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Cell separation was first achieved in 1964 by S. H. Seal, who separated large tumor cells from the smaller blood cells using filtration. Two years later, Pohl and Hawk performed experiments on how cells respond differently to a nonuniform electric field based on the cell type. Such observations were the inception of cell separation methods, which allow isolating a single cell type from a heterogeneous sample.

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Related Experiment Video

Updated: Dec 14, 2025

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

Cell type prioritization in single-cell data.

Michael A Skinnider^1,2, Jordan W Squair^3,4,5, Claudia Kathe^6,7

¹Brain Mind Institute, Faculty of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. michael.skinnider@msl.ubc.ca.

Nature Biotechnology

|July 22, 2020

Summary

This summary is machine-generated.

Augur prioritizes cell types responding to biological perturbations using machine learning. This method outperforms existing techniques and identified neural circuits for locomotion recovery in mice.

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Area of Science:

Computational biology
Single-cell genomics
Machine learning

Background:

Identifying cell-type-specific responses to perturbations is crucial for understanding biological systems.
Existing methods like differential gene expression analysis may not fully capture complex cellular responses.
High-dimensional single-cell data offers rich information but requires advanced analytical tools.

Purpose of the Study:

To introduce Augur, a novel machine learning-based method for prioritizing cell types most responsive to biological perturbations.
To provide a robust framework for analyzing single-cell data across various modalities.
To demonstrate the superiority of Augur compared to conventional methods.

Main Methods:

Augur utilizes a machine learning framework to quantify cell separability in high-dimensional space.
The method was validated on diverse single-cell datasets, including single-cell RNA sequencing, chromatin accessibility, and imaging transcriptomics.
Performance was benchmarked against differential gene expression analysis.

Main Results:

Augur effectively prioritizes cell types exhibiting significant responses to biological perturbations.
The method demonstrated superior performance compared to differential gene expression analysis across multiple single-cell data types.
Augur successfully identified specific neural circuits involved in restoring locomotion in mice after spinal cord neurostimulation.

Conclusions:

Augur provides a powerful and accurate approach for identifying responsive cell types in single-cell studies.
The method's versatility across data types makes it broadly applicable in biological research.
Augur's application in neuroscience highlights its potential for discovering mechanisms of biological recovery and therapeutic interventions.