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

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Assessment of Protocol Impact on Subjectivity Uncertainty When Analyzing Peripheral Blood Mononuclear Cell Flow

Rebecca Grant¹, Karen Coopman², Sandro Silva-Gomes³

¹Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough, Leicestershire LE11 3TU, UK.

Methods and Protocols

|April 3, 2021

Summary

Measurement uncertainty quantifies operator variability in Cell and Gene Therapy (CGT) manufacturing. Following protocols significantly reduced variation in Flow Cytometry data analysis, improving cell count accuracy.

Keywords:

Flow Cytometry data analysis measurement uncertainty subjectivity variation

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Last Updated: Nov 10, 2025

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Flow Cytometric Characterization of Murine B Cell Development

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Published on: January 22, 2021

Area of Science:

Biotechnology
Analytical Chemistry
Process Engineering

Background:

Cell and Gene Therapy (CGT) manufacturing faces variability from multiple sources.
Operator differences significantly contribute to process variability.
Quantifying this variability is crucial for consistent product quality.

Purpose of the Study:

To quantify operator variability in Flow Cytometry data analysis using measurement uncertainty.
To compare variability before and after implementing a standardized protocol.
To identify key steps contributing to variability in CGT manufacturing.

Main Methods:

Participants performed a 5-step Flow Cytometry gating sequence.
Variability was assessed using measurement uncertainty and Coefficient of Variation.
Two study stages: participant judgment vs. diagrammatical protocol adherence.
Measurement uncertainty calculated following Guide to the Expression of Uncertainty in Measurement protocols.

Main Results:

Following a diagrammatical protocol reduced inter-participant variation by 57%.
Measurement uncertainty provided higher resolution in analyzing gating processes.
The primary source of variability was identified in the initial gate for cell isolation.

Conclusions:

Measurement uncertainty is a valuable tool for root cause analysis in CGT manufacturing.
Standardized protocols can significantly reduce operator-induced variability.
Implementing measurement uncertainty can enhance process control and continuous improvement in CGT.