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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: Jul 18, 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

Identifying cancer-associated leukocyte profiles using high-resolution flow cytometry screening and machine learning.

David A Simon Davis¹, Melissa Ritchie¹, Dillon Hammill²

¹Irradiation Immunity Interaction Lab, Australian National University, Canberra, ACT, Australia.

Frontiers in Immunology

|August 21, 2023

Summary

Blood immune cell signatures, particularly monocyte subsets, can serve as biomarkers for cancer detection and progression. Machine learning models trained on these signatures show promise for identifying cancer presence and type.

Keywords:

biomarkers cancer flow cytometry immunology leukocytes machine learning

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Flow Cytometric Analysis of Mitochondrial Reactive Oxygen Species in Murine Hematopoietic Stem and Progenitor Cells and MLL-AF9 Driven Leukemia

Flow Cytometric Analysis of Mitochondrial Reactive Oxygen Species in Murine Hematopoietic Stem and Progenitor Cells and MLL-AF9 Driven Leukemia

Published on: September 5, 2019

Area of Science:

Immunology
Oncology
Bioinformatics

Background:

Machine learning (ML) aids clinical decision-making by modeling complex data.
Identifying novel biomarkers can enhance ML effectiveness in cancer detection.
Cancer-related immune cell signatures are a promising area for biomarker discovery.

Purpose of the Study:

To investigate blood immune cell signatures as potential biomarkers for cancer progression.
To develop and test a pipeline for identifying cancer-specific leukocyte biomarkers.

Main Methods:

Developed a multiparameter cell-surface marker screening pipeline.
Utilized flow cytometry for high-resolution leukocyte population profiling.
Employed CATboost machine learning models for cancer prediction.

Main Results:

Identified a signature of blood leukocyte subsets, including monocyte subsets.
Demonstrated that these signatures can predict the presence and type of cancer in murine models.
Successfully trained ML models to correlate immune cell profiles with cancer status.

Conclusions:

Blood immune cell signatures show potential as robust biomarkers for cancer detection and characterization.
The developed screening pipeline can be adapted for use with human cancer patient blood samples.
This approach offers a novel strategy for enhancing ML-based cancer diagnostics.