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Related Experiment Video

Updated: Jun 6, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Decoding Multicellular Communication Motifs from Spatial Transcriptomics with ALARMIST.

Jiayi Fan, John Hood, James Strong

Biorxiv : the Preprint Server for Biology

|June 5, 2026

Summary

This summary is machine-generated.

ALARMIST reveals complex multicellular communication in tumors by identifying signaling motifs. This framework uncovers drivers of lung and brain cancer progression, offering new insights into tissue microenvironments.

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An Optogenetic Method to Control and Analyze Gene Expression Patterns in Cell-to-cell Interactions

An Optogenetic Method to Control and Analyze Gene Expression Patterns in Cell-to-cell Interactions

Published on: March 22, 2018

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Last Updated: Jun 6, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

An Optogenetic Method to Control and Analyze Gene Expression Patterns in Cell-to-cell Interactions

An Optogenetic Method to Control and Analyze Gene Expression Patterns in Cell-to-cell Interactions

Published on: March 22, 2018

Area of Science:

Computational Biology
Systems Biology
Cancer Research

Background:

Cellular organization relies on complex, multi-cell type signaling networks.
Current spatial transcriptomics methods often overlook higher-order interactions within the tissue microenvironment.

Purpose of the Study:

To develop a computational framework, ALARMIST, for inferring multicellular communication patterns from spatial transcriptomics data.
To identify interpretable communication motifs and their impact on cellular phenotypes.

Main Methods:

ALARMIST (Assessment of Ligand And Receptor Motifs And Impacts in Spatial Transcriptomics) uses a probabilistic approach.
It decomposes spatial signaling into recurrent subnetworks (motifs) involving multiple cell types and ligand-receptor interactions.
The framework estimates downstream phenotypic effects of active motifs on cells.

Main Results:

ALARMIST identified an immune-active vascular motif at the tumor-normal boundary in lung adenocarcinoma (LUAD).
Plasmacytoid dendritic cells were implicated in early lung carcinogenesis.
A hub-and-spoke motif involving macrophages and a GRN-SORT1 axis was found in glioblastoma (GBM), predicting survival in low-grade glioma.

Conclusions:

ALARMIST provides a novel method for analyzing complex multicellular communication in spatial transcriptomics.
The findings highlight specific communication motifs as key drivers of tumor progression in LUAD and GBM.
This approach offers insights into microenvironmental influences on cancer development and patient outcomes.