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

Cyt-Geist: Current and Future Challenges in Cytometry: Reports of the CYTO 2025 Conference Workshops.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2025

Same author

Spatially Resolved Immune Profiling of the Murine Lung Using In Vivo Labeling and Spectral Cytometry.

Current protocols·2025

Same author

Development of a 3D <i>In Vitro</i> Wound Healing Model to Assess the Effect of ADSC-EVs on Vascularization.

Tissue engineering. Part A·2025

Same author

The consequence of mismatched buffers in purity checks when spectral cell sorting.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2024

Same author

SingletSeeker: An unsupervised clustering approach for automated singlet discrimination in cytometry.

Cytometry. Part B, Clinical cytometry·2024

Same author

Controlled infection with cryopreserved human hookworm induces CTLA-4 expression on Tregs and upregulates tryptophan metabolism.

Gut microbes·2024

Same journal

A Novel Laboratorial Approach to Evaluate Bacterial Microleakage of Endodontic Sealers.

Current protocols·2026

Same journal

TRIAGE Toolkit: Streamlined Discovery of Regulatory Genes and Elements.

Current protocols·2026

Same journal

High-throughput Profiling of Pseudouridines in Microbiome-derived Bacterial RNA.

Current protocols·2026

Same journal

Recombinant Protein Expression in Rhodococcus species.

Current protocols·2026

Same journal

Streamlined In Vitro Transcription for Generating Self-Amplifying RNA With Modified Nucleotides.

Current protocols·2026

Same journal

CODEC Library Preparation From Genomic DNA.

Current protocols·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 11, 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

Ensuring Full Spectrum Flow Cytometry Data Quality for High-Dimensional Data Analysis.

Laura Ferrer-Font¹, Geoffrey Kraker², Kathryn E Hally³

¹Hugh Green Cytometry Centre, Malaghan Institute of Medical Research, Wellington, New Zealand.

Current Protocols

|February 6, 2023

Summary

This summary is machine-generated.

This study presents a step-by-step protocol for preparing full spectrum flow cytometry (FSFC) data for high-dimensional analysis. The workflow ensures data quality, reduces artifacts, and enables reproducible results in single-cell studies.

Keywords:

data curation data preparation full spectrum flow cytometry high-dimensional data analysis high-dimensional flow cytometry

More Related Videos

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

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: Aug 11, 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

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

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:

Immunology
Biotechnology
Computational Biology

Background:

Full spectrum flow cytometry (FSFC) enables multi-parameter single-cell analysis.
High-dimensional data analysis offers advantages over manual gating for complex datasets.
Increasing panel complexity necessitates robust data preparation for accurate analysis.

Purpose of the Study:

To develop a methodical protocol for preparing high-dimensional FSFC data.
To ensure data quality and minimize artifacts for downstream analysis.
To aid users in obtaining valid and reproducible results from FSFC data.

Main Methods:

Data cleaning: addressing signal drift, doublets, and aggregates.
Unmixing: ensuring accurate deconvolution of spectral data.
Scaling and batch correction: implementing proper transformations and normalization.
Dimensionality reduction: visualizing the impact of each preparation step.

Main Results:

A validated step-by-step protocol for FSFC data preparation.
Demonstration of quality control impact using dimensionality reduction.
Methodology addresses key challenges in high-dimensional flow cytometry data.

Conclusions:

The developed workflow facilitates efficient and reliable high-dimensional analysis of FSFC data.
Proper data preparation is crucial for accurate interpretation of complex single-cell datasets.
This protocol empowers FSFC users to achieve reproducible and high-quality results.