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Related Concept Videos

Overview Of Cell Separation And Isolation01:20

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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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An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing
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Phenotypically supervised single-cell sequencing parses within-cell-type heterogeneity.

Kevin Chen1, Kivilcim Ozturk2,3, Ryne L Contreras1

  • 1Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA.

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|January 25, 2021
PubMed
Summary
This summary is machine-generated.

Phenotypically supervised single-cell RNA sequencing (pheno-scRNAseq) reveals unique molecular insights into breast cancer cell heterogeneity. This method enhances understanding of cell communication and functional diversity in disease.

Keywords:
Cell BiologyComplex System BiologyTranscriptomics

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Area of Science:

  • Cellular Biology
  • Genomics
  • Cancer Research

Background:

  • Understanding within-cell-type functional heterogeneity is crucial for deciphering cellular communication and diverse behaviors.
  • Single-cell RNA sequencing (scRNAseq) has improved cell heterogeneity studies, but linking cell phenotypes to transcriptomic data remains difficult.

Purpose of the Study:

  • To investigate if phenotypically supervised scRNAseq (pheno-scRNAseq) offers greater insight into heterogeneous cell behaviors compared to unsupervised scRNAseq.
  • To explore the molecular mechanisms underlying phenotypic heterogeneity in breast cancer cells.

Main Methods:

  • A phenotypic cell sorting technique was employed to isolate cells based on distinct phenotypes.
  • Pheno-scRNAseq was performed on invasive and non-invasive cells within a 3D in vitro breast cancer (BRCA) model.
  • Results were compared against phenotype-agnostic scRNAseq analysis.

Main Results:

  • Pheno-scRNAseq identified more unique and selective differentially expressed genes than unsupervised scRNAseq.
  • Functional studies confirmed the utility of pheno-scRNAseq for understanding within-cell-type functional heterogeneity.
  • Migration phenotypes were found to be coordinated with specific metabolic, proliferation, stress, and immune phenotypes.

Conclusions:

  • Pheno-scRNAseq provides novel insights into the molecular systems governing breast cancer cell phenotypic heterogeneity.
  • This approach is valuable for dissecting complex cellular communication and functional diversity in disease contexts.