Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function

Understanding gene function and regulation in homeostasis and disease requires knowledge of the cellular and tissue contexts in which genes are expressed. Here, we applied four single-nucleus RNA sequencing methods to eight diverse, archived, frozen tissue types from 16 donors and 25 samples, genera...

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Vydané v:Science (American Association for the Advancement of Science) Ročník 376; číslo 6594; s. eabl4290
Hlavní autori: Eraslan, Gökcen, Drokhlyansky, Eugene, Anand, Shankara, Fiskin, Evgenij, Subramanian, Ayshwarya, Slyper, Michal, Wang, Jiali, Van Wittenberghe, Nicholas, Rouhana, John M, Waldman, Julia, Ashenberg, Orr, Lek, Monkol, Dionne, Danielle, Win, Thet Su, Cuoco, Michael S, Kuksenko, Olena, Tsankov, Alexander M, Branton, Philip A, Marshall, Jamie L, Greka, Anna, Getz, Gad, Segrè, Ayellet V, Aguet, François, Rozenblatt-Rosen, Orit, Ardlie, Kristin G, Regev, Aviv
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States 13.05.2022
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ISSN:1095-9203, 1095-9203
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Popis
Shrnutí:Understanding gene function and regulation in homeostasis and disease requires knowledge of the cellular and tissue contexts in which genes are expressed. Here, we applied four single-nucleus RNA sequencing methods to eight diverse, archived, frozen tissue types from 16 donors and 25 samples, generating a cross-tissue atlas of 209,126 nuclei profiles, which we integrated across tissues, donors, and laboratory methods with a conditional variational autoencoder. Using the resulting cross-tissue atlas, we highlight shared and tissue-specific features of tissue-resident cell populations; identify cell types that might contribute to neuromuscular, metabolic, and immune components of monogenic diseases and the biological processes involved in their pathology; and determine cell types and gene modules that might underlie disease mechanisms for complex traits analyzed by genome-wide association studies.
Bibliografia:ObjectType-Article-1
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ISSN:1095-9203
1095-9203
DOI:10.1126/science.abl4290