Evidence map›Paper›PMID 41583424›Full record

ArticleFrontiers in immunology2025

ULMnet: inferring physical cell-cell communication networks from scRNAseq data using univariate linear models.

Sodiq A Hameed, Luis Fernando Iglesias-Martinez, Walter Kolch, Vadim Zhernovkov

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Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Sodiq A HameedSystems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland.
Luis Fernando Iglesias-MartinezSystems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland.
Walter KolchSystems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland.
Vadim ZhernovkovSystems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Cells in tissues interact by direct physical contact or over short and long distances via secreted mediators. Cell-cell communication inference has now become routine in downstream scRNAseq analysis but this mostly fails to capture physical cell-cell interactions due to tissue dissociation. Multiplets (mostly doublets) in scRNAseq may represent undissociated physically attached cells that become sequenced together. Hence, identifying multiplets may serve as a good starting point to harness scRNAseq data for physical cell-cell interaction inference. Method: In this study, we develop a computational method, called ULMnet, which utilizes univariate linear models to identify multiplets in scRNAseq datasets, predict their cellular compositions, and infer physical cell-cell interaction networks. ULMnet was applied to multiple datasets ranging from cell pairs, partially dissociated tissues to conventional scRNAseq datasets of healthy and cancer tissues. Result and Discussion: Indeed, our method showed good sensitivity (~56%) with excellent precision (~99%) in predicting FACS sorted doublet cell pairs with known constituents (ground truth), recording comparable or superior performance over two other existing methods. Also, applying it to scRNAseq data of partially dissociated tissues containing real multiplets unraveled physical networks which recapitulated the microanatomical structures of the tested tissues. This further underscores the accuracy in our predictions to capture biologically meaningful interactions. Finally, we tested our method on classical scRNAseq datasets and obtained biologically reasonable results. For example, when tested on classical cancer scRNAseq datasets, we recovered important interactions which followed biologically plausible cell interactions, validated by cell-cell colocalization in matched spatial transcriptomics datasets. This reassured the accuracy of our method in depicting physical interactions only between cells that were truly in close proximity in tissues.

Indexed as

Cell CommunicationComputational BiologyAnimalsHumansLinear ModelsSingle-Cell Gene Expression Analysiscell-cell communicationmachine learning (ML)physical cell-cell interaction networksscRNAseqspatial transcriptomics (ST)univariate linear models

Identifiers

PMID41583424
PMCPMC12827579

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.