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Overview Of Cell Separation And Isolation

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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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Updated: Sep 1, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Cell Layers: uncovering clustering structure in unsupervised single-cell transcriptomic analysis.

Andrew P Blair¹, Robert K Hu², Elie N Farah²

¹Biological and Medical Informatics Graduate Program, University of California, San Francisco, CA 94143, USA.

Bioinformatics Advances

|August 15, 2022

Summary

This summary is machine-generated.

This study introduces Cell Layers, an interactive tool for analyzing single-cell transcriptomics data. It enhances the understanding of cell populations by visualizing clustering results across multiple resolutions.

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Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

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Last Updated: Sep 1, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

Author Spotlight: Integrating Single-Cell Transcriptomics with Organoid Cultures for Advanced Research and Therapeutic Insights

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Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell transcriptomics enables cell population identification through unsupervised clustering.
Current static visualization methods limit the analysis of clustering results to a single resolution parameter.
Researchers often explore multiple resolutions but report only one, potentially losing valuable insights.

Purpose of the Study:

To develop an interactive tool for quantitative investigation of single-cell clustering results across varying resolutions.
To enhance the interpretability of single-cell clustering by linking molecular data with cluster evaluation metrics.
To provide novel insights into cell population heterogeneity and integrity.

Main Methods:

Development of Cell Layers, an interactive Sankey visualization tool.
Quantitative investigation of gene expression, co-expression, and biological processes.
Evaluation of cluster integrity across multiple clustering resolutions.

Main Results:

Cell Layers facilitates the exploration of single-cell clustering at different resolutions.
The tool links molecular data and cluster evaluation metrics for improved interpretability.
Novel insights into cell population structures and relationships across resolutions are revealed.

Conclusions:

Cell Layers offers a powerful method for the comprehensive analysis of single-cell transcriptomics data.
The interactive visualization enhances the understanding of cell populations and their characteristics.
This tool aids researchers in a more thorough evaluation of clustering outcomes.