The diversification of methods for studying cell-cell interactions and communication.


Journal

Nature reviews. Genetics
ISSN: 1471-0064
Titre abrégé: Nat Rev Genet
Pays: England
ID NLM: 100962779

Informations de publication

Date de publication:
18 Jan 2024
Historique:
accepted: 01 12 2023
medline: 19 1 2024
pubmed: 19 1 2024
entrez: 18 1 2024
Statut: aheadofprint

Résumé

No cell lives in a vacuum, and the molecular interactions between cells define most phenotypes. Transcriptomics provides rich information to infer cell-cell interactions and communication, thus accelerating the discovery of the roles of cells within their communities. Such research relies heavily on algorithms that infer which cells are interacting and the ligands and receptors involved. Specific pressures on different research niches are driving the evolution of next-generation computational tools, enabling new conceptual opportunities and technological advances. More sophisticated algorithms now account for the heterogeneity and spatial organization of cells, multiple ligand types and intracellular signalling events, and enable the use of larger and more complex datasets, including single-cell and spatial transcriptomics. Similarly, new high-throughput experimental methods are increasing the number and resolution of interactions that can be analysed simultaneously. Here, we explore recent progress in cell-cell interaction research and highlight the diversification of the next generation of tools, which have yielded a rich ecosystem of tools for different applications and are enabling invaluable discoveries.

Identifiants

pubmed: 38238518
doi: 10.1038/s41576-023-00685-8
pii: 10.1038/s41576-023-00685-8
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2024. Springer Nature Limited.

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Auteurs

Erick Armingol (E)

Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA. earmingo@ucsd.edu.
Department of Paediatrics, University of California, San Diego, La Jolla, CA, USA. earmingo@ucsd.edu.

Hratch M Baghdassarian (HM)

Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA.
Department of Paediatrics, University of California, San Diego, La Jolla, CA, USA.

Nathan E Lewis (NE)

Department of Paediatrics, University of California, San Diego, La Jolla, CA, USA. nlewisres@ucsd.edu.
Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA. nlewisres@ucsd.edu.

Classifications MeSH