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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: Jul 14, 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

Shared resource lab (SRL) strategies for supporting high-dimensional cytometry data analysis.

David M Gravano¹, Aja M Rieger², Lauren Nettenstrom³

¹Stem Cell Instrumentation Foundry, University of California Merced, Merced, California, USA.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology

|October 6, 2023

Summary

This summary is machine-generated.

Cytometry Shared Resource Laboratories (SRLs) play a key role in supporting high-dimensional data analysis. This study explores current strategies, limitations, and challenges faced by SRLs and their users in handling complex cytometry datasets.

Keywords:

education flow cytometry high-dimensional data analysis shared resource lab

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Published on: December 12, 2019

Area of Science:

Single-cell biology
Biotechnology
Data science

Background:

Increasingly complex single-cell data from flow and mass cytometry necessitates advanced analytical approaches.
Cytometry Shared Resource Laboratories (SRLs) are crucial for data acquisition but their role in downstream data analysis is less defined.

Purpose of the Study:

To investigate current strategies employed by SRLs for high-dimensional data analysis support.
To identify limitations and long-term challenges in SRL-provided data analysis support.
To offer recommendations for enhancing SRLs' role in high-dimensional data analysis.

Main Methods:

Conducted two surveys to gather insights from SRLs and users.
Organized a workshop at CYTO 2022 to discuss findings and strategies.
Analyzed responses to identify successful support mechanisms and areas for improvement.

Main Results:

SRLs are actively involved in supporting high-dimensional data analysis, but user needs and SRL capabilities often misalign.
Key challenges include resource limitations, training gaps, and the evolving nature of cytometry data.
Successful strategies involve dedicated data analysis personnel and standardized workflows.

Conclusions:

SRLs are pivotal in facilitating high-dimensional data analysis, requiring strategic development of downstream support.
Addressing identified limitations and challenges is essential for maximizing the impact of single-cell technologies.
Clearer guidelines and resource allocation are needed to empower SRLs in advanced data analysis.