Combination of automated high throughput platforms, flow cytometry, and hierarchical clustering to detect cell state
Version of Record online: 8 JAN 2007
Copyright © 2007 International Society for Analytical Cytology
Cytometry Part A
Volume 71A, Issue 1, pages 16–27, January 2007
How to Cite
Kitsos, C. M., Bhamidipati, P., Melnikova, I., Cash, E. P., McNulty, C., Furman, J., Cima, M. J. and Levinson, D. (2007), Combination of automated high throughput platforms, flow cytometry, and hierarchical clustering to detect cell state. Cytometry, 71A: 16–27. doi: 10.1002/cyto.a.20353
- Issue online: 24 JAN 2007
- Version of Record online: 8 JAN 2007
- Manuscript Accepted: 27 OCT 2006
- Manuscript Revised: 21 MAR 2006
- Manuscript Received: 6 OCT 2005
- Defense Advanced Research Projects Agency (DARPA)
- hierarchical clustering;
- high-throughput experimentation;
- flow cytometry;
- cell-surface antigens;
- cellular phenotypes;
- cellular profiling
This study examined whether hierarchical clustering could be used to detect cell states induced by treatment combinations that were generated through automation and high-throughput (HT) technology. Data-mining techniques were used to analyze the large experimental data sets to determine whether nonlinear, non-obvious responses could be extracted from the data.
Unary, binary, and ternary combinations of pharmacological factors (examples of stimuli) were used to induce differentiation of HL-60 cells using a HT automated approach. Cell profiles were analyzed by incorporating hierarchical clustering methods on data collected by flow cytometry. Data-mining techniques were used to explore the combinatorial space for nonlinear, unexpected events. Additional small-scale, follow-up experiments were performed on cellular profiles of interest.
Multiple, distinct cellular profiles were detected using hierarchical clustering of expressed cell-surface antigens. Data-mining of this large, complex data set retrieved cases of both factor dominance and cooperativity, as well as atypical cellular profiles. Follow-up experiments found that treatment combinations producing “atypical cell types” made those cells more susceptible to apoptosis.
Hierarchical clustering and other data-mining techniques were applied to analyze large data sets from HT flow cytometry. From each sample, the data set was filtered and used to define discrete, usable states that were then related back to their original formulations. Analysis of resultant cell populations induced by a multitude of treatments identified unexpected phenotypes and nonlinear response profiles. © 2007 International Society for Analytical Cytology