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Reconstructing codependent cellular cross-talk in lung adenocarcinoma using REMI.

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This study introduces REMI, a novel graph-based algorithm for reconstructing cell-cell interactions (CCIs) by considering ligand-receptor (LR) dependencies. REMI enhances accuracy in analyzing complex biological microenvironments, particularly in lung adenocarcinoma (LUAD).

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Area of Science:

  • Computational biology
  • Systems biology
  • Bioinformatics

Background:

  • Cellular cross-talk is crucial for biological processes and diseases.
  • Existing computational methods for cell-cell interactions (CCIs) often overlook ligand-receptor (LR) dependencies, limiting accuracy.
  • Accurate reconstruction of cellular interactomes is essential for understanding tissue microenvironments.

Purpose of the Study:

  • To develop a computational approach, REMI (REgularized Microenvironment Interactome), for reconstructing cellular interactomes.
  • To account for dependencies between ligand-receptor (LR) interactions in high-dimensional, small-sample size datasets.
  • To identify clinically relevant CCIs and prognostic signatures in human lung adenocarcinoma (LUAD).

Main Methods:

  • Developed REMI, a graph-based algorithm for predicting LR interactions.
  • Applied REMI to reconstruct the LUAD interactome using bulk flow-sorted RNA sequencing data.
  • Integrated single-cell transcriptomics data to enhance cell type resolution and identify prognostic LR signatures.

Main Results:

  • REMI successfully reconstructed the LUAD interactome, considering LR dependencies.
  • Identified prognostic LR signatures within tumor-stroma-immune subpopulations.
  • Experimentally validated the colocalization of CTGF:LRP6 in malignant cell subtypes, linking it to LUAD progression.

Conclusions:

  • REMI provides a robust computational framework for reconstructing complex cellular interactomes.
  • The approach improves the accuracy of CCI prediction by modeling LR dependencies.
  • Identified specific CCIs with potential prognostic value in LUAD, paving the way for further clinical research.