{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Cherry C"],"funding":["NIBIB NIH HHS","U.S. Department of Health & Human Services | NIH | U.S. National Library of Medicine","NICHD NIH HHS","NIA NIH HHS","NIEHS NIH HHS","U.S. Department of Health & Human Services | NIH | National Institute on Aging","U.S. Department of Health & Human Services | NIH | National Institute of Environmental Health Sciences","NLM NIH HHS","NIAMS NIH HHS","NIGMS NIH HHS","U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering","U.S. Department of Health & Human Services | NIH | Eunice Kennedy Shriver National Institute of Child Health and Human Development"],"pagination":["1228-1238"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9894531"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["5(10)"],"pubmed_abstract":["The understanding of the foreign-body responses to implanted biomaterials would benefit from the reconstruction of intracellular and intercellular signalling networks in the microenvironment surrounding the implant. Here, by leveraging single-cell RNA-sequencing data from 42,156 cells collected from the site of implantation of either polycaprolactone or an extracellular-matrix-derived scaffold in a mouse model of volumetric muscle loss, we report a computational analysis of intercellular signalling networks reconstructed from predictions of transcription-factor activation. We found that intercellular signalling networks can be clustered into modules associated with specific cell subsets, and that biomaterial-specific responses can be characterized by interactions between signalling modules"],"journal":["Nature biomedical engineering"],"pubmed_title":["Computational reconstruction of the signalling networks surrounding implanted biomaterials from single-cell transcriptomics."],"pmcid":["PMC9894531"],"funding_grant_id":["R01EB028796","DP1 AR076959","R01 HD084633","K01 ES025434","R01LM012907","K01ES025434","R35 GM124725","R01 EB028796","R01 LM012373","R01 LM012907","T32 AG058527","5T32AG058527"],"pubmed_authors":["Cherry C","Elisseeff JH","Andorko JI","Cahan P","Fertig EJ","Han J","Maestas DR","Garmire LX"],"additional_accession":[]},"is_claimable":false,"name":"Computational reconstruction of the signalling networks surrounding implanted biomaterials from single-cell transcriptomics.","description":"The understanding of the foreign-body responses to implanted biomaterials would benefit from the reconstruction of intracellular and intercellular signalling networks in the microenvironment surrounding the implant. Here, by leveraging single-cell RNA-sequencing data from 42,156 cells collected from the site of implantation of either polycaprolactone or an extracellular-matrix-derived scaffold in a mouse model of volumetric muscle loss, we report a computational analysis of intercellular signalling networks reconstructed from predictions of transcription-factor activation. We found that intercellular signalling networks can be clustered into modules associated with specific cell subsets, and that biomaterial-specific responses can be characterized by interactions between signalling modules","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Oct","modification":"2025-04-26T18:02:58.492Z","creation":"2025-04-06T15:44:02.236Z"},"accession":"S-EPMC9894531","cross_references":{"pubmed":["34341534"],"doi":["10.1038/s41551-021-00770-5"]}}