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exRNA Atlas Analysis Reveals Distinct Extracellular RNA Cargo Types and Their Carriers Present across Human Biofluids

Author:
Oscar D. Murillo, William Thistlethwaite, Joel Rozowsky, Sai Lakshmi Subramanian, Rocco Lucero, Neethu Shah, Andrew R. Jackson, Srimeenakshi Srinivasan, Allen Chung, Clara D. Laurent, Robert R. Kitchen, Timur Galeev, Jonathan Warrell, James A. Diao, Joshua A. Welsh, Kristina Hanspers, Anders Riutta, Sebastian Burgstaller-Muehlbacher, Ravi V. Shah, Ashish Yeri, Lisa M. Jenkins, Mehmet E. Ahsen, Carlos Cordon-Cardo, Navneet Dogra, Stacey M. Gifford, Joshua T. Smith, Gustavo Stolovitzky, Ashutosh K. Tewari, Benjamin H. Wunsch, Kamlesh K. Yadav, Kirsty M. Danielson, Justyna Filant, Courtney Moeller, Parham Nejad, Anu Paul, Bridget Simonson, David K. Wong, Xuan Zhang, Leonora Balaj, Roopali Gandhi, Anil K. Sood, Roger P. Alexander, Liang Wang, Chunlei Wu, David T.W. Wong, David J. Galas, Kendall Van Keuren-Jensen, Tushar Patel, Jennifer C. Jones, Saumya Das, Kei-Hoi Cheung, Alexander R. Pico, Andrew I. Su, Robert L. Raffai, Louise C. Laurent, Matthew E. Roth, Mark B. Gerstein, Aleksandar Milosavljevic
Source:
Cell 2019 v.177 no.2 pp. 463-477.e15
ISSN:
0092-8674
Subject:
RNA, blood serum, case-control studies, cell communication, exercise, hosts, humans, lipoproteins, models, quantitative polymerase chain reaction, ribonucleoproteins, saliva, urine
Abstract:
To develop a map of cell-cell communication mediated by extracellular RNA (exRNA), the NIH Extracellular RNA Communication Consortium created the exRNA Atlas resource (https://exrna-atlas.org). The Atlas version 4P1 hosts 5,309 exRNA-seq and exRNA qPCR profiles from 19 studies and a suite of analysis and visualization tools. To analyze variation between profiles, we apply computational deconvolution. The analysis leads to a model with six exRNA cargo types (CT1, CT2, CT3A, CT3B, CT3C, CT4), each detectable in multiple biofluids (serum, plasma, CSF, saliva, urine). Five of the cargo types associate with known vesicular and non-vesicular (lipoprotein and ribonucleoprotein) exRNA carriers. To validate utility of this model, we re-analyze an exercise response study by deconvolution to identify physiologically relevant response pathways that were not detected previously. To enable wide application of this model, as part of the exRNA Atlas resource, we provide tools for deconvolution and analysis of user-provided case-control studies.
Agid:
6358229