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Related Concept Videos

Cell Specific Gene Expression01:58

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...

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Stereopy: modeling comparative and spatiotemporal cellular heterogeneity via multi-sample spatial transcriptomics.

Shuangsang Fang1,2, Mengyang Xu2,3, Lei Cao1,2

  • 1BGI Research, Beijing, China.

Nature Communications
|April 21, 2025
PubMed
Summary
This summary is machine-generated.

Stereopy offers a new framework for analyzing spatial transcriptomics data from multiple samples. It helps researchers understand cellular dynamics across space and time, revealing complex biological patterns.

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Area of Science:

  • Spatial transcriptomics
  • Computational biology
  • Systems biology

Background:

  • Analyzing complex biological systems requires tracking cellular dynamics across conditions, time, and space.
  • Integrating multi-sample spatial transcriptomics data to explore cellular heterogeneity is a significant challenge.

Purpose of the Study:

  • To present Stereopy, a flexible framework for modeling and dissecting comparative and spatiotemporal patterns in multi-sample spatial transcriptomics.
  • To provide researchers with enhanced data interpretation abilities for multi-sample spatial transcriptomics data.

Main Methods:

  • Development of a universal container, scope controller, and integrative transformer for multi-sample multimodal data.
  • Implementation of Stereopy for modeling comparative and spatiotemporal patterns.
  • Interactive data visualization for exploring cellular heterogeneity.

Main Results:

  • Stereopy enables investigation of cell communities and genes in pathological changes.
  • The framework facilitates detection of spatiotemporal gene patterns considering spatial and temporal features.
  • Stereopy allows inference of 3D niche-based cell-gene interaction networks, bridging intercellular and intracellular regulations.

Conclusions:

  • Stereopy is a comprehensive bioinformatics toolbox and extensible framework for multi-sample spatial transcriptomics.
  • It empowers researchers with enhanced data interpretation and novel perspectives for data mining.
  • The framework addresses the challenge of integrating multi-sample data for exploring cellular heterogeneity.