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Cell Specific Gene Expression01:58

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Gene expression in prokaryotes is governed by constitutive and regulated systems, allowing cells to balance the production of essential proteins with adaptive responses to environmental changes.Constitutive Gene ExpressionConstitutive, or housekeeping, genes are continuously expressed as they encode proteins vital for fundamental cellular processes. These include enzymes for glycolysis, ribosomal components for protein synthesis, and proteins involved in DNA replication. Their constant...
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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
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Transcriptome Analysis of Single Cells
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scGEM: Unveiling the Nested Tree-Structured Gene Co-Expressing Modules in Single Cell Transcriptome Data.

Han Zhang1, Xinghua Lu1,2, Binfeng Lu3

  • 1Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA.

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|September 9, 2023
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Summary
This summary is machine-generated.

We developed scGEM, a novel Bayesian model, to identify gene co-expression modules (GEMs) that reveal cellular functions in single-cell RNA sequencing data. This method enhances understanding of cell specialization and differentiation.

Keywords:
cellular programgene co-expressing modulenested tree structuresingle cell transcriptometopic model

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Area of Science:

  • Computational Biology
  • Genomics
  • Molecular Biology

Background:

  • Single-cell transcriptome analysis offers high-resolution insights into cellular heterogeneity.
  • Current methods struggle to identify and quantify cellular programs driving cell specialization and differentiation.

Purpose of the Study:

  • To introduce scGEM, a Bayesian model for revealing gene co-expression modules (GEMs) in single cells.
  • To quantify cellular programs underlying cell specialization and differentiation.

Main Methods:

  • Developed scGEM, a nested tree-structured nonparametric Bayesian model.
  • Applied scGEM to single-cell datasets from peripheral blood mononuclear cells and early brain development.
  • Validated scGEM performance against existing methods using simulation and bulk RNA-seq data.

Main Results:

  • scGEM effectively discovers shared and specialized transcriptome signals across diverse cell types.
  • scGEM significantly outperformed other methods in perplexity and topic coherence on simulation data.
  • GEMs from scGEM showed stronger correlations with biological processes like lymphocyte infiltration and cell cycle in cancer cells.

Conclusions:

  • scGEM models hidden cellular functions by identifying transcriptomic programs in single cells.
  • The model elucidates the specialization and generalization of transcriptomic programs across different cell types.
  • scGEM provides a powerful tool for dissecting cellular heterogeneity and function.