A microwell array cytometry system for high throughput single cell biology and bioinformatics

[摘要] (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.

[发布机构] Massachusetts Institute of Technology