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

Elective genomic sequencing for adults in research, clinical and commercial contexts.

BMC medical genomics·2026

Same author

Fault-tolerant 3D reconstruction from 2D spatial proteomics sections.

bioRxiv : the preprint server for biology·2026

Same author

Elective genomic sequencing for adults in research, clinical and commercial contexts.

medRxiv : the preprint server for health sciences·2026

Same author

Coordinated immune-epithelial dynamics in the nasal epithelium protect against respiratory virus infection.

bioRxiv : the preprint server for biology·2026

Same author

Single-cell spatial pharmacobiology identifies conserved stromal barriers to therapeutic antibody delivery in human solid tumors.

Nature biotechnology·2026

Same author

CD39<sup>+</sup>CD49a<sup>+</sup>CD103<sup>+</sup> cytotoxic tissue-resident natural killer cells infiltrate and control solid epithelial tumor growth in mice.

Science translational medicine·2026

Search research articles

Related Experiment Video

Updated: May 20, 2026

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

CytoSPADE: high-performance analysis and visualization of high-dimensional cytometry data.

Michael D Linderman¹, Zach Bjornson, Erin F Simonds

¹Department of Electrical Engineering, Stanford University, Stanford, CA, USA. michael.linderman@mssm.edu

Bioinformatics (Oxford, England)

|July 12, 2012

Summary

CytoSPADE offers high-performance analysis for high-dimensional flow cytometry data, enabling advanced tree-based visualization and interpretation of complex single-cell measurements.

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

Sample Preparation for Mass Cytometry Analysis

Sample Preparation for Mass Cytometry Analysis

Published on: April 29, 2017

Related Experiment Videos

Last Updated: May 20, 2026

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

Sample Preparation for Mass Cytometry Analysis

Sample Preparation for Mass Cytometry Analysis

Published on: April 29, 2017

Area of Science:

Computational Biology
Bioinformatics
Immunology

Background:

Flow cytometry advances allow simultaneous measurement of over 30 protein markers per cell.
High-dimensional cytometry data presents challenges for analysis and visualization.

Purpose of the Study:

To introduce CytoSPADE, a high-performance implementation for analyzing high-dimensional flow cytometry data.
To provide a tool for tree-based analysis and visualization of cytometry data.

Main Methods:

CytoSPADE implements the Spanning-tree Progression Analysis of Density-normalized Events (SPADE) algorithm.
The software is implemented in R, C++, and Java for broad compatibility.

Main Results:

CytoSPADE provides a high-performance interface for SPADE.
Enables effective tree-based analysis and visualization of complex cytometry datasets.

Conclusions:

CytoSPADE facilitates deeper insights into high-dimensional single-cell data.
The tool is freely available for researchers in various operating systems.