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Updated: May 14, 2025

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

Published on: March 1, 2017

Dissecting multilayer cell-cell communications with signaling feedback loops from spatial transcriptomics data.

Lulu Yan¹, Jinyu Cheng², Qing Nie³

¹School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China.

Genome Research

|April 22, 2025

Summary

This summary is machine-generated.

Spatial transcriptomics (ST) reveals complex cell-cell communication (CCC). Our new method, stMLnet, integrates spatial data to identify multilayer signaling feedback loops, improving inference of intercellular and intracellular communication pathways.

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Area of Science:

Computational Biology
Genomics
Systems Biology

Background:

Spatial transcriptomics (ST) offers novel insights into cellular interactions.
Inferring functional cell-cell communication (CCC) from ST data, considering spatial context and signaling, remains challenging.

Purpose of the Study:

To introduce stMLnet, a computational method for identifying multilayer signaling feedback loops in CCC from ST data.
To leverage spatial information and multilayer signaling regulation for enhanced CCC inference.

Main Methods:

stMLnet quantifies spatially dependent ligand-receptor signaling activity using diffusion and mass action models.
The method maps intercellular signaling to intracellular targets.
Benchmarking against seven methods assessed intercellular ligand-receptor inference and intracellular target gene prediction.

Main Results:

stMLnet demonstrated superior performance in both intercellular ligand-receptor inference and intracellular target gene prediction compared to existing methods.
The method proved robust and scalable across diverse ST datasets (seqFISH+, Slide-seq v2, MERFISH, Stereo-seq) with varying resolutions and gene coverages.
stMLnet identified multilayer signaling feedback loops in the inflammatory response within COVID-19 lung tissue ST data.

Conclusions:

stMLnet provides an effective tool for dissecting complex ligand/receptor-target regulation and multicellular signaling circuits from ST data.
This advancement aids in understanding the mechanistic and functional roles of spatial CCC.
The method supports deeper biological insights from spatial transcriptomics studies.