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Related Concept Videos

Flow Cytometry01:23

Flow Cytometry

13.4K
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 2, 2026

Flow Cytometric Characterization of Murine B Cell Development
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Flow Cytometric Characterization of Murine B Cell Development

Published on: January 22, 2021

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Automatic B cell lymphoma detection using flow cytometry data.

Ming-Chih Shih, Shou-Hsuan Stephen Huang, Rachel Donohue

    BMC Genomics
    |February 26, 2014
    PubMed
    Summary
    This summary is machine-generated.

    Automated flow cytometry analysis accurately diagnoses chronic lymphocytic leukemia (CLL) and follicular lymphoma (FL) using a novel computational method, improving diagnostic efficiency for B-lymphocyte neoplasms.

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    Detection and Enrichment of Rare Antigen-specific B Cells for Analysis of Phenotype and Function
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    Area of Science:

    • Hematology
    • Computational Biology
    • Immunology

    Background:

    • Flow cytometry is crucial for diagnosing hematopoietic diseases.
    • Current manual gating strategies are time-consuming, labor-intensive, and prone to error.
    • Advances in technology have increased biomarker analysis but not automated interpretation.

    Purpose of the Study:

    • To develop and validate an automated computational method for diagnosing B-lymphocyte neoplasms.
    • To assess the efficiency and accuracy of the automated system compared to manual methods.
    • To reduce diagnostic errors and improve workflow in hematologic disease diagnosis.

    Main Methods:

    • Utilized 80 sets of flow cytometry data from healthy donors, chronic lymphocytic leukemia (CLL), and follicular lymphoma (FL) patients.
    • Developed a multi-profile detection algorithm for computational analysis.
    • Trained and tested the algorithm using approximately 15% of the data to build cell capture rate profiles.

    Main Results:

    • The automated approach successfully identified 36/37 healthy donors, 18/18 CLL cases, and 12/13 FL cases.
    • Achieved high diagnostic accuracy for both CLL and FL.
    • Demonstrated the feasibility of automated diagnosis in a proof-of-concept study.

    Conclusions:

    • An automated diagnosis of CLL and FL is achievable using a computational method based on cell capture rates.
    • The developed system is efficient and can aid in the diagnosis of B-lymphocyte neoplasms.
    • This approach offers a promising alternative to manual gating for flow cytometry data analysis.