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Related Concept Videos

Adult Stem Cells01:33

Adult Stem Cells

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Stem cells are undifferentiated cells that divide and produce more stem cells or progenitor cells that differentiate into mature, specialized cell types. All the cells in the body are generated from stem cells in the early embryo, but small populations of stem cells are also present in many adult tissues including the bone marrow, brain, skin, and gut. These adult stem cells typically produce the various cell types found in that tissue—to replace cells that are damaged or to continuously...
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Profiling Individual Human Embryonic Stem Cells by Quantitative RT-PCR
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StemSC: a cross-dataset human stemness index for single-cell samples.

Hailong Zheng1,2, Jiajing Xie3, Kai Song1

  • 1Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.

Stem Cell Research & Therapy
|March 22, 2022
PubMed
Summary
This summary is machine-generated.

A new stemness index, StemSC, accurately measures stemness in single cells using gene expression patterns. This method improves upon existing techniques by better correlating with differentiation and identifying cancer stem cells, impacting immunotherapy research.

Keywords:
Cell dedifferentiationCross-datasetSingle-cell analysisStemnessTumor microenvironment

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Area of Science:

  • Cell Biology
  • Genomics
  • Bioinformatics

Background:

  • Stemness, the ability of cells to self-renew and differentiate, is crucial in development and disease.
  • Existing transcriptome-based stemness evaluation methods have limitations, including poor correlation with differentiation time and inability to leverage validated stem cells.
  • There is a need for a robust and versatile stemness index for single-cell analysis.

Purpose of the Study:

  • To develop a novel stemness index for single-cell samples, termed StemSC.
  • To improve the accuracy and applicability of stemness evaluation across diverse biological contexts.
  • To investigate the relationship between stemness, tumor dedifferentiation, immune cell interactions, and patient survival.

Main Methods:

  • Constructed StemSC based on relative expression orderings (REO) of gene pairs.
  • Identified stemness-related genes by analyzing their correlation with differentiation time.
  • Utilized 13 RNA-seq datasets from embryonic stem cells (ESCs) to establish reference REOs for StemSC calculation.

Main Results:

  • StemSC demonstrated higher negative correlations with differentiation time in normal tissues and positive correlations with tumor dedifferentiation in cancer datasets.
  • StemSC showed robustness to batch effects, enabling the identification of stem-like cells in independent tumor datasets.
  • Identified stem-like tumor cells exhibited reduced interactions with anti-tumor immune cells, and high stemness correlated with worse survival in immunotherapy-treated patients.

Conclusions:

  • StemSC is a superior stemness index for cross-dataset analysis, outperforming existing methods.
  • The StemSC index facilitates the exploration of stemness's role in various biological processes, including cancer progression and immune response.
  • This tool aids in understanding the implications of stemness in therapeutic strategies and patient outcomes.