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

Robust FDR Control for Neuroimaging-based Classification via Knockoffs.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026

Same author

MSCA-Net: Multi-Modal Cell Segmentation for Spatial Transcriptomics.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026

Same author

Cellular immunology data enable clinical severity prediction via supervised machine learning.

iScience·2026

Same author

Geometric brain signatures of Alzheimer's disease progression and subtypes.

medRxiv : the preprint server for health sciences·2026

Same author

PD-1 blockade drug holiday improves exhausted progenitor CD8 T cell (Tpex) reinvigoration by avoiding Tpex adaptive resistance.

bioRxiv : the preprint server for biology·2026

Same author

Gene-Modulated Network Diffusion for Improved Modeling of Amyloid- <math><mi>β</mi></math> Spread in Alzheimer's Disease.

bioRxiv : the preprint server for biology·2026

Same journal

Layered social competition coordinates reproductive hierarchy formation in ants.

bioRxiv : the preprint server for biology·2026

Same journal

Combination epigenetic-targeted therapy increases the immunogenicity of poorly immunogenic sarcomas.

bioRxiv : the preprint server for biology·2026

Same journal

Loss of LanC-like proteins delays post-injury regeneration of aging skeletal muscles.

bioRxiv : the preprint server for biology·2026

Same journal

Integrative Transfer Network: Deep Transfer Learning Across Populations and Prediction Targets.

bioRxiv : the preprint server for biology·2026

Same journal

Confidence-supported label-free metabolic imaging with FPhaS phase autofluorescence microscopy.

bioRxiv : the preprint server for biology·2026

Same journal

Sequence-encoded autoinhibition couples mRNA decapping activity to phase separation.

bioRxiv : the preprint server for biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 26, 2025

Measurement of T Cell Alloreactivity Using Imaging Flow Cytometry

Measurement of T Cell Alloreactivity Using Imaging Flow Cytometry

Published on: April 19, 2017

Automated Cytometric Gating with Human-Level Performance Using Bivariate Segmentation.

Jiong Chen^1,2, Matei Ionita^3,4, Yanbo Feng²

¹Department of Bioengineering, University of Pennsylvania School of Engineering and Applied Science, PA, USA.

Biorxiv : the Preprint Server for Biology

|May 20, 2024

Summary

This summary is machine-generated.

A new deep learning framework, UNITO, automates cell gating in cytometry data. This image segmentation approach accurately identifies cell subpopulations, outperforming existing methods and rivaling human expert performance for faster, reproducible analysis.

More Related Videos

Author Spotlight: Advancing Research in Microbial Autoaggregation Using Imaging Flow Cytometry

Author Spotlight: Advancing Research in Microbial Autoaggregation Using Imaging Flow Cytometry

Published on: September 29, 2023

Temporal Tracking of Cell Cycle Progression Using Flow Cytometry without the Need for Synchronization

Temporal Tracking of Cell Cycle Progression Using Flow Cytometry without the Need for Synchronization

Published on: August 16, 2015

Related Experiment Videos

Last Updated: Jun 26, 2025

Measurement of T Cell Alloreactivity Using Imaging Flow Cytometry

Measurement of T Cell Alloreactivity Using Imaging Flow Cytometry

Published on: April 19, 2017

Author Spotlight: Advancing Research in Microbial Autoaggregation Using Imaging Flow Cytometry

Author Spotlight: Advancing Research in Microbial Autoaggregation Using Imaging Flow Cytometry

Published on: September 29, 2023

Temporal Tracking of Cell Cycle Progression Using Flow Cytometry without the Need for Synchronization

Temporal Tracking of Cell Cycle Progression Using Flow Cytometry without the Need for Synchronization

Published on: August 16, 2015

Area of Science:

* Computational Biology
* Immunology
* Data Science

Background:

* High-throughput cytometry generates complex single-cell protein expression data.
* Manual gating is labor-intensive and prone to variability due to biological and technical differences.
* Automated gating methods struggle with unpredictable events like debris and artifacts.

Approach:

* Developed UNITO, a deep learning framework transforming cell classification into image-based semantic segmentation.
* Applied UNITO to three independent cohorts for robust identification of hierarchical cytometric subpopulations.
* Validated UNITO against automated methods and expert immunologist consensus.

Key Points:

* UNITO accurately detects initial gates for single cellular events and subsequent cell gates.
* Outperforms existing automated methods, achieving results comparable to human expert consensus.
* Provides a fully automated pipeline without requiring human input or prior knowledge.

Conclusions:

* UNITO offers a reproducible, interpretable, and efficient solution for cytometry auto-gating.
* Achieves high accuracy and speed, unaffected by sample cell count.
* Enables fast (approx. 2 minutes/sample) and interpretable cell subtype assignment.