High throughput single cell bioinformatics.
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. There is a growing need for experimental techniques that can provide a combination of single cell resolution, large cell populations, and the ability to track cells over multiple time points. In this article, a microwell array cytometry platform was developed to meet this need and investigate the heterogeneity and stochasticity of cell behavior on a single cell basis. The platform consisted of a microfabricated device with high-density arrays of cell-sized microwells and custom software for automated image processing and data analysis. As a model experimental system, we used primary hepatocytes labeled with fluorescent probes sensitive to mitochondrial membrane potential and free radical generation. The cells were exposed to oxidative stress and the responses were dynamically monitored for each cell. The resulting data was then analyzed using bioinformatics techniques such as hierarchical and k-means clustering to visualize the data and identify interesting features. The results showed that clustering of the dynamic data not only enhanced comparisons between the treatment groups but also revealed a number of distinct response patterns within each treatment group. Heatmaps with hierarchical clustering also provided a data-rich complement to survival curves in a dose response experiment. The microwell array cytometry platform was shown to be powerful, easy to use, and able to provide a detailed picture of the heterogeneity present in cell responses to oxidative stress. 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.
Roach
Kenneth L
KL
Center for Engineering in Medicine, BioMEMS Resource Center, Massachusetts General Hospital, Harvard Medical School, Shriners Hospital for Children, Boston, MA, USA.
King
Kevin R
KR
Uygun
Basak E
BE
Kohane
Isaac S
IS
Yarmush
Martin L
ML
Toner
Mehmet
M
D000818:Animals; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D004129:Dimethylpolysiloxanes / Q000737:chemistry; D005260:Female; D005434:Flow Cytometry / Q000379:methods*; D022781:Hepatocytes / Q000166:cytology*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D046228:Microarray Analysis / Q000379:methods*; D046210:Microfluidic Analytical Techniques / Q000379:methods*; D008856:Microscopy, Fluorescence; D009757:Nylons / Q000737:chemistry; D051381:Rats; D011917:Rats, Inbred Lew; D015203:Reproducibility of Results
D004129:Dimethylpolysiloxanes; D009757:Nylons; C085707:poly(dimethylsiloxane)-polyamide copolymer