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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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Related Experiment Video

Updated: Dec 3, 2025

Integrate Imaging Flow Cytometry and Transcriptomic Profiling to Evaluate Altered Endocytic CD1d Trafficking

Integrate Imaging Flow Cytometry and Transcriptomic Profiling to Evaluate Altered Endocytic CD1d Trafficking

Published on: October 29, 2018

Predicting single-cell gene expression profiles of imaging flow cytometry data with machine learning.

Nikolaos-Kosmas Chlis^1,2, Lisa Rausch³, Thomas Brocker³

¹Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany.

Nucleic Acids Research

|October 29, 2020

Summary

IFC-seq is a new machine learning method that predicts gene expression from cell images. This approach integrates imaging data with transcriptomics, adding gene expression insights to imaging flow cytometry datasets without extra cost.

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Last Updated: Dec 3, 2025

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Published on: October 29, 2018

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Flow Cytometry and Single-Cell Analysis for Characterizing Microglia Activation in Early Postnatal Mouse Brain Development

Flow Cytometry and Single-Cell Analysis for Characterizing Microglia Activation in Early Postnatal Mouse Brain Development

Published on: October 3, 2025

Area of Science:

Cell biology
Genomics
Computational biology

Background:

High-throughput imaging and single-cell genomics are key technologies for cellular studies.
Morphological data from imaging can predict cellular states and gene mutations.
Imaging flow cytometry (IFC) offers potential beyond basic cell sorting.

Purpose of the Study:

To introduce IFC-seq, a machine learning method for predicting single-cell gene expression profiles from IFC data.
To integrate uncoupled imaging and transcriptomics datasets using common surface markers.
To enhance existing and new IFC datasets with gene expression information.

Main Methods:

IFC-seq leverages machine learning to model gene expression from cellular morphology.
It integrates separate imaging and transcriptomics data by identifying common surface markers.
A convolutional neural network was used for label-free gene expression prediction from brightfield images.

Main Results:

IFC-seq successfully modeled gene expression for key markers in human blood mononuclear cells.
The method accurately predicted gene expression in mouse myeloid progenitor cells.
Label-free prediction of gene expression from brightfield images was achieved for mouse cells.

Conclusions:

IFC-seq enables the prediction of gene expression profiles from IFC experiments.
The method adds valuable gene expression data to existing and future IFC datasets.
This approach enhances the utility of IFC without additional experimental costs.