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

Non-diffusive slow heat dissipation induces high local temperature in living cells.

Nature communications·2026

Same author

Emulsion-Templated Gel Embedding: A Microfluidics-Free Method for Scalable Cell Encapsulation in Hydrogel Microcapsules.

ACS biomaterials science & engineering·2026

Same author

Combination of CSF α-synuclein seed amplification assay and amyloid-β42 predicts cognitive decline in Parkinson's disease.

Neurobiology of disease·2026

Same author

Intrinsically disordered protein droplet-enhanced oligonucleotide assembly enables rapid oligonucleotide-to-protein expression.

Nucleic acids research·2026

Same author

Whole-genome sequencing reveals a possible molecular basis of sex determination in the dioecious wild yam Dioscorea tokoro.

PLoS genetics·2026

Same author

Calcium-dependent protein kinases participate in RBOH-mediated sustained ROS burst during plant immune cell death.

The New phytologist·2026

Same journal

SqueakPose Studio, an end-to-end platform for pose estimation and real-time edge-AI deployment.

eLife·2026

Same journal

Mechanistic insights into transcriptional regulation of ARHGAP36 expression identify a factor predictive of neuroblastoma survival.

eLife·2026

Same journal

Activity-dependent CO<sub>2</sub> production in the axon triggers opening of Connexin32 in the Schwann cell paranode.

eLife·2026

Same journal

Lipid packing contributes to the confinement of caveolae to the plasma membrane.

eLife·2026

Same journal

A coma pattern-based autofocusing method resolves bacterial cold shock response at single-cell level.

eLife·2026

Same journal

Non-canonical amino acid incorporation enables minimally disruptive labeling of stress granule and TDP-43 proteinopathy.

eLife·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 9, 2025

Sample Preparation for Mass Cytometry Analysis

Sample Preparation for Mass Cytometry Analysis

Published on: April 29, 2017

In silico-labeled ghost cytometry.

Masashi Ugawa^1,2,3, Yoko Kawamura¹, Keisuke Toda¹

¹Thinkcyte Inc, Tokyo, Japan.

|December 21, 2021

Summary

This summary is machine-generated.

This study introduces stain-free flow cytometry using machine learning to predict cell labels from imaging data. This innovative method enables high-throughput cell characterization and sorting without costly or toxic fluorescent stains.

Keywords:

cell biology flow cytometry human imaging flow cytometry immunology inflammation ips cells leukocytes machine learning

More Related Videos

Far-Red Fluorescent Senescence-Associated β-Galactosidase Probe for Identification and Enrichment of Senescent Tumor Cells by Flow Cytometry

Far-Red Fluorescent Senescence-Associated β-Galactosidase Probe for Identification and Enrichment of Senescent Tumor Cells by Flow Cytometry

Published on: September 13, 2022

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

Published on: June 21, 2024

Related Experiment Videos

Last Updated: Oct 9, 2025

Sample Preparation for Mass Cytometry Analysis

Sample Preparation for Mass Cytometry Analysis

Published on: April 29, 2017

Far-Red Fluorescent Senescence-Associated β-Galactosidase Probe for Identification and Enrichment of Senescent Tumor Cells by Flow Cytometry

Far-Red Fluorescent Senescence-Associated β-Galactosidase Probe for Identification and Enrichment of Senescent Tumor Cells by Flow Cytometry

Published on: September 13, 2022

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

Published on: June 21, 2024

Area of Science:

Biotechnology
Cell Biology
Machine Learning

Background:

Flow cytometry is crucial for high-throughput cell characterization and isolation.
Current methods rely on molecular staining, which is expensive and can cause cellular toxicity.
This toxicity is a significant concern for downstream applications like regenerative medicine and diagnostics.

Purpose of the Study:

To develop a high-throughput, stain-free flow cytometry method.
To enable cell characterization and isolation without fluorescent labeling.
To address the limitations of cost and toxicity associated with traditional flow cytometry.

Main Methods:

Introduced 'in silico-labeled ghost cytometry', a stain-free flow cytometry technique.
Utilized machine learning to derive molecular labels from diffractive and scattering imaging data.
Applied compressive imaging methods for real-time cell analysis and sorting.

Main Results:

Accurately assigned molecular labels to cells in real time using only imaging data.
Successfully distinguished between different cell states, human induced pluripotent stem (iPS) cell types, and peripheral white blood cell subtypes.
Demonstrated the efficacy of stain-free modalities for cell characterization.

Conclusions:

In silico-labeled ghost cytometry offers a viable stain-free alternative for cell analysis.
This method has potential applications in cell manufacturing for regenerative medicine.
It can also be used in cell-based diagnostics where fluorescence labeling is undesirable.