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Related Concept Videos

Flow Cytometry01:23

Flow Cytometry

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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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Database-guided Flow-cytometry for Evaluation of Bone Marrow Myeloid Cell Maturation
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Implementing flowDensity for Automated Analysis of Bone Marrow Lymphocyte Population.

Ghazaleh Eskandari1, Sishir Subedi1, Paul Christensen1

  • 1Department of Pathology and Genomic Medicine, Houston Methodist Hospital, Houston, TX, USA.

Journal of Pathology Informatics
|January 24, 2022
PubMed
Summary

The flowDensity algorithm shows promise for automating flow cytometry (FCM) data analysis in bone marrow samples, correlating well with manual gating for lymphocyte subsets but requiring further optimization for small cell populations.

Keywords:
Automated analysisdata analysisdata visualizationflow cytometryflowDensitygating

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Area of Science:

  • Hematology
  • Computational Biology
  • Immunology

Background:

  • Manual gating of flow cytometry (FCM) data for bone marrow analysis is standard but time-intensive.
  • Automated analysis methods are being developed to improve efficiency in cytometry.
  • Clinical adoption of automated FCM analysis algorithms remains limited.

Purpose of the Study:

  • To evaluate flowDensity, an open-source algorithm, for automated classification of lymphocyte subsets in bone marrow biopsy specimens.
  • To compare the performance of flowDensity-based gating with traditional manual gating methods.

Main Methods:

  • Applied flowDensity-based gating to 102 normal bone marrow samples.
  • Compared flowDensity results with manual analysis.
  • Assessed independent cell marker expression for comprehensive analysis and visualization.

Main Results:

  • Demonstrated a correlation between manual and flowDensity-based gating for lymphocyte subsets.
  • Observed a lower correlation for flowDensity in small cell clusters.
  • Successfully identified and visualized lymphocyte subsets through comprehensive expression analysis.

Conclusions:

  • flowDensity presents a promising approach for FCM data analysis.
  • Further optimization of the flowDensity algorithm is necessary for routine clinical and research implementation.