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Related Experiment Video

Updated: Mar 8, 2026

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Characterizing cell subsets using marker enrichment modeling.

Kirsten E Diggins^1,2, Allison R Greenplate^2,3, Nalin Leelatian^1,2

¹Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.

|January 31, 2017

Summary

This summary is machine-generated.

Marker Enrichment Modeling (MEM) objectively identifies cell types from complex single-cell data. This new algorithm improves upon traditional metrics for analyzing immune and cancer cells, offering a quantitative language for cell characterization.

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Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

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Last Updated: Mar 8, 2026

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Enrichment and Characterization of the Tumor Immune and Non-immune Microenvironments in Established Subcutaneous Murine Tumors

Enrichment and Characterization of the Tumor Immune and Non-immune Microenvironments in Established Subcutaneous Murine Tumors

Published on: June 7, 2018

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

Area of Science:

Single-cell biology
Computational biology
Immunology
Cancer research

Background:

Identifying cell types from high-content single-cell data currently requires manual expert analysis.
Existing methods for cell identification lack objectivity and standardization.

Purpose of the Study:

To develop an objective algorithm for cell identity determination from high-content single-cell data.
To create a standardized, quantitative language for describing cell characteristics.

Main Methods:

Developed Marker Enrichment Modeling (MEM), an algorithm that quantifies contextual feature enrichment.
Applied MEM to fluorescence and mass cytometry data for immune and cancer cell subsets.

Main Results:

MEM objectively describes cells by generating human- and machine-readable labels.
MEM demonstrates superior performance compared to traditional metrics in classifying immune and cancer cell subsets.
The algorithm provides a quantitative framework for characterizing cytotypes.

Conclusions:

Marker Enrichment Modeling (MEM) offers an objective and scalable approach to cell identity learning.
MEM enhances the analysis of complex tissues by providing a consistent language for cell type description.
This method facilitates the communication and understanding of cellular heterogeneity in biological research.