Sequential univariate gating approach to study the effects of erythropoietin in murine bone marrow

Ram Achuthanandam, John Quinn, Renold J. Capocasale, Peter J. Bugelski, Leonid Hrebien, Moshe Kam

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Analysis of multicolor flow cytometric data is traditionally based on the judgment of an expert, generally time consuming, sometimes incomplete and often subjective in nature. In this article, we investigate another statistical method using a Sequential Univariate Gating (SUG) algorithm to identify regions of interest between two groups of multivariate flow cytometric data. The metric used to differentiate between the groups of univariate distributions in SUG is the Kolmogorov-Smirnov distance (D) statistic. The performance of the algorithm is evaluated by applying it to a known three-color data set looking at activation of CD4+ and CD8+ lymphocytes with anti-CD3 antibody treatment and comparing the results to the expert analysis. The algorithm is then applied to a four-color data set used to study the effects of recombinant human erythropoietin (rHuEPO) on several murine bone marrow populations. SUG was used to identify regions of interest in the data and results compared to expert analysis and the current state-of-the-art statistical method, Frequency Difference Gating (FDG). Cluster analysis was then performed to identify subpopulations responding differently to rHuEPO. Expert analysis, SUG and FDG identified regions in the data that showed activation of CD4+ and CD8+ lymphocytes with anti-CD3 treatment. In the rHuEPO treated data sets, the expert and SUG identified a dose responsive expansion of only the erythroid precursor population. In contrast, FDG resulted in identification of regions of interest both in the erythroid precursors as well as in other bone marrow populations. Clustering within the regions of interest defined by SUG resulted in identification of four subpopulations of erythroid precursors that are morphologically distinct and show a differential response to rHuEPO treatment. Greatest expansion is seen in the basophilic and poly/orthochromic erythroblast populations with treatment. Identification of populations of interest can be performed using SUG in less subjective, time efficient, biologically interpretable manner that corroborates with the expert analysis. The results suggest that basophilic erythroblasts cells or their immediate precursors are an important target for the effects of rHuEPO in murine bone marrow. The MATLAB implementation of the method described in the article, both experimental data and other supplemental materials are freely available at http://web.mac.com/ acidrap18.

Original languageEnglish (US)
Pages (from-to)702-714
Number of pages13
JournalCytometry Part A
Volume73
Issue number8
DOIs
StatePublished - Aug 2008
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Pathology and Forensic Medicine
  • Histology
  • Cell Biology

Keywords

  • Cluster analysis
  • Erythropoietin
  • FDG
  • Flow cytometry
  • Kolmogorov-Smirnov statistic
  • Multivariate data analysis

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