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10.1101/2022.02.04.479209 http://scihub22266oqcxt.onion/10.1101/2022.02.04.479209 35169794!8845414!35169794     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       bioRxiv 2022 ; ä (ä): ä Nephropedia Template TP {{tp|p=35169794|t=2022. Comparative analysis of cell-cell communication at single-cell resolution |pdf=|usr=}}{{35169794}} gab.com Text Comparative analysis of cell-cell communication at single-cell resolution JO: bioRxiv http://www.kidney.de/pget.php?&tw=1&pmid=35169794 #FOAvip Inference of cell-cell communication (CCC) from single-cell RNA-sequencing data is a powerful technique to uncover putative axes of multicellular coordination, yet existing methods perform this analysis at the level of the cell type or cluster, discarding single-cell level information. Here we present Scriabin aeuro" a flexible and scalable framework for comparative analysis of CCC at single-cell resolution. We leverage multiple published datasets to show that Scriabin recovers expected CCC edges and use spatial transcriptomic data, genetic perturbation screens, and direct experimental manipulation of receptor-ligand interactions to validate that the recovered edges are biologically meaningful. We then apply Scriabin to uncover co-expressed programs of CCC from atlas-scale datasets, validating known communication pathways required for maintaining the intestinal stem cell niche and revealing species-specific communication pathways. Finally, we utilize single-cell communication networks calculated using Scriabin to follow communication pathways that operate between timepoints in longitudinal datasets, highlighting bystander cells as important initiators of inflammatory reactions in acute SARS-CoV-2 infection. Our approach represents a broadly applicable strategy to leverage single-cell resolution data maximally toward uncovering CCC circuitry and rich niche-phenotype relationships in health and disease. Twit Text FOAVip Comparative analysis of cell-cell communication at single-cell resolution ????bioRxiv http://www.kidney.de/pget.php?&tw=1&pmid=35169794 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=35169794 #FOAvip English Wikipedia <ref>{{Cite pmid|35169794}}</ref> Comparative analysis of cell-cell communication at single-cell resolution #MMPMID35169794 Wilk AJ; Shalek AK; Holmes S; Blish CA bioRxiv 2022[Jul]; ä (ä): ä PMID35169794show ga Inference of cell-cell communication (CCC) from single-cell RNA-sequencing data is a powerful technique to uncover putative axes of multicellular coordination, yet existing methods perform this analysis at the level of the cell type or cluster, discarding single-cell level information. Here we present Scriabin aeuro" a flexible and scalable framework for comparative analysis of CCC at single-cell resolution. We leverage multiple published datasets to show that Scriabin recovers expected CCC edges and use spatial transcriptomic data, genetic perturbation screens, and direct experimental manipulation of receptor-ligand interactions to validate that the recovered edges are biologically meaningful. We then apply Scriabin to uncover co-expressed programs of CCC from atlas-scale datasets, validating known communication pathways required for maintaining the intestinal stem cell niche and revealing species-specific communication pathways. Finally, we utilize single-cell communication networks calculated using Scriabin to follow communication pathways that operate between timepoints in longitudinal datasets, highlighting bystander cells as important initiators of inflammatory reactions in acute SARS-CoV-2 infection. Our approach represents a broadly applicable strategy to leverage single-cell resolution data maximally toward uncovering CCC circuitry and rich niche-phenotype relationships in health and disease. äComparative analysis of cell-cell communication at single-cell resolut(epmc).pdf DeepDyve Pubget Overpricing