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Spreading of Chromatin Modifications02:25

Spreading of Chromatin Modifications

The histone proteins in the nucleosomes are post-translationally modified (PTM) to increase or decrease access to DNA. The commonly observed PTMs are methylation, acetylation, phosphorylation, and ubiquitination of lysine amino acids in the histone H3 tail region. These histone modifications have specific meaning for the cell. Hence, they are called "histone code". The protein complex involved in histone modification is termed as "reader-writer" complex.
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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
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Uncover spatially informed variations for single-cell spatial transcriptomics with STew.

Nanxi Guo1,2, Juan Vargas2,3, Samantha Reynoso2,4

  • 1Biostatistics and Informatics PhD Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States.

Bioinformatics Advances
|June 3, 2024
PubMed
Summary
This summary is machine-generated.

STew, a novel spatial transcriptomic method, integrates gene expression and spatial data for enhanced tissue analysis. It accurately identifies cell types and disease niches across various spatial transcriptomic technologies.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Spatial transcriptomics (ST) technologies offer insights into gene expression within tissue contexts.
  • Analyzing ST data to integrate spatial information with gene expression remains a significant challenge.

Purpose of the Study:

  • To introduce STew, a scalable multi-view representation learning method for joint analysis of spatial and gene expression data in ST.
  • To develop a statistical framework for assessing model fit in ST data analysis.

Main Methods:

  • STew employs multi-view representation learning to jointly analyze spatial and gene expression data.
  • A data-driven statistical framework is used to measure the goodness of model fit.
  • Benchmarking was performed on human and mouse ST data with manual annotations.

Main Results:

  • STew demonstrated superior performance in clustering accuracy and spatial domain continuity compared to existing methods.
  • The method is robust to parameter variations and applicable to diverse ST technologies (10× Visium, Slide-seqV2, 10× Xenium).
  • STew identified spatially informed cell type clusters, biologically meaningful axes, and a proinflammatory fibroblast niche in psoriatic skin.

Conclusions:

  • STew provides a scalable and effective approach for analyzing spatial transcriptomic data.
  • The method accurately reveals cell type distributions and disease-relevant spatial niches.
  • STew's linear scalability ensures its utility for large-scale ST datasets.