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Flow Cytometry

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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: May 23, 2025

Preparation and Morphological Analysis of Chick Cranial Neural Crest Cell Cultures

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Published on: June 27, 2022

Fast, flexible analysis of differences in cellular composition with crumblr.

Gabriel E Hoffman^1,2,3,4,5,6, Panos Roussos^1,2,3,4,5,6

¹Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Research Square

|March 10, 2025

Summary

We developed crumblr, a new statistical method to analyze cell type composition changes. This method increases statistical power for identifying cell type frequency shifts in complex datasets.

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Area of Science:

Genomics
Computational Biology
Biostatistics

Background:

Cell type composition is crucial for understanding health and disease.
Single-cell technologies provide high-resolution data on cell types.
Analyzing compositional data from large cohorts presents statistical challenges.

Purpose of the Study:

To introduce crumblr, a scalable statistical method for analyzing cell type composition data.
To enable robust identification of cell type frequency changes in complex study designs.
To improve statistical power in analyzing cell lineage hierarchy.

Main Methods:

Developed crumblr, a method using precision-weighted linear mixed models.
Incorporated random effects to handle complex study designs.
Employed a multivariate approach for statistical testing across cell lineage levels.

Main Results:

Simulations show crumblr increases statistical power and controls false positive rates compared to existing methods.
Demonstrated crumblr's application on diverse single-cell RNA-sequencing datasets.
Successfully analyzed data from aging, tuberculosis, prostate cancer metastasis, and SARS-CoV-2 infection studies.

Conclusions:

crumblr offers a powerful and scalable solution for analyzing cell type compositional data.
The method enhances the ability to detect biologically significant changes in cell type frequencies.
crumblr is applicable to a wide range of single-cell studies in health and disease research.