Multidimensional Data Analysis in Immunophenotyping

Michael R. Loken1

1 HematoLogic Inc., Fred Hutchinson Cancer Research Institute, Seattle, Washington
Publication Name:  Current Protocols in Cytometry
Unit Number:  Unit 10.4
DOI:  10.1002/0471142956.cy1004s00
Online Posting Date:  May, 2001
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Abstract

The complexity of cell populations requires careful selection of reagents to detect cells of interest and distinguish them from other types. Additional reagents are frequently used to provide independent criteria for cell identification. Two or three monoclonal antibodies in combination with forward and right‐angle light scatter generate a data set that is difficult to visualize because the data must be represented in four‐ or five‐dimensional space. The separation between cell populations provided by the multiple characteristics is best visualized by multidimensional analysis using all parameters simultaneously to identify populations within the resulting hyperspace. Groups of cells are distinguished based on a combination of characteristics not apparent in any usual two‐dimensional representation of the data.

     
 
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Table of Contents

  • Defining the Problem of Multidimensional Data Analysis
  • Combining Regions Using Boolean Logic
  • Multidimensional Gating to Separate Overlapping Populations
  • Using Nonrectangular Regions to Identify Discrete Populations
  • Literature Cited
  • Figures
  • Tables
     
 
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Materials

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Figures

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Literature Cited

Literature Cited
   CDC (Centers for Disease Control). 1992. Guidelines for the performance of CD4+ T‐cell determinations in persons with human immunodeficiency virus infections. Morb. Mortal. Wkly. Rep. 41(RR‐8):1‐17.
   Knapp, W., Dorken, B., Gilks, W.R., Reiber, E.P., Schmidt, R. E., Stein, H., and von dem Borne, A.E.G.Kr. 1989. Leukocyte Typing IV, White Cell Differentiation Antigens. Oxford University Press, Oxford, U.K.
   Loken, M.R., Civin, C.I., Shah, V.O., Fackler, M.O., Segers‐Nolten, I., and Terstappen, L.W.M.M. 1992. Characterization of erythroid, lymphoid, and monomyeloid lineages in normal human bone marrow. In Flow Cytometry in Hematology (O.D. Laerum and R. Laerum, eds.) pp. 31‐42. Academic Press, London.
   Stelzer, G.T., Shults, K.E., and Loken, M.R. 1993. CD45 gating for routine flow cyometric analysis of human bone marrow specimens. Ann. N.Y. Acad. Sci. 677:265‐280.
   Terstappen, L.W.M.M., Civin, C.I., Shah, V.O., Hurwitz, C.A., and Laerum, M.R. 1989. Multidimensional flow cytometry as a new approach for discrimination between normal and leukemic cells in peripheral blood and bone marrow. In Progress in Cytometry (A. Jansen, ed.) pp. 4‐29. Becton Dickinson, Erembodegem, Belgium, and San Jose, Calif.
   Terstappen, L.W.M.M. and Loken, M.R. 1992. Leukocyte differential counting. In Flow Cytometry in Hematology (O.D. Laerum and R. Laerum, eds.) pp. 95‐110. Academic Press, London.
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