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spaCI: deciphering spatial cellular communications through adaptive graph model.

Ziyang Tang1, Tonglin Zhang2, Baijian Yang1

  • 1Department of Computer and Information Technology, Purdue University, Indiana, USA.

Briefings in Bioinformatics
|December 22, 2022
PubMed
Summary
This summary is machine-generated.

We developed spaCI, a new method using spatial transcriptomics data to map cell-cell communication. spaCI accurately identifies ligand-receptor interactions and their regulators, aiding disease mechanism discovery.

Keywords:
adaptive graph modelsingle-cell spatial transcriptomicsspatial cell graphtriplet loss

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Area of Science:

  • Computational Biology
  • Genomics
  • Systems Biology

Background:

  • Cell-cell communication is crucial for biological signaling and complex diseases.
  • Single-cell spatial transcriptomics (SCST) offers insights into spatial cell communication landscapes.
  • Existing methods struggle with noisy SCST data and dropout events for accurate communication inference.

Purpose of the Study:

  • To develop an effective method for deciphering cell-to-cell communications from SCST profiles.
  • To accurately infer ligand-receptor (L-R) interactions and identify upstream regulatory factors.
  • To apply the method to real-world datasets for biological insights into complex diseases.

Main Methods:

  • Proposed spaCI, a novel adaptive graph model with attention mechanisms.
  • spaCI integrates spatial locations and gene expression profiles for L-R axis identification.
  • Benchmarked spaCI against existing methods using simulation and real SCST datasets.

Main Results:

  • spaCI demonstrated superior performance in inferring cellular communications compared to current methods.
  • Identified hidden and inconspicuous L-R interactions, such as THBS1-ITGB1 in lung cancer.
  • Revealed SMAD3 as a key regulator in fibroblast-tumor crosstalk, impacting lung cancer prognosis.

Conclusions:

  • spaCI effectively addresses challenges in analyzing SCST data for cellular communication.
  • Facilitates discovery of disease mechanisms, biomarkers, and therapeutic targets.
  • Highlights the importance of spatial context in understanding intercellular signaling.