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Treffer: Hi-C3: a statistical inference-based model for reconstructing higher-order cell-cell communication networks.

Title:
Hi-C3: a statistical inference-based model for reconstructing higher-order cell-cell communication networks.
Authors:
Tong Y; School of Mathematics, South China University of Technology, No. 381 Wushan Road, Tianhe District, Guangzhou 510640, Guangdong, China., Hong R; School of Mathematics, South China University of Technology, No. 381 Wushan Road, Tianhe District, Guangzhou 510640, Guangdong, China., Li M; School of Mathematics, Foshan University, No. 18 Jiangwan 1st Road, Chancheng District, Foshan 528000, Guangdong, China., Yang N; School of Mathematics, South China University of Technology, No. 381 Wushan Road, Tianhe District, Guangzhou 510640, Guangdong, China., Deng W; School of Mathematics, Foshan University, No. 18 Jiangwan 1st Road, Chancheng District, Foshan 528000, Guangdong, China., Tang H; School of Mathematics, Foshan University, No. 18 Jiangwan 1st Road, Chancheng District, Foshan 528000, Guangdong, China., Zeng T; Guangzhou National Laboratory, Xingji 4th Road, Haizhu District, Guangzhou 510005, Guangdong, China.; GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Laboratory, Guangzhou Medical University, Xinzao Town, Panyu District, Guangzhou 511495, Guangdong, China., Liu R; School of Mathematics, South China University of Technology, No. 381 Wushan Road, Tianhe District, Guangzhou 510640, Guangdong, China.
Source:
Briefings in bioinformatics [Brief Bioinform] 2025 Aug 31; Vol. 26 (5).
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: England NLM ID: 100912837 Publication Model: Print Cited Medium: Internet ISSN: 1477-4054 (Electronic) Linking ISSN: 14675463 NLM ISO Abbreviation: Brief Bioinform Subsets: MEDLINE
Imprint Name(s):
Publication: Oxford : Oxford University Press
Original Publication: London ; Birmingham, AL : H. Stewart Publications, [2000-
MeSH Terms:
Cell Communication*/genetics , Algorithms* , Computational Biology*/methods , Models, Biological* , Models, Statistical*, Humans ; Single-Cell Analysis ; Signal Transduction ; Arabidopsis/genetics
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Grant Information:
T2341022 National Natural Science Foundation of China; 42450084 National Natural Science Foundation of China; 12322119 National Natural Science Foundation of China; T2341022 National Natural Science Foundation of China; 12501670 National Natural Science Foundation of China; 12371485 National Natural Science Foundation of China; 42450084 National Natural Science Foundation of China; 2023YFF1204700 National Key Research and Developmen Program of China; 2024A1515011797 Guangdong Basic and Applied Basic Research Foundation; 2022A1515110759 Guangdong Basic and Applied Basic Research Foundation; 2023A1515110176 Guangdong Basic and Applied Basic Research Foundation; 2025A1515011988 Guangdong Basic and Applied Basic Research Foundation; 2024B1212010004 Guangdong Provincial Key Laboratory of Mathematical and Neural Dynamical Systems
Contributed Indexing:
Keywords: higher-order cell–cell communication; intercellular communication; maximum likelihood estimation; simplicial complex; single-cell RNA-seq
Entry Date(s):
Date Created: 20251029 Date Completed: 20251029 Latest Revision: 20251101
Update Code:
20251101
PubMed Central ID:
PMC12570032
DOI:
10.1093/bib/bbaf568
PMID:
41159728
Database:
MEDLINE

Weitere Informationen

Multicellular organisms are composed of diverse cell types that must coordinate their behaviors through communication. Cell-cell communication (CCC) is essential for growth, development, differentiation, and immune response. Recent computational methods have leveraged single-cell RNA sequencing (scRNA-seq) to infer CCC via ligand-receptor interactions (LRIs), with most approaches focusing on pairwise interactions. However, many biological processes are driven by the coordinated action of multiple cell types, underscoring the need to model higher-order cellular interactions beyond pairwise interactions. Inspired by principles of network diffusion and epidemic dynamics, we first model the receptor expression as: a Poisson-distributed random variable biologically regulated by the collective signaling of multiple ligand-producing cell types. Then, we propose Hi-C3, a unified statistical inference-based framework for inferring both conventional pairwise and new higher-order CCC patterns from scRNA-seq data, which is solved via an efficient likelihood-based (EM) algorithm. Particularly, Hi-C3 employed a modified PageRank algorithm to assess the importance of individual cells or cell types within the higher-order network, revealing key cellular communication hubs supported by independent spatial and biological evidence. When applied to diverse datasets from Arabidopsis thaliana and colorectal cancer, Hi-C3 achieved comparable performance to state-of-the-art methods in the inferring pairwise communication while uniquely uncovering complex higher-order cellular communication structures. Collectively, Hi-C3 offers a powerful statistical model and computational framework for uncovering complex and coordinated multicellular signaling structures disregarded by pairwise communication inference methods, and offers novel biology network insights into the logic of cellular organization and communication in both development and disease.
(© The Author(s) 2025. Published by Oxford University Press.)