Patent Application
20050154534
1. Field of the Invention
The present invention relates generally to the field of high throughput screening methods. The present invention relates to a computer-implemented screening system and method that can be used to identify agent mixtures that elicit a desired response and specifically, identify the best set and/or subset of two or three peptones that optimizes cell culture conditions based upon a variety of responses such as antibody secretion, cell number and time to peak antibody secretion.
2. Description of the Related Art
For cells to be used in therapies to treat or cure diseases in humans, it is desirable to control cell fate, e.g., cell survival, proliferation and differentiation, when cells are maintained in culture in vitro. It is therefore necessary to control cell surface receptor interaction with ligands. For example, in order to gain control over interactions between a cell and ligands present on the in vitro culture substrate, a suitable culture substrate, such as polystyrene, can be coated with a polymer which does not allow for cell attachment even when serum proteins are used in the culture media. This coating thus eliminates the uncontrolled and arbitrary adsorption of the serum proteins. Biologically active ligands suitable to interact with cell surface receptors can then be immobilized on this coating while maintaining the biological activity of the ligands. This concept is well known to those skilled in the art. For example, it is known to use hyaluronic acid or algenic acid as a surface coating upon which the cell adhesion ligands can be immobilized using chemistries resulting in stable covalent bonds between the coating and the cell adhesion ligands. This prevents the cell adhesion ligand from being solubilized and leaving the surface. Moreover, the coating itself does not support cell adhesion. This is further described in a copending, commonly owned U.S. patent application Ser. No. 10/259,797, filed on Sep. 30, 2002, the entire content of which is incorporated herein by reference.
Additionally, it is probable that specific mixtures of agents are required in order to achieve a desired cell fate. A great number of growth effector molecules are known. These include growth factors, hormones, peptides, small molecules and extracellular binding molecules. However, it can thus be a tedious task to find the right growth effector or growth effector combinations to achieve a desired cell fate for a given cell type.
Accordingly, a need exists for higher throughput system and methods to identify agents useful to achieve a desired cell fate for a given cell type. This is of particular interest for cells that do not survive or only survive by drastically altering their differentiation state in conventional cell culture systems, a prime example being primary mammalian cells. In particular, there is a need in the art for a computer-implemented, statistically designed experimental method and a system for implementation to systematically explore the interactions between mixtures of factors that are required in order to achieve a desired cell fate. Preferably, the higher throughput system and method would include starting from a list of several possible agents, such as peptones, and implement an optimization strategy to identify the best subset of two or three peptones that optimizes cell culture conditions based upon a variety of responses. The responses can include antibody secretion, cell number and peak antibody secretion periods.
Accordingly, an object of the present invention is to provide an automated system and method for identifying agents that cause a phenotypic change in a cell. The method includes providing receptacles in an array and providing a statistical design including generic factor names, factor levels and experimental runs. The method further includes placing different mixtures of single agents into select ones of the receptacles according to a computer representation of the statistical design and utilizing a software program to generate the computer representation of the design. The software automatically maps the identities of the agents to the generic factor names, maps the concentration or amounts of the agents to the factor levels and maps the locations of the receptacles within the array to the experimental runs. Once the different mixtures have been correctly placed into receptacles in accordance with the computer representation of the design, the placed mixtures are contacted with whole cells that are capable of changing their phenotype.
Another object of the present invention includes providing a method to acquire data indicative of a phenotypic change in the contacted cells and utilizing a processor including an algorithm for comparing the acquired data with the statistical design to identify which of the agent mixtures and/or which single agents are effective in causing the phenotypic change in the contacted cells. The method further includes storing the statistical design, the identities of the agents, the computer representation of the design, the acquired experimental data and the results of the algorithm comparison in one or more databases.
Yet another object of the present invention includes providing a system for implementing the method just described, and includes an array of receptacles, selective ones of which are for receiving (i) different mixtures of single agents and (ii) fluid including cells. The system also includes a statistical design including generic factor names, factor levels, and experimental runs, and a software program for generating a computer representation of the design. The software program automatically maps the identities of the agents to the generic factor names, maps the concentration of or amounts of the agents to the factor levels and maps the locations of the receptacles within the array to the experimental runs. The system also includes acquired experimental data indicative of the phenotypic change in the cells, and a processor including an algorithm for comparing the experimental data with the statistical design to identify the mixtures and/or single agents which are effective in causing the phenotypic change in the cells. Further included in the system are one or more databases for storing the statistical design, the agent identities, the computer representation of the design, the acquired experimental data and the results of the algorithm comparison.
Still another object of the present invention is to apply an automated media optimization technology that enables users to optimize media components (e.g. factors) using the MPM/CATSBA software and robotic liquid-handling platforms. Using specific factors, the MPM/CATSBA software automatically creates statistically designed experiments in a multi-well plate format, and generates the necessary files to prepare the correct experimental conditions using a robotic-liquid-handling platform (e.g. the Biomek FX, Biomek 2000, Tecan Genesis, or any similar platforms). The software and the database it resides on are used to automatically categorize and analyze numerous formats of data (e.g. fluorescence, absorbance, cell counts, and so forth). The software user can perform all relevant statistical analyses in an automated fashion and all relevant reports are automatically generated and stored within the database. After all relevant statistical analyses are performed, the user has the ability to combine results from multiple experiments for a meta-analysis and data mining.
These and other objects, advantages and novel features of the invention will be more readily appreciated from the following detailed description when read in conjunction with the accompanying drawings, in which:
a through 16d are example spreadsheets showing a Plackett-Burman statistical design for the layout of a 96-well plate in accordance with an embodiment of the present invention;
As defined herein, “agents” are growth effector molecules that bind to cells and regulate the survival, differentiation, proliferation or maturation of target cells or tissue. Examples of suitable agents for use in the embodiments of the present invention include peptones, growth factors, extracellular matrix molecules, peptides, hormones and cytokines which can either be in solution or bound to a culture surface, such as a well surface, scaffold surface, bead surface, and so forth.
The term “agent-immobilizing material” is defined herein as a biocompatible polymer that can serve as a link between the culture surface and an agent.
As defined herein, the term “immobilize,” “immobilized,” and the like is to render an agent, i.e., growth effector molecules, immobile on a culture surface, such as a well surface or the surface of a scaffold contained within a well. This term is intended to encompass passive adsorption of the agents to the culture surface, as well as direct or indirect covalent attachment of the agents to the culture surface.
“Factors” are the names of the variables in the experiment, and represent the elements that the experiment changes from one trial or run (e.g., one well) to the next. In the embodiments of the present invention, “factor” is a generic name for a single agent or mixture of single agents. Factors are combined according to a statistical design to form different mixtures in the experiment.
“Statistical Design”, as defined herein, is an experimental design that assists the user in finding a combination of adjustable variables (i.e., factors) to produce the best experimental outcome, dramatically reducing the number of experiments needed to achieve that objective. In the embodiments of the present invention, a suitable statistical design is generated using generic factor names which represent the agents being tested. The design includes factor levels that can be the amounts and/or concentrations of the factors or that can be converted to the actual amounts and/or concentrations of the factors. The design also includes experimental runs which are numbered. Experimental runs specify the combinations of factors and the levels thereof to test, and each can correspond to a single well on a multiwell plate. The experimental runs can be mapped to wells on a generic multiwell plate.
As used herein, the terms “pre-treatment” and “pre-treated” refers to the addition to a surface or other substrate of functional groups which are chemically involved in the covalent bond subsequently formed with the agent-immobilizing material (i.e., a biocompatible polymer). For example, a surface of a microtitre well can be subjected to amino-plasma treatment to create an amine-rich surface onto which the agent-immobilizing material may be coupled.
The term “array,” “receptacle array,” and the like as defined herein is a plurality of unique containers, such as tubes or wells, which are placed in an orderly arrangement, such as rows and columns.
The term “phenotypic”, “phenotypic change”, and the like as defined herein includes the observable physical or biochemical characteristics of an organism, as determined by both genetic makeup and environmental influences. This includes the expression of a specific trait, such as antibody secretion, cell number and time to peak antibody secretion, based on genetic and environmental influences.
As described above, it is likely that mixtures of single agents are required in order to achieve a desired cell fate. For example, growth effector molecules that bind to cell surface receptors and regulate the survival, differentiation, proliferation or maturation of these cells include growth factors, extracellular matrix molecules, peptides, hormones and cytokines, of which there are many examples. It can therefore be a tedious task to find the right growth effector or growth effector combinations to place in contact with the cell to achieve a desired cell fate.
The embodiments of the present invention solve a need in the art by providing a high throughput, computer-implemented method to identify optimal agents for a given cell type. Specifically, the system and method described in greater detail below, examines a list of several possible agents, such as peptones, and identifies the best subset of two or three peptones that optimizes cell culture conditions based on a variety of responses, such as antibody secretion, cell number, and time to peak antibody secretion.
In a first step of flow diagram 10, a user at block 100 either creates an experimental design using a commercially available software such as JMP™, available from SAS Institute of Cary, N.C., or generates a statistical design based on an algorithm that is already included in the software of the system. The design at block 100 includes generic factor names, factor levels, experimental runs, and a mapping of experimental runs to a generic microwell array. The statistical design is stored in a database at block 102. The user then inputs the specific agents at block 104, as well as the concentrations and/or amounts of the specific agents into the software. The user inputs are stored in a database at block 106.
At block 107, the user can select a specific statistical design. Subsequently, at block 108 a software program is utilized in order to generate a computer representation of the specific statistical design. The computer representation of the design can be a spreadsheet which can translate for example, into a 96-well layout. In particular, the software program used to generate the computer representation maps the names of the specific agents to the generic factor names in the design, maps the concentration and/or amounts of the agents to the factor levels in the design, creates experimental runs based on the specific agents and the concentrations and/or amounts, and maps the well locations to the experimental runs on a specific microwell array. The computer representation is then stored in a database at block 110.
At block 112, a computer program is generated for a robotic system based on the computer representation of the design. At block 114 as shown in flow diagram 15 of
Once the agents have been placed into the wells correctly, the robotic system at block 116 dispenses fluid including whole cells into the wells of the microwell array. At block 118 experimental data is acquired which would be indicative of a change in the phenotype of a cell. The acquired data is stored in a database at block 120 so that the experimental data is linked to the computer representation of the design. Then at block 122, a processor is utilized which includes an algorithm to compare the stored experimental data to the stored statistical design to identify the best mixtures and/or best agents, and in particular, which subset of two or three elicited the desired biological response (i.e., elicited a phenotypic change in the cells). Optionally, another algorithm can be used to compare the performance of mixtures of agents or single agents over multiple experiments to determine trends or patterns. In either case, the results of the algorithm comparisons can be stored in a database and displayed to a user at block 124, and can be periodically updated.
The databases shown in
Referring now to
The extracted information is compared at block 202 with the agent mixtures and/or single agents identified in block 122 from
In
Cellular constituents of a cellular pathway can include mRNA levels, protein abundances, protein activities, degree of protein or nucleic acid modification (e.g., phosphorylation or methylation), combinations of these types of cellular constituents, and so forth. Each cellular constituent is influenced by at least one other cellular constituent in the collection by some biological mechanism. The influence, whether direct or indirect, of one cellular constituent on another is presented as an arc between the two cellular constituents and the entire pathway is presented as a network of arcs linking the cellular constituents to the pathway.
In
In order to ascertain certain pathways, proteins, or genes of particular interest, aspects of the biological state of the cell, for example, the transcriptional state, the translational state, or the activity state, can be measured in the presence of a mixture of single agents identified as eliciting a phenotypic change in the cell. This corresponds with block 122 of
Other external components can include user interface device 408, which can be a monitor and keyboard, together with pointing device 410, which can be a “mouse”, or other graphic input devices (not illustrated). Typically, the computer system 400 is also linked to network link 412 which can be part of an Ethernet link to other local computer systems, remote computer systems, or the Internet. This network link 412 allows computer system 400 to share data and processing tasks with other computer systems.
Loaded into the memory 404 are several software components which are both standard and well known to those skilled in the art, and components that are particular to the embodiment of the present invention. These software components collectively result in the computer 400 system to function according to the methods of at least one embodiment of the present invention. The software components are typically stored on hard disks 406. Software component 414 represents the operating system, which is responsible for managing computer system 400 and the network interconnections. An example of a suitable operating system is Windows 98, or Windows NT. Software component 415 is provided for analyzing the image from the microwell plate reader 407, and software component 416 represents common languages and functions conveniently present on system 400 to assist programs implementing the methods which are specific to the embodiment. Languages that can be used to program the analytical methods include Java®, but may also include C, C++, Fortran, Visual Basic or other computer languages.
In one example, the method can be programmed in mathematical software packages which allows symbolic entry of equations and high-level specification of processing, including algorithms to be used, thereby freeing a user of the need to procedurally program individual equations or algorithms. Such packages include Matlab, available from Mathworks of Natick, Mass., Mathematica available from Wolfram Research of Champaign, Ill., S-Plus available from Mathsoft of Seattle Wash., MathCAD available from Mathsoft of Cambridge, Mass., and “R” available from the R Foundation. Accordingly, software component 418 represents the methods as programmed in a procedural language or symbolic package.
In the system of
Software component 502 represents a user interface (UI), which is preferably a graphical user interface (GUI), which is a graphical way to represent the operating system, such as Windows 2000 or X11. User interface 502 provides a user of the computer system 400 with control and input as to the statistical design, specific agents, their concentrations and/or amounts, and, optionally, experimental data. The user interface may also include a means for loading information, such as experimental data from the hard drive 406, from removable media (e.g., CD-Rom), or from a different computer system communicating with the instant system over a network, such as the Internet.
Software component 504 represents the control software, which can be referred to as a UI server, which controls the other software components of the computer system. Software component 506 represents a data reduction and computation component including algorithms which execute the analytic methods. For example, component 506 can include an algorithm for comparing acquired experimental data to the statistical design to identify the best mixtures and/or best agents. The data can be imported into the software and automatically linked to the statistical design so that the data is fully annotated and ready for statistical analysis. Moreover, component 506 can include an algorithm to compare the performance of mixtures or agents over multiple experiments to determine trends or patterns which can be stored and periodically updated if desired. In one embodiment, software component 506 includes a linear regression algorithm. This is a method by which coefficients are estimated for each of the specific agents that are used in the statistical design.
Software component 418 can also include a software component 508 for generating a computer representation of the statistical design, as well as a software component 510 for a robotic system to place agents correctly into the wells of array 409 based on the statistical design stored in database 500. For example, a user can select an option via user interface 502 to generate computer files that can be imported into a robotic sample preparation platform, such as Biomek FX, Biomek 2000, Tecan Genesis, or any similar platform. The computer files can be used to automatically prepare the correct experimental conditions on the microwell array, to culture the cells, and to perform any fluid dispensing, fluid withdrawing or wash steps to carry out assays of phenotype.
The method described above can also be implemented from a customer location that is remote from the actual laboratory where the experiments are being performed. This could involve a web-based interface or the distribution of a thick-client software application to the customer. The level of interaction between the laboratory and the customer could vary. For example, the customer could have complete control of the process or, alternatively, the customer could receive only periodic reports from the laboratory as to the progress in obtaining optimal mixtures of agents.
With reference now to
In the embodiment shown, mixtures 18 of single agents 20, e.g., 20a-d are covalently immobilized to agent-immobilizing material 16. However, some or all of the agents being tested can be in a solution, rather than bound to a culture surface. In one example of the method, mixtures of single agents are covalently immobilized to an agent-immobilizing material on a culture surface, such as the receptacle surface or the surface of a scaffold contained within the receptacle. In yet another example, mixtures of single agents can be passively adsorbed onto a culture surface. Moreover, some or all of the single agents in the mixture can be in solution, and as described above, suitable agents for testing include, but are not limited to, growth factors extracellular matrix molecules, peptides, hormones, and cytokines. Moreover, small molecules, metals, chelators or enzymes can be added as agents to the wells.
Different mixtures 18 of single agents 20 are placed into the receptacles 10 according to a statistical design, which will be described in greater detail below. As shown in
Referring again to
Referring now to
As shown in
[10/10]=[1]
This provides an overall factor concentration that is equivalent to [1] per well.
[5/10]=[0.5]
Therefore, the overall factor concentration in the wells shown in
With reference now to
As shown in
[10/10]=[1]
for an overall factor concentration equivalent to [1] per well.
In scenario 95 of
Therefore, the total concentration of the agents in each receptacle is the same. Based on
The methods described above use a format, such as a microwell array, to screen a plurality of different mixtures of agents in parallel for their ability to bind to a given cell-type and elicit a desired response in the cell. The methods include placing different mixtures of agents into selective wells of a multi-well plate according to a statistical design. The methods can further include the optional step of placing single agents into other wells.
The methods also include delivering a fluid sample comprising a cell-type to the wells. After an appropriate incubation time between the cells and the samples in the various wells, evidence of an interaction between the cells and the well components can be detected, either directly or indirectly. For example, data can be acquired using functional assays, immunocytochemistry, or microscopy to measure responses such as antibody secretion, cell number and peak antibody secretion.
Suitable statistical designs for use with the embodiments of the present invention include, but are not limited, to the following: fractional factorial design, D-optimal design, mixture design and Plackett-Burman design. The statistical design can also be a space-filling design based on a coverage criteria, a lattice design, or a latin square design.
As described above, agents can either be bound to a culture surface (e.g., receptacle surface or scaffold surface) or can be in a solution. For example, in one example, the culture surface, which may be pre-treated, is coated with an agent-immobilizing material. The agent-immobilizing material is desirably a biocompatible polymer which does not support cell adhesion and which can serve as a flexible link, or tether between the culture surface and the agents. Examples of suitable polymers include synthetic polymers like polyethylene oxide (PEO), polyvinyl alcohol, polyhydroxylethyl methacrylate, polyacrylamide, and natural polymers such as hyaluronic acid and algenic acid.
Culture surfaces (e.g., well surfaces) are selected from, but not limited to, the following: polystyrenes, polyethylene vinyl acetates, polypropylene, polymethacrylate, polyacrylates, polyethylenes, polyethylene oxide, glass, polysilicates, polycarbonates, polytetrafluoroethylene, fluorocarbons, and nylon. The culture substrates may also wholly or partially include biodegradable materials such as polyanhydrides, polyglycolic acid, polyhydroxy acids such as polylactic acid, polyglycolic acid and polylactic acid-glycolic acid copolymers, polyorthoesters, polyhydroxybutyrate, polyphosphazenes, polypropyl fumurate, and biodegradable polyurethanes.
The culture surfaces can also be pre-treated. For example, cell culture surfaces bearing primary amines can be prepared by plasma discharge treatment of polymers in an ammonia environment. In one example, an agent-immobilizing material can be covalently attached to these aminated surfaces using standard immobilization chemistries as described in copending, commonly owned U.S. patent application Ser. No. 10/259,797, referenced above.
Two processes used commercially to create tissue culture treated polystyrene are atmospheric plasma treatment, also known as corona discharge, and vacuum plasma treatment, each of which is well known to those skilled in the art. Plasmas are highly reactive mixtures of gaseous ions and free radicals. An amino-plasma treatment or oxygen/nitrogen plasma treatment can be used to create an amine-rich surface onto which biocompatible polymers such as hyaluronic acid (HA) or algenic acid (AA) may be coupled through carboxyl-groups using carbodiimide bioconjugate chemistries, as described in U.S. patent application Ser. No. 10/259,797 referenced above. The resulting surfaces will not allow cells to attach, even in the presence of high, e.g., 10-20%, serum protein concentrations present in the cell culture media.
An example of pre-treated tissue culture polystyrene products that can be used to covalently link the agent via the agent-immobilizing material are the PRIMARIA™ tissue culture products available from Becton Dickinson Labware, which are created using oxygen-nitrogen plasma treatment of polystyrene and which result in the incorporation of oxygen-and nitrogen-containing functional groups, such as amino and amide groups.
Agents such as extracellular matrix proteins, peptides, and so forth can be subsequently covalently coupled to the HA or AA surface described above utilizing the amine groups on the proteins/peptides and either the carboxyl groups on the HA or AA, or aldehyde groups created on the HA or AA by oxidation using a substance such as sodium periodate.
In one example, the terminal sugar of human placental hyaluronic acid can be activated by the periodate procedure as described in a publication by E. Junowicz and S. Charm, entitled “The Derivatization of Oxidized Polysaccharides for Protein Immobilization and Affinity Chromatography,” published by Biochimica et. Biophysica Acta, Vol. 428: 157-165 (1976), the entire content of which is incorporated herein by reference. This procedure entails adding sodium or potassium periodate to a solution of hyaluronic acid, thus activating the terminal sugar which can be chemically cross-linked to a free amino group on an agent such as the terminal amino group on an extracellular matrix protein.
In another example, free carboxyl groups on the biocompatible polymer, such as HA or AA, may be chemically cross-linked to a free amino group on the agent using carbodiimide as a cross-linker agent. Still other standard immobilization chemistries are well known to those skilled in the art and can be used to join the culture surfaces to the biocompatible polymers, and to join the biocompatible polymers to the agents. Additional details are provided in a publication by Richard F. Taylor, Ed., entitled “Protein Immobilization: Fundamentals and Applications”, published by M. Dekker, NY, 1991, the entire content of which is incorporated herein by reference, or in copending U.S. patent application Ser. No. 10/259,797, referenced above.
The agents can be tethered to aminated tissue culture surfaces via biocompatible polymers, or can be tethered via biocompatible polymers to carboxylated surfaces or hydroxylated surfaces using standard immobilization chemistries. Examples of attachment agents include cyanogen bromide, succinimide, aldehydes, tosyl chloride, avidin-biotin, photocrosslinkable agents, epoxides and maleimides. Again, it is noted that the agents can be present in a solution and need not be bound to the culture surface.
As described above, the method provides mixtures of agents, which can be bound to a culture surface or can be in solution, contained within selective ones of the receptacles. Moreover, other receptacles may contain a single agent, and the agents may be combined in any desired proportions. The relative amounts of different agents present in the receptacles can be controlled for example, by the concentration of the agents in a composition which is to be dispensed into the receptacles.
Moreover, where the agents are covalently attached via a biocompatible polymer to the receptacle surface, the loading density can be controlled by adjusting the capacity of the biocompatible polymers bound to the culture surface. This can be accomplished by controlling the number of reactive groups on the polymers that can react with the agents, or by controlling the density of the biocompatible polymer molecules on the culture surface. Furthermore, the agents can first be separately linked to the biocompatible polymers (i.e. tethers), and then the “loaded” tethers can be mixed in the desired proportions and attached to the pre-treated substrate.
The agents can be in a solution and/or can be bound to a surface. For example, the agents can be covalently immobilized via biocompatible polymers to a pre-treated tissue culture surface which is desirably amine-rich. Alternatively, the agents can be immobilized to the receptacle surfaces by passively adsorbing the agents to the surface. Agents can also be pre-immobilized onto solid supports, such as beads, which then can be added to the receptacles. A response in a cell-type contacted with the beads in the receptacles could subsequently be detected. Mixtures of beads comprising single agents may be combined to form agent mixtures. Alternatively, mixtures of single agents can be immobilized to the beads.
The agents can also be immobilized on or impregnated within a scaffold, which can be placed in the receptacle and then contacted with fluid containing the cells. Suitable scaffolds for use in the embodiments described above, and methods for immobilizing agents thereto or therewithin are described in copending, commonly owned U.S. patent application Ser. No. 10/259,817, filed on Sep. 30, 2002, the entire content of which is incorporated herein by reference.
Receptacles for use in the embodiments described above can take any usual form, but are desirably microwells or tubes. Configurations such as microtitre wells and tubes are particularly useful and allow the simultaneous automated assay of a large number of samples to be performed in an efficient and convenient way. Microtitre wells are capable of extensive automation because of automatic pipetters and plate readers. Other solid phases, particularly other plastic solid supports, may also be used.
In one example, the receptacles comprise the wells of a 96-well microtitre plate (i.e., microwell array). Automatic pipetting equipment for reagent addition and washing steps, and color readers already exist for such microtitre plates as known to those skilled in the art. An example of such an automated device includes a pipetting station and a detection apparatus (e.g., plate reader), wherein the pipetting station is capable of performing sequential operations of adding and removing reagents to the wells at specific time points in a thermostatic environment (i.e., temperature controlled environment).
As described above, agents for use in the embodiments include growth effector molecules that bind receptors on the cell surface or are taken up through ion channels or transports and regulate the growth, replication or differentiation of target cells or tissue. In one example, these agents are cell adhesion ligands and/or extrinsic factors. In still other examples, the agents can be extracellular matrix proteins, extracellular matrix protein fragments, peptides, growth factors, cytokines, and combinations thereof, including an example described in greater detail below including sets and subsets of two and three peptones that are use to optimize cell culture conditions.
Preferred agents are growth factors, extracellular matrix molecules, cytokines, peptides, hormones, metals, chelators or enzymes. Examples of growth factors include, but are not limited to, vascular endothelial-derived growth factor (VEGF), epidermal growth factor (EGF), platelet-derived growth factor (PDGF), transforming growth factors (TGFa, TGFβ), hepatocyte growth factor, heparin binding factor, insulin-like growth factor I or II, fibroblast growth factor, erythropoietin nerve growth factor, bone morphogenic proteins, muscle morphogenic proteins, and other factors known to those skilled in the art. Other suitable growth factors are described in a publication by M. B. Sporn and A. B. Roberts, Eds., entitled “Peptide Growth Factors and Their Receptors I”, published by Springer-Verlag, NY, 1990, the entire content of which is incorporated herein by reference.
Such growth factors can be isolated from tissues using methods well known to those skilled in the art. For example, growth factors can be isolated from tissue or can be produced by recombinant means. Epidermal growth factor can be isolated from the submaxillary glands of mice and Genentech, of San Francisco, Calif., produces TGF-β recombinantly. Other growth factors in both natural and recombinant forms are also available from vendors such as Sigma Chemical Co., of St. Louis, Mo., R&D Systems, of Minneapolis, Minn., BD Biosciences, of San Jose, Calif., and Invitrogen Corporation, of Carlsbad, Calif.
Examples of suitable extracellular matrix molecules for use in the embodiment include vitronectin, tenascin, thrombospondin, fibronectin, laminin, collagens, and proteoglycans. Other extracellular matrix molecules are described in a publication by Kleinman et al., entitled “Use of Extracellular Matrix Components for Cell Culture,” published by Analytical Biochemistry 166:1-13 (1987).
Additional agents useful in the method described above include cytokines, such as the interleukins and GM-colony stimulating factor, and hormones, such as insulin. These are described in the literature referenced above and are commercially available.
Cells for use with the embodiments can be any cells that can potentially respond to the agents or that need the agents for growth. For example, cells can be obtained from established cells lines or separated from isolated tissue. Suitable cells include most epithelial and endothelial cell types, for example, parenchymal cells such as hepatocytes, pancreatic islet cells, fibroblasts, chondrocytes, osteoblasts, exocrine cells, cells of intestinal origin, bile duct cells, parathyroid cells, thyroid cells, cells of the adrenal-hypothalamic-pituitary access, heart muscle cells, kidney epithelial cells, kidney tubular cells, kidney basement membrane cells, nerve cells, blood vessel cells, cells forming bone and cartilage, and smooth and skeletal muscles.
Other useful cells can include stem cells which may undergo a change in phenotype in response to a select mixture of agents. Further suitable cells include blood cells, umbilical cord blood-derived cells, umbilical cord blood-derived stem cells, umbilical cord blood-derived progenitor cells, umbilical cord-derived cells, placenta-derived cells, bone marrow derived cells, and cells from amniotic fluid. The cells can be genetically engineered, and/or cultured with agents in a receptacle, such as the well of a 96-well microtitre plate. These cells can be cultured using any of the numerous cell culture techniques well known to those skilled in the art, such as those described in the text by Freshney, entitled “Cell Culture, A Manual of Basic Technique”, 3rd Edition, published by Wiley-Liss, NY, 1994. Other cell culture media and techniques are well known to those skilled in the art and can also be used in the embodiments of the present invention described above.
The cells can be cultured in the presence of agents which are in a solution or which are bound to a standard tissue culture vessel, such as a microtitre plate. The cells can also be cultured in suspension using agents that have been tethered to beads or fibers, preferably on the order of 10 microns in diameter. These particles, when added to culture medium, would attach to the cells, thereby stimulating their growth and providing attachment signals.
In a specific application, the system and method described above can be applied to identify the best subset of two or three peptones that optimizes cell culture conditions based on a variety of responses. Starting from a list of several possible peptones, an optimization strategy can be developed to identify the best subset of two or three peptones that optimizes cell culture conditions based on a variety of responses such as antibody secretion, cell number, and time to peak antibody secretion. Optimization techniques are further discussed in a publication by Taylor et al., entitled “Automated Assay Optimization With Integrated Statistics And Smart Robotics”, published by Journal Of Bimolecular Screening, 5(4): 213-225, August 2000, in a publication by Wolcke et al., entitled “Miniaturized HTS Technologies—uHTS”, published by Drug Discovery Today, 6 (12): 637-646, Jun. 15, 2001, and in a publication by Lutz et al., entitled “Experimental Design For High-Throughput Screening”, published by Drug Delivery Today, 1 (7): 277-286, July 1996, the entire content of each is incorporated herein by reference.
The system and method can be provided as an automated media optimization technology that enables users to optimize media components (i.e. factors) using a MPM/CATSBA software and robotic liquid-handling platforms. Using such specific factors, the software can automatically create statistically designed experiments in a multi-well plate format. The software then generates the necessary files to prepare the correct experimental conditions using a robotic-liquid-handling platform (e.g. the Biomek FX, Biomek 2000, Tecan Genesis, or any similar platforms). The software and the database it resides on can then be used to automatically categorize and analyze numerous formats of data (i.e. fluorescence, absorbance, cell counts, and so forth). The software user can then perform all relevant statistical analyses in an automated fashion and all relevant reports are automatically generated and stored within the database. After all relevant statistical analyses are performed, the user has the ability to combine results from multiple experiments for a meta-analysis and data mining.
In this example, the system and method initiates an assay development and determination of basic cell culture conditions in a first step. Specifically, the user specifies the factors (i.e. peptones) and their concentrations into the software (i.e. MPM/CATSBA) via a GUI, as noted in block 100 of
In a second step, a dilution series is provided in 96 well plates to determine optimal concentrations of peptones one-at-a-time. The computer files are used to automatically prepare the correct experimental conditions on the 96 well plates as noted in block 114 of
Specifically, at block 118 experimental data is acquired which would be indicative of a change in the phenotype of a cell, and is stored in a database at block 120 so that the experimental data is linked to the computer representation of the design. Then at block 122, a processor is utilized which includes an algorithm to compare the stored experimental data to the stored statistical design to identify the optimal concentrations that elicits the desired biological response (i.e., elicited a phenotypic change in the cells). The results of the algorithm comparisons can be stored in a database and displayed to a user at block 124, and can be periodically updated.
The resulting experiments in the 96 well plates are used to identify the best subsets of two and/or three peptones with a verification in shake flasks. That is, the optimization experiments in the 96 well plates determine the best concentrations of the peptones in the best subsets with verification in flasks.
A bake-off then provides the best subsets/best concentrations against customer media and appropriate controls in the 96 well plates followed by verification in shake flasks. Once again returning to
The user can perform all relevant statistical analyses in an automated way from information provided by the software application. Reports can then be generated and the results stored in the database. Based on the results of the statistical analysis, the user may return to the first step to start the next experiment or may proceed to scaling up the best media formulation.
In this specific example, the agents, or factors, are limited to peptones, but the system and method described above is general to applications including any reagents or factors that could be added to cell culture media. In the automated optimization application of the embodiment described above, the resulting tables described in greater detail below use an eight peptone set, but the number may be varied without affecting the strategy although specific design parameters would require modifications.
This specific example is directed at the detection of the best subsets of two and three peptones, but the number of peptones to be included in the best subsets evaluation could be increased or decreased within the same strategy although as noted above, specific designs parameters would require modifications. Additionally, the examples below incorporates 96 well plates, however the actual format of the plates could be changed and the overall strategy would still be valid. If larger or smaller plates were used, the designs would need to be revised accordingly.
In this example, the system and method is directed towards peptone combinations of two and three peptones at a time in order to determine which subsets are best. With eight peptones, wherein each is provided having at least two different concentration levels, the embodiment can do all of the two-way combinations (i.e. 8 choose 2=28) at both a higher and lower (i.e. higher/higher and lower/lower) concentration on one plate as shown in TABLE 1, with several wells left over for controls. Additionally, outer wells are not used due to evaporation. For example, in TABLE 1, CG Soy is evaluated in concentrations of 3.0 mg/mL (i.e. lower concentration for CG Soy) and 4.0 mg/mL (i.e. higher concentration for CG Soy) with the remaining peptones, such as Phytone in concentrations of 9.0 mg/mL (i.e. lower concentration for Phytone) and 10.0 mg/mL (i.e. higher concentration for Phytone), respectively.
On the second plate, the system and method is directed towards three-way combinations (i.e. 8 choose 3=56) at a single respective concentration level for each peptone on one plate as shown in TABLE 2, with several wells provided for controls. The controls on each plate allow comparisons between the plates and allow for additional statistical analysis. For example, in TABLE 2, CG Soy is evaluated in a concentration of 3.0 mg/mL (i.e. single concentration for CG Soy) with the remaining peptones, such as Phytone in a concentration of 9.0 mg/mL (i.e. single concentration for Phytone) and Phytone UF in a concentration of 2.0 mg/mL (i.e. single concentration for Phytone UF).
An example array of plate 1 is shown in TABLE 1, wherein the best subsets of two peptones are detected from a set of eight peptones, in addition to a number of control wells and replicated wells from plate 2.
In TABLE 1, eight peptones are used in the evaluation, including CG SOY, Phytone, Phytone UF, Proteose 3, Select Soytone, Wheat, Yeastolate, and Yeastolate Plus. Each peptone is included in either a low concentration or a high concentration. For example, CG Soy is used in concentrations including 3.0 mg/mL as a low concentration, and 4.0 mg/mL as a high concentration. These values can vary between peptones, as shown by comparison with Phytone which is used in concentrations including 9.0 mg/mL as a low concentration, and 10.0 mg/mL as a high concentration. The Well ID number indicates the microwell array well into which the indicated concentrations of peptone combinations are placed.
As illustrated in TABLE 1, a robotic system places the selected combinations of desired peptone concentrations into wells of a microwell array based on the computer representation created in steps 100-112 of
The system and method then acquires experimental data indicative of a phenotypic change in the contacted cells. Specifically, in this example, indicative data includes growth (i.e. proliferation) and secretion of antibodies (i.e. IgG Secretion/Cell proliferation). The system can than can store the experimental data in a database with links to the computer representation of the experimental design. In doing so, an algorithm can then be applied to compare experimental data to statistical designs to identify the best peptone combination, and more specifically, the one best two peptone combination for inclusion in a subset concentration evaluation as described in greater detail below. The procedure can then be repeated for subsets of three peptones from the set of eight peptones.
An example array of plate 2 is shown in TABLE 2, wherein the best subsets of three peptones are detected in addition to a number of control wells and replicated wells from plate 1.
In TABLE 2, the eight peptones of TABLE 1 are used again in the evaluation, including CG SOY, Phytone, Phytone UF, Proteose 3, Select Soytone, Wheat, Yeastolate, and Yeastolate Plus. In this evaluation, each peptone is included in a single concentration for each respective peptone. For example, CG Soy is used in a single concentration value of 3.0 mg/mL. As noted above, these values can vary between peptones, as shown by comparison with Phytone which is used in a single concentration value of 9.0 mg/mL. Also as noted above, the Well ID number indicates the microwell array well into which the indicated concentrations of peptone combinations are placed.
As illustrated in TABLE 2, a robotic system once again places the selected combinations of desired peptone concentrations into wells of a microwell array based on the computer representation created in steps 100-112 of
As with TABLE 1, the system and method then acquires experimental data indicative of a phenotypic change in the contacted cells, and stores the experimental data in a database with links to the computer representation of the experimental design. In doing so, an algorithm can then be applied to compare experimental data to statistical designs to identify the best peptone combinations, and more specifically, the three best three peptone combinations for inclusion in a subset concentration evaluation as described in greater detail below.
In this example, the system and method is applied to determine optimum concentrations of subset combinations of two and three peptones selected from the group of eight (i.e. optimization). The result of the best subset experiments above provides several combinations of two and three peptones together that are determined as being the best out of those tested. These subsets of peptones will be carried on to another automated experiment as described in greater detail below, in which the best concentrations will be determined for each subset using standard statistical methods as noted in block 126 and/or 128 of
The following protocol of TABLE 3 is a generic template for an optimization plate examining one best subset of two peptones as resulting from TABLE 1, and three best subsets of three peptones as resulting from TABLE 2. The peptones in the different sets may overlap. For each set of peptones, a central composite design is laid out on the plate. Using the inner 60 wells of the plate, various combinations of two and three peptone best subsets can be evaluated for an optimum concentration.
In TABLE 3, the values reflect the coded levels of the peptones used. The peptones in TABLE 3 are assigned as generic factors, F01, F02, . . . , F11. Although only eight peptones were included in this example, the peptones in the different subsets may overlap. In this example, F01 and F02 represent the peptones included in the best subset of two peptones to now be optimized as determined from TABLE 1. The subsets F03-F05, F06-F08, and F09-F11 represent the three best subsets of three peptones to now be optimized as determined from TABLE 2.
The Well ID number of TABLE 3 indicates the microwell array well into which the indicated concentrations of peptone combinations are placed. The Design ID column indicates which subset concentrations are being varied. For example, the Design ID column has the value of 1 for all of the wells in which the concentrations of the subset of two peptones are varied. The Design ID column has values 2, 3 and 4 for the wells in which the concentrations of the three subsets of three peptones are varied (i.e. F03-F05, F06-F08, and F09-F11), respectively. Whenever an NA is present in TABLE 3, the indicated factor, or peptone, is not included in that well.
With reference to TABLE 3, the first column indicates the Well ID number for each of the experimental runs in the 96 well plate. There are 60 runs in this example. The numbers in TABLE 3 in the columns labeled F01, F02, . . . , F11 (−1.41, −1, 0, 1, 1.41, etc.) represent coded values for the levels, or concentrations, of the factors, or peptones, respectively. From gathered information, a range of possible optimum concentrations is determined for each peptone, within which an optimum concentration is believed to exist. This range is assigned relative values using techniques such as Response Surface Methodology (RSM) as described in greater detail below.
In this example, for columns F01 and F02, the values of −1.41 and 1.41 correspond to the maximum and minimum concentrations that are hypothesized to span the range of possible optimum concentrations for the first two peptones, respectively. The concentrations of the peptones that correspond to the coded values of −1, 0, and 1 lie between the maximum and minimum concentrations of −1.41 and 1.41, and can be determined by a simple linear transformation. For example, the coded level of zero corresponds to the concentration midway between the maximum and minimum concentrations.
For the columns F03 to F11, the values of −1.68 and 1.68 correspond to the maximum and minimum concentrations that are hypothesized to span the range of possible optimum concentrations for the corresponding peptones assigned as F03 to F11. The concentrations corresponding to −1, 0, and 1 can be determined as described above. A specific example of a procedure for defining such ranges and subsequently assigning relative values is described in greater detail below.
For example, if from TABLE 1, the best combination of two peptones is found to be CG Soy at a concentration of 3.0 mg/mL and Phytone at a concentration of 9.0 mg/mL, these values could then be applied to the optimization of TABLE 3. As noted above for TABLE 3, F01 and F02 represent the peptones included in the best subset of two peptones as determined from TABLE 1 to be optimized.
In the optimization experiment, a concentration range is determined for each peptone, within which an optimum concentration is believed to exist and this range is assigned relative values. When determining this range, the current best values (i.e. from TABLE 1) can be chosen as a center value, and higher and lower values can then be selected to define the range around the current best values to explore for the optimum. The range should be wide enough to include a best estimate as to the true optimum, but narrow enough to provide a good statistical model. In many applications, this may require inputs from skilled users, such as cell biologists and statisticians.
For the above example, the range for CD Soy for use in TABLE 3 can be assigned relative values based upon the following defined range.
A unit change of one in coded values is 1 mg/mL in concentration values, therefore,
A unit change of one in coded values is 2 mg/mL in concentration values, therefore,
A similar procedure applies to the best combination of three peptones found in TABLE 2. As noted above for TABLE 3, the subsets F03-F05, F06-F08, and F09-F11 represent the three best subsets of three peptones as determined from TABLE 2 to be optimized.
For example, if from TABLE 2, the best combination of three peptones is found to be CG Soy at a concentration of 3.0 mg/mL, Phytone UF at a concentration of 2.0 mg/mL and Wheat at a concentration of 7.0 mg/mL, these values could then be applied to the optimization of TABLE 3. As above, the range is selected as a best estimate as to a region that should contain the optimum. An example calculation for one of these three peptone ranges is presented below. The range for Wheat for use in TABLE 3 can be assigned relative values based upon the following defined range.
Z0=0 corresponds to a concentration of 7.0 mg/mL of Wheat
A unit change of one in coded values therefore is 0.5 mg/mL in concentration values, therefore,
In many applications of the above embodiment, different concentration levels may be chosen for the same factor in the two and three variable optimization experiments. That is, where a factor is present in both a best combination of two and three peptones, and therefore used in multiple places in TABLE 3, the ranges of the single peptone in TABLE 3 need not be identical.
The generic factor names are provided in the top row of TABLE 3 and correspond to various subsets of the peptones listed in TABLES 1 and 2 in this example. In particular, the actual peptones in the best subset of two peptones resulting from the evaluation of the peptones in TABLE 1 would be substituted for the generic factors F01 and F02. The actual peptones in the first best subset of three peptones resulting from the evaluation of the peptones in TABLE 2 would be substituted for the generic factors F03, F04, and F05, and so on. Since the same peptone may appear in multiple best subsets, the same peptone may correspond to more than one of the generic factor names F01, F02, . . . F11.
As with TABLES 1 and 2, a robotic system places the selected subsets of desired peptone concentration variations into wells of a microwell array in an automated procedure. The evaluation of TABLE 3 results in sufficient space for placing two-way combinations of two peptones having minimum, or low (i.e. −1.41), mid-low (i.e. −1), mid (i.e. 0), mid-high (i.e. 1) and maximum, or high (i.e. 1.41) concentration levels, and placing three-way combinations of three peptones having minimum, or low (i.e. −1.68), mid-low(i.e. −1), mid (i.e. 0), mid-high (i.e. 1) and maximum, or high (i.e. 1.68) concentrations into the wells of a single plate. These concentrations however may not necessarily correspond to the concentrations shown in TABLES 1 and 2 for the same peptones, but as noted above, are concentrations that are hypothesized to span the range of possible optimum concentrations for a specific peptone.
The system and method then acquires experimental data indicative of a phenotypic change in the contacted cells and stores the experimental data in a database with links to the computer representation of the experimental design. In doing so, an algorithm can then be applied to compare experimental data to statistical designs to identify the best peptone concentration values.
The experiments can be further repeated with a subset to arrive at an optimum subset of factors for producing a desired response in a cell. Moreover, the experiment can be repeated wherein the concentration of the agents are varied. Follow-up experiments can also be performed with the subset of single agents that had statistically significant main effects or by combining a subset of the best single agents with a subset identified in the best mixtures.
One example of the results provided by the above embodiment are illustrated in
The embodiment described above can be completed in less time than conventional experiments. In particular, all of the best subsets of two peptones can be evaluated on a single plate (i.e. TABLE 1), and all of the best subsets of three peptones can be evaluated on a separate single plate (i.e. TABLE 2). Both of these plates, including replicates thereof, provide results (i.e. subsets) that can be evaluated at the same time in a single experiment on yet another separate plate (i.e. TABLE 3). The automated implementation of this is much faster and more efficient than conventional experiments that are not conducted in multiwell plates. Because the subsets of each size (i.e. two and three) are all evaluated on the same plate, respectively, the data obtained is more directly comparable and reliable. In addition, the follow-up optimization experiment allows the several subsets of two and three peptones to be optimized in the same experiment on the same plate. This is more efficient than conducting the experiments on separate plates, at separate times, and/or in alternative formats to multiwell plates.
Using a software package such as the MPM/CATSBA software, the above embodiments of the present invention remove many of the inherent human errors in cell culture and cellular experimentation through the automated implementation of an optimization strategy. The embodiments allow the implementation of software that makes the physical plate layouts from a complex statistical design in an automated fashion. The complex plate layouts then enable an automated evaluation and determination of solutions to best subset problems in a more efficient manner than currently available. Specifically, in this example the automated implementation of the optimization strategy is used to efficiently identify the best subset of peptone combinations, and thereafter, the best peptone concentrations that optimize cell culture conditions based upon antibody secretion, cell number and time to peak antibody secretion values.
Through a combined knowledge of experimental design and robotics for automated sample preparation, the embodiments integrate computers into traditional cell culture and use this technology to optimize biological systems as opposed to simply optimizing assay conditions.
All plate layouts, liquid-handling commands, and data analysis functions are automatically generated using the software. This automated platform removes most human errors from the experimental process. Prior to using this technology, experiments were either manually programmed into a robotic liquid-handling platform or experiments were created by hand on the benchtop. Both of these experimental approaches are highly likely to contain inherent errors due to manual manipulation and programming errors.
Additionally, the MPM software automatically analyzes all data and may be used to suggest follow-up optimization experiments. This system allows solutions to more complex media optimization problems in a more highly efficient manner. Additionally, the strategy for picking best subsets and jointly optimizing the concentrations for those subsets is novel in both design and implementation. This results in the ability to create complex plate layouts in an automated fashion and leads to very different experiments and observations than would be available in a manual system. For example, synergistic effects can be observed in certain combinations of media components.
The above embodiment further provides much faster pipetting speeds, all providing greater cost savings, improved data analysis times and robotic programming times. The reagent cost savings is calculated by multiplying the number of repeats required by the number of optimization experiments required for each experimental approach, then dividing the conventional result by the above optimization result.
The embodiment described above could be implemented from a customer location that is remote from the actual laboratory where the experiments are being performed. This could involve a web-based interface or the distribution of a thick-client software application to the customer. The level of interaction could range from as simple as dynamically generated reports showing the current status of the optimization to complete customer control of the process. Additionally, the embodiments are applicable to custom media optimization services, as well as custom data, reagent and experimental design management services.
Additional statistically designed experiments in accordance with the embodiments of the present invention are described in greater detail below.
An oxygen/nitrogen plasma is used by Becton Dickinson Labware to create PRIMARIA™ tissue culture products. In particular, oxygen/nitrogen plasma treatment of polystyrene products results in incorporation of oxygen- and nitrogen-containing functional groups, such as amino and amide groups. For this experiment, HA was coupled to the amine-rich surface on PRIMARIA™ multi-well plates through carboxyl groups on HA using carbodiimide bioconjugates chemistries well known in the art, such as those described in “Protein Immobilization: Fundamentals and Applications” Richard S. Taylor, Ed. (M. Dekker, NY, 1991) or as described in copending, commonly owned U.S. application Ser. No. 10/259,797, filed Sep. 30, 2002.
ECM agents were covalently attached to the HA polymer tethered to the culture surface from Example 1. In particular, aldehyde groups were created on HA by oxidation using the periodate procedure described in E. Junowicz and S. Charm, “The Derivatization of Oxidized Polysaccharides for Protein Immobilization and Affinity Chromotography,” Biochimica et. Biophysica Acta, Vol. 428: 157-165 (1976). This procedure entailed adding sodium periodate to a solution of HA, thus activating the terminal sugar. Subsequently, the activated HA was coupled to the amine groups on the ECM proteins using standard immobilization chemistries, such as those described in “Protein Immobilization: Fundamentals and Applications” Richard F. Taylor, Ed. (M. Dekker, NY, 1991) or copending U.S. application Ser. No. 10/259,797, filed Sep. 30, 2002.
In the present example, the statistical design is a mixture design. This design was used to identify pairs of factors, or single factors that had a positive effect on a cell response, and allows us to look at interactions between two ECMs. In this example, 10 single ECMs, each representing a single “factor” are used to created ECM mixtures for placement into the wells of a 96-well plate as shown in
In this example, a group of 10 adhesion ligands was selected and a 96-well array was chosen as the format for this screen. To eliminate border effects due to uneven evaporation, only the inner 60 wells of the 96-well array are to be used for the experiment. Wells in the outer rows and columns of the plate can thus be used for suitable controls.
The following 10 adhesion ligands were selected based on their common use as cell culture reagents, commercial availability and price: Collagen I (CI), Collagen III (CIII), Collagen IV (CIV), Collagen VI (CVI), elastin (ELA), fibronectin (FN), vitronectin (VN), laminin (LAM), polylysine (PL), and polyornithine (PO).
A statistical design was developed with special consideration of the surface chemistry requirements. In particular, in this experiment the scenario shown in
With reference now to
MC3T3-E1 cells, originated from Dr. L. D. Quarles, Duke University, and were kindly provided by Dr. Gale Lester, University of North Carolina at Chapel Hill. These cells were grown using standard cell culture techniques. MC3T3-E1 is a well-characterized and rapidly growing osteoblast cell line that was chosen because it attaches aggressively to most commonly used tissue culture surfaces.
Cells were removed from cell culture flasks using trypsin-EDTA according to methods well known in the art. Cells were enumerated, spun down and resuspended in media containing no serum or, alternatively, in media containing 10% fetal calf serum. Cells were plated into the wells of a 96-well microarray according to the layout shown in
An image analysis software package (Meta Morph, Universal Imaging Corporation, a subsidiary of Molecular Devices, Downingtown, Pa.) was used to enumerate the fluorescently labeled cell nuclei in
In
In order to optimize the surfaces, one can follow two leads, e.g., the “best well” composition or the “best factors”. The determination of “best factors” is made following rigorous statistical analysis of the experimental results.
In the “best well” approach, the well with the best experimental outcome is chosen for further optimization. In the example shown in
In the “best factors” approach, the experimental results are analyzed using statistical models. For the above-described example, a mixture-model analysis of the MC3T3-E1 data shows that Collagen IV, laminin, and poly-L-lysine (marginal effect) appear to increase the cell count when present at significant quantities with no serum as shown in
With reference now to
It is noted that both the “best well” and “best factors” approaches are valid, but each approach can lead to different surface compositions. In the present example, the “best well” approach would lead to a surface comprising Collagen-type VI and Collagen-type III, while the “best factor” approach would lead to a surface comprising Collagen VI and laminin.
Design
The present example describes a Plackett-Burman (PB) design as shown in
Proposed Acquisition of Data and Statistical Analysis
Cells are plated into the wells of a 96-well plate in accordance with the design shown in the spreadsheet of
Following the first screen, the main effects are estimated and reviewed. By “main effects”, it is meant the effect of a single agent acting independently. Interaction effects mean the combined effects of more than one single agent when the agents act in concert (not independently). At this point, relevant interactions among the agents typically are not estimated in the statistical model, but interactions among the agents would be expected to result in the best experimental runs, i.e., best wells. After the first round of screening, the best wells and the factors that are included in these wells (level=“1”) are identified. Follow-up experiments can be performed for each best well using all the factors included in the well, whether or not they had a positive, neutral, or negative effect in the preliminary statistical analysis. The experiments can be repeated with a subset of the agents identified in the best well so as to arrive at an optimum subset of factors for producing a desired response in a cell. Moreover, the experiment can be repeated, wherein the concentration of the agents in a best well are varied. Follow-up experiments can also be performed with the subset of single agents that had statistically significant main effects or by combining a subset of the best single agents with a subset identified in the best mixtures.
It has been proposed that the control of cellular phenotypes via extracellular conditions is governed by high order interactions among the factors in the extracellular environment. The Plackett-Burman design presented here is believed to provide good statistical estimates of the main effects and also provides the opportunity to observe a diverse set of combinations of factors among its experimental runs. In this case, higher-order interactions would be expected to result in specific experimental runs being “best wells” over and above what could be predicted by the individual main effects of the agents in the best wells.
Although only a few exemplary embodiments of the present invention have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the exemplary embodiments without materially departing from the novel teachings and advantages of this invention. Accordingly, all such modifications are intended to be included within the scope of this invention as defined in the following claims.
Related subject matter is disclosed in U.S. Patent Application of Heidaran et al., entitled “Computer Software And Algorithms For Systems Biologically Linked To Cellular Phenotype”, Ser. No. 10/662,713, filed on Sep. 15, 2003, the entire content of which is incorporated herein by reference. Additional related subject matter is disclosed in U.S. patent application Ser. No. 09/359,260, entitled “Methods, Apparatus And Computer Program Products For Formulating Culture Media”, filed Jul. 22, 1999, in U.S. patent application Ser. No. 10/662,640, entitled “High Throughput Method To Identify Ligands For Cell Attachment”, filed Sep. 15, 2003, in U.S. patent application Ser. No. 09/608,892, entitled “Peptides For Use In Culture Media”, filed Jun. 30, 2003, and in U.S. patent application Ser. No. 10/260,737, entitled Methods And Devices For The Integrated Discovery Of Cell Culture Environments”, filed Sep. 30, 2002, the entire content of each is incorporated herein by reference
