Patent Application
20160010138
Systems, methods, computer programs, and computer readable mediums for evaluating microorganisms are described.
Critically ill patients that acquire a microbial infection must begin effective antimicrobial therapy as quickly as possible. Application of the computer-based systems, devices, methods, and computer programs of the present disclosure to provide organism detection, determination of growth, identification, antimicrobial susceptibility testing, and/or antimicrobial resistance characterization may substantially reduce the time from sample to result, thereby increasing the potential for successful therapeutic outcomes.
In various aspects, systems, methods and computer readable mediums are provided evaluating microorganisms. In a first aspect, a computer-based system configured to analyze microorganism information is provided having one or more processors and a tangible, non-transitory memory. The computer-based system may also have a user interface. A first value associated with an attribute of a microorganism is determined by the computer-based system, based on first information obtained from a microorganism detection system. A second value associated with an attribute of the microorganism is determined, optionally by the computer-based system, based on second information obtained from a microorganism detection system. A growth rate is determined by reference to the first value and the second value. The determined growth rate is compared to a control growth rate.
In a second aspect, a computer-based system configured to analyze microorganism information is provided having one or more processors and a tangible, non-transitory memory. The computer-based system may also have a user interface. A first value associated with an attribute of a microorganism is determined by the computer-based system, based on first information obtained from a microorganism detection system. A second value associated with an attribute of the microorganism is determined by the computer-based system based on second information obtained from a microorganism detection system. A growth rate is determined, optionally by the computer-based system, by reference to the first value and the second value. The determined growth rate is compared to a control growth rate.
In a third aspect, a computer-based system configured to analyze microorganism information is provided having one or more processors and a tangible, non-transitory memory. The computer-based system may also have a user interface. A first value associated with an attribute of a microorganism is determined by the computer-based system, based on first information obtained from a microorganism detection system. A second value associated with an attribute of the microorganism is determined by the computer-based system based on second information obtained from a microorganism detection system. A growth rate is determined by the computer-based system by reference to the first value and the second value. The determined growth rate is compared, optionally by the computer-based system, to a control growth rate.
In another aspect, systems, methods and computer readable mediums capable of determining a growth rate of one or more microorganisms are described. A computer-based system configured to analyze microorganism information is provided having one or more processors and a tangible, non-transitory memory. The computer-based system may also have a user interface. A first value associated with a growth rate of a microorganism is determined by the computer-based system, based on information obtained from a microorganism detection system, optionally in response to subjecting a microorganism to at least one of a first event and a first condition. A second value associated with a growth rate of a reference microorganism is obtained, optionally from a microorganism detection system. A proportional relationship between the first value and the second value may be compared by the computer-based system.
In various embodiments, the systems, methods and computer readable mediums described may be capable of evaluating microorganisms. For example, a method may comprise: determining, by a computer-based system configured to analyze microorganism information and comprising a processor and a tangible, non-transitory memory, a first value associated with an attribute of a microorganism, based on first information from a microorganism detection system; determining, by the computer-based system, a second value associated with the attribute of the microorganism, based on second information from the microorganism detection system; determining, by the computer-based system, a growth rate associated with the first value and the second value; and comparing, by the computer-based system, the growth rate to a control growth rate.
In various embodiments, the systems, methods and computer readable mediums described may be capable of determining a growth rate of one or more microorganisms. For example, the method may comprise: determining, by a computer-based system configured to analyze microorganism information and comprising a processor and a tangible, non-transitory memory, a first value corresponding to a growth rate of a microorganism, based on information from a microorganism detection system, in response to subjecting a microorganism to at least one of a first event and a first condition; obtaining, by the computer-based system, a second value corresponding to a growth rate of a reference microorganism; and determining, by the computer-based system, a proportional relationship between the first value to second value.
In various embodiments, the systems, methods and computer readable mediums described may be capable of determining a growth rate of one or more microorganisms. A method may comprise: detecting, by a computer-based system configured to analyze microorganism information and comprising a processor and a tangible, non-transitory memory, first microorganism information from a microorganism detection system; detecting, by the computer-based system, second microorganism information from the microorganism detection system; parsing, by the computer-based system, first microorganism information and second microorganism information into a plurality of microorganism information value subsets, wherein a first microorganism information value subset created from first microorganism information and second microorganism information value subset created from second microorganism information are associated with a location; associating, by the computer-based system, the first microorganism information value subset and the second microorganism information value subset; determining, by the computer-based system, a first growth rate of a microorganism, based on the first microorganism information value subset and the second microorganism information value subset, in response to subjecting a microorganism to at least one of a first event and a first condition; obtaining, by the computer-based system, a second value corresponding to a reference growth rate; and determining, by the computer-based system, a proportional relationship between the first value to second value.
In various embodiments, the second value may be determined in response to an event.
In various embodiments, the second value may be determined in response to a second condition and a second event.
In various embodiments, the microorganism may be an individuated microorganism.
In various embodiments, the microorganism may be subjected to a condition.
In various embodiments, the condition may be associated with the event.
In various embodiments, the control growth rate may be at least one of a predetermined growth rate and a dynamically determined growth rate.
In various embodiments, the event may be at least one of a predetermined time, a dynamically determined mass, a number of individuated microorganisms, and a number of clones.
In various embodiments, the condition may be at least one of a temperature, a growth medium condition, a carbon source, a nitrogen source, an amino acid, a nutrient, a salt, a metal ion, a cofactor, a pH, a trace element, a dissolved gas, an antimicrobial agent, an aerobic condition, and an anaerobic condition.
In various embodiments, the condition may be at least one of static and dynamic. In various embodiments, the microorganism information may comprise a plurality of values associated with a plurality of attributes evaluated simultaneously.
In various embodiments, a change in measured signal intensity associated with a microorganism attribute or clone, a clone signal intensity curve shape likelihood, or another variant response function is determined by a computer-based system.
In various embodiments, a tracking error likelihood is determined by a computer-based system.
In various embodiments, a growth likelihood value is determined, by a computer-based system, based on the clone signal intensity curve shape likelihood and tracking error likelihood.
In various embodiments, microorganism susceptibility is determined, by a computer-based system, based on a comparison of the growth likelihood value to a reference range.
In various embodiments, a signal associated with the microorganism is rendered, by a computer-based system, into a plurality of signal approximations.
In various embodiments, the plurality of signal approximations are planes comprising a plurality of point amplitudes corresponding to microorganism locations.
In various embodiments, the plurality of signal approximations are combined, by a computer-based system, to create a microorganism model.
In various embodiments, the point amplitudes are analyzed, by a computer-based system, in association with at least one of background information and noise information.
In various embodiments, the plurality of signal approximations are filtered, by a computer-based system, to eliminate at least one of background information and noise information.
In various embodiments, locations associated with point amplitudes corresponding to microorganisms are registered by a computer-based system.
In various embodiments, a second value may be obtained from a reference growth curve associated with a reference microorganism.
In various embodiments, an event may include at least one of a predetermined time, a dynamically determined mass, a number of individuated microorganisms, and a number of clones.
In various embodiments, a condition may be at least one of a temperature, a growth medium condition, a carbon source, a nitrogen source, an amino acid, a nutrient, a salt, a metal ion, a cofactor, a pH, a trace element, a dissolved gas, an antimicrobial agent, an aerobic condition, and an anaerobic condition.
In various embodiments, the proportional relationship between a first growth rate value and a second growth rate value may be evaluated, optionally by a computer-based system, against a reference range.
In various embodiments, at least one of the following is identified, by a computer-based system: microorganism susceptibility to an antimicrobial agent, microorganism resistance to an antimicrobial agent, microorganism expression of a virulence factor, microorganism hypervirulence, and polymicrobial specimens.
In various embodiments, at least one of the following is identified, by a computer-based system: microorganism susceptibility to an antimicrobial agent, microorganism resistance to an antimicrobial agent, microorganism expression of a virulence factor, microorganism hypervirulence, and polymicrobial specimens.
In various embodiments, at least one of the following is identified, in association with a computer-based system: microorganism susceptibility to an antimicrobial agent, microorganism resistance to an antimicrobial agent, microorganism expression of a virulence factor, microorganism hypervirulence, and polymicrobial specimens.
In various embodiments, microorganism susceptibility to an antimicrobial agent is identified, by a computer-based system, in response to the proportional relationship between the first growth rate value and the second growth rate value falling one of within and outside of the reference range.
In various embodiments, microorganism susceptibility to an antimicrobial agent is identified, in association with a computer-based system, in response to the proportional relationship between the first growth rate value and the second growth rate value falling one of within and outside of the reference range.
In various embodiments, a microorganism that is not susceptible to an antimicrobial agent is identified, by a computer-based system, in response to the proportional relationship falling within the reference range.
In various embodiments, a microorganism that is not susceptible to an antimicrobial agent is identified, in association with a computer-based system, in response to the proportional relationship falling within the reference range.
In various embodiments, a microorganism that is not susceptible to an antimicrobial agent is identified, by a computer-based system, in response to the proportional relationship falling outside of the reference range.
In various embodiments, a microorganism that is not susceptible to an antimicrobial agent is identified, in association with a computer-based system, in response to the proportional relationship falling outside of the reference range.
In various embodiments, a microorganism that is resistant to an antimicrobial agent is identified, by a computer-based system, in response to the proportional relationship falling within the reference range.
In various embodiments, a microorganism that is resistant to an antimicrobial agent is identified, in association with a computer-based system, in response to the proportional relationship falling within the reference range.
In various embodiments, a microorganism that is resistant to an antimicrobial agent is identified, by a computer-based system, in response to the proportional relationship falling outside of the reference range.
In various embodiments, a microorganism that is resistant to an antimicrobial agent is identified, in association with a computer-based system, in response to the proportional relationship falling outside of the reference range.
In various embodiments, a microorganism expressing a virulence factor is identified, by a computer-based system, in response to the proportional relationship falling within the reference range.
In various embodiments, a microorganism expressing a virulence factor is identified, in association with a computer-based system, in response to the proportional relationship falling within the reference range.
In various embodiments, a microorganism expressing a virulence factor is identified, by a computer-based system, in response to the proportional relationship being outside of the reference range.
In various embodiments, a microorganism expressing a virulence factor is identified, in association with a computer-based system, in response to the proportional relationship being outside of the reference range.
In various embodiments, a microorganism that is hypervirulent is identified, by a computer-based system, in response to the proportional relationship being within the reference range.
In various embodiments, a microorganism that is hypervirulent is identified, in association with a computer-based system, in response to the proportional relationship being within the reference range.
In various embodiments, a microorganism that is hypervirulent is identified, by a computer-based system, in response to the proportional relationship being outside of the reference range.
In various embodiments, a microorganism that is hypervirulent is identified, in association with a computer-based system, in response to the proportional relationship being outside of the reference range.
In various embodiments, a polymicrobial specimen is identified, by a computer-based system, in response to two or more proportional relationships falling within and/or outside of a reference range.
In various embodiments, a polymicrobial specimen is identified, in association with a computer-based system, in response to two or more proportional relationships falling within and/or outside of a reference range.
The accompanying drawings are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate aspects and embodiments of the disclosure, and together with the description, serve to explain the principles of the disclosure, wherein:
The detailed description of various aspects and embodiments herein makes reference to the accompanying drawing figures, which show various aspects and embodiments and implementations thereof by way of illustration and best mode, and not of limitation. While these aspects and embodiments are described in sufficient detail to enable those skilled in the art to practice the embodiments, it should be understood that other aspects and embodiments may be realized and that mechanical and other changes may be made without departing from the spirit and scope of the present disclosure. Furthermore, any reference to singular includes plural aspects and embodiments, and any reference to more than one component may include a singular aspect and embodiment. Likewise, any ordination of a device, system, or method or of a component or portion thereof with designations such as “first” and “second” is for purposes of convenience and clarity and should not be construed as limiting or signifying more than an arbitrary distinction. Moreover, recitation of multiple aspects and embodiments having stated features is not intended to exclude other aspects and embodiments having additional features or other aspects and embodiments incorporating different combinations of the stated features.
Systems, methods and computer program products are provided in various aspects and embodiments of the present disclosure. References to “various embodiments,” “one embodiment,” “an embodiment,” “an example embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described. After reading the description, it will be apparent to one skilled in the relevant art(s) how to implement the disclosure in alternative embodiments.
Various aspects and embodiments of the present disclosure can be realized by any number of systems, computer hardware, devices, computer software and computer readable mediums, devices compositions, organisms, processes and methods configured to perform the intended functions. Stated differently, other systems, devices, methods, and the like can be implemented or incorporated herein to perform the intended functions. It should also be noted that the accompanying drawing figures referred to herein are not drawn to scale, but can be exaggerated to illustrate various aspects of the present disclosure, and in that regard, the drawing figures should not be construed as limiting. Finally, although the present disclosure can be described in connection with various principles and beliefs, the present disclosure is not intended to be bound by any particular theory.
In various aspects, the systems, methods and computer readable medium (collectively “systems”) described herein are capable of determining microorganism information (e.g., data describing a microorganism attribute). More specifically, the systems described herein are capable of identifying and quantifying individuated microorganism characteristics (e.g., growth). The microorganism information may be used to identify and characterize one or more microorganisms in a specimen or sample and/or recommend treatment options based on a microorganism event (e.g., inclusion or exclusion of one or more antimicrobial agents from a treatment regimen).
In various aspects, the systems can identify individuated microorganisms and evaluate microorganism growth under or in response to various conditions. For example, the microorganism may be exposed to a first condition that stimulates growth (e.g., an increase in temperature) and/or a second condition that inhibits growth (e.g., an antimicrobial agent). As such, the system can be capable of determining microorganism identification, growth, antimicrobial susceptibility and/or resistance, and/or providing a variety of analytical outputs based on a multi-variable or multi-factorial analysis.
In various aspects, the systems described herein may be implemented as hardware, hardware-software, or software elements. These systems may comprise one or more modules, analyzers, hardware components, software components, computer programs and/or the like.
As used herein, the terms “microorganism” and “organism” mean a member of one of following classes: bacteria, fungi, algae, and protozoa, and can also include, for purposes of the present disclosure, viruses, prions, or other pathogens. In various embodiments, bacteria, and in particular, human and animal pathogens, are evaluated. Suitable microorganisms include any of those well established in the medical art and those novel pathogens and variants that emerge from time to time. Examples of currently known bacterial pathogens, for example, include, but are not limited to genera such as Bacillus, Vibrio, Escherichia, Shigella, Salmonella, Mycobacterium, Clostridium, Cornyebacterium, Streptococcus, Staphylococcus, Haemophilus, Neissena, Yersinia, Pseudomonas, Chlamydia, Bordetella, Treponema, Stenotrophomonas, Acinetobacter, Enterobacter, Klebsiella, Proteus, Serratia, Citrobacter, Enterococcus, Legionella, Mycoplasma, Chlamydophila, Moraxella, Morganella, and other human pathogens encountered in medical practice. Similarly, microorganisms may comprise fungi selected from a set of genera such as Candida, Aspergillus, and other human pathogens encountered in medical practice. Still other microorganisms may comprise pathogenic viruses (sometimes human pathogens) encountered in medical practice, including, but not limited to, orthomyxoviruses, (e.g., influenza virus), paramyxoviruses (e.g., respiratory syncytial virus, mumps virus, measles virus), adenoviruses, rhinoviruses, coronaviruses, reoviruses, togaviruses (e.g., rubella virus), parvoviruses, poxviruses (e.g., variola virus, vaccinia virus), enteroviruses (e.g., poliovirus, coxsackievirus), hepatitis viruses (including A, B and C), herpes viruses (e.g., Herpes simplex virus, varicella-zoster virus, cytomegalovirus, Epstein-Barr virus), rotaviruses, Norwalk viruses, hantavirus, arenavirus, rhabdovirus (e.g., rabies virus), retroviruses (including HIV, HTLVI, and II), papovaviruses (e.g., papillomavirus), polyomaviruses, picornaviruses, and the like. With respect to viruses, in general, the methods and compositions of the disclosure may be used to identify host cells harboring viruses.
As used herein, the term “microorganism” can be used to refer to a single cell, such as a single or individual bacterial cell. The term “microorganism” may also be used to refer to a clone comprising more than one cell, such as a group of cells or organisms produced asexually from a single progenitor cell or ancestor. In various embodiments and as used herein, the term “microorganism” may also refer to a group of cells that may be genetically distinct (i.e., arising from more than one progenitor cell or ancestor) but may be physically associated and evaluated as a single “microorganism.”
As used herein, the term “in association with a computer-based system,” and similar phrasing, in reference to any process step, process step output (e.g., a growth curve), and the like (e.g., identification), means that it is either directly performed by the computer-based system, or based upon data, information, outcomes or reports from the computer-based system, or both. In reference to the term with other process steps, inputs, and the like, the same might be either performed directly by the computer-based system, or performed in sequence to, or parallel with, the computer-based system.
The present disclosure provides systems of detecting microorganisms within samples (e.g., sample 110 discussed herein). Samples, including, for example, samples in solution, may comprise any number of sources, including, but not limited to, bodily fluids (including, but not limited to, blood, urine, serum, lymph, saliva, anal and vaginal secretions, perspiration, peritoneal fluid, pleural fluid, effusions, ascites, purulent secretions, lavage fluids, drained fluids, brush cytology specimens, biopsy tissue, explanted medical devices, infected catheters, pus, biofilms, and semen) of virtually any organism, with mammalian samples, particularly human samples, and environmental samples (including, but not limited to, air, agricultural, water, and soil samples) finding use in the system of the present disclosure. In addition, samples can be taken from food processing, which can include both input samples (e.g., grains, milk, or animal carcasses), samples in intermediate steps of processing, as well as finished food ready for the consumer. The value of system of the present disclosure for veterinary applications should be appreciated as well, for example, with respect to its use for the analysis of milk in the diagnosis and treatment of mastitis, or the analysis of respiratory samples for the diagnosis of bovine respiratory disease.
Samples can range from less than a milliliter up to a liter for certain respiratory lavage fluids, and can further range in bacterial concentration from less than one bacterium to greater than 109 bacteria per milliliter. Furthermore, the sample can be present in blood, urine, sputum, lavage fluid or other medium. Sample concentration may be used to concentrate the sample so that bacteria that are present in small numbers can be effectively introduced into the system, as well as so the background liquid medium can be normalized, or in some cases eliminated or reduced, to provide consistent sample properties upon introduction to a system. It should be noted, however, that various samples may be used without concentration or other modification within the scope of the present disclosure.
In various aspects, a first value associated with an attribute of a microorganism is determined by a computer-based system, based on first information obtained from a microorganism detection system. A second value associated with an attribute of the microorganism is determined by the computer-based system based on second information obtained from a microorganism detection system. A growth rate is determined by the computer-based system by reference to the first value and the second value. In further aspects, a first value associated with a growth rate of a microorganism is determined by a computer system, based on information obtained from a microorganism detection system, optionally in response to subjecting a microorganism to at least one of a first event and a first condition. As used herein, terms such as “determining growth,” “detecting growth,” “determining a growth rate” and similar variations thereof may be used interchangeably with respect to various aspects and embodiments of the present disclosure. The definitions of “growth” and other conceptually related terms are further defined below.
As used herein, the term “growth” may include any measurable change attributable to or occurring within the life history of an organism, whether that change occurs under static external conditions or in response to a change in an internal or external event or condition. The term “growth” can be used to describe any change, regardless of whether the change is positive or negative. “Growth” can also be used to describe a lack of growth, or neutral growth, where there may be no measurable change, or no net change, in a measured value of an attribute of a microorganism. “Growth” can be used to refer to one or more changes associated with a single microorganism, or growth can be used to refer to a net or collective change in a group, collection, or population of organisms, whether derived from a single parental cell or from multiple parental cells. “Positive growth” in the case of microorganisms that are cells (e.g., bacteria, protozoa, and fungi) can refer to an increase in an attribute of a microorganism, including such attributes as, mass, cell divisions (e.g., binary fission events or cell doubling resulting in the production of daughter cells), cell number, cell metabolism products, or any other experimentally observable attribute of a microorganism.
In accordance with various embodiments, microorganism size, (whether in one, two, or three dimensions), need not be used to evaluate growth with respect to microorganisms that are individual cells or individuated, multicellular clones; in various embodiments, size may not be a useful or informative metric of growth, and in fact may be misleading. However, in various embodiments and in accordance with a definition of “microorganism” wherein a microorganism may comprises a group or population of cells originating from a plurality of unrelated progenitor cells, the size of the microorganism may be evaluated as an attribute that may be measured with respect to a determination of growth, insofar as the size of the microorganism may serve to represent the mass or number of the cells of which the microorganism is comprised. In the case of viruses, “growth” can refer to the reproduction of viruses, generally within a host cell, and can include host cell lysis, in the case of lytic viruses. Thus, “growth” of a virus may sometimes be detected as a loss of the discrete host cell.
Various methods of detection of an attribute of a microorganism are described in greater detail below. For example, detecting positive growth of a microorganism can include detecting either an increase in the mass of the microorganism and/or detecting the occurrence of one or more cell divisions as evidenced by the production of daughter cells. In various embodiments and as described in greater detail herein, detection of a change in the mass of a microorganism may be performed using any of a variety of methods that may directly or indirectly measure a change in the quantity of mass of a microorganism.
As used herein, the term “mass” may be used in various senses with respect to the measurement or detection of microorganism growth. For example, the term “mass” may be used in a formal sense to describe a quantity of matter, such as the mass of a microorganism or the change in the mass of a microorganism as might be determined using a microbalance. In general, however, as used herein, the term “mass” may also be used to describe a measure that may be indirectly or directly related to, or serve as a proxy for, a measurement of a quantity of matter. For example, a measurement of an increase in the size or number of microorganisms may be described as an increase in the “mass” of the microorganism(s) (i.e., the “biomass” of the microorganisms, such as may be determined by various measurement techniques that assess, directly or indirectly, changes in an apparent “mass” of a microorganism, clone, or population, where the measured change may or may not correlate exactly with a change in the “mass” as an actual quantity of matter). Any method that may be used to evaluate the “mass” of a microorganism, whether by a direct determination of a quantity of matter, or by a measure of a quantity that may be directly or indirectly related to the quantity of matter of a microorganism, can be used to detect growth for purposes of the systems and methods of the present disclosure.
As mentioned herein, “detecting growth” can also refer to detecting a lack of positive growth and/or to detecting negative growth. For example, a microorganism life cycle can include one or more phases wherein “growth” may not be ascertainable using certain measurement techniques, such as during periods traditionally referred to as a “lag phase” or “death phase.” Likewise, some antimicrobial agents act by retarding positive growth, while failing to produce cell mortality. In such cases, detecting little or no change in the size or mass of a microorganism, for example, may be included within the evaluation of “growth.” Explained differently, in the absence of an antimicrobial agent, a microorganism may exhibit positive growth, but in the presence of the agent, a lack of growth may be significant, even if the microorganism does not die. Thus, in some cases, such as the forgoing example, small (or decreased) changes may be measured in a period of time relative to the mass, increase in number, or any other attribute of microorganism that may be observed; however, these changes may be meaningfully distinguishable from positive growth.
In addition, processes such as bacterial programmed cell death (e.g., apoptosis and/or autophagic cell death) may be considered negative growth. In general, detection of negative growth relies on changes, usually but not always decreases, in microorganism mass, number, or any other attribute that can be measured. In various embodiments, detection of cell death may further include the use of an indicator of a condition associated with cell death or a lack of cell viability, such as a mortal stain or a change in intrinsic fluorescence.
As used herein, “detecting growth” can also refer to detecting changes in an attribute of a microorganism that may be in a growth phase other than that associated with logarithmic or exponential phase growth. In other words, “detecting growth” can comprise detection of changes in an attribute of a microorganism in a phase of growth that might traditionally be referred to as lag phase, stationary phase, or death phase, for example. Changes associated with such phases may be neutral or negative with respect to changes in the size, “mass,” or “biomass.” As described above, the lack of measurable changes in number, “mass,” or “biomass” may be significant and serves as a measure of “growth.” However, in various embodiments, other attributes that may nonetheless be measured and exhibit measureable changes may also be used to derive a measure of growth, as used herein. Thus, detection of growth can relate to measurement of attributes associated with, for example, homeostasis, catabolism, cell death, or necrosis. These attributes can include, for example, measurement of metabolite production, protein production, cell membrane integrity, ion channel activity, gene transcription, and the like. Various attributes of a microorganism that may be measured with respect to a determination of a growth rate, along with various methods that may be used to detect and measure those attributes, are described in greater detail below.
Thus, “detecting growth” can refer to detecting positive growth, detecting a lack of growth, e.g., detecting cells that are not actively dividing but are not growing positively, and detecting negative growth, for example, cell death.
In various embodiments, detecting growth may be performed at the individual or discrete microorganism level, rather than at a gross colony or population level. Thus, “detecting growth of a discrete microorganism” may be performed as an evaluation of growth of an individual cell or clone, for example, in a period of time such that a small population of daughter cells can be formed, but prior to the ability to visually see a colony or population with the naked eye. This aspect of various embodiments has been described as “quantum microbiology,” wherein individuated microorganisms (i.e., discretely and/or repeatably identifiable microorganisms, whether individual cells or clones comprising more than one cell, or microorganisms comprising more than clone in close physical approximate and treated as an individual microorganism, as defined above) can be analyzed as described herein. In various embodiments, devices such as biosensors, microfluidics chambers, microfluidics cartridges, and other specialized devices may be used to facilitate microorganism individuation and detection. Systems, methods, and devices that enable individuation and detection of discrete microorganisms in accordance with various embodiments of the present disclosure are described in detail in U.S. Pat. Nos. 7,341,841 and 7,687,239, which are herein incorporated by reference in their entirety.
In various embodiments, the ability to analyze or measure changes in the attributes of individuated microorganisms may, for example, enable growth to be detected in a short time frame in comparison to traditional microbiological methods, such as minutes or hours rather than days. Similarly, growth may be detected within an absolute time frame of only a few cell doubling events of a microorganism, rather than the tens or hundreds of doubling events that may be required to assess growth and/or susceptibility with traditional methods. Furthermore, certain embodiments of the present disclosure do not require an initial growth of microorganisms (either liquid or solid) prior to an evaluation of growth; rather, some methods are sufficiently sensitive to enable starting with direct-from-specimen biological samples with no growth or culturing prior to the assay. In general, the methods of the disclosure can be performed within and rely on measures of growth that may be made within an absolute timeframe within which a microorganism present in the sample under conditions suitable for growth may undergo from 1 to about 10 doubling events, with from about 1 to about 4 being particularly useful, and 1 to 2 being ideal in situations where the “time to answer” is being minimized.
In various aspects, “detecting growth” may be performed using a computer-based system, as described in greater detail below. In various aspects, a computer-based system can comprise a processor, non-transitory memory, and an interface. The computer-based system may be configured to perform method steps and/or execute instructions on a computer readable medium or computer program. In these embodiments, detection of growth and/or a determination of a growth rate may be made by integrating microorganism information associated with the detection and/or measurement of one or more attributes of a microorganism over a period of time. In various embodiments, detection of growth and/or a determination of a growth rate, or a lack thereof, for a microorganism need not be based solely on a direct or absolute assessment of cell viability, change in size or mass, performance of metabolic processes (i.e., homeostasis, anabolic, or catabolic processes), reproduction, or the like, but instead may be based on a probabilistic assessment that a measured change in one or more attributes is likely to correspond to growth. Thus, in various embodiments and as described in detail below, detection of growth and/or a growth rate may be determined based on measurement of a change in one or more attributes over time and a determination of a statistical probability of whether the measured change corresponds to growth, as compared to a control or reference.
In various aspects, a method for detection of growth and/or determination of a growth rate may comprise determining, by a computer-based system configured to analyze microorganism information, values associated with an attribute of a microorganism, based on information obtained from a microorganism detection system. Microorganism detection systems and/or methods that may be used to provide microorganism information in accordance with various embodiments, such as first information, second information, and the like, are described in greater detail below.
In various aspects, a method may comprise determining, by the computer-based system, a first value associated with an attribute of a microorganism based on first information obtained from a microorganism detection system. A second value associated with an attribute of the microorganism may be determined, optionally by the computer-based system, based on second information obtained from a microorganism detection system. A growth rate of the microorganism may be determined by reference to the first value and the second value. The determined growth rate may be compared to a control growth rate. In various embodiments, the growth rates may be compared by the computer-based system.
In various aspects, a first value associated with a growth rate of a microorganism is determined by the computer-based system, based on information obtained from a microorganism detection system, optionally in response to subjecting a microorganism to at least one of a first event and a first condition. A second value associated with a growth rate of a reference microorganism is obtained, optionally from a microorganism detection system. In various aspects, a growth rate determined for a microorganism and a reference growth rate are compared. A proportional relationship between the first value and the second value may be compared by the computer-based system. In various embodiments, a relationship between a first value associated with a growth rate of a microorganism and a second value associated with a growth rate of a reference microorganism need not be compared by the computer-based system.
As explained in more detail herein, a growth rate may be determined based on a statistical probability of whether a measured change corresponds to growth may comprise a product of a plurality of factors or likelihoods derived from an assessment of various factors. A control against which an identification of growth (i.e., the statistical probability that a process interpreted as growth is occurring) is made may be an internal control, such as a control run contemporaneously with the sample for which a determination of growth is being made. In other embodiments, a control may be a predetermined standard. For example, a sample for which a growth rate is to be determined may be measured in an under a set of standardized conditions for which one or more reference growth rates (for example, a library of reference or standard growth curves) are available, and a determination of a growth rate may be made for the sample based on comparison of the experimentally determined growth rate against one or more reference growth rates. In various embodiments, such reference growth rates may be empirically determined by a user at a time that is separate from the experimental determined growth rate for a sample. In other embodiments, reference growth rates under standard conditions for a microorganism detection system may be determined by a third party, and the reference growth rates may be obtained from the third party by a user for experimental determination of a growth rate of a sample.
Determination of a growth rate, or a determination that a probability that a measured change of an attribute of a microorganism in a sample comprises growth in a statistically or clinically meaningful sense, may be made for a sample based not only on comparison of a sample growth rate against one or more reference growth rates, but may also comprise consideration of additional factors that may modify the calculation of the probability of growth based on comparison of the measured attribute to a reference growth rate. For example, measurements of an increase in cell volume may be used to determine a growth rate, but those same measurements of cell volume may also be fit to models or references relative to models of cell shape that may or may not be integrated into the reference growth rates used for comparison. In this manner, conformity (or lack thereof) of attributes of cell size and/or shape changes relative to various expected models of cell morphology may modify the probability that the measured microorganism attribute change corresponds to a reference growth rate. Similarly, other factors, such as the number of cell divisions and the probability that the observed cell or group of cells in a measurement made at a first time corresponds to the observed cell or group of cells in measurement made at a second or subsequent times may also modify to determination of the probability that a sample or microorganism therein is demonstrating growth.
In various embodiments, a determination of a growth rate of a microorganism in response to a condition may provide clinically meaningful or useful information, for example, where the microorganism originates from a patient sample and the condition comprises an antimicrobial agent. The growth rate determined for a microorganism, compared against a reference growth rate, in accordance with various embodiments of the present disclosure, may facilitate identifying whether the microorganism is susceptible to the antimicrobial agent and/or whether the microorganism is resistant to the antimicrobial agent. For example, susceptibility of a microorganism to an antimicrobial agent may be identified by determining a rate of growth in response to a condition comprising a concentration of the antimicrobial agent and comparing the growth rate of the microorganism to a reference growth rate. If a proportional relationship of the growth rates is determined to be outside of a reference range or below a threshold criterion, for example, the microorganism may be identified as susceptible to the antimicrobial agent.
As used herein, “susceptibility” includes instances in which an antimicrobial agent has an inhibitory effect on the growth of a microorganism or a lethal effect on the microorganism. “Susceptibility” further includes the concept of a minimum inhibitory concentration (“MIC”) of an antimicrobial agent, defined as a concentration of an antimicrobial agent that will arrest growth of a microorganism. Identification of “susceptibility,” or a lack of susceptibility for example, using the system and method described herein, may provide information that may be useful to a clinician in making a decision regarding antimicrobial agent therapy for a patient.
In various embodiments, for example, if a proportional relationship of growth rates is determined to be outside of a reference range or above a threshold criterion, a microorganism may be identified as resistant.
As used herein, “resistant” includes instances in which a microorganism is not substantially affected by an antimicrobial agent. For example, resistance may be identified by determining that a microorganism's growth is not substantially affected by a MIC of an antimicrobial agent. It is important to note that identification of resistance provides no information as to susceptibility or identification of clinically therapeutic treatment options, with the exception that the antimicrobial agent to which a microorganism is identified as resistant may be ruled out as a viable clinical therapeutic agent, assuming that the resistant microorganism is a pathogenic microorganism responsible for an infection in a patient. In various embodiments, “resistance” may be related to known mechanisms associated with a particular microorganism genotype, wherein the mechanism providing antimicrobial agent resistance to the microorganism is genetically encoded and expressed to confer a resistant phenotype.
In various embodiments, determination of a growth rate may also facilitate identifying whether a microorganism demonstrates intermediate susceptibility to an antimicrobial agent. Traditional clinical AST testing procedures often report information related to the identity of a pathogen in a sample and a table of antimicrobial agents to which a pathogen or other microorganism is susceptible, intermediate, or resistant (“SIR”). In accordance with various embodiments, the system and methods disclosed herein may be used to rapidly provide similar information, including empirically determined resistance and susceptibility data based on subjecting a microorganism to a condition comprising an antimicrobial agent, rather than data that may simply rely on performing microorganism identification alone.
In various embodiments, determination of the growth rate of a microorganism may further be used to identify that the microorganism is, for example, expressing a virulence factor or hypervirulent. For example, a microorganism may demonstrate an altered growth rate due to expression of one or more virulence factors that may be associated with enhanced pathogenicity. In various embodiments, a growth rate of a microorganism expressing a virulence factor (or a hypervirulent microorganism) may exhibit a growth rate that is higher than a reference organism, or a growth rate that is lower than a reference organism.
As used herein, an “attribute of a microorganism” can be any detectable or measurable feature or characteristic of a microorganism. An attribute can be directly associated with a microorganism, for example, a feature or characteristic that is physically located on or within or otherwise directly physically associated with a microorganism. Such a directly associated attribute can include, for example, the size, shape, mass, intrinsic fluorescence, cell surface features, membrane integrity, genomic DNA, ribosomal RNA subunits, etc. A directly associated attribute can also include, for example, both directly and indirectly bound markers such as may be used in indirect immunofluorescence microscopy. As used herein, an attribute can also include a feature that is indirectly associated with a microorganism, such as proteins, ions, osmolytes, metabolites, or any other chemical or macromolecular substance that may be secreted, released, or exchanged into a medium and detected directly or indirectly. For example, colorimetric indicators may be used as substrates in enzyme-linked immunosorbent assays. Furthermore, an attribute can be a feature or characteristic of a microorganism, regardless of whether tne microorganism is viable or dead, intact or disrupted. Any value related to the presence of a microorganism that may be observed, detected, or measured, using any technique, whether presently available or yet to be developed, is within the scope of an attribute of a microorganism as used in the present disclosure.
In various embodiments, microorganisms to be analyzed may be maintained in or subjected to one or more conditions suitable for growth. For example, a microorganism detection system may include one or more sample vessels in which microorganism samples are placed for detection and analysis. In various embodiments and as illustrated in
In various embodiments and as used herein, a “condition” can be any parameter related to or having an influence on microorganism growth. For example, a “condition” can include parameters required for or beneficial to microorganism growth, and a “condition” can include parameters that may inhibit microorganism growth. Likewise, a “condition” can refer to a single parameter or variable that may influence microorganism growth, or a “condition” may collectively refer to a set of parameters or variables. In accordance with various embodiments, a “condition” may simply refer to the passage of time. A “condition” can include any traditional microbiological culture medium that may be known to a person of ordinary skill in the art, and a “condition” can further include any growth (or selective) medium comprising any combination of medium components, whether defined or undefined (complex). Examples of medium components and classes of components include carbon sources, nitrogen sources, amino acids, extracts, salts, metal ions, cofactors, vitamins, dissolved gasses, and the like. Similarly, a “condition” can include various components that might be added to a medium to influence the growth of a microorganism, such as selective and non-selective antimicrobial agents that may inhibit or arrest microorganism growth, modulating agents (i.e., agents that may alter microorganism growth but are not considered antimicrobial agents), or enrichment agents (e.g., substances that may be required for auxotrophic microorganisms, such as hemin, or substances that may be required by fastidious organisms) or other components that may encourage microorganism growth. A “condition” can also include other environmental parameters separate from the composition of a culture medium, such as light, pressure, temperature, and the like. Similarly, a condition can include any of a variety of other parameters that might occur or be imposed, such as: a host organism defensive material or cell (e.g., human, defensin proteins, complement, antibody, macrophage cell, etc.), a surface adherent material (i.e., surfaces intended to inhibit growth, kill cells, etc.), a physiological, metabolic, or gene expression modulating agent (e.g., host defense activation with co-cultured host cells), a physiological salt, metabolite, or metabolic waste materials (such as may be produced by living microorganisms or used to simulate late-stage culture growth conditions (i.e., stationary phase conditions)), a reduction in nutrient media (simulating, for example, stationary phase conditions), or a bacteriophage infection (actual or simulated). Furthermore, a “condition” may be static (e.g. a fixed concentration or temperature) or dynamic (e.g. time-varying antimicrobial concentration to simulate pharmacokinetic behavior of intermittent infusions; or to simulate any endogenous or exogenous process affecting microbe response). These definitions of “condition” are intended to be illustrative, rather than exhaustive, and, as used herein, a “condition” can include any endogenous or exogenous parameter that may influence a microorganism.
In various embodiments, a system may include a temperature regulated incubation chamber in which the sample cassette may be maintained during microorganism detection and analysis. In various embodiments, a system may include ability to provide for temperature regulation of the cassette or sample chambers such as by using Peltier elements, resistive heating elements, or temperature regulation of circulating liquid medium for growth during or between evaluations. Temperature regulation may comprise maintaining a microorganism at a fixed temperature during an analysis period, or may comprise providing changing temperatures according to a predetermined temperature profile. A temperature regimen comprising changing temperature can include temperature changes at predetermined temperature change slopes or ramps. In various embodiments, temperature regulation may comprise simultaneously providing different temperatures or temperature profiles for individual chambers or flowcells in a cassette during an analysis period.
In some variations the system may be further configured to accelerate microorganism (and particularly bacterial) growth relative to standard clinical microbiological culturing conditions. Microorganism growth may be accelerated while evaluating growth by changing, for example, the temperature, composition, and/or oxygen content of the media. For example, increasing the temperature may provide an increased rate of microorganism growth and enable a determination of a rate of growth using the system and method disclosed herein in a shortened time frame relative to incubation at a temperature used in standard AST methods.
In various embodiments, microorganisms in a sample or specimen are introduced into a microorganism detection system. A microorganism detection system may comprise a specialized device to facilitate microorganism individuation, growth, and/or detection. In various embodiments, a specialized device for microorganism individuation comprises, for example, a biosensor or a disposable cartridge such as those described in U.S. Pat. Nos. 7,341,841 and 7,687,239.
In various embodiments, a multiplexed automated digital microscopy (MADM) microorganism detection system comprises a computer-based system and may be a bench top instrument that combines a disposable fluidic cartridge with automated microscopy and image analysis software. The MADM system can include, among other features, automated sample distribution to multiple on-board analysis chambers providing integrated electrokinetic concentration (EKC) and imaging, electrophoretic concentration to a capture and imaging surface using transparent indium tin oxide (ITO) electrodes and redox buffer system, phase contrast, darkfield, and fluorescence microscopy, XYZ motion control including autofocus, off-board (instrument-based) pumps and fluid media, on-board reagent reservoirs (antibodies, stains, antibiotics), and active on-device valving for fluidic network control.
Evaluations can be performed using the system, with off-board specimen preparation (i.e., simple centrifugation or filtration sample preparation). The MADM system can provide rapid concentration of bacteria to assay capture and imaging surface using electrokinetic concentration. Targeted bacterial identification can be performed by fluidic introduction of species specific antibodies followed by fluorescently labeled secondary antibodies, with automated epi-fluorescent microscopy. In various embodiments, individual clones can be mapped, and growth rate determination exploits registered time-lapse image analysis, processed to derive growth rate information (e.g., doubling times and growth rate constants). The MADM system can also provide on-board, near real-time antibiotic susceptibility testing (AST).
In various embodiments, a flowcell for use with a microorganism detection system can include indium tin oxide (ITO; conductive and transparent) coated glass as top and bottom layers, optionally with an adsorptive chemical coating on the bottom surface. A sample containing microorganisms may be introduced and a potential applied. Since bacteria are generally negatively charged, they migrate to the positively charged surface, where they may adsorb to the chemical coating. After electrokinetic concentration, the device may be automatically filled with growth media (TSB). All subsequent assay steps may be performed in media and microorganism viability may be maintained throughout the process.
Once the microorganisms present in the sample have been individuated, individual microorganisms can be interrogated (e.g., optically, spectroscopically, bioelectroanalytically, etc.) using the microorganism detection system to measure an attribute of, characterize, and/or identify the microorganisms in the sample. The interrogation or detection of an attribute of a microorganism can take place in any suitable manner, including a non-invasive manner that does not interfere with the integrity or viability of the microorganism, that is, attributes of a microorganism present in a sample can be detected and measured while the microorganism remains in the sample cassette and/or remains intact. Moreover, in various embodiments, attributes of a microorganism may be detected while the organism remains viable and/or capable of undergoing growth. The ability to identify the microorganisms in a non-invasive manner, optionally coupled with keeping the sample contained (e.g., sealed within a sample cassette or equivalent device) throughout the analysis process, along with automation of the procedure, may contribute to reduced handling of potentially pathogenic samples and may increase the safety of an identification or AST process relative to traditional clinical microbiological methods. Furthermore, the ability to characterize and/or identify microorganisms, for example, by direct interrogation of a direct-from-specimen sample without further processing of the sample (e.g., resuspension, plating, and growth of colonies) can greatly increase the rapidity with which identification/characterization can be made.
Any of a number of detection systems and/or methods that may provide an ability to detect an attribute of a microorganism may be used in accordance with various aspects and embodiments. In some embodiments, systems and/or methods that may provide real-time or near real-time detection are used. These include brightfield imaging, darkfield imaging, phase contrast imaging, fluorescence imaging, upconverting phosphor imaging, chemiluminescence imaging, evanescent imaging, near infra-red detection, confocal microscopy in conjunction with scattering, surface plasmon resonance (“SPR”), atomic force microscopy, and the like. Likewise, various combinations of detection systems and/or methods may be used in parallel or in complementary fashion to detect one or more attributes of a microorganism in accordance with the present disclosure.
Spectroscopic methods can be used to detect one or more attributes of the microorganisms. These may include intrinsic properties, such as a property present within the microorganism in the absence of additional, exogenously provided agents, such as stains, dyes, binding agents, etc. Optical spectroscopic methods can be used to analyze one or more extrinsic attributes of a microorganism, for example, a property that can only be detected with the aid of additional agents. A variety of types of spectroscopy can be used, including, for example, fluorescence spectroscopy, diffuse reflectance spectroscopy, infrared spectroscopy, terahertz spectroscopy, transmission and absorbance spectroscopy, Raman spectroscopy, including Surface Enhanced Raman Spectroscopy (“SERS”), Spatially Offset Raman spectroscopy (“SORS”), transmission Raman spectroscopy, and/or resonance Raman spectroscopy or any combination thereof.
Spectroscopic detection can be carried out by any technique known to those of skill in the art to be effective for detecting and/or identifying one or more intrinsic or extrinsic attributes of a microorganism. For example, front face fluorescence (where the excitation and emitted light enters and leaves the same optical surface, and if the sample is generally optically thick, the excitation light penetrates a very short distance into the sample and can be used for identification of microorganisms. Other forms of measurement, such as epifluorescence, reflectance, absorbance, and/or scatter measurements, can also be employed.
Typically, the light source, or excitation source, results in the excitation of the sample, followed by measurement of the emission of fluorescence of the sample at predetermined time points or continuously. Similarly, the reflected light from interaction of the excitation source with the sample may be measured to provide pertinent data for identification and/or characterization. The emission from the sample may be measured by any suitable means of spectral discrimination, such as by employing a spectrometer.
A sample illumination source, or excitation source, may be selected from any number of suitable light sources as known to those skilled in the art. Any portion of the electromagnetic spectrum that produces usable data can be used.
Detection systems and/or methods may be used that rely on fluorescence signal (e.g., intrinsic fluorescence or fluorescence due to the presence of added indicator dyes) due to excitation by a UV, visible spectrum, or IR light source. The light sources can be continuum lamps such as a deuterium or xenon lamps for UV and/or a tungsten halogen lamp for visible/IR excitation. Since these light sources have a broad range of emission, the excitation band can be reduced using optical bandpass filters. Other methods for emission wavelength spectral width that may be utilized include an acousto-optic tunable filter, liquid crystal tunable filter, an array of optical interference filters, prism spectrograph, and the like. Alternatively, lasers are available in discrete wavelengths from the ultraviolet to the near infra-red. Any of a variety of fluorescence signal-based multiplexing methods will be known to those skilled in the art and are within the scope of the present disclosure.
Alternatively, light-emitting diodes can be used as narrowband excitation light sources. LED's are available from a peak wavelength of 240 nm to in excess of 700 nm with a spectral width of 20-40 nm. The same methods for the reduction of spectral width can be incorporated with the LED's to improve discrimination between excitation and emission spectra. In various embodiments, a plurality of narrowband light sources, such as LEDs or lasers, may be spatially and/or temporally multiplexed to provide a multi-wavelength excitation source.
The emission from the sample may be measured by any suitable means of spectral discrimination, most preferably employing a spectrometer. The spectrometer may be a scanning monochromator that detects specific emission wavelengths whereby the output from the monochromator is detected by a photomultiplier tube and/or the spectrometer may be configured as an imaging spectrograph whereby the output is detected by an imaging detector array such as a charge-coupled device (“CCD”) camera or detector array. In one embodiment, a discriminator allows the observation of the fluorescence and/or scattering signal by a photodetection means (such as a photomultiplier tube, avalanche photodiode, CCD detector array, a complementary metal oxide semiconductor (“CMOS”) area sensor array and/or electron multiplying charge coupled device (“EMCCD”) detector array. Fluorescence signal strength at several different wavelengths are acquired and saved in a computer memory.
The detection of growth could also be accomplished using Raman spectroscopy. Raman spectroscopy is a non-contact technique where the sample is illuminated by laser radiation. The scattered light is either elastically or inelastically scattered by interaction with the molecules which comprise the microorganism. The elastically scattered light is referred to as Rayleigh scattering and the inelastically scattered light is Raman scattering. Raman spectroscopy has been shown to be a potentially viable method of microorganism identification and/or characterization by examination of the vibrational spectra of the microorganism.
The laser illumination and scattering collection optics are designed to focus the beam to a near-diffraction limited spot size. This size ensures adequate laser signal on the microbe since Raman scattering is very inefficient. The collection optics are designed to efficiently capture scattered light and couple it into an optical spectrometer for analysis. The Raman signal can be acquired at one or more locations and the subsequent signal averaged.
Once Raman spectra are obtained, they may be analyzed for location and strength of key peaks in the spectra. This data may be compared to a stored reference data set of known microorganisms so that determinations of, for example, Gram type, morphological information, and species identification, can be obtained. A reference data set from known microorganisms can be obtained in the system and methods described herein, or may be obtained from a third party.
To enhance Raman (SERS) and fluorescence signals, microorganisms could either be coated with gold and/or silver nanoparticles in a sample preparation step, and/or the inner optical surface could be pre-coated with metal colloids of particular size and shape. In various embodiments, the nanoparticles may be associated with microorganisms in a centrifugation step.
Spectra such as fluorescence spectra obtained using various methods described above may be used to perform identification of microorganisms. Reference spectra may be obtained for known microorganisms, thus allowing for correlation of measured sample data with characterization of the microorganisms of interest using various mathematical methods known to those skilled in the art. The measured test data from known microorganisms is stored in machine-readable memory, e.g., within the instrument itself or within an associated data processing device, such as a connected computer-based system. For example, the data from samples being tested by the instrument may be compared with the baseline or control measurements utilizing software routines known to or within the ability of persons skilled in the art to develop. More particularly, the data may be analyzed by a number of multivariate analysis methods, such as, for example, General Discriminant Analysis (“GDA”), Partial Least Squares Discriminant Analysis (“PLSDA”), Partial Least Squares regression, Principal Component Analysis (“PCA”), Parallel Factor Analysis (“PARAFAC”), Neural Network Analysis (“NNA”) and/or Support Vector Machine (“SVM”). These methods may be used to classify unknown microorganisms of interest in the sample being tested into relevant groups (e.g., species) based on existing nomenclature, and/or into naturally occurring groups based on the organism's metabolism, pathogenicity and/or virulence in designing the system for evaluating, detecting and/or characterizing the organism as described herein.
Microorganisms associated with a detection system can be interrogated using mass spectrometry techniques, such as MALDI-TOF mass spectrometry, desorption electrospray ionization (“DESI”) mass spectrometry, GC mass spectrometry, LC mass spectrometry, electrospray ionization (“ESI”) mass spectrometry and Selected Ion Flow Tube (“SIFT”) spectrometry.
A bioelectroanalytical microorganism detection system may be used to detect and measure the mass increase of individual microorganisms in near real time. Such a system can include, for example, a system configured to measure impedance or to perform electrochemical impedance spectroscopy. In accordance with various embodiments, the system can provide a rapid and accurate evaluation of the growth dynamics for the population of viable organisms in the sample.
A bioelectroanalytical quantitative growth measurement system may comprise a sample device, such as a disposable microfluidic cartridge with a surface having an array of discrete, individually addressable electrodes suitable for performing impedance measurements, electrochemical impedance spectroscopy, or other bioelectroanalytical measurements. In various embodiments, a microelectrode or a nanoelectrode array may be used. An opposing plane of the microfluidics cartridge may likewise comprise an electrode or an electrode array. In various embodiments, microorganisms introduced into a sample device may become physically associated with a surface or location of the sample device. For example, microorganisms may be electrokinetically concentrated to a surface of a microfluidic cartridge prior to performing bioelectroanalysis. In various other embodiments, microorganisms introduced into the cartridge or other device may be associated with a surface of the device and/or confined at a discrete location of the device (e.g., an addressable location on a planar surface or a discrete, recessed well) using other forms of passive or active cell movement, such as settling, fluid flow, cell trapping, centrifugation, etc. The array of micro- or nanoelectrodes may be used to measure charges associated with a microorganism cell wall and/or the release of ions or other osmolytes from microorganisms in the cartridge. In various embodiments, the electrode size combined with the sensitivity and dynamic range of the bioelectroanalytical measurement system may be suitable to respond proportionately to the mass of viable microorganism structure adjacent to the electrode. For example, the sensitivity of an individual bioelectroanalytical sensor may be suitable to detect ions released or exchanged into the medium by a metabolically active microorganism adjacent the sensor, while a neighboring sensor more distant from the cell detects a smaller ion concentration due to increased diffusion of ions with increased electrode or sensor distance.
For example, an electrode located near the center of mass of a cell or clone may provide a greater bioelectroanalytical response or measurement value than an electrode located adjacent the edge of a cell or otherwise only partially occupied by or in proximity with a cell. A series of measurements, near continuous measurements, or continuous measurements may be taken at each electrode in an array over time, and the system may be suitable to obtain and record frequent or near real time measurements. In this manner, a bioelectroanalytical measurement system may provide data obtained from multiple discretely addressed electrodes for a given microorganism. Likewise, the system may obtain, for example, thousands of discretely addressed electrode measurements at each time in an experimental time course. The data output may thus resemble optical image data comprising discrete pixels with unique addresses, each electrode registering a resistance that may vary within a significant dynamic range dependent proportional to the presence of live and/or growing microorganisms. The data acquired by a bioelectroanalytical measurement system may be processed in accordance with the processes performed by analysis module 140 as described in detail herein with respect to image data.
A variety of other microorganism detection systems and/or methods have been used to detect and/or determine values associated with various attributes of a microorganism, including, for example, optical density, nephelometry, densiometry, flow cytometry, capillary electrophoresis, analytical chemistry and indicator-based methods of metabolite detection, protein output, molecular diagnostics, quartz crystal microbalance, bioluminescence, microcantilever sensors, and asynchronous magnetic bead rotation, among others, and are also included within the various aspects and embodiments.
Of the various approaches that have been described herein, some, such as various optics-based methods, impedance, surface plasmon resonance, and atomic force microscopy, are compatible with non-destructive measurement or detection of individual, living microorganisms and can be used to evaluate microorganism growth and/or development of a multicellular clone. Some of these methods are furthermore capable of resolving and providing multiple measures or data points for a particular, individuated microorganism at any given point in time. Any method, as may be currently in practice or developed in the future, may be used to determine a value associated with an attribute of a microorganism for use in determining a growth rate, as disclosed herein.
In various aspects and embodiments, and with reference to
In various aspects and embodiments, sample 110 may be any suitable biological sample containing a microorganism. For example, sample 110 may be a biological fluid (e.g., blood or other bodily fluid), a laboratory specimen from a culture, or any other suitable sample containing a microorganism. Sample 110 may be collected from a patient in a healthcare setting. Moreover, sample 110 may be collected for diagnostic, treatment, scientific, or any other suitable purpose.
In various aspects and embodiments, input system 120 may be any system capable of receiving, processing, handling, dispersing and/or otherwise preparing a sample. Input system 120 may comprise a sample input capable of receiving samples 110 from any suitable source (e.g., a vial, a test tube, a culture, an assay, and/or the like). Input system 120 may be operatively coupled to sample analyzer 130. More specifically, input system 120 may comprise a distribution system capable of preparing and routing samples to a sample analyzer 130. The distribution system may comprise a manifold capable of receiving a plurality of samples. The distribution system may also comprise one or more pumps and plumbing to route the plurality of samples to the sample analyzer. Input system 120 may be capable of processing and/or preparing sample 110 prior to, during, or after transport of sample 110 from the distribution system to sample analyzer 130.
In various aspects and embodiments, sample analyzer 130 may be any hardware, software, or hardware-software system capable of evaluating and collecting data about sample 110. Sample analyzer 130 may comprise any suitable microorganism evaluation, measuring, and data collection devices. Sample analyzer 130 may comprise any instrument or be capable of performing any evaluation and/or data collection process, steps, and/or method described herein with respect to microorganism detection. More specifically, sample analyzer 130 may be capable of detecting, evaluating, characterizing, or otherwise analyzing one or more individuated microorganisms.
In various embodiments, analysis module 140 may be any hardware, software, or hardware-software system capable of evaluating and collecting data about sample 110. Analysis module 140 may be operatively coupled and/or in electronic communication with sample analyzer 130. The functions performed by analysis module 140 may be performed using any combination of hardware, including, for example, a computer-based system, a special purpose computer, a general-purpose computer, a distributed computer system, a consolidated computer system, or a remote server or computer-based system. Analysis module 140 may be capable of receiving and processing microorganism information from sample analyzer 130. For example, analysis module 140 may be capable of receiving image data associated with a microorganism. The image data may represent one or more individuated microorganisms, populations of individually identifiable microorganisms, other information associated with a sample, information regarding debris and noise, and any other suitable information collected, analyzed and/or received by sample analyzer 130. Moreover, analysis module 140 may be operatively coupled and/or in electronic communication with input system 120. Analysis module 140 may receive patient and/or sample data from input system 120 and/or sample analyzer 130 (e.g., information indicating the source of the sample, characteristics of the sample, sample collection information, and/or the like).
Analysis module 140 may be further capable of processing and/or analyzing the microorganism information. For example, analysis module 140 may be capable of parsing the information, assess various characteristics of a microorganism (e.g., location, growth rate, mass, doubling, and/or the like). Analysis module 140 may also be capable of identifying parsed data that is not indicative of or associated with a microorganism (e.g., background, debris, noise, and/or the like). Further, analysis module 140 may be capable of associating various characteristics of one or more microorganisms with events and/or conditions. For example, analysis module 140 may be configured to evaluate a growth rate of an object and/or object site over time. Analysis module 140 may also evaluate multiple events and/or conditions over time. For example, analysis module 140 may be capable of evaluating a growth rate over time and associated with growth rate with specific events or conditions (e.g., the introduction of heat, the introduction of an antimicrobial agent, and/or the like).
The multivariable analysis capability of analysis module 140 also provides computer-based system 100 with an ability to make a recommendation or determination about a microorganism based on one or more events or conditions. For example, based on a change or a lack of change of a growth rate in response to an event or condition (e.g., the introduction of an anti-microbial agent), analysis module 140 may be capable of determining a characteristic (e.g., susceptibility to the antimicrobial agent) or the identity of the microorganism. As will be described in greater detail herein, analysis module 140 may evaluate and characterize the changing or lack of change of the microorganism information and render a recommendation or determination of the microorganism characteristic.
In various embodiments, healthcare IS 150 may be any hardware, software, or hardware-software system capable of evaluating, receiving, processing, associated, and/or displaying microorganism information about sample 110. Healthcare IS 150 may be operatively or electronically coupled to analysis module 140 and/or any other component of system 100. Healthcare IS 150 may comprise one of more portals that are accessible to a healthcare provider. For example, Healthcare IS 150 may comprise an electronic medical record (“EMR”) or other suitable patient health data management system that is capable of providing information and recommendations about a patient's condition.
In various embodiments and with reference to
In various embodiments, computer-based system 100 may detect, measure, track, and analyze individuated microorganisms based on optical image data, such as digital photomicrographs acquired using any of a variety of methods and imaging modes well known to a person of skill in the art, various examples of which are further described below. System 100 may measure microorganism attributes and perform data analysis using measured signal intensity values, such as, for example, pixel intensity values from a digital image. In various embodiments, non-optical methods may be used for detection, data acquisition, and analysis, and any form of quantitative data or measured signal intensity values that may be acquired by any of a variety of measurement systems may be suitable for analysis by system 100. In various embodiments, microorganism information acquired by a non-optical method may be processed in a manner similar to that described in detail herein with respect to pixel intensity values derived from image data.
In various embodiments, system 100 is fully automated and capable of handling various noise levels and signal intensity ranges, independent of illumination heterogeneities, and applicable to different individuated microorganism evaluation systems and methods (e.g., imaging modes, including dark-field, fluorescent, and phase contrast images, and/or the like). For example, locally determined background signal intensities may be used to compensate for illumination heterogeneities that may be introduced as a function of irregular illumination intensity from the illumination source or an irregular interaction of light emitted from the illumination source with the sample cassette.
Examples of image properties that make microorganism detection and clone tracking non-trivial include: high and varying levels of noise, uneven illumination or background signal from a signal source, large amount of debris, and non-immobilized microorganisms.
In various embodiments, system 100 is capable of detecting individuated microorganisms. System 100 may be capable of performing, for example, method 200 and/or method 300.
In various embodiments and with reference to
This analysis may include the evaluation of any suitable attribute of elements of the sample. In this way, sample analyzer 130 may evaluate and measure, characterize, sense or otherwise quantify one or more physical or non-physical attributes of elements of a sample to determine element information. System 100 may process and/or quantify these attributes in any suitable fashion. For example, system 100 may assign a first value to the each attribute or each element (Step 220). This first value may be a different first value for each detected, identified and/or analyzed attribute of each microorganism.
The first value may be initially analyzed against a predetermined or dynamically determined reference range. This reference range may be associated with an attribute to be measured. In response to the attribute being within the reference range, system 100 may identify the element associated with the attribute as an element to be monitored. In response to the attribute being outside the reference range, system 100 may determine that the element associated with the attribute is an element that is not monitored.
In various embodiments, the sample and/or one or more elements may be subject to a condition (Step 230). The condition may be any suitable condition described herein. The condition may be introduced at any suitable time. For example, the condition may be introduced as part of sample preparation. The condition may also be introduced in response to and/or at a time or event after system 100 has identified or is evaluating the elements. The condition may also be introduced to the entire sample or to one or more portions of the sample.
In various embodiments, system 100 may evaluate the elements in response to and/or based on the condition. System 100 may be capable of collecting and or associating element information with the condition. Moreover, system 100 may be capable of collecting and associating element information based on or as a function of the condition. For example, the condition may be an exposure to a specific substance or time. In this example, system 100 may be capable of evaluating, correlating, and storing information about element attributes (or changes in an element's attributes in response to the condition and/or time). In this way, system 100 may be capable of determining a second value associated with an element attribute in response to an event (e.g, time) (Step 240). The second value may characterize a change or no change in an attribute and/or the element. Like with the first value, the second value may be a different second value for each detected, identified and/or analyzed attribute of each element. Moreover, the second value may be a function of the first value and/or proportionally associated with the first value. As such, the second value, when compared to or analyzed with the second value may describe the nature of change of the first value. For example, system 100 may use the first value and the second value to determine a rate of change of an attribute (e.g., a grown rate of an element). Moreover, system 100 may use the second value or a comparison of the first value and the second value to determine whether an element is an element of interest (e.g., a microorganism).
The second value may also be analyzed against a second predetermined or dynamically determined reference range. This second reference range may be associated with the attribute being measured. In response to the attribute being within the second reference range, system 100 may identify the element associated with the attribute as an element to be analyzed or evaluated. In response to the attribute being outside the second reference range, system 100 may determine that the element associated with the attribute is not relevant for further analysis or evaluation (e.g., the element can be discarded or ignored).
In various embodiments, system 100 may be capable of assessing the response of the element to a condition (Step 250). For example, system 100 may determine a rate of change (e.g., a growth rate of an element) in the presence of the condition and in response to the event, based on the first value and the second value. This rate of change of an attribute of the element may be compared to a known or dynamically determined control rate of change (Step 260). In this way, system 100 may be capable of making a recommendation about an element, a condition, and/or an event based on the rate of change of the attribute measured. For example, the recommendation may comprise a determination that an element is susceptible to a condition based on a rate of change correlating with a control rate of change (or an associated rate of a control rate of change). The recommendation may comprise a determination that an element is resistant to a condition based on a rate of change not correlating with a control rate of change (or an associated rage of a control rate of change).
Microorganism Detection Based on Microorganism Information
In various aspects, computer-based system 100 may perform signal detection and analysis of individuated elements of a sample (Step 305). In accordance with various embodiments and as described in detail herein, microorganism information or data from sample analyzer 130 may comprise digital photomicrograph images, such as a set of dark field and/or fluorescent digital photomicrograph images obtained over a period of time. In other embodiments, microorganism information from sample analyzer 130 may comprise non-image data. Although the functions that may be performed by analysis module 140 are generally described in relation to image data in the present disclosure, the various functions described herein may be capable of parsing data from sample analyzer 130 to identify local singularities (e.g., potential individuated organisms or discrete clones) regardless of whether the data comprises digital images having pixel intensity data, or comprises other forms of data wherein sample elements are measured and represented with quantitative signal value and location information values (i.e., microorganism information values). The output for analysis module 140 following data analysis may comprise a matrix or table of discretely identified clones and associated information obtained over the course of sample evaluation (i.e., values associated with attributes of the microorganism, or microorganism information value subsets). In various embodiments and as described in greater detail herein, identification and tracking of discrete clones and evaluation of attributes of those discrete clones may facilitate bacterial species identification, such as by fluorescence in situ hybridization, as well as assessment of clone growth, such as for antibiotic susceptibility testing.
In various embodiments, analysis module 140 may perform steps including seeding, registration, object model fitting, and clone tracking, outlined below and described in greater detail herein. Analysis module may perform these steps in any logical combination and sequence. In various embodiments, certain steps, for example, tracking, may not be required due to features of the microorganism detection system used to perform sample evaluation. The various steps that may be performed by analysis module 140 for image and non-image data are described briefly in the following paragraphs and in greater detail below.
Seeding.
In various embodiments, seeding may be performed by analysis module 140 as part of microorganism analysis. A seeding step may comprise performing multiple functions including smoothing, denoising, and background estimation and segmentation. Image smoothing and denoising may be performed using techniques including spatial convolution with filters. Background estimation and segmentation may be used to identify regions of the images or other data that potentially correspond to clones (i.e. foreground information) and which regions contain purely background information. With these components distinguished, the seeding phase may also output a list of coordinates or addresses identified as potential clones (i.e. seed points) for downstream analysis.
Registration.
Registration of seed points or sample objects may also be performed by analysis module 140 in accordance with various embodiments. For example, following seeding, each image may be compared with the previous image in a time-lapse series to compute a step-wise registration shift at subpixel resolution. This shift is essentially two numbers which represent the translation (on the x and y axes) in pixels that is required for the images to be aligned. The registration process is also capable of detecting images which are too far out of alignment and should not be considered for further analysis.
Microorganism Object Model Fitting.
In various embodiments, object model fitting may also be performed by analysis module 140. The area or data around each seed point may be evaluated to form a list of models. These models describe a potential cell, and may include, for example, various measurable or detectable attributes of a sample element such as signal intensity, size, shape, and orientation. Object model fitting may also evaluate which portions of an image or non-image data are changing as time progresses. This observation is critical in distinguishing cells from other measurement or data artifacts including, for example, dust particles.
Tracking.
In various embodiments, analysis module 140 may also track cells or clones throughout the acquired data in a dataset. For example, for image data, after cells have been identified for all of the images by object model fitting, analysis module 140 may track the cells by performing cluster analysis of the cells to identify groups of cells that belong to the same clone. This cluster analysis is computed via a distance measure, as new cells from clones will appear close to the parent cell. Once cells have been sorted into groups mapping to the correct clone, the increase (or decrease) in cell count, aggregate signal area, and aggregate signal intensity across each image or other data form is computed for each clone.
The various processes that may be performed by analysis module 140 are described in greater detail in the following sections.
In various embodiments, detected signals and/or particles from data (e.g., an image) describing sample 110 (e.g., potential individuated organisms) are seeded or identified using a wavelet transform following an initial cell candidate detection procedure, explained in more detail below. In various embodiments, signal detection and/or cell candidate detection is applied to every image in a time-lapse stack. More specifically, analysis module 140 is capable of detecting signals, spots, and/or singularities in data that are greater than a threshold (Step 310). The threshold or background pixel signal evaluated in the cell candidate detection procedure may be comprised of imaging sensor artifacts, signal noise, and structure in the background such as illumination non-uniformities. The background structure and noise characteristics and data produced by the cell candidate detection procedure may be used in subsequent image processing steps such as model fitting, described in detail in Example 11. Analysis module 140 may detect signals having various properties including, for example, a range of intensities, a range of sizes, and a range of shapes. Each of the various properties may be associated with a predetermined threshold that characterizes the level of a significant signal versus noise. In this way, analysis module 140 may identify a signal as noise where a signal is not within a reference range (e.g., is not a significant signal). For example, in cases of image analysis for images with uneven background illumination, analysis module 140 is able to determine whether a signal is a significant signal because the signal is evaluated relative to a background signal-related threshold.
In various embodiments, analysis module 140 may employ a cell candidate detection procedure to distinguish foreground pixels in an image from background pixels that may arise due to imaging sensor artifacts, signal noise, and background structures or signal variations that may be due to non-uniform illumination and the like. For example, image sensor artifacts can comprise bad pixels, including hot, warm, and cold pixels wherein the signal registered by the sensor does not correspond to an actual sample element or lack thereof in the imaged sample. The non-linear response of such pixels to incident photons may be detected and suppressed or eliminated from further data processing in the cell candidate detection procedure. In various embodiments, a cell candidate detection procedure may suppress bad pixels using a local smoothing kernel. The local smoothing kernel may be convolved with input image I to interpolate and suppress bad pixels in accordance with the following functions:
Following interpolation of bad pixels, a second local smoothing kernel may be applied to reduce the effects of shot noise, or statistical noise resulting from photons arriving at an image sensor's photo-sensitive wells:
In various non-optical microorganism detection systems, other sources of statistical noise and non-linear signal responses may similarly occur and be suppressed or eliminated.
Analysis module 140 may employ a wavelet transform for signal analysis. Analysis module 140 may employ the wavelet transformation and allow the signal to decompose the signal into one or more wavelet functions. These functions can be applied to the image. In response to applying the wavelet functions to the image, the analysis module 140 may generate one or more wavelet coefficients that represent an image on several levels of resolution (e.g., a stack of image planes).
For example, analysis module 140 may employ one or more wavelet transforms by successively constructing approximations of one or more signals (e.g., an image) at stepped or various resolution levels. For example, analysis module 140 may create a signal plane for a resolution level of an image, having a general amplitude of approximately zero and that has one or more points of amplitude (e.g., greater than or less than zero). These points of amplitude may correspond to particles (e.g., microorganisms, noise, foreign objects, debris, and/or the like) that can be filtered, identified or otherwise analyzed. As a result, analysis module 140 may reduce a signal (e.g., an image) to a sequence of approximations corresponding to the various resolution levels of the signal. Moreover, analysis module 140 may determine a sequence of the wavelet coefficients (e.g., points of amplitude) that characterize the details of the image. These wavelet coefficients, when considered together with the approximations at the various levels of resolution, describe the signal in a way that highlights the changes in the signal (e.g., the sample elements in the image), which may not be characterized in detail in the various approximation of the signal.
In various embodiments, analysis module 140 may employ wavelet transforms because the wavelet transforms are suitable for signal characterization (e.g., object detection in images). By employing wavelet transforms, analysis module 140 produces a characterization of a signal that is relatively sparse. Put another way, because the approximation of the signal plane has an amplitude that is near zero, a point of signal amplitude may identify sample elements in a signal plane, and the wavelet transform is helpful for identifying individuated microorganisms in a signal. This is so because the individuated organism can be described by a point of amplitude in the plane (e.g., a deviation for the near zero amplitude). Generally, this property of wavelet transforms may provide a representation where most of the wavelet coefficients are close to zero and only those that correspond to significant portions or features of the signal (e.g., an individuated organism or particle) are large. Moreover, the points of amplitude corresponding to significant portions and/or features of the signal persist through several resolution levels of the signal.
In various embodiments, by employing wavelet transforms, analysis module 140 may not require significant computation resources. In this way, analysis module 140 may perform signal analysis with minimal and/or generally available computer resources, because the wavelet transformation analysis is computationally efficient.
In various embodiments, analysis module 140 may employ any suitable wavelet function and associated wavelet transform for signal approximation. Each level k of the wavelet transform starts from an approximation image Ak−1 and produces two images. For example, analysis module 140 may determine a wavelet coefficient image Wk by solving:
with high pass filtering, where A0 is an original image, Ak and Wk for k=1, . . . , 8 are approximation and wavelet coefficient images respectively on a resolution level k, indices (m, n)={(−k, 0), (0, −k), (k, 0), (0, k)} for levels k=1, 3, 5, 7 and (m, n)={(−k, −k), (k, −k), (k, k), (−k, k)} for levels k=2, 4, 6, 8, and i, jε{image area coordinates}. In response to determining Wk, analysis module 140 may approximate signal Ak by low-pass smoothing of Ak−1, by solving:
where Ak and Wk for k=1, . . . , 8 are approximation and wavelet coefficient images respectively on a resolution level k, indices (m, n)={(−k, 0), (0, −k), (k, 0), (0, k)} for levels k=1, 3, 5, 7 and (m, n)={(−k, −k), (k, −k), (k, k), (−k, k)} for levels k=2, 4, 6, 8, and i, jε{image area coordinates}.
In various embodiments, analysis module 140 may then reduce or eliminate noise in the signal by filtering out small wavelet coefficients that correspond to noise. Locations associated with points of amplitude within a predetermined threshold are retained as potential sample elements (e.g., organism locations) and points of amplitude outside the predetermined reference range are discarded as noise.
In various embodiments, analysis module 140 may determine an estimation of the wavelet coefficients that correspond to signal without noise on each resolution level of the wavelet transform. This estimation may be based on a Gaussian noise assumption and a non-informative prior distribution for the wavelet coefficient. The non-informative prior distribution may indicate that there is no initial assumption associated with data distribution. The wavelet coefficient estimation for one resolution level may be calculated by solving:
where σk is a standard deviation of the wavelet coefficients that correspond to noise on the resolution level k and Wksignal (i, j) is a wavelet coefficient at location (i, j) on a resolution level k of a signal without noise.
The wavelet coefficient estimation may use a value of σk for each resolution level of the wavelet transform. In various embodiments, analysis module 140 may estimate σ, based on an assumption that most of the wavelet coefficients correspond to noise. This assumption may allow analysis module 140 to determine noise wavelet coefficients using a median of the absolute value of wavelet coefficients by solving:
where the pixels (i, j) and corresponding Wk (i, j) values included in the noise estimate are dependent on the resolution level k. To determine the resolution level k, analysis module 140 may decompose an original image into various levels (e.g., 8) of the wavelet transform as described above. This decomposition provides eight wavelet coefficient planes of the same size as the original image. Analysis module 140 may use one or more high resolution planes or the signal to filter out the noise. For each plane, analysis module 140 may deflate or normalize the wavelet coefficients by determining Wksignal(i, j). As a result, analysis module 140 may identify noise by determining which of the wavelet coefficients deflate or normalize to zero.
In various embodiments, analysis module 140 may also estimate σk values for each plane. For example, analysis module 140 may create a noise mask for each plane. For the first plane (e.g., the highest resolution plane) the mask may include all wavelet coefficients. For the second, third, and fourth planes, the mask may include only the locations that are detected as noise (e.g., wavelet coefficients deflated or normalized to zero) in the previous plane. Analysis module 140 estimates the noise standard deviation for each plane based on the determination of σk by using the wavelet coefficients that are covered by the noise mask.
In various embodiments, analysis module 140 may analyze the lower resolution planes (e.g., the fifth to eighth planes) to detect significant wavelet coefficients (e.g., sample elements or signal features that are not noise and that may correspond to organisms). Analysis module 140 may employ the same wavelet coefficient deflation procedure, namely the estimation of σk. However, analysis module 140 may employ a control based on a predetermined or dynamically determined rule. The rule may provide that the wavelet coefficients are set to zero in response to the wavelet coefficients being deflated or normalized to zero on both planes from paired planes (e.g., the fifth and sixth planes and/or the seventh and eighth planes in an eight plane analysis). In response to the normalization or deflation not being zero, the deflated wavelet coefficients can be set to one. As a result, each location corresponding to a one is not discarded altogether. Rather, the location is monitored as a potential seed site (e.g., a site where an organism is present), as described in more detail below. σk values are estimated using all wavelet coefficients of the plane, there is no mask. This may be a two-step filtering process. For example, the first filtering step may detect noise and the second filtering step may detect particles. A mask may be used to define areas of an image that contain only noise wavelet coefficients.
In various embodiments, analysis module 140 may calculate wavelet coefficient correlation planes by taking products of the deflated wavelet coefficients at the same location on all of the various wavelet coefficient planes. This correlation may allow analysis module 140 to identify one or more correlation points across the various planes. In this way, analysis module 140 is able to determine potential microorganism locations, of variable size and morphology, in the presence of noise and uneven background illumination with a manageable frequency of false positive events while minimizing the frequency of false negative events.
In various embodiments, analysis module 140 may employ a discrete wavelet transform to construct a per-pixel estimate of significant foreground presence, and thereby identify seed locations for candidate cells. The discrete wavelet transform may also decompose each image into high- and low-frequency components at multiple levels of resolution. A discrete wavelet transform may be used to decompose each image into an approximation image A and a coefficients image C at every level of resolution k, for example, k=1, . . . , 8. Thus, for every level of resolution k, the discrete wavelet transform comprises two convolutions, including a high-pass filter of the previous approximation image Ak−1 to produce a coefficients image Ck by solving:
C
k(i,j)=Ak−1(i,j)−¼Σm,nAk−1(i+m,j+n),
and a low-pass filter of Ak−1to produce an updated approximation image Ak by solving:
The presence of significant coefficients is then determined from analyzing coefficient images Ck in accordance with the following process. First, resultant wavelet coefficients are interpreted to construct a noise presence mask using the first four coefficient images k=1, . . . , 4 by solving:
where σk=median(|Ck(i, j)|/0.6745 for i,j such that Wnoise(i, j)=0, is an estimate of noise at a given resolution.
Next, two pairs of wavelet coefficient images, k=5,6 and k=7,8, are used to estimate contributions due to cell candidates. For k=5,6:
where σk=median(|Ck(i, j|)/0.6745 for all i,j. Wsignal2 (i,j) is computed for k=7, 8 using an analogous function.
The final wavelet coefficient image is a product of noise and two signal coefficient images, which results in a non-zero coefficient determined for significant (i.e., lower frequency) foreground events only:
P(i,j)=Wnoise(i,j)Wsignal1(i,j)Wsignal2(i,j).
Candidate cell locations, or seed locations, are then computed by a local maxima operator:
Seed(n)=argmax[i±m,j+n](P(i+m,j+n)),
where (m, n)ε({−2,2}, {−2,2}).
Microorganism Object Model Fitting
In various embodiments, analysis module 140 may assign unique identifiers (e.g., locations) to signals (Step 315). By monitoring the locations, analysis module 140 may estimate morphological parameters of the identified signal objects (e.g., detected potential organisms).
In various embodiments, analysis module 140 may determine potential locations based on the particle seeding from a signal. Analysis module 140 may further evaluate the identified locations to determine which of the identified locations correspond to the actual microorganisms. As such, analysis module 140 may model each identified location (Step 320). For example, analysis module 140 may estimate particular attributes of a particular sample element. The particular attributes may be associated with a microorganism location, such that microorganisms attributes, activities and/or the like can be further evaluated, characterized, or estimated by system 100. More specifically, analysis module 140 may create a microorganism object characterized by length and width in signal parameters (e.g., pixels in the context of an image, impedance registering electrodes in a microelectrode array, and the like), orientation angle in the plane on a signal (e.g., in radians), and height in signal intensity units of an image for each identified location. Additionally, analysis module 140 may determine the signal intensity of an object associated with the identified location (e.g., microorganism signal intensity may be calculated as a microorganism object parameter). In various embodiments, the signal intensity may be a composite value representing a plurality of measured attributes, including, for example, a plurality of pixel intensities associated with a sample element.
Analysis module 140 may estimate, determine, and/or characterize the dimensional parameters of objects at identified sites by fitting microorganism models to data associated with each location. The model may be characterized as a second-order surface such as, for example:
M
ij(x,y)=a2x2+b2y2+cxy+d,
illustrated in
Analysis module 140 may fit the surface associated with the location by minimizing the squared error to an original image at the location (i,j). The coefficients of the surface a, b, c and d are used to determine the parameters of the object associated with the location. For example, analysis module may calculate the length, width, and orientation angle of the object using the eigenvalue decomposition of the matrix
Microorganisms may look like brighter circular or rod-like spots on a darker background. Based on this determination, analysis module 140 may discard certain identified locations. For example, only objects and/or associated object locations with both positive eigenvalues or a larger positive eigenvalue may be retained as potential microorganisms. The height of the object associated with the location may be assigned the value of d.
Using the parameters determined for each object model at each identified location, analysis module 140 may create a microorganism object as a rectangular box with a length and width equal to the corresponding microorganism object's length and width and the height of 1. Analysis module 140 may smooth the box by convolving with a Gaussian kernel of width 7. The resulting object height may be scaled up to the height of the microorganism object parameter d. This smoothed box may be used to calculate an error of the microorganism object as an absolute deviation between the smoothed box and the original image at the corresponding location. The error may be used as a threshold range or limit to identify microorganism object sites (e.g., organisms for evaluation) and eliminate debris and/or foreign objects that are not microorganism objects. For example, analysis module 140 may identify microorganism objects with errors of less than 300 as detected microorganisms. In response to meeting the threshold range, analysis module 140 may represent each selected microorganism as a microorganism object with corresponding parameters.
In various embodiments, analysis module 140 may evaluate detected image objects by employing model fitting in accordance with a process set forth in greater detail in Example 11, below. In various embodiments, model fitting may rely on products of other processes performed by analysis module 140, such as seeding (described above) and registration (described below).
In various embodiments, analysis module 140 may determine and/or calculate a microorganism composite value parameter (e.g., a composite value representing a plurality of measured or estimated attributes). The microorganism composite value parameter may be determined based on the sum of measured object values minus background values. For example, a composite value may comprise the signal intensity of pixels in the original image covered by the microorganism object box minus image background for these pixels. If microorganism objects overlap, analysis module 140 may distribute the signal intensity value between overlapping microorganism object locations in proportion to the values of the microorganism object boxes over the corresponding pixel.
In various embodiments, analysis module 140 may create a background signal based on the first signal of the signal sequence (with the least number of microorganisms). The first signal may be transformed by deleting all areas that belong to any microorganism object (regardless of whether the microorganism object is retained as a microorganism or discarded because of a large fitting error). In response to the transformation, analysis module 140 may substitute the pixel values in these areas with the median of the lower 50th percentile of 800 closest pixels that do not belong to any other identified location and corresponding microorganism object. Analysis module 140 also smoothes the background image by a median filter of size 10 in order to eliminate noise. The smoothed background image may be saved and used as the background for all images in the sequence.
As such, analysis module 140 is able to estimate and/or characterize the length, width, and/or height of microorganism objects based on image local curvatures at identified locations. Moreover, analysis module 140 may reduce associated errors by eliminating identified locations and associated seed objects in response to the fit quality of microorganism object being outside a predetermined reference range with respect to the original image.
Clone Tracking
In accordance with various embodiments, analysis module 140 may track an individuated microorganism. Clone tracking may not be required in various embodiments, such as methods of microorganism handling and data acquisition that do not rely on data processing steps for distinguishing and identifying individual microorganisms. For example, and as described in greater detail below in Example 13, various methods may be used whereby individual cells or clones are physically isolated or contained at a discrete location with no risk of relocation, overlapping growth, or the like. However, in accordance with various embodiments, microorganisms may be able to move or relocate over the course of data acquisition, and clone tracking may be required to ensure that data is acquired for the same individual microorganism over time and thereby contribute to data integrity.
In various embodiments, analysis module 140 may track each microorganism from a first image through the image sequence individually. Analysis module 140 may assign microorganisms in the first image to clones (e.g., divided or new microorganisms at a monitored object location). Moreover, analysis module 140 may create clone tracks as combinations of individual microorganism tracks from the same clone. This allows analysis module 140 to evaluate which microorganisms belong to which clone for the first image of the sequence, where the number of microorganisms is the smallest and clones may be well separated. By creating an assignment or track for each clone, analysis module 140 may determine a baseline associated with microorganisms and clones from the first signal. Analysis module 140 may then evaluate the clone progress and/or behavior automatically using the microorganism track assignments.
In various embodiments for which clone tracking is implemented, analysis module 140 may employ one or more rules for determining track assignments. For example, a track may be a sequence of sets of microorganisms assigned to a particular track in each signal of a signal sequence. The track may start as one microorganism, and each microorganism of the track can be associated with zero, one, or several microorganisms in the next signal of the sequence. Each microorganism in the first signal of the signal sequence can be a start of a microorganism track. In various embodiments, the start or origin of each microorganism track corresponds to the first signal of the sequence, with no new tracks created thereafter. Tracks can end before the end of a signal sequence. Microorganisms in a subsequent signal of the sequence should have predecessors in the previous signal. Microorganisms that are tagged as non-growing can be assigned as a predecessors to no more than one microorganism in the next signal of a sequence. The microorganism in the next signal should represent the same microorganism as its predecessor with high probability. Tracks may be created by associating microorganisms in a subsequent signal of the signal sequence with the closest microorganisms in the previous signal. This may be done by evaluating a change in a characteristic or event (e.g., a location) associated with a subject cell and an associated microorganism.
Registration
In various embodiments, the first signal sequence can be registered to eliminate alignment shifts from one signal to the next. Signal registration can be performed on signal sequences regardless of whether correlating elements between the signals are present in the signals. Correlating elements may be used as an alignment marker (e.g., fiducial marks in an image). Otherwise, the detected microorganisms may be used as alignment markers. Registration may be determined by finding the minimal translation of a signal that minimizes the mean squared error of its alignment markers with the previous signal in the sequence.
In accordance with various embodiments, registration may be performed employing a process described in greater detail in Example 12, below.
In response to registration, non-growing microorganism objects may be detected. For the first five cycles of the signal sequence each microorganism may be matched to the closest microorganism in the next signal in the sequence. As a result, analysis module 140 may determine a list of single-microorganism tracks through the first five cycles of the signal sequence. Analysis module 140 may calculate two measures of volume difference for all possible pairs of microorganisms in a track. The first measure may be the size of the non-overlapping volume of the microorganism models that are placed at the same location but retain their orientation. The second measure may be the difference in volume between the microorganism models regardless of their orientations. For the five cycle tracks there can be ten pairs of microorganisms that produce two vectors of difference measures of length ten. A microorganism in the first signal of the sequence can tagged by analysis module 140 as non-growing if the square root of the mean of the squares is less than 0.3 for the first vector of difference measures and is less than 0.5 for the second vector.
The implementation of the distance measure calculation for all microorganisms in a first image to all microorganisms in a second matrix may be computationally expensive, especially in cases of hundreds or thousands of microorganisms. Therefore, the distance measure may be calculated iteratively in order make the computational expense of this component of the tracking process feasible.
More specifically, analysis module 140 may place each microorganism from the first signal in the same signal with all microorganisms of the second signal. For each such microorganism configuration an adjacency matrix can be calculated with a Gaussian similarity function:
Analysis module 140 may determine that this adjacency matrix can be interpreted as a transition probability matrix of a random walk that jumps randomly between microorganisms. The distance parameter σdist2 of the similarity function may be initially set to 60. The distance parameter may be set to this level to make the similarity function greater than zero for most microorganisms that are ancestors of the same microorganism.
Each microorganism in the first signal may be assigned a vector of likelihoods of being a predecessor for all microorganisms in the second signal based on the distance parameter. The Laplacian of the adjacency matrix s(i,j) may be constructed. Analysis module 140 may obtain eigenvectors that correspond to the smallest eigenvalues (<0.0005) of the Laplacian. These eigenvectors may approximate indicator functions of microorganism clusters in the microorganism configuration initially constructed. The eigenvectors may be combined as columns into the matrix U, and the matrix Q=UUT may be constructed. A row of a matrix Q that corresponds to a microorganism from the first signal is taken as a likelihood vector of this microorganism being the predecessor for the microorganisms on the second signal.
In various embodiments, as likelihood vectors for all microorganisms in the first signal being are collected, analysis module may associate each microorganism in the first signal with zero, one, or several microorganisms in the second signal. For each microorganism in the second signal, analysis module 140 evaluate whether any identified microorganism object may be a predecessor microorganism in the first signal, based on the highest likelihood value. Based on the highest likelihood value, analysis module 140 may make an association between microorganisms if the likelihood value is the highest for all first signal microorganisms.
In various embodiments, analysis module 140 may identify and separately analyze non-growing microorganisms. For example, analysis module 140 may associate non-growing microorganisms with zero, or one microorganism in the second signal, and the microorganism in the second signal should have similar signal characteristics to the microorganism in the first signal. For example, analysis module may use a similarity threshold of five-fold for both volume difference measures calculated during non-growing microorganism detection. Analysis module 140 may tag microorganisms in a subsequent signal that have been associated with non-growing microorganisms in a previous signal as non-growing microorganisms to propagate non-growing microorganisms through the signal sequence.
All microorganisms that are assigned a track association are deleted from the second image and the association step is repeated until there are no possible associations left between two images. In this way, microorganisms identified in the second image are associated with and/or assigned to a clone in the first image.
In various embodiments, if there are non-associated microorganisms left in subsequent signal analysis, module 140 initiates a tracking procedure. A new microorganism configuration associated with the subsequent signal for each microorganism of the previous signal may be created by removing all previously associated microorganisms from the subsequent signal except the ones that have been associated with the particular previous signal. As a result, analysis module 140 may increase σdist2 and the non-associated microorganisms and repeat tracking through the signal sequence.
Analysis module 140 may associate and/or cluster microorganisms in the first signal of the signal sequence into clones. Analysis module 140 may associate the clusters based on the clones having the substantially similar spectral clustering distances which were identified during tracking. As such, analysis module 140 may create clone tracks that are combinations of microorganism tracks that start from the first image microorganisms of the same clone.
In various embodiments, analysis module 140 may detect potential growing clones based on a probability analysis. For example, analysis module 140 may assign a probability of being a growing clone or a value corresponding to a likelihood of being a growing clone to each tracked clone. Likewise, analysis module 140 may also assign a probability of being a growing clone to any clone for which an attribute may be measured over time, regardless of whether a clone tracking function is performed by analysis module 140. The probability value can be any suitable value. For example, the likelihood may be a value between zero and one, where one represents the highest likelihood. Analysis module 140 may also be configured to determine a growth likelihood value for each tracked clone. In various embodiments, a growth likelihood may be calculated based on pixel intensity data derived from an optical image. For example, the growth likelihood value may be calculated as a product of two likelihood values (e.g., the clone signal intensity curve shape likelihood and the tracking error likelihood). In various embodiments, a growth likelihood may be calculated based on non-optical data, such as mass data, impedance data, analytical chemistry data, and the like. In various embodiments, a growth likelihood may be calculated based on any attribute or combination of attributes that may be measured using any suitable method.
In various embodiments, analysis module 140 may further clarify, refine, and/or eliminate some tracked clones. For example, clones that are not tracked to the end of signal sequence may be assigned a growth likelihood value of zero.
In various embodiments, analysis module 140 may further characterize one or more tracked close based on signal intensity curves of tracked clones. The signal intensity curve for a clone may be quantified as the sequence of sums of intensities of a population (e.g., each individuated microorganism) assigned to the particular clone, which is evaluated or tracked in each signal of the signal sequence. For example, a cubic polynomial p2x2+p3x+p4 may be fitted to or approximate the natural logarithm of the signal intensity curve, where parameters p1, p2, and p3x and the mean squared error of the fit characterize features of the signal intensity curve. These features may serve as input for two logistics regression functions that in turn output the two likelihood values (e.g., clone signal intensity curve shape likelihood and the tracking error likelihood).
In various embodiments, analysis module 140 may determine the tracking error likelihood. The tracking error likelihood may be determined based on a logistic regression function that takes the mean squared error of the fit of the signal intensity curve as an input. This likelihood can represent an assumption that the logarithm of the signal intensity curve of a growing clone should have a signal intensity curve that is well approximated by a cubic polynomial.
In various embodiments, analysis module 140 may determine the clone signal intensity curve shape likelihood logistic regression function and take the cubic polynomial parameters p1, p2, and p3 as input. Based on this input, analysis module 140 may determine that the logarithm of the signal intensity curve of a growing clone should have a specific shape.
In various embodiments, analysis module 140 may determine any suitable variant response function. Similarly, analysis module 140 may be capable of characterizing a response based on any mathematical function and/or estimating the mathematical function associated with a response.
In various embodiments, the logistic regression functions have been constructed by fitting logistic regression models to a training set of tracked clones. The training set included examples of tracked growing and non-growing clones from several evaluations such as those described herein.
In various embodiments, analysis module 140 may fit a cubic polynomial to the logarithm of the signal intensity curve on a clone-by-clone basis for the signal data sequence. Analysis module 140 may use a logistic regression function in the cubic polynomial parameter space to assign a growth likelihood value for a clone.
In various embodiments, growth likelihood information and other data that may be derived from analysis of growth by analysis module 140 may be used to perform antibiotic susceptibility data analysis. Antibiotic susceptibility data analysis uses measurements that reflect the changing mass of clones over time to detect bacterial susceptibility to antibiotics. The growth data may be represented by a growth profile for each clone.
In various embodiments, other growth parameters may be determined from signal intensity curves or growth curves derived for a population of growing clones using various forms of data other than optical image data. Growth parameters may include, for example, the number of growing clones, a ratio of sample number of growing clones to a control number of growing clones, a sample clone division rate, a ratio of a sample clone division rate to a control clone division rate, growth probability, a growth time score, a sample fast clones growth probability, a ratio of a sample fast clones growth probability to a control fast clones growth probability, a division rate for sample fast clones, and the like.
Regression analysis of various growth parameters for strains with known MICs may be used to produce a concentration susceptibility model for a bacterial species and an antibiotic combination. This model, when applied to growth parameters calculated for an unknown bacteria sample, may be used to output a continuous score. The score may be used to describe antibiotic susceptibility of the bacteria sample on a continuous scale. The susceptibility score may be thresholded at known breakpoints to output an estimated MIC value.
In various embodiments, the examples described herein can demonstrate evaluation of various aspects of microorganism attributes, as defined herein. The examples described herein can also demonstrate determination and/or characterization of changes in attributes (e.g., growth rate), in response to, for example, events or conditions. Moreover, the examples described herein may demonstrate analysis of the changes in attributes to make a determination of alternation of growth rates as compared to reference growth rates. Based on these determinations, the systems, methods and computer readable mediums (e.g., system 100), may perform the evaluation steps and provide one or more outputs based on the determined alterations.
The ability of the system of the present disclosure to provide immunochemical and microscopic identification and quantitative growth rate measurement by evaluating in near real time and in situ the mass increase of individual microorganisms was assessed. The system provides a rapid, accurate evaluation of growth dynamics for the population of viable organisms in the sample.
Bacterial species display unique surface antigens enabling specific immunolabeling and identification with a multiplexed automated digital microscopy (MADM) system. Proof of concept was demonstrated by concentrating a sample of mixed Klebsiella pneumoniae and Haemophilus influenzae to a surface and incubating with a mixture of anti-Kp and anti-Hi. Subsequent species-specific secondary fluorescent labeling identified bacterial species via fluorescent imaging, results of which are shown in
Growth rates were determined in accordance with various processes disclosed herein to identify microorganisms via threshold discrimination, and track growth by tabulating integrated intensity through a timed sequence of dark field images. Growth constants and doubling times of individual clones are derived as explained herein, and can be expressed as either individualized or aggregate subpopulation values.
Standard methods of microorganism growth rate quantitation measure optical density (OD600) changes in a growing suspension culture. The experimental system was used to determine the aggregate growth rates of a panel of ten clinically relevant bacterial species, with the results compared to suspension culture measurements. The average difference of 18% sd 5% demonstrates a high degree of concordance in the methods.
Individual bacterial clone growth tracking enables evaluation of antibiotic susceptibility and resistance characteristics on a clonal basis within hours with near real time measurement.
Bacteremia due to multiple drug resistant organisms (MDRO) is increasing in frequency and growing in complexity. Critically ill patients who acquire a bloodstream infection must begin adequate antibiotic therapy as quickly as possible. For critically ill patients, resistance can render initial therapy ineffective, delaying the start of effective antimicrobial therapy. The requirement for overnight culture creates an unacceptable delay. Delay also prolongs exposure to broad-spectrum empiric therapy, creating selective pressure favoring emergent resistance. Systems and methods in accordance with various embodiments of the present disclosure, such as the multiplexed automated digital microscopy (MADM) system referred to with respect to the following examples, have the potential to reduce turnaround time by rapidly analyzing live bacteria extracted directly from a clinical specimen, eliminating the need for colony isolates. The purpose of this pilot study was to determine MADM system sensitivity, specificity, speed, and technical requirements for same-day analysis of live organisms extracted directly from blood. Tests used two of the most common ICU pathogens, Staphylococcus aureus (SA) and Pseudomonas aeruginosa (PA).
The MADM system used a custom microscope and pipetting robot, plus an evaluation system, as described herein. 32-channel disposable cassettes (
Simulated blood specimens consisted of isolates spiked into 10 mL each of 29 aliquots of two short-fill CPD blood bank bags to make approximately 5 CFU/mL of bacterial target species, confirmed by quantitative culture. Spiked isolates included 14 Staphylococcus aureus (SA), 3 Pseudomonas aeruginosa (PA), or 12 non-target Gram-negative bacilli species. Dilution of each sample with 30 mL of modified TSB culture medium promoted growth. 20 additional control aliquots contained no spikes. 4-hour incubation at 35° C., followed with brief spin cleanup, ended with pellet resuspension into an electrokinetic buffer to make 1 mL samples for MADM system analysis. 20 μL sample aliquots were then pipetted into 14 cassette flowcells. A 5-minute low-voltage electrokinetic capture was performed to concentrate microorganisms on the lower surface of each flowcell where a capture coating immobilized the bacterial cells.
Liquid (40° C.) Mueller-Hinton agar with and without antimicrobials was then exchanged through each channel and gelled. Separate pairs of channels received antibiotics, which included the following antibiotics at the concentrations indicated: 32 μg/mL amikacin (AMK), 8 μg/mL imipenem (IPM), 6 μg/mL cefoxitin (FOX), or 0.5 μg/mL clindamycin (CLI). Cooling then gelled the agar, followed by incubation at 35° C. with microscope imaging at 10-minute intervals for 3 hours (concurrent with identification in other channels).
The system acquired dark-field images every 10 minutes. The analyzer applied identification algorithms to each individual immobilized cell that exhibited growth. 6 channels provided data for ID algorithms to score individual organisms and their progeny clones. ID variables included cell morphology, clone growth morphology, clone growth rate, and other factors. The analyzer computed ID probability based on the number of related clones and their scores. The system required 40 or more clones that exceeded a threshold score in order to proceed with analysis.
Controls included quantitative culturing, disk diffusion tests for isolate resistance phenotype, and 20 blood samples without spikes.
Culture confirmed that normal growth occurred in the prepared samples. Organism detection required ≧4 growing clones (GC). Recovery yielded SA GC counts that exceeded CFU as determined by culturing because of near-complete clump disruption in most samples. Counting combined results in multiple channels when appropriate. Identification required ≧40 GC, and each phenotype test required ≧40 GC. The MADM system detected growth in 29/29 spiked samples and no growth in 20/20 non-spiked controls. Growth sufficient for ID occurred in 23/29 samples in the fixed 4-hour growth period. 4 SA samples clumped excessively, precluding ID scoring. 2 PA samples grew too slowly (<1.1 div/hr) to achieve 40 GC in the growth period (5 hours would suffice). SA growth rates were ≧1.5 div/hr. The MADM system identified 1/1 PA and 10/10 SA. One false PA ID occurred out of 22 non-target samples to yield 100% sensitivity and 97% specificity. The false ID was attributable to a known imaging abernation, later corrected.
The MADM system was used to identify drug resistance in 19/20 adequate samples with one false MSSA, yielding drug resistance phenotyping results with 89% sensitivity and 100% specificity. Table 2 summarizes SA data for overall concordance with comparator results.
This pilot study asked whether major pathogens grow quickly enough to enable same-day diagnostic testing directly with bacteremic blood samples using microscopy. The MADM system had previously been used to analyze small numbers of live microbial cells extracted from other specimen types.
This study demonstrated that 4 hours of growth in a common nutrient medium provides enough live clones for MADM system analysis with fast-growing cells (>1.1 div/hour growth rate in the conditions tested). PA required slightly longer times for adequate testing, estimated at 5 hours. Given the number of GC required for a test (40 with the study prototype), number of tests, and the slowest target organism growth, straightforward calculation derives the minimum growth duration needed. Fastest possible turnaround time results from maximizing growth rate while minimizing the GC needed per test, and minimizing the required number of tests and their duration.
S. aureus
Within 8 hours starting with blood, automated microscopy successfully identified target pathogens and detected drug resistance phenotypes for a major species of live bacterial cells extracted directly from a small volume of simulated bacteremic blood. Diagnostic analysis using individual live-cell methods enables rapid turnaround without first requiring colony isolates. The probabilistic identification scoring achieved high concordance with clinical lab results. Resistance phenotype analysis also achieved high concordance. This analytical strategy can also use responses of individual clones to identify organism subpopulations and resistance phenotypes within polymicrobial specimens.
Application of systems and methods in accordance with various embodiments of the present disclosures, such as MADM system analysis, enables diagnostic analysis of live microorganisms extracted after brief growth in culture medium with high specificity and sensitivity.
Infections due to Gram negative bacteria expressing extended-spectrum beta-lactamases (ESBLs) are increasing in frequency and growing in complexity. ESBL drug resistance expression can be difficult to accurately detect by standard culturing methods, and is associated with multiple drug resistance in nosocomial and community infections. For critically ill patients, the likelihood for treatment success is related to the time required to initiate effective antimicrobial therapy. At best, confirmatory tests now require at least one day to perform with isolate culturing. In contrast, automated microscopy has the potential to reduce turnaround time by detecting complex resistance phenotypes directly in positive culture broth. The purpose of our study was to determine the sensitivity, specificity, and speed of automated microscopy to detect ESBL expression in clinically significant isolates of Enterobacteriaceae.
Direct observation of microorganism response to antibiotic exposure was performed on a disposable 32-channel fluidic cassette (
Multiple institutions provided clinical strains. The collection included 24 strains of Enterobacteriaceae known to be ESBL-positive, and 32 known to be ESBL-negative. Strains included K. pneumoniae (14 neg, 11 pos); K. oxytoca (1 neg); E. coli (12 neg, 7 pos); P. mirabilis (1 pos); E. cloacae (3 neg, 4 pos), E. aerogenes (1 pos); S. marcescens (1 neg); and C. freundii (1 neg). Quality control strains included CLSI standard strains from the ATCC. CLSI ESBL confirmatory disk diffusion tests (DD) served as the comparator.
10 mL of banked whole blood in CPD was spiked into BACTEC Plus Aerobic/F bottles (BD), followed by an isolate spike using the above-listed clinical isolates to make 103 CFU/mL final concentration. Bottles incubated overnight (16 hours) at 35° C. with agitation. Centrifuged 100 μL culture aliquots were resuspended in 500 μL of a low ionic strength buffer to lyse blood cells. Further 5,000-fold dilution used an electrokinetic buffer to produce inocula. 20 μL buffer-resuspended aliquots of the inocula were pipetted into each flowcell and electrokinetic concentration captured live cells onto the flowcell surfaces. Antibiotics were then introduced into each flowcell. ESBL detection used 4 separate fluidic channels, each receiving a 20 μL inoculum containing 256 μg/mL of ceftazidime (CAZ) or cefotaxime (CTX) with or without 4 μg/mL of clavulanic acid (CA). Total preparation time averaged 20 minutes prior to placing the cassette on the microscope stage. Each 20× magnification field of view for microscopy contained approximately 4 to 40 growing clones, and each flowcell channel contained 40 separate fields of view. The system acquired darkfield images of 40 separate fields of view for each condition (each flowcell) every 10 minutes for 3 hours, computed the mass of each channel's cell population during the test, and compared the mass ratio in the antibiotic-only to its paired +CA channel. Thus, the system performed 6 concurrent assays in separate flowcells for each isolate, summarized in Table 3, below:
The clone-by-clone growth analysis was performed for each isolate under each test condition using the image analysis algorithm. The system classified ESBL status by computing the growth in each drug with or without CA, using the ratio of clone mass. The system classified an isolate as ESBL positive if the mass ratio achieved a threshold, compound criteria using both drugs. The comparator was a CLSI confirmatory ESBL disk diffusion (DD) assay.
The MADM system correctly classified 24 of 24 ESBL-positive strains (100% sensitivity), and 30 of 32 ESBL-negative strains (94% specificity) within three hours after inoculum introduction. The average maximum mass ratio of ESBL-negative isolates was 2.4±5.4 s.d. while the average for ESBL-positives was 96±113 s.d. Sensitivity was 100% (CI 84-100%) and specificity 94% (CI 78-99%). MADM system results are summarized in Table 4.
One false-positive occurred for a K. pneumoniae strain that had DD values of 11 and 15 mm, just below the 5 mm difference required, and produced microcolonies in the zones. It is indicated as datum “A” in
This study demonstrates direct 3-hour blood culture pathogen ESBL resistance phenotype detection using automated microscopy. It extends other MADM system studies that used respiratory specimens and additional resistance phenotypes. Direct measurement of the magnitude and kinetics of clavulanate synergy enabled sensitive, specific, and rapid detection of the ESBL phenotype using a single challenge concentration of each antibiotic. The analytical speed of the automated system was consistent with that required to help de-escalate empiric therapy in critically ill bacteremic patients.
Nosocomial infections due to multiple drug resistant (MDR) bacteria are increasing in frequency and growing in complexity. For critically ill patients, resistance can render initial therapy ineffective, delaying the start of effective antimicrobial therapy. But standard diagnostic cultures introduce a 2-3 day delay to provide guidance. Rapid, same-day, direct-from-specimen ID and AST of respiratory specimens could reduce clinical morbidity and mortality. Systems and processes for evaluating microorganism in accordance with various embodiments of the present disclosure, such as a multiplexed automated digital microscopy (MADM) system described and used in the following example, have the potential to reduce turnaround time by rapidly analyzing bacteria extracted directly from a clinical specimen. A pilot study using respiratory specimens was performed to compare analysis performed using a multiplexed automated digital microscopy (MADM) system with traditional cultures-based approaches. The purpose of our study was to determine the speed and accuracy of a MADM system as an alternative to culturing with same-day quantitation, identification, and resistance phenotyping. Customized MADM systems used commercial inverted microscopes with 12-bit monochrome cameras. A computer system ran custom image analysis and experiment control software. 32-channel disposable cassettes (
A total of 281 de-identified remnant respiratory specimens were collected from hospital and commercial sources. The specimens included 25 endotracheal aspirates (ETA) of unknown age obtained from a specimen vendor. Also included were 230 mini-bronchoalveolar lavage (mini-BAL) and 26 ETA specimens obtained from Denver Health Medical Center (DHMC). DHMC specimens were 7-21 days old. AST results were available for all mini-BAL specimens but not for ETA. Accompanying reports also included semi-quantitative ID used to select 92 bacteria-positive specimens for analysis using the MADM system. Targets included Pseudomonas aeruginosa, Acinetobacter baumannii complex, and Staphylococcus aureus. Controls used standard culturing methods (Cx).
After culture re-test, 79 specimens demonstrated quantitative culture (qCx) values above the diagnostic threshold (1e4 for mini-BAL and 1e5 CFU/mL for ETA). These specimens were prepped for introduction into the MADM system. The system performed quantitative ID for Staphylococcus aureus (STAU), Pseudomonas aeruginosa (PSAE), and Acinetobacter spp. (ABCC). Concurrent quantitative culture was performed on specimens. The MADM system performed resistance phenotype tests on STAU-containing specimens for cefoxitin (FOX) MRSA phenotyping and clindamycin (CLI) resistance. The system tested ABCC- and PSAE-containing specimens for amikacin (AN) and imipenem (IMP) resistance.
Specimen preparation used a brief procedure to release bacteria, reduce imaging background, and suspend bacteria in a low ionic strength buffer. We rejected 13 specimens reported as positive but for which repeat qCx failed to confirm content. We rejected 10 samples with heavy interfering background when dilution to OD600=0.3 yielded organism counts inadequate for analysis. We rejected 7 samples for other technical deficiencies. The remaining 62 specimens were tested using the MADM system.
20 μL samples of prepared specimen were pipetted into independent flowcell channels and a low-voltage electrical field was applied for 5 minutes. The electrical field concentrated bacteria onto a functional surface coating that immobilized the bacteria on the lower flowcell surface. Each flowcell channel received only one type of test reagent solution that contained a selective agent if required (only for channels used to test AB, sulbactam 32 μg/mL).
The instrument acquired images at 10-minutes intervals for 180 minutes, using 10 fields of view in each flowcell channel through a 20× objective. Imaging used darkfield illumination. Identification variables included response to selective agents (AB with sulbactam), cell morphology, growth morphology, and growth rate.
Identification algorithms applied to each individual immobilized bacterial cell. The system measured the amount of change in mass over time to compute growth rates. Identification consisted of computing and combining probability scores for morphology, response to selective media, and growth rates to produce a receiver operating characteristic curve (ROC) to derive classification criteria.
Dark field illumination revealed specimen matrix residue pixel blobs with a broad range of size and morphology. The system distinguished live microorganisms by requiring measurable growth as well as morphologic criteria in accordance with various aspects of the present disclosure.
MADM system results were concordant with repeat qCx in 59/62 specimens. Identification scoring algorithms for STAU yielded 14/14 true positives (TP), 45/45 true negatives (TN), 2 false positives (FP), and 1 false negative (FN); for ABCC 1/1 TP, 60/61 TN, 1 FP; for PSAE 3/3 TP, 59/59 TN. Two specimens yielded false positives for STAU and one yielded a STAU false negative. Overall ID performance was 95% sensitivity and 99% specificity. 2 specimens had STAU that expressed the MRSA phenotype by MADM (FOX) and Cx (OXA), and one by MADM system analysis only. None of the STAU expressed CLI-resistance. All PSAE and ABCC were susceptible to IMP and AN. MADM system resistance detection was concordant with hospital AST results except with one STAU sample (MSSA by oxacillin MIC, MRSA by FOX in the MADM system). For the 186 ID tests, Table 5 summarizes performance. Times to results were 1 hour for specimen prep and 3 hours to all analytical results for a total of 4 hours specimen-to-answer. Table 6 summarizes resistance phenotype results, with one discordant MRSA false positive.
Table 6. Target-positive specimens.
2 false positive STAU IDs resulted from incorrect speciation (1 chained cocci, 1 Enterococcus). Test optimization or fastidious media could improve future versions. One ABCC false positive was Enterobacter sp. The false negative STAU had too few clones to meet the call criterion. Scanning more fields of view resolves this problem. The MRSA discordance arose in Cx with oxacillin, which is no longer considered the most reliable phenotyping agent (FOX, as used in the MADM system analysis). The small number of cells required for analysis is compatible with the bacterial concentration at BAL diagnostic threshold of 104 CFU/mL and ETA at 105 CFU/mL.
Conclusions
A multiplexed automated digital microscopy system in accordance with various embodiments of the present disclosure accurately analyzed live immobilized bacteria extracted directly from mini-BAL and ETA specimens. Total specimen-to-answer time was 4 hours.
Infection with heteroresistant organisms can be difficult or impossible to detect by standard antibiotic susceptibility culturing methods using MIC criteria. Of particular concern, heteroresistance by S. aureus to vancomycin (VAN) may be emerging as a diagnostic challenge. The magnitude of the problem remains obscure because VAN-heteroresistant S. aureus (hVISA) exhibits MICs within the susceptible range but may lead to VAN failure. This leaves the microbiological laboratory community unable to perform adequate epidemiological and clinical studies. The purpose of our study was to determine assay criteria for multiplexed automated digital microscopy (MADM) system to rapidly identify the hVISA phenotype in individual live organisms using abbreviated population analysis profiles (PAP). Numbers of individual organisms tested fell within the range obtainable directly from lower respiratory specimens.
Customized MADM systems in accordance with various embodiments of the present disclosure used commercial inverted microscopes with 12-bit monochrome cameras. The computer-based system ran custom image analysis and experiment control software, as described herein. 32-channel disposable cassettes (
We characterized Staphylococcus aureus (SA) clinical isolates along with isolates from a Centers for Disease Control (CDC) SA collection using 48-hour broth microdilution abbreviated population analysis profiles (BMD-PAP), which served as the control. A total of 30 isolates were characterized. We also applied BMD-PAP to hVISA reference strain Mu3 (ATCC 700698), and measured areas under the curve (PAP-AUC) in all tests.
BMD-PAP consisted of serial isolate concentrations from 1 to 106 CFU/mL dropped onto sectors of VAN agar plates containing from 0 to 6 mg/mL in 10 steps (non-doubling dilutions) and counting colonies. An isolate met the hVISA+ detection threshold criterion if its BMD-PAP-AUC≧0.9 Mu3 AUC. This study did not attempt to discriminate between hVISA and VISA, as designated by the plus sign in hVISA+.
For MADM system analysis, each independent flowcell channel, an example of which is illustrated in
The instrument acquired images at 10-minutes intervals for 90 minutes, using three fields of view in each flowcell channel through a 20× objective and darkfield illumination. In each channel, the system approximately 1,000 growing clones. It then counted the number of clones from the same population sample that exhibited at least 4-fold gain in mass by the end of a 4-hour analysis period. Computation normalized the latter count by dividing it by the initial count. The abbreviated AUC was also determined and compared to microdilution PAP AUC.
By plotting abbreviated PAPs for these normalized counts, we then selected an AUC value for the abbreviated PAP region that yielded the best discrimination between hVISA+ and VSSA strains determined by BMD-PAP AUC and the Mu3 reference AUC.
BMD-PAP detected 15 hVISA+ isolates (3 CDC strains, 12 screened clinical isolates) and 15 VSSA (12 CDC strains). One MADM system evaluation with a VSSA strain contained too many organisms to count and was censored as a technical error, leaving 29 total comparisons. The MADM system correctly classified 14/15 hVISA+ strains, and 14/14 VSSA strains. As illustrated in the plot of MADM-PAP-AUC vs. BMD-PAP-AUC (arbitrary units for areas) (
Growth analysis by the MADM system revealed an identification criterion for using abbreviated PAPs of individual clones growing in the presence of different VAN concentrations to identify non-susceptible S. aureus subpopulations in isolates obtained from various sources. The comparator method used an analogous PAP with broth cultures and the generally accepted classification criterion against a stable reference strain (Mu3).
MADM system PAPs for positive strains had down-sloping characteristics of heteroresistance as did the BMD-PAPs. This study identified a narrow range of VAN concentrations to use for expanded studies, enabling efficient and rapid automation. At its present state, the MADM system appears applicable for use with clinical isolates to identify hVISA+ within 4 hours. This enables replication with larger screening studies to help estimate phenotype prevalence as well as characterizing statistical performance.
The small number of cells required is also compatible with the number available from lower respiratory tract specimens at the diagnostic threshold. Additional research with polymicrobial specimens will determine potential for inclusion in a practical rapid diagnostic system.
A multiplexed automated digital microscopy (MADM) system in accordance with various embodiments of the present disclosure identified hVISA+ isolates in 4 hours with 93% sensitivity and 100% specificity in a collection of 29 isolates characterized by a broth microdilution methods for population analysis profiling.
Standard clinical VAP diagnosis is imprecise, with subsequent treatment often delayed and associated with increased morbidity, mortality (28-d MR=30%) and hospital costs. Quantitive culture (qCx) of bronchoalveolar lavage (BAL) is usually obtained only after VAP is clinically diagnosed. Surveillance of at-risk mechanically ventilated (MV) adults with multiple BALs is associated with significantly more antibiotic-free days & fewer deaths. However, surveillance qCx requires 48-72 hours for results from conventional labs. Susceptibility testing requires an additional day.
Surveillance microbiological testing for rapid bacterial identification and antibiotic resistance testing was evaluated using a multiplexed automated digital microscopy (MADM) system in accordance with various embodiments of the present disclosure to assess whether it could sensitively identify patients who subsequently develop VAP when compared to usual microbiological approaches using conventional culture methods of lower respiratory samples from patients at risk for VAP and reduce time to initiation of treatment and reduce failure rates of initial therapy.
Adult MICU patients with identified surrogate were included within 72 hours of intubation and if anticipated to require MV for >48 h. Moribund state or pregnancy were exclusions. Surveillance mini-BAL (Combicath, Plastimed) was performed on Day 1, 3, 5, 7 and 10 of MV. Samples were split and processed for both a) routine respiratory quantitative microbiological culture and sensitivity assays (>48 h result availability) and b) rapid (<8 hour) flowcell/surface-capture assays using the MADM system. Viable microorganisms were identified using growth analysis enhanced by a focused VAP antibody panel (S. aureus, P. aeruginosa, A. baumannii). Untypable organisms were also reported. Sensitivity was assessed using growth analysis. Bacterial species and antimicrobial agent resistance mechanisms are summarized in Table 7. Attending physicians were blinded to MADM system results.
BAL sample were prepared, removing debris and separating microorganisms from other sample material. Sample microorganisms were introduced into a multichannel fluidic cassette as described elsewhere herein. Bacteria were concentrated and retained on the lower surface of all flowcells using low-voltage electrical field (5 min) Antibody labeling of bacteria in flowcells was used to aid identification.
The automated digital microscopy system was used to perform darkfield imaging of 10 fields of view every 10 minutes for 180 minutes in each flowcell channel. Initial epifluorescence imaging was also performed for antibody detection. The system used identification algorithms in accordance with various embodiments of the present disclosure for each individual immobilized microorganism exhibiting growth and determined growth rates of progeny for the duration of the test. Identification consisted of probability scores based on microorganism morphology and growth rates. Antibody labeling identification data was also incorporated. Observed microorganisms were then classified as STAU, PSAE, or non-target. Antibiotic responses were used to aid identification when appropriate. Quantitation was performed by counting identified microorganisms and computing original specimen CFU/mL.
Conventional clinical microorganism identification was performed by DHMC micro lab using standard CLSI procedures. Clinical microbiological data was provided to ICU clinicians for medical decision making. Identification information generated using the MADM system was prospectively performed but not available for clinical decision making.
S. aureus (STAU)
P. aeruginosa (PSAE)
A. baumannil (ABCC)
Primary outcome assumptions were made relative to study power and sample size: 1) 10% incidence of VAP, 2) 40 h difference in clinically reportable VAP target (QCx BAL ID (48 h)+resistance (18 h) vs. MADM system determination of BAL ID (4 h)+resistance (2 h)), and 3) 80% power, two-tailed α≦0.01 requires 35 patients, assuming a median of 2 mini-BAL per patient (˜8 unique isolates).
A total of 77 mini-BALs (median 2; range 1-7 per patient) were performed on 33 MV patients. Patient demographics and BAL safety and surveillance statistics are presented in Tables 8 and 9. Study results are summarized in Tables 10-12. 20 (61%) patients had diffuse or patchy chest x-ray infiltrates and 3 patients had no infiltrates on enrollment. 70 BAL samples were tested using a MADM system.
10.5 (6.5-18.2)
S. maltophilia
S. maltophilia
K. pneumo
Candida spp.
Mini-BAL based surveillance for VAP is both feasible and safe in ventilated at-risk patients. MADM system-based microbiological surveillance for VAP demonstrated sensitivity (86%) and specificity (97%), with a significant reduction in time to clinically available bacterial ID and resistance (approx 40-66 hr lead time) for multiple organisms and resistance types. In 5 of 7 (63%) mini-BAL samples with a target organism above threshold by QCx, MADM system-based ID would have resulted in important and earlier antibiotic changes/additions. As shown here, systems in accordance with various embodiments of the present disclosure, such as the MADM system, is a promising approach for rapid surveillance in patients at risk for VAP.
Nosocomial infections due to multi-resistant Gram negative bacteria are increasing in frequency and growing in complexity. Pseudomonas aeruginosa (PA) and Acinetobacter baumannii (AB) are major causes of nosocomial infection and difficult to manage because of multi-drug resistance. Enterobacteriaceae that acquire the KPC carbapenemase are also likely to co-exist with multi-drug resistance in addition to presenting formidable detection challenges. Conventional phenotyping methods require growth of large numbers of bacteria, which increases the total time-to-result. For critically ill patients, the likelihood for success is indirectly related to the time required to administer effective antimicrobial therapy. However, standard tests require 2-3 days to characterize antimicrobial resistance patterns using culture-based methods. In contrast, various systems and methods in accordance with various aspects of the present disclosure, such as multiplexed automated digital microscopy (MADM) systems, have the potential to reduce turnaround time by direct detection of antimicrobial resistance phenotypes in bacteria extracted from a clinical specimen. The purpose of this study was to determine the sensitivity, specificity, and speed of automated microscopy to detect major resistance phenotypes associated with multi-drug resistance in significant Gram-negative clinical isolates.
A MADM system was used with a purpose-built 32-channel disposable fluidic cassette (
Clinical isolates of Pseudomonas aeruginosa (PA), Acinetobacter baumannii (AB), and Klebsiella pneumoniae (KP) were tested. Test agents included amikacin (AN), imipenem (IMP), ceftazidime (CAZ), ertapenem (ETP), aminophenylboronic acid (APB), and benzo(b)thiophene-2-boronic acid (BTB). The boronic acids inhibit the KPC enzyme as well as AmpC. Table 13 summarizes organisms and test conditions. Test results are expressed as nonsusceptible (NS) or susceptible (S).
Isolates were grown on blood agar, suspended colonies in tryptic soy broth for 2 hours, then centrifuged and resuspended log-phase bacteria in low ionic strength electrokinetic buffer.
MADM system analysis was performed using a 32-channel disposable fluidic cassette (
10 μL aliquots of 5E+7 CFU/mL were pipetted into separate flowcells for each isolate and test condition. Microorganisms were electrokinetically concentrated onto the flowcell detection surface with an electrical field to the positively charged lower surface to immobilize cells and yield 10-100 bacteria per field of view (
Table 14 summarizes assay performance. Sensitivity and specificity were, respectively: PA-AN (33/37) 89% and (33/35) 94%; AB-IMP (24/26) 92% and (65/66) 98%; AAB-CAZ (58/59) 98% and (14/17) 82%; KPETP (6/6) 100% and (13/13) 100%; KPC/APB (5/6) 83% and (13/13) 100%; KPC/BTB (4/6) 67% and (13/13) 100%.
Direct analysis of small numbers of bacteria using ADM identified resistance phenotypes in non-fermenters and in K. pneumoniae within 3 hours. The experimental method met the objectives of using a small number of cells, achieving rapid results, and having accuracy approaching those of standard tests in identifying major resistance phenotypes, including difficult-to-detect KPC-positive organisms. Cell number was consistent with that previously shown adequate to rapidly identify pathogens from organisms extracted directly from a polymicrobial patient specimen. Further optimization may further decrease the total assay time and improve test performance.
Assay kinetics enabled sensitive, specific, and rapid detection of each phenotype using a single challenge concentration of each antibiotic.
Hospital acquired infections (HAI), and particularly nosocomial pneumonia, are leading causes of morbidity and mortality in critically ill patients. Acinetobacter spp., including A. baumannii and several other Acinetobacter genomospecies, are important pathogens in the ICU.
Hospital-adapted Acinetobacter harbors numerous antibiotic resistance mechanisms and presents serious diagnostic challenges. Because these organisms are often highly drug resistant, their identity and phenotype markedly influence the choice of therapy.
Culture-based systems are able to identify Acinetobacter spp. but require initial enrichment culturing and colony isolation. Culturing methods therefore require as long as 48 hours for positive identification and antibiotic susceptibility testing. This is too long for managing critical infectious diseases because initial therapy must assure adequate control of disease progression.
Molecular methods shorten the identification, but cannot differentiate between live and dead, nor intact or fragmented bacteria, nor can they quantify specimen contents. These are important criteria for many types of specimen, particularly in diagnosing pneumonia.
In order to eliminate the delays required for culturing, it would be desirable to analyze live organisms extracted directly from a patient specimen. Such a method would require species identification and enumeration, as well as the ability to determine the viability of individual cells.
The purpose of this investigation was to characterize a method for rapid identification of Acinetobacter spp. extracted directly from a mock specimen using fluorescent-labeled antibody paired with automated growth tracking of individual bacteria to determine viability. The experimental methods tested in this study are intended to become part of a new rapid diagnostic system using bacteria extracted directly from a patient specimen without prior enrichment culturing or colony isolation.
Acinetobacter spp. and non-Acinetobacter isolates were obtained from ATCC and JMI Laboratories (N. Liberty, Iowa). The collection included 19 A. baumannii and 1 Acinetobacter genomospecies-13, plus 28 non-Acinetobacter isolates of species often found in respiratory specimens.
Direct observation of bacteria was performed on a disposable fluidic cassette inserted into a custom bench-top instrument that combines automated digital microscopy, motion control, and analysis module.
The cassette contained multiple independent flowcells. Flowcells were constructed with transparent top and bottom surfaces to allow microscope imaging. Each surface had a transparent electrode coating, forming an electrophoresis chamber. The bottom surface was coated with poly-L-lysine to immobilize bacteria upon surface contact.
Colonies from agar plates were resuspended in tryptic soy broth (TSB) and grown for 2 hours. Mock specimens were made by spiking log phase bacteria (approx. 5×106 CFU/mL) into bronchoalveolar lavage (BAL) fluid from non-infected sheep. A specimen was then centrifuged on Percoll to reduce debris, washed and resuspended in electrokinetic capture buffer, and pipetted into a cassette's sample wells. Tests were also performed on isolates without BAL. For experiments on live/dead mixtures, live organisms were mixed with formalin-killed bacteria in a 1:1 ratio (McFarland standard).
Application of an electrical field caused bacteria to migrate to the positively-charged lower electrode during a capture step. The bacteria adhered to the surface coating, permitting subsequent medium exchanges. Photomicrographs illustrating microorganisms in a flowcell before and after surface capture are shown in
Polyclonal antibodies were developed in chickens and isolated using acid precipitation of yolk proteins followed by tangential flow filtration using a 100 kDa filter. Antibodies specific for Acinetobacter surface antigens were isolated from the yolk preparation by affinity purification.
Antibody staining of immobilized bacteria was performed by incubation in the affinity-purified IgY for 5 minutes in a 1% BSA/TSB staining solution. Primary antibody binding was followed by washing and detection of bound IgY using 5-minute incubation in goat anti-chicken antibody conjugated to Alexa-555. Quantitative image analysis computed the mean intensity of cell staining and the percentage of cells that stained above a threshold level criterion.
The instrument acquired time sequenced images for each of the flowcells at 10-minute intervals. For growth measurement, the image analyzer computed mass changes using dark field imaging mode. Clones were considered to be growing if they exhibited at least 50% increase in integrated intensity over the 40 minute growth period.
To test feasibility for polymicrobial multiplexing, 1:1 mixed species of live Acinetobacter and Pseudomonas aeruginosa were spiked into BAL. Staining for P. aeruginosa used rabbit O-typing antisera and goat antirabbit antibody conjugated to Alexa-488.
Anti-Acinetobacter antibody labeled 16 of 20 strains of Acinetobacter spp. and did not label 25 of 28 strains of non-Acinetobacter species commonly found in respiratory specimens (Table 15).
A. baumannii
Acinetobacter gsp. 13
Pseudomonas aeruginosa
Stenotrophomonas maltophilia
Haemophilus influenzae
Klebsiella pneumoniae
Escherichia coli
Enterobacter aerogenes
Enterobacter cloacae
Staphylococcus aureus
Staphylococcus epidermidis
Staphylococcus haemolyticus
Staphylococcus pneumoniae
Staphylococcus pyogenes
Staphylococcus salivarius
Capture time was fixed at 300 seconds. Electrokinetic transport moved all bacteria above the capture area to the surface, determined by focusing at different levels above the surface. Growth of immobilized bacteria began after TSB wash without an appreciable lag time (<10 min.).
Antibody did not detectably bind to BAL debris. Over 90% of live cells extracted from the live control mock BAL specimen met the growth criterion, indicating that sample preparation capture, and labeling did not adversely affect viability. None of the spiked dead cells exhibited growth. A mixture of live and formalin-killed cells resulted in staining of both live and dead cells (
Growth measurement clearly differentiated between growing and non-growing individual clones after approximately 30 minutes of growth measurement. In the mixed live/dead mock specimen, 33% of clones met the viability criterion. (
A mixture of live Acinetobacter and Pseudomonas exhibited the expected staining with respective antibodies. Of 344 total cells observed, 221 stained with Acinetobacter antibody and 123 stained with P. aeruginosa antibody.
This set of conditions demonstrated the feasibility of concurrent color multiplexing with multiple antibodies.
Polyclonal antibody developed against surface antigens of Acinetobacter spp. showed the potential for multiplexed identification in the presence of interfering species commonly seen in respiratory specimens. Electrokinetic immobilization and species immuno-identification did not significantly affect cell viability. The experimental methods were able to quantify the ratio of live cells in a mock specimen. Immuno-identification combined with automated growth) tracking of immobilized bacteria represents a rapid and potentially powerful approach to indentifying and differentiating intact live Acinetobacter spp. cells from dead or dormant cells directly from high-titer specimens.
Mechanisms of broad-spectrum resistance to B-lactam antibiotics present serious clinical challenges, particularly with critically ill patients. Methicillin resistant S. aureus (MRSA) has become a major pathogenic phenotype that requires rapid identification in order to assure adequate initial therapeutic coverage. MRSA is associated with multiple drug resistance mechanisms in addition to conferring total β-lactam resistance. Laboratories need new methods to rapidly determine all major antibiotic resistance phenotypes. Conventional phenotyping methods require growth of large numbers of bacteria, which lengthens the total time-to-result. New methods requiring small numbers of organisms for testing could potentially obviate the need for overnight culturing and enable direct-from-specimen analysis.
Multiplexed direct cellular phenotyping offers a rapid alternative method, requiring relatively small numbers of cells. It has the potential to overcome the inherent limitations of other rapid methods, such as gene-based detection, for which resistance expression lacks a direct molecular marker correlate. Direct cellular phenotyping shows evidence of meeting analytical challenges such as inducibility and heteroresistance that now complicate antibiotic susceptibility testing.
This study tested multiplexed assay methods intended to enable a new rapid diagnostic system that will use bacteria extracted directly from a patient specimen without prior enrichment or colony isolation. The purpose was to determine whether the novel direct cellular phenotyping methods meet requirements for speed and accuracy in simultaneously identifying two unrelated and clinically important resistance mechanisms in S. aureus using small numbers of bacterial cells.
Direct observation of bacterial response to antibiotic exposure was performed on a custom disposable 32-flowcell cassette (
A collection of oxacillin borderline-MIC isolates was provided by the CDC. The collection included 78 mecA-positive and 56 mecA-negative strains, plus one strain with mutated mecA that produced a variant PBP2a protein of unknown clinical significance. Tests also included CLSI QC strains (data not shown), ATCC 43300 (MRSA), BAA-976 (macrolide efflux), BAA-977 (inducible MLSB phenotype), and 29213 (susceptible control). 44 of the mecA-positive and 14 of the mecA-negative isolates were either constitutively or inducibly resistant to clindamycin (CLI) according to D-test results. Table 16 lists the CLI resistance phenotype counts by mecA status.
Colonies from agar plates were resuspended in broth and grown for 2 hours. Log phase S. aureus were resuspended in electrokinetic capture buffer at 1×106 CFU/mL. A 10 μL sample was pipetted into each flowcell of the cassette, and the cassette placed into the instrument.
Electrophoresis for 5 minutes concentrated bacteria to the flowcell surface. Bacteria adhered to the capture coating, permitting subsequent medium exchanges. Each 444×592 μm field of view contained approximately 100-500 bacterial cells. All assays used Mueller-Hinton broth (MHB) as a wash medium and reagent vehicle.
For each isolate, the system performed concurrent assays in separate flowcells: a growth control, a non-induction FOX test, a FOX-induced FOX test, a non-induction CLI test, and an ERY-induced CLI test. Prior studies had established 1 hr of 1 μg/mL FOX followed by 3 hrs of 6 μg/mL FOX as standard conditions. Other studies had established 1 hr of 0.1 μg/mL ERY followed by 3 hrs of 0.5 μg/mL CLI as standard.
The instrument acquired images for each of the flowcells at 10-minute intervals. The system performed growth rate measurements on the entire bacterial population within each field of view.
Prior studies established growth-rate interpretation criteria after the challenge period. For MRSA identification, mecA-positive isolates had growth rates greater than 0.1 divisions per hour (div/hr), and mecA-negative isolates had rates less than 0.1 div/hr. For MLSB identification, CLI-resistant isolates had growth rates greater than 0.4 div/hr and CLI-susceptible isolates had rates less than 0.4 div/hr.
Growth began after bacterial immobilization and MHB wash without an appreciable lag time (<10 min)
78 of the 79 mecA-positive strains were classified as MRSA, and 56 of the 56 mecA-negative strains as MSSA (
The experimental method performed using a system and method in accordance with various embodiments of the present disclosure met the objectives of minimal starting cell count, rapid time to result, and demonstrated accuracy comparable to that of FOX-DD and D-zone tests in identifying the MRSA phenotype and CLI resistance in this oxacillin MIC borderline collection.
Further optimization of the induction concentration and challenge concentration may further decrease the total assay time using systems and methods disclosed herein.
Analysis using a MADM system in accordance with various embodiments of the present disclosure required orders of magnitude fewer cells (100-500) and a dramatically decreased period of time (4 hrs) for MRSA and MLSB identification as compared to the number of cells (approx. 104-105) and length of time (days) required by conventional microbiological methods. If combined with compatible concentration and in situ identification methods, the rapid direct phenotyping method enabled the system and methods of the present disclosure has the potential to eliminate the need for overnight culturing and colony isolation with patient specimens such as bronchoalveolar lavage fluid, wound swabs, and other high-titer specimens. The analytical speed of the automated system was consistent with that required to guide initial empiric therapy in critically ill patients.
Current microbiological methods use the absence or presence of bacterial cell growth and/or division to determine the effects of antibiotics on bacteria. Unfortunately, standard clinical microbiology approaches to determination of the minimum inhibitory concentration (MIC) of an antibiotic such as those provided by CLSI and EUCAST do not account for slowly growing clones or for clones that may grow after a dormant period. Survival of these persister clones may have dire consequences for a patient if ignored during the selection of an antibiotic treatment schema. Systems and methods in accordance with various embodiments of the present disclosure are capable of detecting not only microorganism growth and/or division, but can also detect several other indicators of bacterial activity, enabling a more thorough characterization of persister clones during antibiotic susceptibility testing.
In order to identify persister clones capable of retaining activity after exposure to antibiotics, a variety of indicators that revealed different physiological states were tested (Table 17). Physiologic indicators of active bacteria include indicators of growth, responsiveness to external stimulus, transcription, translation, energy dependent activity, enzyme activity, and an intact permeability barrier. The indicators tested experimentally were grouped according to the following functional categories of physiologic states to which they are responsive, which included indicators of respiratory and metabolic activity, and membrane integrity.
Active bacterial cells maintain a proton gradient across their cell membranes, creating a membrane potential that may be measured using fluorescent dyes. However, the dyes used in this study to evaluate membrane potential are toxic to bacteria and can only be used as an endpoint assay.
DiBAC4(3) (Molecular Probes) is an anionic dye that enters depolarized cells (i.e., cells lacking a membrane potential) and fluoresces in the red channel when it binds to intracellular proteins or membranes. Using this indicator, 18.5% of an overnight refrigerator stock (control) and 95% of heat-killed E. coli stained with 10 g/ml DiBAC4(3) after a 15 min incubation period at room temp.
DiOC2(3) (Molecular Probes) is a dye that fluoresces in the red channel when it is highly concentrated and self-associates in cells with an intact membrane potential. Fewer DiOC2(3) molecules are able to enter inactive cells and will exhibit a green fluorescence as a single molecule. The red:green fluorescence ratio is used to normalize the fluorescence results of cells with differing sizes. Carbonylcyanide-m-chlorophenylhydrazone (CCCP) is a proton ionophore that disrupts the proton gradient and was used to treat E. coli cells in an experimental sample that was compared to an untreated overnight refrigerator stock. No significant difference in red:green fluorescence ratios were observed between the O/N refrigerator stock and CCCP-treated E. coli, and further experimentation would be required to determine whether different dye concentrations would be effective for evaluation of cells with intact versus depolarized cell membranes using the system of the present disclosure.
CTC (5-Cyano-2,3-ditolyl tetrazolium chloride) (Sigma) is a redox dye that produces an insoluble fluorescent formazan when it is reduced. Metabolically active bacteria that reduce CTC retain the formazan intracellularly and are detectable based on the fluorescence of the formazan. Overnight refrigerator stock cultures of both E. coli and S. aureus were fluorescently labeled with the reduced form of CTC after 25 min at 35° C. with 4 mM CTC in 0.9% NaCl.
2-NBDG (Molecular Probes) is a fluorescent derivative (green channel) of D-glucose that is internalized by active bacteria. Overnight refrigerator stock cultures of E. coli and K. pneumoniae were fluorescently labeled after approximately 1 min of exposure to 1 μM 2-NBDG. However, overnight refrigerator cultures of H. influenzae, S. maltophilia, and S. aureus did not fluoresce despite independent verification of the viability of the stock cultures used for) the 2-NBDG experiment. While S. maltophilia does not metabolize glucose and served as a negative control providing results that conformed with expectations, the failure of H. influenza and S. aureus indicates that further experimentation is required and other carbon sources may provide better results and/or compatibility with a wider array of microbial species.
C12-Resazurin (Molecular Probes) is a redox dye that is reduced to a red-fluorescent C12-resorufin by metabolically active bacteria. The dyes is non-toxic and stable in culture media according to manufacturer literature. Overnight refrigerator stock cultures of S. aureus were able to reduce C12-Resazurin based on detection of the red-channel fluorescence. Unexpectedly, both heat-killed (2-7 hr post heat-kill) and isopropyl alcohol killed S. aureus were also fluorescently labeled with C12-Resazurin. Further experimentation would be required to evaluate the suitability of this dye for detection of metabolically active bacteria while reducing false positive results.
Carboxyfluorescein diacetate/carboxyfluorescein diacetate succinimidyl ester (CFDA/CFDA-SE; Molecular Probes) compounds are converted into amine-reactive fluorescent molecules when cleaved by esterases present within active cells. When CFDA-SE is taken up by live cells, the fluorescent molecule produced by esterase cleavage and amine reaction is retained inside the cell. Hence, if the cell eventually becomes inactive, these cells will continue to fluoresce. The product formed by uptake and processing of CFDA, on the other hand, should be more “leaky” over time and may exit a dead cell. The difference in kinetics of dye exit between an active vs. inactive cell is unknown. Therefore, although the dyes are non-toxic, they would likely be most effective as an endpoint assay. In experiments performed using the MADM system, heat-killed cells and isopropyl alcohol killed cells demonstrated fluorescence when dyes were added immediately after treatment. A two hour post-heat-kill delay was sufficient to eliminate the possible residual esterase activity observed according to CFDA-SE assays (i.e., no fluorescence was observed with a delayed assay following heat-kill). Shorter delay times have not yet been investigated but may be compatible with avoiding false positive results while providing a shorter assay period.
YO-PRO-1 (Molecular Probes) is a DNA-intercalcater that penetrates damaged membranes but not intact membranes. Experiments were conducted to verify non-toxicity of the dye to various test microorganisms, including E. coli, S. aureus, and H. influenza. This indicator has been successfully used as an indicator to assay membrane permeability in the aforementioned species, and the use of YO-PRO-1 to evaluate antibiotic susceptibility of individual microorganisms in a collection of large numbers of microorganisms is described in detail in U.S. Pat. No. 7,341,841.
Propidium iodide (PI; Sigma) is another red fluorescent DNA-intercalcater that penetrates damaged membranes but not intact membranes. Initial studies using PI were very promising and appeared comparable to YO-PRO-1. However, fluorescent signal intensity vs. background was not as high as for YO-PRO-1.
Plasmolysis is a method of detecting membrane integrity by exposing cells to a very hypertonic solution, such as may be performed by subjecting a cell to osmotic pressure by manipulating the concentration of a solute, for example, sodium chloride. Active cells will shrink in size in response to the osmotic pressure while inactive cells are unable to respond and will remain the same size. The ability to evaluate plasmolysis to assess cell membrane integrity was tested in E. coli, with 77% of cells from overnight refrigerator stock cultures decreasing in size when exposed to 0.9% NaCl, whereas 90% of heat killed cells did not exhibit a response. While certain reports suggest that plasmolysis may be more difficult to detect in gram-negative bacteria than in gram-positive cells, our results in experiments with E. coli indicate that plasmolysis may observed in this gram-negative organism using the system of the present disclosure.
Various dyes were successfully used and are compatible with the system and methods disclosed herein as indicators of cell viability that can be added at the outset of a growth determination evaluation (i.e., non-toxic cell membrane permeability indicators such as YO-PRO-1), while others can be used as endpoint indicators of cell activity or viability following growth evaluation and the results of such assays may be overlaid or correlated with growth determination conclusions generated using the growth analysis module as described herein.
In accordance with various embodiments, data analysis may comprise fitting detected sample elements or objects to models of microorganism shapes. Individual cellular object candidates are generally of circular (i.e., spherical in three dimensions) and/or rod-like (i.e., ellipsoid in three dimensions) shapes. Once clones start growing, the signal shape and signal intensity of each microorganism object or cell is assumed to be a superposition of a number of basic shapes or shape models to which detected image shapes may be compared in a model fitting process described in greater detail in the present example. The model fitting process may be applied to each detected microorganism or cellular object candidate. Such an approach allows for detection robust to noise and neighboring objects as well as artifact rejection. Goodness-of-fit metrics are applied to discard microorganism objects that do not fit microorganism shape models within specified parameters, i.e., detected objects that do not “obey” the models are rejected. This approach also allows for effective signal intensity estimation of the clones (i.e., clone intensity), which is the ultimate goal of the processing pipeline.
The present example describes application of microorganism object model fitting to image data. However, data obtained using a variety of other detection systems and/or methods may be processed as described below. As used in the present example, the term “cumulative image” refers to a per-pixel aggregate of registered changed in stack images over time. Similarly, the term “cumulative background image” refers to a per-pixel aggregate of registered changes in background images over time.
Registration information derived from a registration process performed by the analysis module, such as the registration procedure described in Example 12, is used to align both signal and background images from consecutive frames. In order to constrain the model fitting procedure to areas of the image that are changing (i.e., to focus the analysis only on growing clones), a mask of significant change in signal-to-background relationship in the vicinity of the detections is constructed according to the following functions:
where (m, n) are dimensions of rectangles bounding seed detections, N=m*n,
maxBgChange=max(CumBgk−1)
and
CumI
k
=I
K−1+(register(IK,offsetK)−IK−1)
CumBG
k
=BG
K−1+(register(BGK,offsetK)−BGK−1).
Registered versions of both cumulative and background images for fitting models to IK are generated according to the following functions:
CumI
R
K=register(CumIK,offsetK)
BG
R
K=register(BGK,offsetK)
Before model fit is tested, the input stack image IK is preprocessed to remove hot pixels which can skew the intensity profiles of small cells in a manner similar to that performed during a seeding process, described elsewhere herein. The result of preprocessing is image I′K.
The direction of alignment may be determined for signal and background images in accordance with the following process. A predefined grid of 5×5 pixels with intensities centered on each of the Seed(k) locations may be fitted with a second order surface of the form M(x, y)=a2x2+b2y2+cxy+d, illustrated in
A least squares fit may be found by solving for the four coefficients (Levenberg-Marquard) for all pixels on the 5×5 grid simultaneously if they are within TrackMask(i,j).
The following vector of eigenvalues comprises parameters of the modeling ellipse and includes the semi-minor axis, the semi-major axis, the tangent of the orientation angle, and height:
A model mask (binary) ModelMask is the generated based on the dimensions of the fitted model:
modelWidth=2*(longAxis+5)+1
modelHeight=modelWidth
Values are assigned according to the following function:
The model mask is smoothed with a 7×7 Gaussian kernel and updated as follows:
The model is retained if certain constraints are satisfied, such as goodness-of-fit criteria set forth by the following function:
Each model is evaluated based on the underlying image data and the following parameters are computed:
for i,j such that Model′Mask (i,j)>0. N is number if pixels in model mask.
modelIntensity=d−(1+(1.1−1)*10)*modelBackground.
The aggregate error of a set of overlapping models is computed by integrating over differences of the actual values under models and the fitted errorFit.
As a result of the model parameter estimation, outliers can be discarded.
If errorFit>300 or errorFit<−100, the model is considered invalid. Also, a model may be considered invalid if its dimensions are excessively large, such as:
majorAxis>8, minorAxis>6.5.
The background image may be updated by incorporating the per-pixel background values for the newly estimated models.
The cumulative background image may also be updated based on information from the newly estimated models.
In accordance with various embodiments, time-lapse imagery of the same location within a channel is acquired by using the same commanded location of the stage apparatus. This process may be subject to inaccuracies due to stage imperfections and possible movement of the sample cassette relative to the stage. It is important to align images from the stack to the same sampling grid since tracking growth of a clone becomes greatly simplified. Also, as a result of registration, per-pixel signal and noise statistics over the full stack becomes accessible.
In accordance with various embodiments, the following procedure is applied to every consecutive pair of images in the time-lapse stack.
Since permanent fiducial marks may not be available in any of the images in the stack, the frame content of two consecutive frames −Ik and Ik+1, may be used for alignment. It should be noted that the registration module disclosed in the present example implements 2-D translational registration only; however, in accordance with various embodiments, three-dimensional translational registration is also possible and within the scope of the present disclosure. The following procedure is employed to estimate the content to be used in the registration process:
First, a starting subregion R of image dimension for content estimation is identified as the center of the image:
The center of R is then refined by placing it at the location of the most significant coefficient within the central region:
CenterR=argmax[i,j]P(i,j), for all i,jεR.
The region is then convolved with an edge-finding high pass filter consisting of two 7×7 kernels—Hvertical, Hhorizontal—for both Ik and Ik+1 as follows:
R
Kedge
=R
K
H
vertical
+R
K
H
horizontal,
retaining non-zero values only for those pixels that have not been identified as background according to the following function:
The above procedure is repeated for Ik+1 to obtain RK+1,edge, except no restrictions are placed on including background pixels. Both “feature” images are then reduced in size by a factor of 4 and smoothed with a Gaussian kernel:
R
K,feature
=N(0,0.0002)resize(RKedge,0.25)
R
K+1,feature
=N(0,0.0002)resize(RK+1,edge,0.25)
Smoothed statistics on feature region RK,feature are then computed as follows:
where N is the number of pixels traversed with m={−10,10}, n={−10,10}.
Values of RK,VAR(i,j) are enforced to be positive:
Smoothing of the variance estimate above is performed as follows:
where N is the number of pixels traversed with m=−{−20,20}, n={−20,20}.
An offset between two frames Ik and Ik+1 is computed by shifting RK+1,feature against RK,feature with a predetermined step and evaluating the difference between the two. The offset resulting in the minimum difference is the final registration solution, solved according to the following functions:
where={−1,1}, N=9, and number of iterations maxStep=15. It should be noted that the difference is evaluated only for those locations (i,j) that represent significant variance, such that:
R
K,VAR
(i,j)>mean(RK,VAR
Final registration offset for images Ik and Ik+1 is then RegOffsetK,K+1=offset upon convergence on the minimum difference according to the above process.
An impedance-based quantitative growth measurement system is used to measure the growth of physically isolated individual microorganisms in near real time. The system provides a rapid and accurate evaluation of the growth dynamics for the population of viable organisms in the sample. The present example does not rely on a computer-based system to locate, model, and track discrete microorganisms, but instead monitors discrete impedance sensors for changes in measured value over time that may correspond to growth of a microorganism.
The system disclosed herein may be a bench top instrument that combines a disposable microfluidic cartridge comprising a plurality of individual wells, each having an integrated impedance detector. The number of wells is in large excess of the number of bacteria present in the sample such that either through the expected low concentration level or through dilution, the likelihood or probability is such that a single microorganism or no microorganisms are present in a single well. An example of a 4×4 array of wells is illustrated in
The impedance values are logarithm transformed and fitted with a cubic polynomial, then the population of clones are converted into a measure of growth and/or susceptibility as described elsewhere herein.
A sample comprising microorganisms is loaded into a system comprising a disposable microfluidic cartridge with a surface having a detection surface with an array of discrete, individually addressed microelectrodes suitable for performing impedance measurements. Microorganisms introduced into the cartridge are electrokinetically concentrated to the detection surface of the cartridge prior to performing bioelectroanalysis. Growth medium compatible with bioelectroanalysis is introduced to the chamber. The array of microelectrodes is used to measure cell wall charges and the release of ions and other osmolytes from microorganisms concentrated on the cartridge surface comprising the microelectrodes. A series of impedance measurements are recorded at each electrode in the array over time and signal analysis and detection is performed using analysis module 140 as described elsewhere herein. Impedance values measured over time for each discrete electrode in the array may be plotted, and impedance signal intensity data may resemble pixel intensity data acquired using various optical-based methods. Exemplary data is provided in
It will be apparent to those skilled in the art that various modifications and variations can be made in the present disclosure without departing from the spirit or scope of the disclosure. Thus, it is intended that the present disclosure cover the modifications and variations of this disclosure provided they come within the scope of the appended claims and their equivalents.
Any communication, transmission and/or channel discussed herein may include any system or method for delivering content (e.g. signals, images, data, information, metadata, etc), and/or the content itself. The content may be presented in any form or medium, and in various embodiments, the content may be delivered electronically and/or capable of being presented electronically.
In various embodiments, the methods described herein are implemented using the various particular machines described herein. The methods described herein may be implemented using those particular machines described herein, and those hereinafter developed, in any suitable combination, as would be appreciated immediately by one skilled in the art. Further, as is unambiguous from this disclosure, the methods described herein may result in various transformations of certain articles.
For the sake of brevity, conventional data networking, application development and other functional aspects of the systems (and components of the individual operating components of the systems) may not be described in detail herein. Furthermore, the connecting lines shown in the various figures contained herein are intended to represent exemplary functional relationships and/or physical couplings between the various elements. It should be noted that many alternative or additional functional relationships or physical connections may be present in a practical system.
The various system components discussed herein may include one or more of the following: a host server or other computing systems including a processor for processing digital data; a memory coupled to the processor for storing digital data; an input digitizer coupled to the processor for inputting digital data; an application program stored in the memory and accessible by the processor for directing processing of digital data by the processor; a display device coupled to the processor and memory for displaying information derived from digital data processed by the processor; and a plurality of databases. Various databases used herein may include: client data; merchant data; financial institution data; and/or like data useful in the operation of the system. As those skilled in the art will appreciate, user computer may include an operating system (e.g., a Windows, UNIX, Linux, Solaris, Mac OS, or other suitable operating system) as well as various conventional support software and drivers typically associated with computers.
The present system or any part(s) or function(s) thereof may be implemented using hardware, software or a combination thereof and may be implemented in one or more computer systems or other processing systems. However, the manipulations performed by embodiments were often referred to in terms, such as matching or selecting, which are commonly associated with mental operations performed by a human operator. No such capability of a human operator is necessary, or desirable in most cases, in any of the operations described herein. Rather, the operations may be machine operations. Useful machines for performing the various embodiments include general purpose digital computers or similar devices.
In fact, in various embodiments, the embodiments are directed toward one or more computer-based systems capable of carrying out the functionality described herein. The computer-based system includes one or more computers and/or one or more processors. The processor is connected to a communication infrastructure (e.g., a communications bus, cross over bar, or network). Various software embodiments are described in terms of this exemplary computer system. A computer system can include a display interface that forwards graphics, text, and other data from the communication infrastructure (or from a frame buffer not shown) for display on a display unit.
A computer system also includes a main memory, such as for example random access memory (RAM), and may also include a secondary memory. The secondary memory may include, for example, a hard disk drive and/or a removable storage drive. The removable storage drive reads from and/or writes to a removable storage unit in any suitable manner. As will be appreciated, the removable storage unit includes a computer usable storage medium having stored therein computer software and/or data.
In various embodiments, secondary memory may include other similar devices for allowing computer programs or other instructions to be loaded into a computer system. Such devices may include, for example, a removable storage unit and an interface.
A computer system may also include a communications interface. Communications interface allows software and data to be transferred between a computer system and external devices. Software and data transferred via communications interface are in the form of signals which may be electronic, electromagnetic, optical and/or other signals capable of being received by communications interface. These signals are provided to communications interface via a communications path (e.g., channel).
The terms “computer program medium” and “computer usable medium” and “computer readable medium” are used to generally refer to media such as removable storage drive and a hard disk installed in hard disk drive. These computer program products provide software to a computer system.
Computer programs (also referred to as computer control logic) are stored in main memory and/or secondary memory. Computer programs may also be received via communications interface. Such computer programs, when executed, enable the computer-based system to perform the features as discussed herein. In particular, the computer programs, when executed, enable the processor to perform the features of various embodiments. Accordingly, such computer programs represent controllers of the computer-based system.
In various embodiments, software may be stored in a computer program product and loaded into computer system using removable storage drive, hard disk drive or communications interface. The software, when executed by the processor, causes the processor to perform the functions of various embodiments as described herein. In various embodiments, hardware components such as application specific integrated circuits (ASICs). Implementation of the hardware state machine so as to perform the functions described herein will be apparent to persons skilled in the relevant art(s).
A web client includes any device (e.g., personal computer) which communicates via any network, for example such as those discussed herein. Such browser applications comprise Internet browsing software installed within a computing unit or a system to conduct online transactions and/or communications. These computing units or systems may take the form of a computer or set of computers, although other types of computing units or systems may be used, including laptops, notebooks, tablets, hand held computers (e.g., smartphones), set-top boxes, workstations, computer-servers, main frame computers, mini-computers, PC servers, pervasive computers, network sets of computers, personal computers.
In various embodiments, a web client may or may not be in direct contact with an application server. For example, a web client may access the services of an application server through another server and/or hardware component, which may have a direct or indirect connection to an Internet server. For example, a web client may communicate with an application server via a load balancer. In an exemplary embodiment, access is through a network or the Internet through a commercially-available web-browser software package.
In various embodiments, components, modules, and/or engines of system 100 (e.g., Healthcare IS 150) may be implemented as micro-applications or micro-apps. Micro-apps are typically deployed in the context of a mobile operating system. The micro-app may be configured to leverage the resources of the larger operating system and associated hardware via a set of predetermined rules which govern the operations of various operating systems and hardware resources. For example, where a micro-app desires to communicate with a device or network other than the mobile device or mobile operating system, the micro-app may leverage the communication protocol of the operating system and associated device hardware under the predetermined rules of the mobile operating system. Moreover, where the micro-app desires an input from a user, the micro-app may be configured to request a response from the operating system which monitors various hardware components and then communicates a detected input from the hardware to the micro-app.
As used herein, the term “network” includes any cloud, cloud computing system or electronic communications system or method which incorporates hardware and/or software components. Communication among the parties may be accomplished through any suitable communication channels, such as, for example, a telephone network, data network, Internet, point of interaction device, online communications, satellite communications, off-line communications, wireless communications, transponder communications, local area network (LAN), wide area network (WAN), virtual private network (VPN), networked or linked devices, and/or any suitable communication or data input modality.
The various system components may be independently, separately or collectively suitably coupled to the network via data links which includes, for example, a connection to an Internet Service Provider (ISP) over the local loop as is typically used in connection with standard modem communication, cable modem, Dish networks, ISDN, Digital Subscriber Line (DSL), or various wireless communication methods.
“Cloud” or “Cloud computing” includes a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. Cloud computing may include location-independent computing, whereby shared servers provide resources, software, and data to computers and other devices on demand.
As used herein, “transmit” may include sending electronic data from one system component to another over a network connection. Additionally, as used herein, “data” may include encompassing information such as commands, queries, files, data for storage, and the like in digital or any other form.
The system contemplates uses in association with web services, utility computing, pervasive and individualized computing, security and identity solutions, autonomic computing, cloud computing, commodity computing, mobility and wireless solutions, open source, biometrics, grid computing and/or mesh computing.
Any databases discussed herein may include relational, hierarchical, graphical, or object-oriented structure and/or any other database configurations. Moreover, the databases may be organized in any suitable manner, for example, as data tables or lookup tables. Each record may be a single file, a series of files, a linked series of data fields or any other data structure. Association of certain data may be accomplished through any desired data association technique such as those known or practiced in the art.
The data set annotation may be used for other types of status information as well as various other purposes. For example, the data set annotation may include security information establishing access levels. The access levels may, for example, be configured to permit only certain individuals, levels of employees, companies, or other entities to access data sets, or to permit access to specific data sets based on the transaction, merchant, issuer, user or the like. Furthermore, the security information may restrict/permit only certain actions such as accessing, modifying, and/or deleting data sets. In one example, the data set annotation indicates that only the data set owner or the user are permitted to delete a data set, various identified users may be permitted to access the data set for reading, and others are altogether excluded from accessing the data set. However, other access restriction parameters may also be used allowing various entities to access a data set with various permission levels as appropriate.
One skilled in the art will also appreciate that, for security reasons, any databases, systems, devices, servers or other components of the system may consist of any combination thereof at a single location or at multiple locations, wherein each database or system includes any of various suitable security features, such as firewalls, access codes, encryption, decryption, compression, decompression, and/or the like. Encryption may be performed by way of any of the techniques now available in the art or which may become available.
Middleware may include any hardware and/or software suitably configured to facilitate communications and/or process transactions between disparate computing systems. Middleware components are commercially available and known in the art. Middleware may be implemented through commercially available hardware and/or software, through custom hardware and/or software components, or through a combination thereof. Middleware may reside in a variety of configurations and may exist as a standalone system or may be a software component residing on the Internet server.
Practitioners will also appreciate that there are a number of methods for displaying data within a browser-based document. Data may be represented as standard text or within a fixed list, scrollable list, drop-down list, editable text field, fixed text field, pop-up window, and the like. Likewise, there are a number of methods available for modifying data in a web page such as, for example, free text entry using a keyboard, selection of menu items, check boxes, option boxes, and the like.
In various embodiments, systems may be described herein in terms of functional block components, screen shots, optional selections and various processing steps.
In various embodiments, systems are described herein with reference to screen shots, block diagrams and flowchart illustrations of methods, apparatus (e.g., systems), and computer program products. These functional blocks may be realized by any number of hardware and/or software components configured to perform the specified functions. These functional blocks flowchart illustrations, and combinations of functional blocks in the block diagrams and flowchart illustrations, respectively, may also be implemented by computer program instructions.
These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions that execute on the computer or other programmable data processing apparatus create a computer system capable of implementing the functions specified in the flowchart block or blocks. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart block or blocks. The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.
Accordingly, functional blocks of the block diagrams and flowchart illustrations support combinations of hardware, software, and/or hardware-software systems for performing the specified functions, combinations of steps for performing the specified functions, and program instruction means for performing the specified functions. It will also be understood that each functional block of the block diagrams and flowchart illustrations, and combinations of functional blocks in the block diagrams and flowchart illustrations, can be implemented by either special purpose hardware-based computer systems which perform the specified functions or steps, or suitable combinations of special purpose hardware and computer instructions. Further, illustrations of the process flows and the descriptions thereof may make reference to user windows, webpages, websites, web forms, prompts, etc. Practitioners will appreciate that the illustrated steps described herein may comprise in any number of configurations including the use of windows, webpages, web forms, popup windows, prompts and the like. It should be further appreciated that the multiple steps as illustrated and described may be combined into single webpages and/or windows but have been expanded for the sake of simplicity. In other cases, steps illustrated and described as single process steps may be separated into multiple webpages and/or windows but have been combined for simplicity.
The term “non-transitory” is to be understood to remove only propagating transitory signals per se from the claim scope and does not relinquish rights to all standard computer-readable media that are not only propagating transitory signals per se. Stated another way, the meaning of the term “non-transitory computer-readable medium” and “non-transitory computer-readable storage medium” should be construed to exclude only those types of transitory computer-readable media which were found in In Re Nuijten to fall outside the scope of patentable subject matter under 35 U.S.C. §101.
Benefits, other advantages, and solutions to problems have been described herein with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any elements that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as critical, required, or essential features or elements of the disclosure. The scope of the disclosure is accordingly to be limited by nothing other than the appended claims, in which reference to an element in the singular is not intended to mean “one and only one” unless explicitly so stated, but rather “one or more.” Moreover, where a phrase similar to ‘at least one of A, B, and C’ or ‘at least one of A, B, or C’ is used in the claims or specification, it is intended that the phrase be interpreted to mean that A alone may be present in an embodiment, B alone may be present in an embodiment, C alone may be present in an embodiment, or that any combination of the elements A, B and C may be present in a single embodiment; for example, A and B, A and C, B and C, or A and B and C. Although the disclosure includes a method, it is contemplated that it may be embodied as computer program instructions on a tangible, non-transitory memory or computer-readable carrier, such as a magnetic or optical memory or a magnetic or optical disk. All structural, chemical, and functional equivalents to the elements of the above-described exemplary embodiments that are known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the present claims. Moreover, it is not necessary for a device or method to address each and every problem sought to be solved by the present disclosure, for it to be encompassed by the present claims. Furthermore, no element, component, or method step in the present disclosure is intended to be dedicated to the public regardless of whether the element, component, or method step is explicitly recited in the claims. No claim element herein is to be construed under the provisions of 35 U.S.C. 112(f), unless the element is expressly recited using the phrase “means for.” As used herein, the terms “comprises”, “comprising”, or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
The disclosure further includes the various aspects, embodiments and teachings set forth in appendices A, B, and C, each of which are incorporated into the disclosure in their entirety for all purposes.
This Patent Cooperation Treaty application claims priority to U.S. Patent Application No. 61/798,105 entitled “Rapid Determination of Microbial Growth and Antimicrobial Susceptibility” and filed Mar. 15, 2013, the contents of which are hereby incorporated by reference in their entirety.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US14/30745 | 3/17/2014 | WO | 00 |
| Number | Date | Country | |
|---|---|---|---|
| 61798105 | Mar 2013 | US |
