Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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.
In...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Large language model derived regular expressions for sleep phenotyping from electronic health record: a feasibility study.

Sleep advances : a journal of the Sleep Research Society·2026

Same author

PANDA pediatric arousal neural detection architecture.

NPJ digital medicine·2026

Same author

Dim light melatonin onset profile endotypes in a real-world clinical population with home-based, self-collected salivary assessments: Identification, distribution, and potential clinical relevance.

Sleep medicine·2026

Same author

Quantification of Ventilatory Control in Sleep Apnea: From Physiological Insight to Computable Loop Gain.

Sleep·2026

Same author

Delayed Arousal Response to Sleep Apnea Encodes Mortality.

medRxiv : the preprint server for health sciences·2026

Same author

Facility-Measured Sleep Electroencephalographic Microstructures in Long COVID.

Sleep·2026

Search research articles

Related Experiment Video

Updated: Sep 29, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

Assessment of Automated Flow Cytometry Data Analysis Tools within Cell and Gene Therapy Manufacturing.

Melissa Cheung¹, Jonathan J Campbell², Robert J Thomas¹

¹Centre for Biological Engineering, Loughborough University, Loughborough LE11 3TU, Leicestershire, UK.

International Journal of Molecular Sciences

|March 25, 2022

Summary

Manual flow cytometry analysis introduces variability in cell and gene therapy manufacturing. This study validates computational tools using synthetic data, revealing significant differences in their accuracy and reproducibility for cell identification.

Keywords:

ATMP manufacturing automated data analysis tools flow cytometry regulatory compliance

More Related Videos

Automated Counterflow Centrifugal System for Small-Scale Cell Processing

Automated Counterflow Centrifugal System for Small-Scale Cell Processing

Published on: December 12, 2019

Multicolor Flow Cytometry-based Quantification of Mitochondria and Lysosomes in T Cells

Multicolor Flow Cytometry-based Quantification of Mitochondria and Lysosomes in T Cells

Published on: January 9, 2019

Related Experiment Videos

Last Updated: Sep 29, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

Automated Counterflow Centrifugal System for Small-Scale Cell Processing

Automated Counterflow Centrifugal System for Small-Scale Cell Processing

Published on: December 12, 2019

Multicolor Flow Cytometry-based Quantification of Mitochondria and Lysosomes in T Cells

Multicolor Flow Cytometry-based Quantification of Mitochondria and Lysosomes in T Cells

Published on: January 9, 2019

Area of Science:

Biotechnology
Computational Biology
Immunology

Background:

Flow cytometry is crucial for cell and gene therapy manufacturing, but manual data analysis introduces operator variability.
Computational tools offer potential to reduce bias, yet regulatory confidence and validation remain challenges.
Automated cell identification methods require rigorous assessment for reliable therapeutic applications.

Purpose of the Study:

To evaluate the accuracy and reproducibility of six unsupervised clustering algorithms for flow cytometry data analysis.
To investigate the impact of cell population separation and distribution (normal/skewed) on algorithm performance.
To establish a framework for validating automated cell identification tools using synthetic flow cytometry datasets.

Main Methods:

Generation of synthetic flow cytometry datasets with controlled population separation and distribution characteristics.
Application of six distinct unsupervised clustering algorithms (Flock2, flowMeans, FlowSOM, PhenoGraph, SPADE3, SWIFT) to analyze the synthetic data.
Comparative analysis of algorithm outputs to assess accuracy, reproducibility, and performance under varying data conditions.

Main Results:

Significant variability observed in outputs from different software platforms analyzing identical synthetic datasets.
Algorithm accuracy decreased as cell cluster separation diminished, with flowMeans and Flock2 showing reduced performance.
SWIFT exhibited poor performance with skewed cell populations, while FlowSOM, PhenoGraph, and SPADE3 demonstrated relative robustness.

Conclusions:

Synthetic flow cytometry datasets are valuable for validating automated cell identification methods.
Performance of automated tools varies significantly, particularly with challenging datasets (low separation, skewed populations).
This validation approach enhances confidence in automated cell characterization for therapeutic manufacturing.