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Flow Cytometry01:23

Flow Cytometry

The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Related Experiment Video

Updated: Jun 3, 2026

Multicolor Flow Cytometry-based Quantification of Mitochondria and Lysosomes in T Cells
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Multicolor Flow Cytometry-based Quantification of Mitochondria and Lysosomes in T Cells

Published on: January 9, 2019

Statistical file matching of flow cytometry data.

Gyemin Lee1, William Finn, Clayton Scott

  • 1Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA. gyemin@eecs.umich.edu

Journal of Biomedical Informatics
|March 17, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for imputing missing data in high-dimensional flow cytometry, improving cell subpopulation analysis. The approach overcomes limitations of traditional methods by using restricted nearest neighbor imputation and a novel EM algorithm for clustering with missing data.

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Area of Science:

  • Biotechnology
  • Computational Biology
  • Immunology

Background:

  • Flow cytometry enables rapid measurement of cell markers.
  • Traditional analysis uses limited markers, hindering high-dimensional insights.
  • Limited flow cytometer channels necessitate multiple assays with overlapping markers.

Purpose of the Study:

  • To address the challenge of imputing high-dimensional flow cytometry data from overlapping marginal observations.
  • To develop an improved imputation method that avoids spurious subpopulations.
  • To enable robust multidimensional analysis of flow cytometry data.

Main Methods:

  • Nearest neighbor imputation restricted to cell subpopulations.
  • Clustering with missing data using a novel expectation-maximization (EM) algorithm.
  • EM algorithm initialization using domain knowledge to address ill-posed mixture model fitting.

Main Results:

  • Demonstrated that simple nearest neighbor imputation can create artificial cell subpopulations.
  • Introduced a novel imputation technique that preserves true subpopulations.
  • Successfully applied the method to real-world flow cytometry datasets.

Conclusions:

  • The proposed method effectively imputes high-dimensional flow cytometry data.
  • This approach enhances the accuracy of multidimensional flow cytometry analysis.
  • The technique facilitates more comprehensive understanding of cell populations.