4.8 Article

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

期刊

CELL
卷 177, 期 2, 页码 463-+

出版社

CELL PRESS
DOI: 10.1016/j.cell.2019.02.018

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资金

  1. NIH Common Fund's exRNA Communication Program
  2. NIH [5U54 DA036134, UH2/UH3 TR000923, UH2/UH3 TR000901, UH3 TR000906, 5U19 CA179512, HL133575, P30 DK63720, K23-HL127099, R01-HL136685, R01AG059729, UH3 TR000943, R35 CA209904, CA217685, R01-HL 122547]
  3. NCI, NIH [5RO1 CA163849]
  4. NIH Initiative for Maximizing Student Development [2R25 GM056929]
  5. NCI, Center for Cancer Research, NIH
  6. American Cancer Society Research Professor Award
  7. Frank McGraw Memorial Chair in Cancer Research
  8. COMMON FUND, OFFICE OF THE DIRECTOR
  9. NATIONAL CANCER INSTITUTE [ZIABC011502] Funding Source: NIH RePORTER

向作者/读者索取更多资源

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.

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