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Related Concept Videos

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: May 9, 2026

Quality-Controlled Sputum Analysis by Flow Cytometry
07:22

Quality-Controlled Sputum Analysis by Flow Cytometry

Published on: August 9, 2021

Computational analysis optimizes the flow cytometric evaluation for lymphoma.

Fiona E Craig1, Ryan R Brinkman, Stephen Ten Eyck

  • 1University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.

Cytometry. Part B, Clinical Cytometry
|July 23, 2013
PubMed
Summary
This summary is machine-generated.

Computational analysis aids in distinguishing germinal center B-cell lymphoma (GC-L) from lymphoid hyperplasia (GC-H). The CD5-CD19+CD10+CD38- B-cell population showed high predictive power for GC-L diagnosis.

Keywords:
CD38computational analysisflow cytometrylymphoma

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Quality-Controlled Sputum Analysis by Flow Cytometry
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Multiplexed Fluorescent Immunohistochemical Staining, Imaging, and Analysis in Histological Samples of Lymphoma
07:52

Multiplexed Fluorescent Immunohistochemical Staining, Imaging, and Analysis in Histological Samples of Lymphoma

Published on: January 9, 2019

Area of Science:

  • Hematology
  • Immunophenotyping
  • Computational Biology

Background:

  • Clinical laboratories increasingly use high-color flow cytometry.
  • Traditional methods for panel design and data analysis are often subjective.
  • Distinguishing germinal center B-cell lymphoma (GC-L) from lymphoid hyperplasia (GC-H) remains a challenge.

Purpose of the Study:

  • To develop an optimal flow cytometric strategy for reliably differentiating GC-L from GC-H.
  • To apply computational tools for objective panel design and data analysis.
  • To identify specific cellular populations indicative of GC-L.

Main Methods:

  • Analysis of GC-H and GC-L cases using flow cytometry (CD45, CD20, kappa, lambda, CD19, CD5, CD10, CD38).
  • Application of computational tools (flowType, RchyOptimyx) to construct diagnostic cellular hierarchies.
  • Manual reanalysis to confirm computational findings.

Main Results:

  • The CD5-CD19+CD10+CD38- B-cell population demonstrated the highest diagnostic predictive power.
  • Significantly higher frequencies of CD10+/CD38- B-cells were observed in GC-L compared to GC-H (p=0.0001).
  • Challenges included CD10+ granulocytes, variable CD38 expression, and antigen staining intensity.

Conclusions:

  • Computational analysis and cellular hierarchy construction effectively guide manual flow cytometry analysis.
  • CD38 expression is a key marker for evaluating B-cells with a CD10+ germinal center phenotype.
  • Neoplastic cell evaluation requires accounting for increased antigen expression variability.