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Flow Cytometry

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 28, 2025

Flow Cytometry to Estimate Leukemia Stem Cells in Primary Acute Myeloid Leukemia and in Patient-derived-xenografts, at Diagnosis and Follow Up

Flow Cytometry to Estimate Leukemia Stem Cells in Primary Acute Myeloid Leukemia and in Patient-derived-xenografts, at Diagnosis and Follow Up

Published on: March 26, 2018

A lasso and random forest model using flow cytometry data identifies primary myelofibrosis.

Feng Zhang¹, Ya-Zhe Wang², Yan Chang²

¹Fujian Provincial Key Laboratory on Hematology, Fujian Medical Center of Hematology, Fujian Institute of Hematology, Clinical Research Center for Hematological Malignancies of Fujian Province, Fujian Medical University Union Hospital, Fuzhou, China.

Cytometry. Part B, Clinical Cytometry

|April 22, 2024

Summary

Flow cytometry aids in diagnosing myeloproliferative neoplasms (MPNs). It accurately distinguishes primary myelofibrosis (PMF) from other MPNs and helps differentiate essential thrombocythemia (ET) from prefibrotic PMF, guiding treatment decisions.

Keywords:

flow cytometry immunophenotyping overt primary myelofibrosis polycythemia vera prefibrotic primary myelofibrosis thrombocythemia

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Database-guided Flow-cytometry for Evaluation of Bone Marrow Myeloid Cell Maturation

Database-guided Flow-cytometry for Evaluation of Bone Marrow Myeloid Cell Maturation

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Database-guided Flow-cytometry for Evaluation of Bone Marrow Myeloid Cell Maturation

Database-guided Flow-cytometry for Evaluation of Bone Marrow Myeloid Cell Maturation

Published on: November 3, 2018

Area of Science:

Hematology
Oncology
Immunophenotyping

Background:

Classical Philadelphia-negative myeloproliferative neoplasms (MPNs), including essential thrombocythemia (ET), polycythemia vera (PV), and primary myelofibrosis (PMF), are challenging to differentiate using morphology and molecular markers alone.
Accurate diagnosis and differentiation are crucial for guiding appropriate treatment strategies in MPN patients.

Purpose of the Study:

To clarify the application of flow cytometry in the diagnosis and differential diagnosis of classical Philadelphia-negative MPNs.
To identify specific flow cytometry markers and models for distinguishing between ET, PV, and PMF subtypes.

Main Methods:

Retrospective analysis of immunophenotypes, clinical characteristics, and laboratory findings from 211 Ph-negative MPN patients (ET, PV, pre-PMF, overt PMF) and 47 controls.
Utilized lasso and random forest models to identify key variables for PMF diagnosis.
Employed classification and regression tree models to differentiate between ET and pre-PMF.

Main Results:

PMF showed distinct differences from ET and PV in white blood cells, hemoglobin, peripheral blood blast cells, abnormal karyotype, and WT1 gene expression.
PMF differed from controls in CD34+ cells, granulocyte and monocyte phenotypes, plasma cell percentage, and dendritic cells, with a significantly lower plasma cell percentage.
A five-variable flow cytometry panel identified PMF with 90% sensitivity and specificity. A model using CD34+CD38- cells and platelet counts distinguished ET from pre-PMF with 94.3% and 83.9% accuracy, respectively.

Conclusions:

Flow immunophenotyping is a valuable tool for diagnosing PMF and differentiating it from ET and PV.
Specific flow cytometry markers and models can effectively distinguish between ET and pre-PMF, aiding in treatment decisions.
This approach enhances diagnostic accuracy for classical Ph-negative MPNs, improving patient management.