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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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Updated: May 8, 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

  • 1Division of Hematopathology, Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.

Cytometry. Part B, Clinical Cytometry
|September 5, 2013
PubMed
Summary
This summary is machine-generated.

Computational analysis using flow cytometry effectively distinguishes germinal center B-cell lymphoma (GC-L) from germinal center hyperplasia (GC-H). The CD10+CD38- B-cell population showed significant diagnostic utility, aiding in accurate classification.

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 in flow cytometry are often subjective.
  • Distinguishing germinal center B-cell lymphoma (GC-L) from germinal center hyperplasia (GC-H) remains a diagnostic challenge.

Purpose of the Study:

  • To determine the optimal flow cytometric strategy for reliably differentiating GC-L from GC-H.
  • To apply computational tools for identifying cell populations correlated with diagnostic outcomes.

Main Methods:

  • Flow cytometric immunophenotyping data from GC-H and GC-L cases were analyzed.
  • Utilized computational tools (flowType, RchyOptimyx) to construct cellular hierarchies.
  • Key markers included CD45, CD20, kappa, lambda, CD19, CD5, CD10, and CD38.

Main Results:

  • The CD5-CD19+CD10+CD38- population demonstrated the highest predictive power for distinguishing GC-L from GC-H.
  • Manual reanalysis confirmed significantly higher CD10+/CD38- B-cells in GC-L compared to GC-H (P=0.0001).
  • Challenges included CD10+ granulocytes, variable CD38 expression on B-cells, and antigen staining intensity variations.

Conclusions:

  • Computational analysis and cellular hierarchy construction effectively guided manual analysis of complex flow cytometry data.
  • CD38 expression is crucial for evaluating B-cells with a CD10+ germinal center phenotype.
  • Neoplastic cell evaluation requires accounting for increased antigen expression variability, unlike non-neoplastic cells.