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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: Aug 29, 2025

Sample Preparation for Mass Cytometry Analysis

Sample Preparation for Mass Cytometry Analysis

Published on: April 29, 2017

Automated Cell Phenotyping for Imaging Mass Cytometry.

Sindhura Thirumal, Amoon Jamzad, Tiziana Cotechini

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|September 10, 2022

Summary

A new deep learning model automates cell phenotyping for imaging mass cytometry (IMC) data. This method reduces the need for biological expertise, enabling efficient analysis of complex tissue samples like bladder cancer biopsies.

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Last Updated: Aug 29, 2025

Sample Preparation for Mass Cytometry Analysis

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Published on: April 29, 2017

Single-cell Analysis of Immunophenotype and Cytokine Production in Peripheral Whole Blood via Mass Cytometry

Single-cell Analysis of Immunophenotype and Cytokine Production in Peripheral Whole Blood via Mass Cytometry

Published on: June 26, 2018

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

Area of Science:

Biomedical Engineering
Computational Biology
Cancer Research

Background:

Imaging mass cytometry (IMC) offers high-plex protein imaging on single tissue slides.
Accurate cell phenotyping is essential for IMC data analysis.
Current phenotyping relies heavily on prior biological knowledge.

Purpose of the Study:

To develop a deep learning model for automated cell phenotyping in IMC data.
To reduce the dependency on manual biological expertise for cell type identification.
To apply and validate the model on bladder cancer patient biopsy tissues.

Main Methods:

Development of a deep convolutional autoencoder-classifier.
Training the model to classify cells into four basic types.
Validation using feature importance analysis on bladder cancer tissue data.

Main Results:

Successful automation of cell phenotyping for high-dimensional IMC data.
Demonstration of biological relevance of identified features through importance analysis.
Effective classification of cell types in bladder cancer patient samples.

Conclusions:

Deep learning provides a powerful tool for automating cell phenotyping in IMC.
The developed model streamlines analysis and reduces the need for extensive biological knowledge.
This approach has significant potential for advancing high-dimensional IMC data interpretation.