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Related Concept Videos

Flow Cytometry01:23

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 17, 2026

Multicolor Flow Cytometry-based Quantification of Mitochondria and Lysosomes in T Cells
06:22

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

Published on: January 9, 2019

A survey of flow cytometry data analysis methods.

Ali Bashashati1, Ryan R Brinkman

  • 1Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC, Canada V5Z 1L3.

Advances in Bioinformatics
|January 6, 2010
PubMed
Summary
This summary is machine-generated.

Manual flow cytometry (FCM) data analysis is time-consuming and error-prone. This review explores automated FCM analysis methods to overcome current limitations and improve reproducibility in health research.

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Area of Science:

  • Biomedical Engineering
  • Immunology
  • Computational Biology

Background:

  • Flow cytometry (FCM) is a critical technology in health research and clinical practice for tasks like disease diagnosis and treatment monitoring.
  • Current FCM data analysis is predominantly manual, leading to significant limitations including time consumption, errors, and lack of standardization.

Purpose of the Study:

  • To review state-of-the-art automated flow cytometry data analysis approaches.
  • To outline current challenges and future directions in developing fully automated FCM data analysis tools.

Main Methods:

  • A framework is introduced to systematically report components of FCM data analysis pipelines.
  • Review of existing literature on automated FCM data analysis techniques.

Main Results:

  • Manual FCM analysis is a bottleneck due to its time-intensive, error-prone, and non-reproducible nature.
  • Various automated approaches are emerging to address these limitations.

Conclusions:

  • Automation of FCM data analysis is crucial for improving efficiency, accuracy, and reproducibility in biomedical research.
  • Further development is needed to establish fully automated, standardized FCM analysis tools.