WO2019133098A2

WO2019133098A2 - Microfluidic platform for time-resolved tissue and organism analysis

Info

Publication number: WO2019133098A2
Application number: PCT/US2018/056790
Authority: WO
Inventors: Richard Novak; Youngjae CHOE; Bret NESTOR
Original assignee: President And Fellows Of Harvard College
Priority date: 2017-10-20
Filing date: 2018-10-19
Publication date: 2019-07-04
Also published as: WO2019133098A3

Abstract

A microfluidic system includes an automated platform for culturing embryos with hyperspectral imaging. The microfluidic system further includes a plurality of static chips on the automated platform for providing a fresh medium exchange with minimal embryo motion. The plurality of static chips is compatible with traditional benchtop tools.

Description

MICROFLUIDIC PLATFORM FOR TIME-RESOLVED TISSUE

AND ORGANISM ANALYSIS

CROSS-REFERENCE TO RELATED APPLICATION(S)

[0001] This application claims priority to and benefit of ET.S. Provisional Patent Application No. 62/575,265, filed October 20, 2017, which is hereby incorporated by reference herein in its entirety.

GOVERNMENT SUPPORT

[0002] This invention was made with government support under W911NF-16-C-0050 awarded by the Department of Defense/D ARPA. The government has certain rights in the invention.

FIELD OF THE INVENTION

[0003] The present invention relates generally to microfluidic devices and systems for cell culture and/or cell assays, and, specifically, to a static well chip that is compatible with pipetting or robotic systems.

BACKGROUND OF THE INVENTION

[0004] Whole organisms, such as the Xenopus embryo, enable analysis of normal embryonic development, response to infection, and development of therapeutic countermeasures. As such, whole organisms are ideal for screening compounds as part of the therapeutic development pipeline, but current screens rely on mice and other larger animals that are not amenable to scaled-up screens of hundreds or thousands of embryos. Embryos from fish and amphibians are attractive model systems in that they are small (millimeter scale), easy to culture, and well-representative of human pathways. Although Xenopus and zebrafish embryos are now used by many companies and academic groups to screen drugs, most rely on genetic or other reporters of pathway function and typically are conducted as endpoint assays. In a previous patent application (i.e., International Application Publication No. WO 2017/027838 Al), we presented an initial microfluidic culture device to increase the throughput of embryos being cultured while enabling time-lapse imaging for high-content phenotype screening. That device is an enclosed system that is not compatible with pipetting or robotic systems. Furthermore, existing technologies do not provide adequate throughput or leverage the possible information content. [0005] The present disclosure is directed to providing a microfluidic platform that solves the above and other needs.

SUMMARY OF THE INVENTION

[0006] According to one aspect of the present disclosure, a microfluidic system includes an automated platform for culturing embryos with hyperspectral imaging. The microfluidic system further includes a plurality of static chips on the automated platform for providing a fresh medium exchange with minimal embryo motion. The plurality of static chips is compatible with traditional benchtop tools

[0007] According to another aspect of the present disclosure, a method is directed to culturing embryos with hyperspectral imaging via a microfluidic automated platform. The method further includes providing a fresh medium exchange via a plurality of static chips of the microfluidic automated platform, the plurality of static chips being compatible with traditional benchtop tools. The method further includes maintaining minimal embryo motion while providing the fresh medium exchange.

[0008] Additional aspects of the disclosure will be apparent to those of ordinary skill in the art in view of the detailed description of various embodiments, which is made with reference to the drawings, a brief description of which is provided below.

BRIEF DESCRIPTION OF THE DRAWINGS

[0009] FIG. 1 illustrates an Xenopticon frog embryo platform.

[0010] FIG. 2 illustrates top views of a plurality of different static chip designs.

[0011] FIG. 3A shows distribution plots of standard error of position for the chip designs of FIG. 2.

[0012] FIG. 3B shows distribution plots of standard error of position for two of the chip designs of FIG. 2 with control and infected embryos.

[0013] FIG. 4 illustrates time-resolved tissue tracking.

[0014] FIG. 5 shows a chart of regression of Xenopus metrics.

[0015] FIG. 6 shows a front view of a static frog chip design, according to one embodiment.

[0016] FIG. 7 shows an isometric view of the design of FIG. 6.

[0017] FIG. 8 shows a front view of a static frog chip design, according to another embodiment.

[0018] FIG. 9 shows an isometric view of the design of FIG. 8. [0019] FIG. 10 shows a front view of a static frog chip design, according to yet another embodiment.

[0020] FIG. 11 shows an isometric view of the design of FIG. 10.

[0021] FIG. 12 shows a front view of a static frog chip design, according to yet another alternative embodiment.

[0022] FIG. 13 shows an isometric view of the design of FIG. 12.

[0023] FIG. 14 shows a front view of a static frog chip design, according to yet another alternative embodiment.

[0024] FIG. 15 shows an isometric view of the design of FIG. 14.

[0025] FIG. 16 shows a front view of a static frog chip design, according to yet another alternative embodiment.

[0026] FIG. 17 shows an isometric view of the design of FIG. 16.

[0027] FIG. 18 shows a front view of a static frog chip design, according to yet another alternative embodiment.

[0028] FIG. 19 shows an isometric view of the design of FIG. 18.

[0029] FIG. 20 shows a front view of a static frog chip design, according to yet another alternative embodiment.

[0030] FIG. 21 shows an isometric view of the design of FIG. 20.

[0031] FIG. 22 shows a system for imaging and analyzing embryos.

[0032] FIG. 23 A shows a false color image of a dead tissue sample.

[0033] FIG. 23B shows a false color image of a live tissue sample.

[0034] FIG. 24A shows a transformed hyperspectral image of the dead tissue sample of FIG. 23 A.

[0035] FIG. 24B shows a transformed hyperspectral image of the live tissue sample of FIG. 23B.

[0036] FIG. 25 shows a graph illustrating data obtaining from repeatedly imaging embryos over a time period.

[0037] FIG. 26 shows survival rate versus time curves for the embryos of FIG. 25.

[0038] FIG. 27 shows a method for imaging and analyzing tissue samples such as embryos.

[0039] While the invention is susceptible to various modifications and alternative forms, specific embodiments have been shown by way of example in the drawings and will be described in detail herein. It should be understood, however, that the invention is not intended to be limited to the particular forms disclosed. Rather, the invention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention as defined by the appended claims.

DETAILED DESCRIPTION

[0040] While this invention is susceptible of embodiment in many different forms, there is shown in the drawings and will herein be described in detail preferred embodiments of the invention with the understanding that the present disclosure is to be considered as an exemplification of the principles of the invention and is not intended to limit the broad aspect of the invention to the embodiments illustrated. For purposes of the present detailed description, the singular includes the plural and vice versa (unless specifically disclaimed); the words“and” and“or” shall be both conjunctive and disjunctive; the word“all” means “any and all”; the word“any” means“any and all”; and the word“including” means “including without limitation.” Where a range of values is disclosed, the respective embodiments include each value between the upper and lower limits of the range.

[0041] The microfluidic platform of the present disclosure is generally designed as a static well chip that maintains certain features of previous designs, such as imaging of embryos from the side (more features), long term viability and normal development. However, the microfluidic platform has a new design that focuses on an optimized static well design that combines lateral imaging with ease of handling by being compatible with standard pipettors, including multipipettors.

[0042] For example, an automated microfluidic platform - the Xenopticon platform - is directed to culturing Xenopus laevis embryos with hyperspectral imaging for time-resolved data acquisition, as well as automated development metrics. The Xenopticon platform is directed to understanding pathogen infection and response to therapeutic interventions, and is useful in observing inter-subject variability of response to Aeromonas hydrophila infection. The Xenopticon platform is further helpful in demonstrating the use of an automated culture system for Xenopus embryo analysis that may be applicable to therapeutic development.

[0043] Furthermore, image analytics are added to process hyperspectral images (16 spectra) to identify tissue regions and track them during development. The image analytics are useful for assessing embryo viability and predict outcomes well ahead of the actual time of death. Additionally, the microfluidic platform is useful for measuring impact of interventions, including drugs and drug candidates, on infection and other diseases. The measurements are helpful in providing data to develop therapeutic regimes in terms of time and dose ranges to more quickly allow a drug to enter clinical testing.

[0044] Static wells/chips include a thermoplastic microfluidic/mesofluidic device embossed or injection-molded to form an embryo culture region and a medium reservoir separated by small posts. Static wells/chips are optimized by measuring embryo position in the chips over development, and the best chips minimized motion of embryos. Posts are designed to contain embryos while providing adequate medium diffusion to the embryo, allowing culture of chips for several days without having to replenish medium with no loss of viability. A gas permeable membrane is used to seal the device, which is useful for static chips because there is no fluid perfusion that would otherwise deliver oxygen and remove waste/C02.

[0045] According to another feature, tissue tracking is done by analyzing hyperspectral images to obtain regions of similar“color.” These regions and geometric features are processed using various algorithms to obtain hundreds to thousands of unique metrics per embryo that can be measured over time. Unique combinations of features are used to define tissue types of interest. This allow analysis of phenotypes and tissues in wild-type embryos without any manipulation of any kind.

[0046] Referring to FIG. 1, the Xenopticon platform 10 has the capability to automatically image up to 672 individual embryos in less than 15 min in 16 colors. The platform 10 generally includes a hyperspectral camera or microscope 12, a thermoelectric cooler 14, a plurality of chips 16, a chip manifold 18 to hold the plurality of chips 16, and a backlight 20. The plurality of chips 16 can include perfused chips 22A and/or static chips 22B. Perfused chips 22A provide fresh medium exchange with minimal embryo motion for accurate imaging over one week of development. Static chips 22B offer greater ease of use and compatibility with traditional benchtop tools.

[0047] Static chips 22B are fabricated in polycarbonate or styrene-ethylene-butadiene styrene elastomer via hot embossing. An air permeable membrane seals the chips 16 and provides adequate gas exchange for embryo respiration. Both chip designs enable normal embryo development for up to one week.

[0048] Referring to FIG. 2, a variety of different static chip designs 24A-24F may be used. FIG. 3A shows graphs 26A-26F of the standard error of position for the different static chip designs 24A-24F. FIG. 3B shows graphs 28A and 28B of the standard error of position of control and infected embryos for static chip design 24B, and graphs 28C and 28D of the standard error of position of control and infected embryos for static chip design 24D. Static chip design optimization is generally illustrated, with static well/chip designs optimized using the imaging system to minimize embryo motion over several days while providing adequate growth conditions and efficient sample handling.

[0049] Referring to FIG. 4, time-resolved tissue tracking is directed to hyperspectral image stacks that are parsed into the separate 16 colors, with 2D regions identified using the K- nearest neighbor algorithm, and 100s- 1, 000s of unique metrics are generated to describe tissue properties. Unique tissue signatures enable tracking of tissue regions during development without the need for transgenic reporters or other labels, such as the gut tracking time lapse shown here. Imaging can also be used to track infection. The time lapse on the right shows A. hydrophila infection of an embryo.

[0050] Referring to FIG. 5, an automated development assessment shows a time course of development of 14 embryos demonstrating discrimination of live and dead embryos. Control embryos 30A (white markers with black dashes inside) develop normally (4/4), while 5/5 infected embryos 30B (white markers with nothing inside) die. Embryos 30C treated using deferoxamine mesylate (DFOA) (black markers with white dashes inside) survive at a rate of one embryo out of five. Support vector machine classification automates live/dead determination with >96% accuracy.

[0051] According to some benefits, the Xenopticon platform provides automation of embryo handling and a time-resoled analytical framework for studying Xenopus embryo development. Additionally, observation of distinct infection response time courses for individual embryos in treated and untreated infections may provide further insight into pathogen and therapeutic dynamics.

[0052] Referring to FIGs. 6-21, different chip designs are illustrated in accordance with the above-described features. FIGs. 6 and 7 show front and isometric views of chip design 32. FIGs. 8 and 9 show front and isometric views of chip design 34. FIGs. 10 and 11 show front and isometric views of chip design 36. FIGs. 12 and 13 show front and isometric views of chip design 38. FIGs. 14 and 15 show front and isometric views of chip design 40. FIGs. 16 and 17 show front and isometric views of chip design 42. FIGs. 18 and 19 show front and isometric views of chip design 44. FIGs. 20 and 21 show front and isometric views of chip design 46.

[0053] Additional aspects of chip designs are generally described in International Application Publication No. WO 2017/027838, titled“Microfluidic Devices And Systems For Cell Culture And/Or Assay,” filed on August 12, 2016, and which is incorporated by reference in its entirety. [0054] Referring now to FIG. 22, a system 100 for imaging and analyzing embryos includes a platform 102 for culturing the embryos, a plurality of chips 104 disposed within the platform for storing the embryos during incubation and keeping the embryos stationary, a hyperspectral camera 106, and one or more processing devices 108. The system is configured to periodically image all of the embryos that are stored within the chips with the hyperspectral camera. In some implementations, the system can image each individual embryo once every fifteen minutes. The one or more processing devices can be configured to perform a variety of analytical techniques to analyze the embryos, identify and track infected tissue within the embryos, determine whether the embryos are alive or dead, and monitor the responses of the embryos to a variety of biological processes and stimuli.

[0055] FIGS. 23A and 23B show a false color image 48A of a dead embryo and a false color image 48B of a live embryo in respective chips during and/or after incubation. FIGS. 24A and 24B show a transformed hyperspectral image 50A of the dead embryo and a transformed hyperspectral image 50B of the live embryo, respectively. The hyperspectral images can be obtained using the hyperspectral camera, and generally are 16-color images. Each pixel in the hyperspectral images can have up to 16 different values. The hyperspectral images can be used to analyze the severity of any infection present in the embryos. As shown in FIGS. 24A and 24B, the transformed hyperspectral images 50A, 50B of the embryos can include a scale 52 to indicate whether the tissue at a given location in the image is infected. In some implementations, the transformed hyperspectral images 50A, 50B are analyzed to determine what percentage of the tissue of the embryo is infected. An amount of infected tissue above an infection threshold can indicate that the embryo is dead, while an amount of infected tissue below the infection threshold can indicate that the embryo is alive.

[0056] In some implementations, an infection threshold of 50% can be used to delineate between live embryos and dead embryos. In this implementation, a determination that at least 50% of the tissue in the embryo is infected results in the embryo being classified as dead, while less than 50% of the tissue being infected results in the embryo being classified as alive. This binary analysis of the image scan generally be carried out by the one or more processing devices, or can be carried out by a human operator.

[0057] In other implementations, the one or more processing devices can automatically utilize more complex image analysis techniques. In one implementation, a classifier can be trained to look for infected tissue in the hyperspectral images and determine whether the embryo is alive or dead. The classifier generally looks at every pixel in the image that represents a portion of the embryo and determines whether the color of the pixel indicates an infection or not. In some implementations, a hidden Markov statistical model can be applied to link image time series together. In these implementations, a variety of assumptions can be made about the survival and death of the embryos can be made. For example, one assumption of the classifier is that a truly dead embryo will not come back to life. The classifier can generally determine the viability of the embryos with about 93% accuracy.

[0058] FIG. 25 shows a graph 54 illustrating data obtained from repeatedly imaging eight different embryos or sets of embryos over a time period exceeding 100 hours. Specifically, the graph 54 in FIG. 25 shows the fraction of the tissue of the embryo that is infected. The legend shows that the different embryos or sets of embryos each have a different number of colony forming units, e.g., the different sets of embryos were generally infected with a different amount of a pathogen (such as bacterial cells, fungal cells, or infectious agents). Embryos containing greater numbers of colony forming units (i.e. embryos that were infected with a larger amount of pathogen) generally had a larger percentage of tissue that was infected at a given point in time, confirming the viability of the model.

[0059] FIG. 26 shows two graphs that illustrate survival rate vs. time curves for the eight sets of embryos of FIG. 25. Each of the curves plot the percentage of the embryos in each set that is considered to be still alive against the amount of time elapsed since the embryo became infected. The graph on the left shows the survival curves of the eight sets of embryos based on a human estimating whether or not the embryos are dead or alive at each point in time. The graph on the right shows the survival curves for the same eight sets embryos that were analyzed using the classifier with the hidden Markov model discussed herein. As shown in the graphs, the percentage of embryos still alive in each set drops off more rapidly for embryos having higher concentrations of colony forming units, e.g., embryos that were initially infected with a larger amount of a pathogen.

[0060] As shown by comparing the two charts however, the classifier generally results in a more accurate survival curve with a finer time resolution. By using the automated classifier, the system 100 can more accurately determine whether any given embryo is alive at a certain point in time. This allows the time of death of any embryos to be determined much more precisely than previous implementations. In some implementations using the automated classifier, the time of death of the embryos can be determined to the nearest 15 minutes. The classifier is also more accurate than a human estimator, as the model resulted in an accuracy of about 90.0% as compared to an accuracy of about 84.2% with a human estimator. The automated classifier also had a true positive rate of about 93.5% and a true negative rate of about 89.8%. [0061] FIG. 27 shows a method 200 for imaging and analyzing tissue samples such as embryos. At step 202, a plurality of chips are provided within a platform. The chips can be any of the chips disclosed herein. The chips can include static chips or perfused chips. The chips can also include any of chips 32, 34, 36, 38, 40, 42, 44, or 46. At step 204, a plurality of embryos are incubated within the plurality of chips. Generally, each individual embryo is incubated in its own respective chip.

[0062] At step 206, a plurality of images is obtained of each embryo for each of a plurality of points in time. Generally, the embryos are continually imaged over a certain time period. In one example, the plurality of embryos is repeatedly and sequentially imaged over 15-minute time periods such that each embryo is imaged every 15 minutes. In this example, the first embryo in the sequence may be imaged at 0 minutes, 15 minutes, 30 minutes, 45 minutes... until the end of the analysis. Other time periods can also be used. Generally, the images of the embryos are hyperspectral images captured with a hyperspectral camera. At step 208, the hyperspectral images are analyzed to identify infected and non-infected tissue in each of the embryos. For a given embryo, the images at each point in time are analyzed to identify infected and non-infected tissue for each point in time.

[0063] At step 210, it is determined whether each embryo is alive or dead at each point in time that the images of the embryos were obtained, based on the analysis of the images and the amount of infected and/or non-infected tissue. This determination can be made by a human visually analyzing the images, the one or more processors 108 of the system 100, or any combination. Moreover, any suitable method of determining whether each embryo is alive or dead can be utilized, including the methods disclosed herein.

[0064] As the images are being analyzed and it is being determined whether each embryo is alive or dead at each point in time that the images were obtained, survival characteristics for the embryos can be determined. Generally, the plurality of embryos is separated into at least two different portions for comparison purposes. Thus, at step 212, a first set of survival characteristics for a first portion of the embryos can be determined. At step 214, a second set of survival characteristics for a second portion of the embryos can be determined. At step 216, the first set of survival characteristics and the second set of survival characteristics are compared.

[0065] Generally, the survival characteristics can be any attribute of the embryos that one wishes to measure and compare. In some implementations, the survival characteristics may be survival curves such as those illustrated in FIG. 26. In other implementations, the survival characteristics may measure the response of the embryos to one or more stimuli, such as infection, treatment, light/dark, heat, humidity, etc. The survival characteristics do not necessary relate spec

[0066] The first and second portions of the plurality of embryos may include embryos treated with an infectious agent (such as a pathogen, bacteria, fungus, etc.) and embryos treated with both an infectious agent and a treatment agent. By monitoring the infection in the tissues of the embryos and determining whether each embryo is alive or dead at each point in time, a user and/or the system 100 can produce sets of survival characteristics that are effected by the infectious agent and the treatment agent. Thus, the user and/or the system 100 can monitor the effect the treatment agent has on infected embryos.

[0067] In some implementations, the plurality of embryos may include additional portions. For example, a third portion of the embryos may be a control portion that does not have an infectious agent or a treatment agent introduced thereto. Additional portions may include different types of treatment agents to analyze different potential treatments for the same infectious agent, or different types of infectious agents to analyze the effect a given treatment agent has on different infectious agents.

[0068] In some implementations, an infectious agent and a treatment agent may be introduced to embryos in both a first portion of the plurality of embryos and a second portion of the plurality of embryos. The first portion of the plurality of embryos can be illuminated during analysis, while the second portion of the plurality of embryos can be kept in the dark during analysis. By imaging the embryos and analyzing the images to determine survival characteristics, the user and/or the system 100 can monitor the effect that illumination has on the efficacy of the treatment agent. In other implementations, the time when a treatment agent is administered to different portions of the plurality of embryos can be altered so as to determine whether small shifts in treatment timing effect clinical outcomes. Generally, the treatment agent can be introduced to both the first and second portions of the plurality of embryos under different conditions and/or the first and second portions of the plurality of embryos can be incubated, imaged, and analyzed under different conditions to determine the effects on the efficacy of the treatment agent.

[0069] In still other implementations, tissue samples other than embryos may be used, such as eyes, muscles, organs, or any suitable type of tissue. In even other implementations, the hyperspectral images captured by the hyperspectral camera can be utilized to isolate other metrics of interest without having to re-do any experiments. Generally, any of the implementations described herein may be combined as needed. [0070] The system 100 can be configured to enable generation of very high content data for large numbers of embryos/tissue samples in parallel with a fine time resolution. In some implementations, the capacity of the system 100 is approximately 700 embryos with a 15 minute imaging time period. Implementations with fewer embryos can provide an even finer time resolution less than 15 minutes.

[0071] By utilizing method 200 and other techniques with system 100, a large number of embryos can be analyzed at once, and their times of death can be determined to the nearest 15 minutes. This l5-sminute resolution can generally change with the number of embryos being analyzed at once, as fewer embryos results in less time to capture a hyperspectral image of each embryo, meaning that any given embryo can be imaged more often. This provides the system with the capability to track the response of an embryo to a variety of different rapid biological processes.

[0072] Each of these embodiments, implementations, and obvious variations thereof is contemplated as falling within the spirit and scope of the claimed invention, which is set forth in the following claims. Moreover, the present concepts expressly include any and all combinations and sub-combinations of the preceding elements and aspects.

Claims

CLAIMS What is claimed is:

1. A microfluidic system, comprising:

an automated platform for culturing embryos with hyperspectral imaging;

a plurality of static chips on the automated platform for providing a fresh medium exchange with minimal embryo motion, the plurality of static chips being compatible with traditional benchtop tools.

2. The microfluidic system of claim 1, wherein the hyperspectral imaging is directed to time-resolved data acquisition.

3. The microfluidic system of claim 1, wherein the hyperspectral imaging is directed to automated development metrics.

4. The microfluidic system of claim 1, wherein the automated platform automatically images up to 672 individual embryos.

5. The microfluidic system of claim 4, wherein the automated platform automatically images the individual embryos in less than fifteen minutes.

6. The microfluidic system of claim 5, wherein the automated platform automatically images the individual embryos in sixteen colors.

7. The microfluidic system of claim 1, wherein one or more of the plurality of static chips is fabricated in polycarbonate or styrene-ethylene-butadiene styrene elastomer via hot embossing.

8. The microfluidic system of claim 1, further comprising an air-permeable membrane that seals the plurality of static chips and provides adequate gas exchange for embryo respiration.

9. The microfluidic system of claim 1, further comprising a lateral imaging device.

10. The microfluidic system of claim 1, wherein the automated platform is compatible with standard pipettors and multipipettors.

11. The microfluidic system of claim 1, wherein the automated platform cultures Xenopus laevis embryos.

12. The microfluidic system of claim 1, wherein the automated platform identifies tissue regions and tracks them during development.

13. The microfluidic system of claim 1, wherein one or more of the plurality of static chips include a medium reservoir separated by one more posts.

14. The microfluidic system of claim 13, wherein the one or more posts are designed to contain embryos while providing medium diffusion to the embryo.

15. The microfluidic system of claim 1, further comprising a gas permeable membrane for sealing the automated platform.

16. A method for culturing embryos, the method comprising:

culturing embryos with hyperspectral imaging via a microfluidic automated platform; providing a fresh medium exchange via a plurality of static chips of the microfluidic automated platform, the plurality of static chips being compatible with traditional benchtop tools; and

maintaining minimal embryo motion while providing the fresh medium exchange.

17. The method of claim 16, further comprising automatically imaging hundreds of individual embryos in less than fifteen minutes and in a plurality of colors.

18. The method of claim 16, further comprising hot embossing one or more of the plurality of static chips using polycarbonate or styrene-ethylene-butadiene styrene elastomer materials.

19. The method of claim 16, further comprising imaging the embryos with a lateral imaging device.

20. The method of claim 16, further comprising identifying tissue regions of the embryos and tracking them during development.

21. A method for tracking and analyzing tissue sample development, comprising:

providing a plurality of chips;

incubating a plurality of tissue samples, each of the plurality of tissue sample being incubated in a respective one of the plurality of chips;

obtaining an image of each of the plurality of tissue samples for each of a plurality of points in time;

analyzing the images of each of the plurality of tissue samples to identify infected and non-infected tissue within each of the plurality of tissue samples for each point in time;

determining whether each of the plurality of tissue samples is alive or dead for each point in time based on the analysis of the images;

determining a first set of survival characteristics for each tissue sample in a first portion of the plurality of tissue samples;

determining a second set of survival characteristics for each tissue sample in a second portion of the plurality of tissue samples; and

comparing the first set of survival characteristics and the second set of survival characteristics.

22. The method of claim 21, wherein the tissue sample is an embryo, an eye, a muscle, an organ, or any combination thereof.

23. The method of claim 21, further comprising introducing an infectious agent into each tissue sample of the first portion and the second portion of the plurality of tissue samples to thereby infect each tissue sample in the first portion and the second portion of the plurality of tissue samples.

24. The method of claim 23, further comprising a treatment agent into each tissue sample of the second portion of the plurality of tissue samples.

25. The method of claim 24, wherein the comparing of the first set of survival characteristics and the second set of survival characteristics analyzes the effect of the treatment agent has on the infected tissue samples.

26. The method of claim 24, wherein a third portion of the plurality of tissue samples has no infectious agent and no treatment agent introduced thereto so as to serve as a control group of tissue samples.

27. The method of claim 23, wherein a first type of treatment agent is introduced into each tissue sample in the first portion of the plurality of tissue samples, and wherein a second type of treatment agent is introduced into each tissue sample in the second portion of the plurality of tissue samples.

28. The method of claim 23, wherein a treatment agent is introduced into each tissue sample in the first portion of the plurality of tissue samples under a first set of conditions, and wherein the treatment agent is introduced into each tissue sample in the second portion of the plurality of tissue samples under a second set of conditions, the first set of conditions being different than the second set of conditions.

29. The method of claim 23, wherein a treatment agent is introduced into each tissue sample in the first portion and the second portion of the plurality of tissue samples, the tissue samples in the first portion of the plurality of tissue samples being imaged and analyzed under a first set of conditions, the tissue samples in the second portion of the plurality of tissue samples being imaged and analyzed under a second set of conditions, the first set of conditions being different than the second set of conditions.

30. The method of claim 21, wherein the first set of survival characteristics and the second set of survival characteristics each include survival curves for the first and second portions of the plurality of tissue samples.

31. The method of claim 21, wherein the analyzing and the determining are performed by an automated classifier.

32. The method of claim 21, wherein the automated classifier utilizes a hidden Markov model.