4.6 Article

Optimizing exosomal RNA isolation for RNA-Seq analyses of archival sera specimens

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PLOS ONE
卷 13, 期 5, 页码 -

出版社

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0196913

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资金

  1. K99/R00 Pathway to Independence Award from the NIH [R00CA184415]
  2. Ann and Sol Schreiber Mentored Investigator Award from Ovarian Cancer Research Fund Alliance [458799]
  3. American Cancer Society Early Detection Professorship [SIOP-06-258-01-COUN]
  4. National Center for Advancing Translational Sciences (NCATS) [UL1TR000124]
  5. Sao Paulo Research Foundation (FAPESP) [2017/08211-1, 2016/06413-3, 2015/0725-5]
  6. Henry Ford Hospital [A30935]

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Exosomes are endosome-derived membrane vesicles that contain proteins, lipids, and nucleic acids. The exosomal transcriptome mediates intercellular communication, and represents an understudied reservoir of novel biomarkers for human diseases. Next-generation sequencing enables complex quantitative characterization of exosomal RNAs from diverse sources. However, detailed protocols describing exosome purification for preparation of exosomal RNA-sequence (RNA-Seq) libraries are lacking. Here we compared methods for isolation of exosomes and extraction of exosomal RNA from human cell-free serum, as well as strategies for attaining equal representation of samples within pooled RNA-Seq libraries. We compared commercial precipitation with ultracentrifugation for exosome purification and confirmed the presence of exosomes via both transmission electron microscopy and immunoblotting. Exosomal RNA extraction was compared using four different RNA purification methods. We determined the minimal starting volume of serum required for exosome preparation and showed that high quality exosomal RNA can be isolated from sera stored for over a decade. Finally, RNA-Seq libraries were successfully prepared with exosomal RNAs extracted from human cell-free serum, cataloguing both coding and non-coding exosomal transcripts. This method provides researchers with strategic options to prepare RNA-Seq libraries and compare RNA-Seq data quantitatively from minimal volumes of fresh and archival human cell-free serum for disease biomarker discovery.

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