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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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Related Experiment Video

Updated: Jul 31, 2025

Fluorescence-Guided Matrix-assisted Laser Desorption/Ionization with Laser-Induced Postionization Mass Spectrometry of Individual Rat Neural Cells

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Published on: May 23, 2025

Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples.

Hratch Baghdassarian^1,2, Daniel Dimitrov³, Erick Armingol^1,2

¹Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA.

Biorxiv : the Preprint Server for Biology

|May 10, 2023

Summary

This study integrates LIANA and Tensor-cell2cell to robustly identify cell-cell communication across multiple samples. The combined tools offer flexible method selection and unsupervised deconvolution for biological insights.

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Last Updated: Jul 31, 2025

Fluorescence-Guided Matrix-assisted Laser Desorption/Ionization with Laser-Induced Postionization Mass Spectrometry of Individual Rat Neural Cells

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Published on: May 23, 2025

Transcriptome Analysis of Single Cells

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Published on: April 25, 2011

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

Published on: March 1, 2017

Area of Science:

Computational Biology
Single-cell Genomics
Systems Biology

Background:

Cell-cell communication inference is crucial for understanding biological processes.
Existing tools have limitations in generalizability, sample analysis, and method diversity.
Increasing complexity of single-cell datasets necessitates advanced computational methods.

Conclusions:

The integrated protocol enhances the analysis of cell-cell communication in complex single-cell datasets.
Provides a generalizable and flexible approach for deciphering intercellular signaling.
Accelerates biological discovery through efficient data analysis and visualization.