A microwell array cytometry system for high throughput single cell biology and bioinformatics
Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2009.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2009
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| Online Access: | http://hdl.handle.net/1721.1/47850 |
| _version_ | 1826189060971954176 |
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| author | Roach, Kenneth L. (Kenneth Lee), 1979- |
| author2 | Mehmet Toner. |
| author_facet | Mehmet Toner. Roach, Kenneth L. (Kenneth Lee), 1979- |
| author_sort | Roach, Kenneth L. (Kenneth Lee), 1979- |
| collection | MIT |
| description | Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2009. |
| first_indexed | 2024-09-23T08:09:14Z |
| format | Thesis |
| id | mit-1721.1/47850 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T08:09:14Z |
| publishDate | 2009 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/478502022-01-13T07:53:45Z A microwell array cytometry system for high throughput single cell biology and bioinformatics Roach, Kenneth L. (Kenneth Lee), 1979- Mehmet Toner. Harvard University--MIT Division of Health Sciences and Technology. Harvard University--MIT Division of Health Sciences and Technology Harvard University--MIT Division of Health Sciences and Technology. Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2009. Includes bibliographical references (p. 91-101). Recent advances in systems biology and bioinformatics have highlighted that no cell population is truly uniform and that stochastic behavior is an inherent property of many biological systems. As a result, bulk measurements can be misleading even when particular care has been taken to isolate a single cell type, and measurements averaged over multiple cell populations in a tissue can be as misleading as the average height at an elementary school. Unfortunately, there are relatively few experimental systems available at present that can provide a combination of single cell resolution, large cell populations, and the ability to track individual cells over multiple time points. Those systems that do exist are often difficult to automate and require extensive user intervention simply to generate the raw data sets for later analysis. The goal of this thesis project was to develop a powerful, inexpensive, and easy-to-use system that meets the above requirements and can serve as a platform for single cell bioinformatics. Our current system design is composed of two basic parts: 1) a customizable PDMS device consisting of one or more microwell arrays, each with associated alignment and identification features, and 2) a suite of custom software tools for automated image processing and data analysis. The system has a number of significant advantages over competing technologies such as flow cytometry and standard image cytometry. Unlike flow cytometry, the cells are not in suspension, and individual cells can be tracked across multiple time points or examined before and after a treatment. (cont.) Unlike most image cytometry approaches, the cells are arranged in a spatially defined pattern and physically separated from one another, greatly simplifying the required image analysis. The automated analysis tools require only a minimal amount of user intervention and can easily generate multi-channel fluorescence time courses for tens of thousands of individual cells in a single experiment. For visualization purposes, tools are provided to annotate the original fluorescence images or movies with the results of later analysis, and several quality control routines are available to identify improperly seeded wells or debris. The microwell array cytometry platform has allowed us to investigate a number of biological problems that would be difficult or impossible to tackle with standard techniques. Our earliest work focused on correlating pre-stress cell states with post-stress outcomes, with a major focus on the cryopreservation of primary hepatocytes. In particular, we wanted to know whether cell survival was dominated by extrinsic factors such as ice crystal nucleation, or intrinsic factors such as the energetic state of the cell. In one set of studies, we found that cells with a high initial mitochondrial content or mitochondrial membrane potential, as measured by Rh123 or JC-1 staining, were significantly less likely to survive the freezing process. This demonstrated that intrinsic cell factors do play a major role in cryopreservation survival, but perhaps more importantly demonstrated the power and versatility of the microwell system by tracking individual cells across a treatment as extreme as freezing the entire device. In another set of cryopreservation experiments, cells were transiently transfected with a GFP-tagged protective protein and the resulting cell population, with its range of expression levels, was used to generate dose response curves with single cell resolution for the protein's protective effect. (cont.) More recently, our efforts have focused on generating single cell fluorescence time courses and using bioinformatics techniques such as hierarchical and k-means clustering to visualize the data and extract interesting features. More specifically, the behavior of primary hepatocytes under oxidative stress and protective metabolic manipulation was examined using a combination of mitochondrial and free radical sensitive dyes. The resulting time courses could not only be compared between the treatment groups, but a number of distinct response patterns could be identified within each treatment group. This variation in response patterns represent potentially important information that would be missed using bulk techniques or flow cytometry. In addition, membership in each response cluster was correlated between multiple dyes and with the initial state of each cell. Using a live / dead methodology, dose response curves, survival curves, and survival time distributions were also generated for each treatment condition and further subdivided based on the initial cell state and cluster assignments. We believe that our microwell array cytometry platform will have general utility for a wide range of questions related to cell population heterogeneity, biological stochasticity, and cell behavior under stress conditions. We have really just begun exploring rich data sets of this type, and with additional work there is a great potential for groundbreaking results in many areas of biology and bioinformatics. Though we have applied techniques from gene expression analysis, there are a number of significant differences between the type of data generated by gene chips and that generated in high-throughput single cell experiments. These differences also make single cell biology a fruitful area for the development of novel bioinformatics techniques and theories. by Kenneth L. Roach. Ph.D. 2009-10-01T15:52:09Z 2009-10-01T15:52:09Z 2009 2009 Thesis http://hdl.handle.net/1721.1/47850 430341443 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 101 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Harvard University--MIT Division of Health Sciences and Technology. Roach, Kenneth L. (Kenneth Lee), 1979- A microwell array cytometry system for high throughput single cell biology and bioinformatics |
| title | A microwell array cytometry system for high throughput single cell biology and bioinformatics |
| title_full | A microwell array cytometry system for high throughput single cell biology and bioinformatics |
| title_fullStr | A microwell array cytometry system for high throughput single cell biology and bioinformatics |
| title_full_unstemmed | A microwell array cytometry system for high throughput single cell biology and bioinformatics |
| title_short | A microwell array cytometry system for high throughput single cell biology and bioinformatics |
| title_sort | microwell array cytometry system for high throughput single cell biology and bioinformatics |
| topic | Harvard University--MIT Division of Health Sciences and Technology. |
| url | http://hdl.handle.net/1721.1/47850 |
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