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Discovering paracrine regulators of cell type composition from spatial transcriptomics using SPER.

Tianxiao Zhao1, Adam L Haber2

  • 1Institute of Systems Genetics, New York University Grossman School of Medicine, New York, NY, 10016, United States.

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Summary
This summary is machine-generated.

We developed Spatial Paired Expression Ratio (SPER), a new computational method to find intercellular regulatory factors influencing cell composition in spatial transcriptomics data. SPER identifies paracrine drivers of cellular changes in tissues.

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Area of Science:

  • Computational Biology
  • Genomics
  • Systems Biology

Background:

  • Tissue cell type composition is crucial for function, and deviations are linked to disease.
  • Spatial transcriptomics allows simultaneous gene expression and cell type analysis.
  • Identifying intercellular regulatory factors influencing cell composition remains a challenge.

Purpose of the Study:

  • To develop a computational method for identifying intercellular regulatory factors from spatial transcriptomics data.
  • To evaluate the spatial dependence between transcript abundance and cell type proportions.
  • To discover paracrine drivers of cellular compositional changes.

Main Methods:

  • Development of Spatial Paired Expression Ratio (SPER), a computational approach.
  • Evaluation of SPER using simulated data to detect paracrine drivers.
  • Application of SPER to mouse brain and human lung spatial transcriptomics data.

Main Results:

  • SPER accurately detects paracrine drivers of cellular abundance in simulated data.
  • Genes identified by SPER are enriched for extracellular secretion.
  • SPER-identified genes participate in known receptor-ligand interactions, suggesting regulatory roles.

Conclusions:

  • SPER is a novel computational approach to discover paracrine drivers of cellular composition from spatial transcriptomics.
  • The method facilitates the identification of intercellular regulatory factors.
  • SPER provides insights into tissue homeostasis and disease mechanisms.