Patent Application
20090034822
The present invention relates to methods, apparatus and computer program products for characterising cells and for use in assessing the effect of treatments on cells. In particular, the invention relates to identifying bi-nucleated cells and assessing the effect of different treatments administered to cells on cellular activities, actions of properties, including promotion, prevention, delay or other inhibition, based on captured images of the treated cells.
A number of methods exist for investigating the effect of a treatment or a potential treatment, such as a drug or pharmaceutical, on an organism. One approach is to investigate how the treatment affects the organism at the cellular level so as to try and determine the mechanism of action by which the treatments affects the organism. One approach to assessing the effects at a cellular level is to capture images of cells that have been subject to a treatment. However, it can be difficult to accurately determine or otherwise quantify the effect of a treatment using captured cell image based techniques owing to the inherent difficulties of capturing and processing visual information. Hence, there is a need for improved algorithms for analyzing image derived data in order to accurately and reliably characterise the effects at a cellular level of a treatment and also the treatment itself.
One area where this would be particularly beneficial is in the area of oncology and cancers. It is believed that tumours are the result of a break down in the normal regulation of cell division, which normally occurs through a process known as the cell cycle. The cell cycle has a number of stages. In eukaryotic cells, the cell cycle generally consists of four stages G1, S (the DNA synthesis phase), G2 and mitosis. The stages G1, S and G2 are collectively referred to as interphase. During mitosis, the nuclei of eukaryotic cells divide and in parallel, the cytoplasm divides by a process known as cytokinesis. As a cell leaves G2, it enters the prophase of mitosis during which the nuclear membrane breaks down and the chromosomes condense. Next metaphase occurs during which the chromosomes are aligned on the equator of the mitotic spindle owing to the action of tubulin containing spindle fibres. Next anaphase occurs during which the daughter chromosomes are pulled toward the poles of the cell by the mitotic spindle. Telophase follows, in which the chromosomes decondense and nuclear membranes form around them and the cell is transiently binuclear. At the same time, a cleavage furrow forms across the equator of the cell which tightens and eventually divides the cell into two daughter cells and this is cytokinesis.
As cytokinesis is an important part of the cell cycle, it would be advantageous to be able to reliably characterise a cell population in terms of the proportion of cells undergoing cytokinesis (“cytokinetic cells”), or cells in which cytokinesis failed, as this could give a mechanism for robustly investigating the effects of various treatments on the division of cells which could be of use in the drug discovery field or generally in better understanding the interaction between a treatment and cellular operations and activities.
The present invention therefore addresses these issues and provides methods and apparatus for characterising cells, assessing the effects of treatments on cells, and specific algorithms for analysing data derived from images of cells and cell components so as to characterise a cellular property, within a population of cells, based on measures and indications of the existence of bi-nucleated cells.
The present invention provides in one aspect, methods, apparatus and software for characterising cellular properties and also for characterising the effects of treatments on cells.
In one aspect of the invention, a method is provided for identifying bi-nuclear cells. A first image of marked cells can be captured. The first image can be processed to obtain a first feature of the cells. The first feature can be analyzed to determine whether the first feature indicates that the cell is a bi-nuclear cell. Those cells for which the first feature is indicative of a bi-nuclear cell can be identified as a bi-nuclear cell.
In another aspect of the invention, a method is provided for assessing the affect of a treatment on a cell. A population of cells can be exposed to the treatment. An image of the cells can be captured. Cellular features can be obtained from the image. The cellular features can be analyzed to assess a property of the cellular feature which is characteristic of bi-nuclear cells. The abundance of bi-nuclear cells can be determined.
In another aspect of the invention, a method is provided for characterising cells. The number of concave portions in the outline of a captured image of a nuclear component of a cell can be determined. The cell can then be characterized based on the number of concave portions.
In another aspect of the invention, a method is provided for identifying bi-nuclear cells. A pair of nuclear components can be identified from a captured image of a nuclear component of cells. A measure of the amount of the cytoplasmic component between the pair of nuclear components can be determined from a captured image of the cytoplasmic component of the cells. The cells can then be characterised based on the amount of the cytoplasmic component.
In another aspect of the invention, a method is provided for identifying pairs of nuclei. A pair of nuclear components can be identified from a captured image of a nuclear component of the cells. A nearest neighbour nuclear component to the pair of nuclear components can be identified. The cells associated with the pair of nuclear components can be characterised based on the separation of the pair of nuclear components and the separation of the next nearest neighbour nuclear component from the pair of nuclear components.
Other aspects of the invention include computer program products and computing devices which can provide the various method aspects of the invention.
These and other features and advantages of the present invention will be described below in more detail with reference to the associated drawings.
Generally, this invention relates to processes and apparatus for use in analysing captured images of cells and components of cells in order to identify bi-nuclear cells, i.e. a single cell having two nuclei. This can occur in cytokinetic cells, i.e. cells undergoing cytokinesis during the cell cycle but whose cytoplasm has not yet divided. The invention can be used to investigate the effect of treatments administered to cells by determining the proportion or number of bi-nuclear cells following a treatment. For example a large number of bi-nuclear cells could be indicative of a treatment that inhibits cytokinesis as otherwise the cytoplasm would divide and cytokinesis would be completed. The failure of cytokinesis would lead to the emergence of a significant number of bi-nuclear cells. However, the methods are not limited to investigating the effect of a treatment administered to the cells on cytokinesis. The methods and apparatus presented in the following can also be used in order to investigate, or otherwise quantify, other cellular behaviour in which bi-nuclear cells can result as will be apparent from the following discussion.
The invention also relates to computer programs, machine-readable media on which is provided instructions, data structures, etc. for performing the processes of the invention. Features of cell components, in particular the nucleus and components of the cytoplasm, which have been derived from captured images of cells are analyzed in order to provide some indication on the extent of occurrence of a biologically relevant phenomenon, such as cytokinesis, the failure of cytokinesis or other phenomena for which bi-nuclear cells are a distinguishing feature. The indication can then be used to help classify or otherwise categorise a treatment that has been applied to the cells.
The general method includes the identification of bi-nuclear cells using images captured by an image capture system. Typically an image will be captured of a cell or plurality of cells, depending on the magnification at which the image is captured and certain markers can be used to highlight in the captured image the component of the cell of interest. The term “marker” or “labelling agent” refers to materials that specifically bind to and label cell components. These markers or labelling agents should be detectable in an image of the relevant cells. Typically, a labelling agent emits a signal whose intensity is related to the concentration of the cell component to which the agent binds. Preferably, the signal intensity is directly proportional to the concentration of the underlying cell component. The location of the signal source (i.e., the position of the marker) should be detectable in an image of the relevant cells.
Preferably, the chosen marker binds indiscriminately with its corresponding cellular component, regardless of location within the cell. Although in other embodiments, the chosen marker may bind to specific subsets of the component of interest (e.g., it binds only to sequences of DNA or regions of a chromosome). The marker should provide a strong contrast to other features in a given image. To this end, the marker should be luminescent, radioactive, fluorescent, etc. Various stains and compounds may serve this purpose. Examples of such compounds include fluorescently labelled antibodies to the cellular component of interest, fluorescent intercalators, and fluorescent lectins. The antibodies may be fluorescently labelled either directly or indirectly.
As part of the general method, the effect of a stimulus or treatment on cells can be investigated using the algorithms described herein. The term “treatment” or “stimulus” refers to something that may influence the biological condition of a cell. Often the term will be synonymous with “agent” or “manipulation.” Stimuli may be materials, radiation (including all manner of electromagnetic and particle radiation), forces (including mechanical (e.g., gravitational), electrical, magnetic, and nuclear), fields, thermal energy, and the like. General examples of materials that may be used as stimuli include organic and inorganic chemical compounds, biological materials such as nucleic acids, carbohydrates, proteins and peptides, lipids, various infectious agents, mixtures of the foregoing, and the like. Other general examples of stimuli include non-ambient temperature, non-ambient pressure, acoustic energy, electromagnetic radiation of all frequencies, the lack of a particular material (e.g., the lack of oxygen as in ischemia), temporal factors, etc.
Specific examples of biological stimuli include exposure to hormones, growth factors, antibodies, or extracellular matrix components. Or exposure to biologics such as infective materials such as viruses that may be naturally occurring viruses or viruses engineered to express exogenous genes at various levels. Biological stimuli could also include delivery of antisense polynucleotides by means such as gene transfection. Stimuli also could include exposure of cells to conditions that promote cell fusion. Specific physical stimuli could include exposing cells to shear stress under different rates of fluid flow, exposure of cells to different temperatures, exposure of cells to vacuum or positive pressure, or exposure of cells to sonication. Another stimulus includes applying centrifugal force. Still other specific stimuli include changes in gravitational force, including sub-gravitation, application of a constant or pulsed electrical current. Still other stimuli include photobleaching, which in some embodiments may include prior addition of a substance that would specifically mark areas to be photobleached by subsequent light exposure. In addition, these types of stimuli may be varied as to time of exposure, or cells could be subjected to multiple stimuli in various combinations and orders of addition. Of course, the type of manipulation used depends upon the application.
As part of the processing of captured images, certain features of the cells can be extract using suitable image processing techniques. The algorithms of the present invention can take this feature data as input in order to carryout their analysis. As used herein, the term “feature” refers to a property of a cell or population of cells derived from cell images and includes the basic “parameters” extracted from a cell image. The basic parameters are typically morphological, concentration, and/or statistical values obtained by analyzing a cell image showing the positions and concentrations of one or more markers bound within the cells. Examples of the various features used by the algorithms are given later on herein. It will be appreciated in the following that some of the algorithms of the present invention can work directly from the feature data, e.g. nuclear position and shape, and do not need to themselves process the images from which the feature data has been obtained, whereas other of the algorithms process image data or use other information contained in an image, together with any required feature data.
With reference to
The cellular features derived from the captured images are then analysed in step 104 in order to identify cells exhibiting the biological phenomenon of relevance. In a preferred embodiment, the cellular features are analysed in order to identify bi-nuclear cells. Some quantitative measure of the extent to which the biological phenomenon is expressed in the cellular population covered by the images can then be determined. The measure can then be used in step 106 to assess the effect of a treatment on the cells. Although the following description will focus on inhibition of cytokinesis, the invention is not limited to assessing the effect of a treatment on cytokinesis alone. The invention can also be applied to investigating the effect of a treatment on the nucleus of cells as a result of other mechanisms of action.
Generally, a wide number of cell components can be detected and analyzed. Cell components can include proteins, protein modifications, genetically manipulated proteins, exogenous proteins, enzymatic activities, nucleic acids, lipids, carbohydrates, organic and inorganic ion concentrations, sub-cellular structures, organelles, plasma membrane, adhesion complex, ion channels, ion pumps, integral membrane proteins, cell surface receptors, G-protein coupled receptors, tyrosine kinase receptors, nuclear membrane receptors, ECM binding complexes, endocytotic machinery, exocytotic machinery, lysosomes, peroxisomes, vacuoles, mitochondria, Golgi apparatus, cytoskeletal filament network, endoplasmic reticulum, nuclei, nuclear DNA, nuclear membrane, proteosome apparatus, chromatin, nucleolus, cytoplasm, cytoplasmic signalling apparatus, microbe specializations and plant specializations.
After the cells have been appropriately stained, a treatment 114 can be applied to the cells. A treatment can be of any type which can affect the behaviour of a cell as explained above. The cell may be treated using a chemical agent which can be any type of chemical or chemical compound and may in particular be a potential drug or any other type of therapeutic agent. Typically, a chemical agent may be delivered in a solution and/or with other compounds or treatments, and at varying dose levels. The cells may also be exposed to a biological treatment, such as a virus, protein or by having the cells' DNA modified by any other means by which a biological effect may be exerted on the cells.
After the cells have been treated, in a next step 116 images of the cells and cellular components are captured using any suitable image capture system. A particular embodiment of a suitable image capture system is shown in
Sometimes corrections must be made to the measured intensity. This is because the absolute magnitude of intensity can vary from image to image due to changes in the staining and/or image acquisition procedure and/or apparatus. Specific optical aberrations can be introduced by various image collection components such as lenses, filters, beam splitters, polarizers, etc. Other sources of variability may be introduced by an excitation light source, a broad band light source for optical microscopy, a detector's detection characteristics, etc. Even different areas of the same image may have different characteristics. For example, some optical elements do not provide a “flat field.” As a result, pixels near the center of the image have their intensities exaggerated in comparison to pixels at the edges of the image. A correction algorithm may be applied to compensate for this effect. Such algorithms can be developed for particular optical systems and parameter sets employed using those imaging systems. One simply needs to know the response of the systems under a given set of acquisition parameters.
After images of the cells and cell components have been captured 116, the captured images are processed 118 so as to extract cellular features from the images or subsequent analysis. Any suitable image processing steps may be carried out in order to extract relevant cellular features.
After image correction, a segmentation process 134 is carried out on the images in order to identify individual objects or entities within the image. Any suitable segmentation process may be used in order to obtain nuclear and cellular objects. Typically nuclear DNA markers provide a strong signal and there is a high contrast in the image and an edge detection based segmentation process can be used. For segmenting cells, a watershed type method can be used instead. The segmentation process typically identifies edges where there is a sudden change in intensity of the cells in the image and then looks for closed connected edges in order to identify an object. Segmentation will not be described in greater detail as it is well understood in the art and so as not to obscure the present invention.
Additional operations may be performed prior to, during, or after the imaging operation 116 of
In a specific embodiment, a correction algorithm may be applied prior to segmentation to correct for changing light conditions, positions of wells, etc. In one example, a noise reduction technique such as median filtering is employed. Then a correction for spatial differences in intensity may be employed. In one example, the spatial correction comprises a separate model for each image (or group of images). These models may be generated by separately summing or averaging all pixel values in the x-direction for each value of y and then separately summing or averaging all pixel values in the y direction for each value of x. In this manner, a parabolic set of correction values is generated for the image or images under consideration. Applying the correction values to the image adjusts for optical system non-linearities, mis-positioning of wells during imaging, etc.
Generally the images used as the starting point for the methods of this invention are obtained from cells that have been specially treated and/or imaged under conditions that contrast the cell's marked components from other cellular components and the background of the image. Typically, the cells are fixed and then treated with a material that binds to the components of interest and shows up in an image (i.e., the marker). Preferably, the chosen agent specifically binds to nuclear DNA, but not to most other cellular biomolecules.
At every combination of dose, cell line, and compound, one or more images can be obtained. As mentioned, these images are used to extract various parameter values of relevance to a biological, phenomenon of interest. Generally a given image of a cell, as represented by one or more markers, can be analyzed to obtain any number of image parameters. These parameters are typically statistical or morphological in nature. The statistical parameters typically pertain to a concentration or intensity distribution or histogram.
Some general parameter types suitable for use with this invention include a cell, or nucleus where appropriate, count, an area, a perimeter, a length, a breadth, a fiber length, a fiber breadth, a shape factor, a elliptical form factor, an inner radius, an outer radius, a mean radius, an equivalent radius, an equivalent sphere volume, an equivalent prolate volume, an equivalent oblate volume, an equivalent sphere surface area, an average intensity, a total intensity, an optical density, a radial dispersion, and a texture difference. These parameters can be average or standard deviation values, or frequency statistics from the descriptors collected across a population of cells. In some embodiments, the parameters include features from different cell portions or cell types.
Examples of some specific cellular and nuclear features and parameters that may be extracted from the captured images during step 136 are included in the following table. Other features and parameters can also be used without departing from the scope of the invention.
After the features have been extracted 136 from the image they are stored 120 in database 186, and analysis of the features is carried out in order to assess the effect of the treatment on the cells.
A first algorithm 200 can be used to characterises the nuclear morphology of individual cells. This algorithm can be used to determine whether a nuclear object in an image can be considered to be a single or multi-nuclear object. Hence this algorithm can be used where only a nuclear stain has been used and helped to categorise the effect of the treatment on the nuclei of cells, e.g. as expressed in the nuclear division immediately prior to cytokinesis. A second algorithm 300 takes into account inter-nuclear properties in order to determine whether a particular cell can be characterised as being bi-nuclear. It is particularly suitable for assessing the effect of a treatment on cytokinesis, or inhibition thereof, in a population of cells. As this algorithm uses information relating to the cytoplasm, a cytoplasmic marker is also used in conjunction with the nuclear marker information so as to try and characterise cells as cytokinetic or not. The inter-nuclear algorithm 300 can be used alone, or subsequent to the nuclear morphology algorithm 200 as will be described in greater detail below. These two algorithms can be used to classify the nuclear status of each cell.
A third pairing algorithm 400 can be used to identify a pairing characteristic of cells within a cellular population. Contrary to the other two algorithms, this algorithm does not determine whether a particular cell is bi-nuclear or not, but rather provides a measure of the number of bi-nuclear cells in a population of cells, without assigning each individual cell to a particular class. In a particular embodiment, the pairing algorithm can identify pairs of nuclear objects which can be likely characterised as corresponding to a cell undergoing cytokinesis. Therefore this algorithm can also give a measure of the proportion of cytokinetic cells in the population. The pairing algorithm can be used alone or can be used in conjunction with either or both of the other algorithms. Preferably, the nuclear morphology algorithm is used in order to identify mono-nucleate objects before carrying out the pairing algorithm to identify likely cytokinetic cells.
After one or more of the algorithms has been carried out, at step 150 some measure or measures of the abundance of bi-nuclear cells in the cellular population is determined. A separate measure can be obtained from each algorithm or the separate measures can be combined to provide a single measure. For example the proportion of cells in the cellular population which are undergoing, failed to, or have recently undergone cytokinesis can be obtained. The measure of bi-nuclear cells, which can provide a measure of the inhibition of cytokinesis (as the greater the number of bi-nuclear cells, the less prevalent cytokinesis), obtained in step 150 is then used in step 160 in order to categorise or otherwise classify the treatment.
The metric obtained in step 150 can be evaluated against control or standard values in order to categorise a treatment. For example a treatment may be categorised as prohibiting cytokinesis, inhibiting cytokinesis or having no significant effect on cytokinesis. The treatment may be carried out by simply comparing the proportion of bi-nuclear cells for the treated sample with the proportion of bi-nuclear cells in a standard or controlled sample. Some statistical measure of the difference between the cytokinesis metric for the treated cells and the same cytokinesis metric evaluated for different treatments and/or control samples may be used in order to provide a confidence in the categorisation of the treatment as having an effect on cytokinesis. Any suitable statistical test may be used, such as Fisher's exact test or a Student T-test. These tests, and other statistical tests, can be used to determine the confidence with which it can be assumed that the treated cells and control cells do come from distinct groups and hence that the treatment has had a genuine effect on the treated cells. Other statistical tests can be used.
With reference to
The algorithm 200, takes as input data 204 representing the outline of a single segmented nuclear object 204. As illustrated in
In a first step, the algorithm 200 smoothes 206 the outline of the nuclear object so as to remove or reduce the roughness. In a preferred embodiment, the outline is smoothed by converting the outline into an irregular polygon 268 as illustrated in
At step 208, the algorithm looks for concave regions in the smoothed outline of the nuclear object. In the embodiment illustrated, the concave regions are concave vertices. In one embodiment, the algorithm picks an initial vertex and determines the external angle subtended at that vertex by the adjacent lines of the polygon. For example, at the vertex 270, the external angle is represented by β. As β is greater than 180°, this vertex is not concave, but convex, and so can be discarded for further processing. At vertex 272, the external angle subtended is represented by α. As α is less than 180°, this vertex is a concave vertex and so is retained for further processing. The algorithm evaluates each vertex and measures at step 210 the external angle subtended. If the measured angle of a vertex is 180° or greater, then the vertex can be discarded as not being concave. Those vertices for which the measured angle is less than 180°, are identified as candidate valid concave vertices and are then further evaluated by the algorithm. The algorithm uses the measured angles in order to characterise the candidate valid vertices and the associated region of the object outline as being concave or not.
In a preferred embodiment, a region in the outline of the nuclear object is identified as being concave if the angle subtended by the candidate concave vertex corresponding to that region of the outline falls below a threshold value. As illustrated in greater detail in
After a candidate concave vertex has been evaluated, the algorithm determines 216 whether there are any remaining concave candidate vertices in the outline to be evaluated, and if so returns to step 212 where the angle for the next region is evaluated. Processing loops 218 in this way until all the candidate concave vertices have been evaluated.
After the outlines have been evaluated, then all of the nuclear objects are classified at step 220 based on the number of valid concave vertices identified each the object's outline.
At step 226, a nuclear object in the image is classified as multi-nucleate if its outline has two or more valid concave vertices and if the total intensity of radiation detected for the object exceeds a first threshold. The total intensity of the nuclear object image is proportional to the nuclear DNA present in the actual nuclei. Therefore the total intensity of the nuclear image is compared with a first threshold intensity value to determine whether the amount of DNA present in the actual object is indicative of there being more than two nuclei or not. The total intensity for the nuclear image object is looked up and compared with the first threshold and if the intensity of the nuclear object exceeds the threshold, then this reinforces the belief that the object can be classified as being a multi-nucleate (i.e. more than two nuclei) object. Hence the cell associated with the multi-nuclear object can be classified accordingly as multi-nuclear. Any threshold which allows multi-nuclear objects to be discriminated from bi-nuclear objects can be used. In a preferred embodiment, the threshold is set at 1.9 times the average of the total intensity for all of the nuclear objects in the image.
The nuclear intensity threshold provides a second criterion after the number of valid concave vertices in order to reinforce the classification of the cell and make it more reliable. However, the thresholding step does not have to be used. Further, other properties of the nucleus can be used to provide a secondary criterion by which to discriminate truly multi-nuclear objects. Further more, more than one secondary criterion can be used. Any other feature or property of the nucleus which relates to the likely number of actual nuclei present can be used to provide the secondary check criterion and indeed more than one check criterion can be used. However, the total intensity of a captured image of a nuclear object whose nuclear DNA has been stained is a reliable indicator of the amount of DNA present in the nucleus, and has been found to provide a suitable check criterion.
This scenario is illustrated in
At step 228, for each of the remaining objects, it is determined if the nuclear object has more than one valid concave vertex, and whether the total intensity for the object exceeds a second threshold, different to the first threshold. The second threshold is lower than the first threshold. In a preferred embodiment, the second threshold is approximately 1.1 times the average of the total intensity for all of the nuclear objects in the image. If the object passes both of these criteria, then the nuclear object can be classified as including two actual nuclei and therefore being bi-nucleate, and the associated cell classified accordingly.
The remaining objects are classified in step 230 as being mono-nucleate, i.e. having a single nuclear object.
Hence as a result of step 220, the physical cell associated with the nuclear object that has been imaged has been classified as being mono, bi or multi nucleate. Hence, cells which have two nuclei close together, identified as bi-nucleate in the algorithm, are likely to be cells which have not undergone cytokinesis and therefore the algorithm helps to identify cytokinetic cells based on the morphology of captured images of nuclear components. However, the algorithm is not limited only to identifying cytokinetic cells, or cells in which cytokinesis has been disrupted, and can be used to identify other biological phenomena in which the number of nuclei associated with a cell or cells can be used as a predictor or indicator of the biological mechanisms occurring.
After all the nuclear object images have been evaluated, the nuclear morphology algorithm is completed at step 224. Hence the nuclear morphology algorithm has identified the nuclear objects in the image and the associated cells in the cell population covered by the image, as being mono-nucleate, cytokinetic or multi-nucleate.
Returning to the general method illustrated in
Characterisation of the treatment can be based on a simple comparison of the proportion of bi-nuclear cells in the treated population with the typical proportion of bi-nuclear cells in a control population. If there has been an increase, then the treatment can be characterised as inhibiting cytokinesis as the cytoplasm of these cells is not dividing even though nuclear division has occurred. If there is no significant difference between the controlled cell population and treated cell population, then the treatment can be categorised as neutral. If there is a decrease, then the treatment may be categorised as promoting cytokinesis. Other categorisations of the treatment are also envisaged.
Further, statistical tests can be used to determine whether the difference between the treated cell population and control population can be considered to be significant or not. For example, a Fisher's exact test or a Student T-test could be applied to the number or proportion of bi-nuclear cells in the treated and control cell populations in order to evaluate whether the determined measure of bi-nuclear cells, and hence the categorisation of the treatment, can be considered to be significant or not.
In a first step 304, the algorithm 300 identifies candidate pairs of nuclei using segmented nuclear objects for the cellular population. The process then obtains a measure of the amount of cytoplasmic material between the nuclei of the candidate pairs at step 306. A candidate pair is then classified at step 308 depending on whether the measure of cytoplasmic material between the nuclei can be considered to be indicative of a bi-nuclear cell or not. The method completes at step 309. The results of the algorithm can then be fed into step 150 and a measure of bi-nuclear abundance for the cellular population can be calculated.
With reference to
At further optional step 328, objects which fall within the edge of the captured image field of view can be flagged so as to remove them from consideration. It is possible that objects falling within the perimeter of the image will not be fully presented in the image and therefore are inaccurate representations of the actual nuclear object. At further optional method step 330, cells which have previously been identified as being mitotic can also be flagged.
At step 332, corresponding generally to step 304, candidate pairs of nuclear objects are identified. For each object, the separation between that object and the remaining nuclear objects in the image is determined based on the centroids of the nuclear objects. Using the separations of the nuclear objects, each nuclear object has its nearest neighbour identified. It is then determined whether the nearest neighbour for that first object and the nearest neighbour object form a mutually nearest neighbour pair. This involves determining whether the first object is also the nearest neighbour of the first object's nearest neighbour. If the pair of objects are mutually nearest neighbours, i.e. the first object is the nearest neighbour of its nearest neighbour, then the pair of nuclei are identified as a candidate pair at step 332. At step 334, the set of candidate pairs identified in step 332 is searched, and those pairs including nuclear objects which have been flagged previously are removed from consideration, e.g. pairs including mitotic cells, edge objects, objects too big or too small or bi- or multi-nuclear objects are removed from further consideration. This helps to identify mutually nearest pairs of apparently mono-nucleate objects which are not undergoing some other cellular process.
As highlighted above, steps 324 to 330 of flagging different types of nuclear objects are optional. Further, step 334 of filtering out unsuitable nuclear objects can be carried out before step 332 of identifying pairs of mutually nearest neighbour nuclear objects. Hence the step of identifying candidate pairs is only carried out on those objects which are believed to be mono-nucleate nuclear objects not undergoing some other biological process. However, it is preferred that filtering of pairs be carried out after all objects have been evaluated to identify mutually nearest neighbour pairs.
At step 336, a measure of the amount of cytoplasm between each mutual nearest neighbour pair of objects is obtained. This step is equivalent to general method step 306. In a particular embodiment, this step is carried out by determining the amount of tubulin present between a pair of nuclei. In particular, the intensity of a captured cellular image of a marker for tubulin is used to calculate or measure the amount of tubulin between the pair of nuclei.
At step 344, portion 366 of line 360 extending between the edges of the nuclei is mapped on to image data for the cytoplasmic marker. In a preferred embodiment, the image data is the detected intensity for a tubulin marker.
Although tubulin has been described above, the invention is not limited to the use of tubulin as a cytoplasmic marker, and other cytoplasmic markers can be used, such as antibodies or fluorescent markers specific to actin, some protein kineses, metabolic enzymes, ATP and other similar cytoplasmic components and structures.
Process flow then returns to the main method and at step 338, each pair of nuclei is classified using the tubulin intensity calculated for each pair. Each pair is classified using a classifier module which has been trained using a control group of cells to identify tubulin threshold intensities against which the calculated tubulin intensity for each pair is compared.
The tubulin intensity data is collected at step 352 and at step 354, data equivalent to a histogram of tubulin intensity measurements for each pair is calculated. It is not necessary to plot a histogram but data indicating the proportion of pairs having a certain tubulin intensity as a function of tubulin intensity (IT) is derived.
In greater detail, the percentile corresponding to the intensity threshold to be used can be estimated by assuming a given percentile of the cytokinetic pairs amongst all the image objects in the control cell population. Nobj is the number of objects in the image and Npair is the number of mutually nearest neighbour pairs from the DMSO control well cellular images. For a given object percentile, Qobj, which is assumed to be the proportion of cytokinetic objects, and with Ncyto being the number of cytokinetic pairs in the DMSO control wells, then Qobj=Ncyto×100/(Nobj−Ncyto). So that Ncyto=(Nobj×Qobj)/(100+Qobj). Therefore, the estimated percentage of cytokinetic pairs in the training data is Qpair=(Ncyto×100)/Npair. Practically a Qobj of about 3% has been found to provide reliable results so that the pair percentile is set at QDMSO=100−(Nobj×300)/(Npair×103). The tubulin intensity, IT(3%), corresponding to this percentile for the DMSO training data is then used as the threshold for discriminating between bi-nuclear and non-bi-nuclear pairs of mutually nearest neighbour nuclear objects.
Hence, from the histogram data, the tubulin intensity, IT(3%), corresponding to the 3% of the population having the highest inter-nuclear intensity measurements is obtained and the threshold used in the classifier 338 in the inter-nuclear algorithm 300 is set at this threshold instep 358. The threshold to use can vary between cell types and cell lines, and so cell specific thresholds can be used and similarly the proportion of the cellular population used to identify the threshold value can vary depending on the cell type and cell line.
Returning to step 338, the classifier evaluates each pair of nuclear objects and if the measured tubulin for the pair of objects meets or exceeds the threshold intensity, then the pair of nuclei can be classified as belonging to a bi-nuclear cell as the nuclei are adjacent and the amount of cytoplasmic material between them can be considered sufficiently large to be indicative of the nuclei being present in the same cell and not merely separate adjacent cells.
After each pair in the population has been classified, a bi-nuclear cell abundance metric can be calculated at step 339 to give a measure of the proportion of objects within the cellular population in the image which can be considered to be bi-nuclear cells. One bi-nuclear abundance metric, referred to as a pairing index or metric, that can be used is given by Ncyto×100/(Nobj−Ncyto), where Nobj is the number of objects considered and Ncyto is the number of cytokinetic/bi-nuclear pairs identified from those same objects.
This pairing metric can be used alone or in combination with the cytokinesis metric obtained from the nuclear morphology algorithm in order to categorise the treatment at step 160.
The pairing algorithm 400, with reference to
At step 434, the separation of the centroids for all the nuclear objects are computed to provide a matrix of pair wise nuclear object separations. At step 436, for each object, the five closest nuclear objects are identified and the separation between the object under consideration and its five nearest neighbours is calculated using the perimeters, or outlines, of the objects, rather than their centroids. It is not essential that the distances be computed between the perimeters and the separation between objects can be computed in other ways. However, using the distance between perimeters has been found to fit the nearest neighbour distributions better than other methods, such as the distance between object centroids. Then at step 438, for each object, and using the perimeter separations, the objects nearest neighbour (nn), e.g. 414 in
A nuclear object is then selected for evaluation. At step 444 it is determined if the nearest neighbour separation for the object is less than the nearest neighbour threshold. If not, then the nearest neighbour object is not sufficiently close for the objects to form a pair and so that object can be discarded and a next object is evaluated at step 450. If at step 444 it is determined that the nearest neighbour of an object is sufficiently close for the object to constitute a pair with its nearest neighbour, then the separation of the next nearest neighbour to the object, (e.g. 416 and 412 in
The calculation of the nearest neighbor (nn) and next nearest neighbor (nnn) thresholds will now be briefly described. The thresholds to use are a function of the number of nuclei in the image. The thresholds are set so that if the nuclei were placed randomly on the image, then we would expect 20% of the nuclei to be classified as paired regardless of the number of nuclei in the image. The following formulae for the thresholds use some results from Spatial Statistics which can be found in Statistics for Spatial Data by Noel Cressie, 1993 published by John Wiley & Sons, Inc. which is incorporated herein by reference for all purposes.
The distribution of nearest neighbors for point objects generated as independent events from a uniform distribution (“complete spatial randomness”) is known as is given by g(w)=2π2w exp(−πλw2) where w is a dummy variable and λ=n/s is the density of objects, where n is the number of objects and s is the size of the image. From this distribution function, the expected proportion of nearest neighbor distances less than α is given by P(nn<α)=1-exp(−λα2). Hence for a certain proportion of objects, p (e.g. 20% in this example), the nearest neighbor distance αnm corresponding to the proportion of objects p is given by αnm=√−(s/π)log(1−p). Therefore, for a proportion p the nn threshold can be calculated as αnm and is used in step 444.
Using a similar approach, the next nearest neighbor (nnn) threshold is given by αnm=√−(s/πk2)log(1−pk2) which provides the nnn threshold used in step 446.
Each isolated pair can be considered to be a bi-nuclear cell and so the proportion of bi-nuclear cells in the population of cells can be obtained at step 460. As explained above, in step 160, a z-test can be used to compare the proportion of bi-nuclear cells for a treated cell population with the proportion of bi-nuclear cells for a control cell population in order to determine whether the affect of the treatment can be considered to be statistically significant. This can then be used in classifying the treatment, e.g. as inhibiting cytokinesis if there is a statistically relevant large proportion of bi-nuclear cells in the treated cell population.
Generally, embodiments of the present invention employ various processes involving data stored in or transferred through one or more computer systems. Embodiments of the present invention also relate to an apparatus for performing these operations. This apparatus may be specially constructed for the required purposes, or it may be a general-purpose computer selectively activated or reconfigured by a computer program and/or data structure stored in the computer. The processes presented herein are not inherently related to any particular computer or other apparatus. In particular, various general-purpose machines may be used with programs written in accordance with the teachings herein, or it may be more convenient to construct a more specialized apparatus to perform the required method steps. A particular structure for a variety of these machines will appear from the description given below.
In addition, embodiments of the present invention relate to computer readable media or computer program products that include program instructions and/or data (including data structures) for performing various computer-implemented operations. Examples of computer-readable media include, but are not limited to, magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROM disks; magneto-optical media; semiconductor memory devices, and hardware devices that are specially configured to store and perform program instructions, such as read-only memory devices (ROM) and random access memory (RAM). The data and program instructions of this invention may also be embodied on a carrier wave or other transport medium. Examples of program instructions include both machine code, such as produced by a compiler, and files containing higher level code that may be executed by the computer using an interpreter.
CPU 502 is also coupled to an interface 510 that connects to one or more input/output devices such as such as video monitors, track balls, mice, keyboards, microphones, touch-sensitive displays, transducer card readers, magnetic or paper tape readers, tablets, styluses, voice or handwriting recognizers, or other well-known input devices such as, of course, other computers. Finally, CPU 502 optionally may be coupled to an external device such as a database or a computer or telecommunications network using an external connection as shown generally at 512. With such a connection, it is contemplated that the CPU might receive information from the network, or might output information to the network in the course of performing the method steps described herein.
Although the above has generally described the present invention according to specific processes and apparatus, the present invention has a much broader range of applicability. In particular, aspects of the present invention is not limited to any particular kind of cellular process and can be applied to virtually any cellular process where an understanding of the affect of a treatment on a cell is desired. Thus, in some embodiments, the techniques of the present invention could provide information about many different types or groups of cells, substances, cellular processes and mechanisms of action, and genetic processes of all kinds. One of ordinary skill in the art would recognize other variants, modifications and alternatives in light of the foregoing discussion.
| Number | Date | Country | |
|---|---|---|---|
| Parent | 10615116 | Jul 2003 | US |
| Child | 12130850 | US |
