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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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Updated: Aug 17, 2025

Techniques for the Analysis of Extracellular Vesicles Using Flow Cytometry

Techniques for the Analysis of Extracellular Vesicles Using Flow Cytometry

Published on: March 17, 2015

Building predictive disease models using extracellular vesicle microscale flow cytometry and machine learning.

Robert J Paproski^1,2, Desmond Pink^1,2, Deborah L Sosnowski¹

¹Department of Oncology, University of Alberta, Edmonton, AB, Canada.

Molecular Oncology

|December 15, 2022

Summary

This summary is machine-generated.

A new machine learning platform analyzes extracellular vesicles (EVs) in blood. This blood test accurately identifies high-grade prostate cancer, offering a promising diagnostic tool.

Keywords:

cancer prediction diagnostic test extracellular vesicles machine learning microflow cytometry prostate cancer

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Last Updated: Aug 17, 2025

Techniques for the Analysis of Extracellular Vesicles Using Flow Cytometry

Techniques for the Analysis of Extracellular Vesicles Using Flow Cytometry

Published on: March 17, 2015

Setting a Successful Sorting for Extracellular Vesicle Isolation

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Published on: October 11, 2024

Flow Cytometric Analysis of Extracellular Vesicles from Cell-conditioned Media

Flow Cytometric Analysis of Extracellular Vesicles from Cell-conditioned Media

Published on: February 12, 2019

Area of Science:

Biotechnology
Oncology
Nanomedicine

Background:

Extracellular vesicles (EVs) in biofluids carry disease-specific biomarkers.
Tumor-derived EVs influence cancer progression and metastasis.
Analyzing circulating EVs is a potential non-invasive diagnostic method.

Purpose of the Study:

To develop a machine learning approach for analyzing extracellular vesicles.
To create a diagnostic platform for predicting high-grade prostate cancer from blood samples.

Main Methods:

Combined microscale flow cytometry with machine learning for EV analysis.
Utilized tissue- and disease-specific biomarkers to build predictive models.
Developed and validated the Extracellular Vesicle Machine Learning Analysis Platform (EVMAP) in a 215-patient cohort.

Main Results:

EVMAP significantly improved the prediction of high-risk prostate cancer.
The developed blood test demonstrated clinical utility for cancer detection.
The platform successfully identified high-grade prostate cancer in patient samples.

Conclusions:

The EVMAP platform offers a novel, non-invasive approach for cancer diagnosis.
Machine learning analysis of EVs holds significant clinical potential for early disease detection.
This diagnostic platform enhances prostate cancer prediction through a blood test.