title: Combination of automated high throughput platforms, flow cytometry, and hierarchical clustering to detect cell state.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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. CONCLUSIONS 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.
authors.affiliation: Transform Pharmaceuticals, Incorporated, A Unit of Johnson & Johnson, 29 Hartwell Ave., Lexington, Massachusetts 02421, USA. ckitsos@tpius.jnj.com
authors.forename: Christine M
authors.initials: CM
authors.lastname: Kitsos
authors.forename: Phani
authors.initials: P
authors.lastname: Bhamidipati
authors.forename: Irena
authors.initials: I
authors.lastname: Melnikova
authors.forename: Ethan P
authors.initials: EP
authors.lastname: Cash
authors.forename: Chris
authors.initials: C
authors.lastname: McNulty
authors.forename: Julia
authors.initials: J
authors.lastname: Furman
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Cima
authors.forename: Douglas
authors.initials: D
authors.lastname: Levinson
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D005434:Flow Cytometry / Q000379:methods*; D018922:HL-60 Cells; D006801:Humans