Communications BiologyGenomicsPeer-reviewed journal article
Selective enrichment of plasma cell-free messenger RNA in cancer-associated extracellular vesicles
Hyun Ji KimiD, Matthew J. RamesiD, Florian Goncalves, C. Ward KirschbaumiD, Breeshey Roskams-HieteriD, Elias SpiliotopoulosiD, Joséphine BriandiD, Aaron Reid DoeiD, Joseph EstabrookiD, Josiah T. WagneriD, Emek DemiriD, Gordon Brent MillsiD, Thuy T. M. NgoiD
Abstract
Extracellular vesicles (EVs) have been shown as key mediators of extracellular small RNA transport. However, carriers of cell-free messenger RNA (cf-mRNA) in human biofluid and their association with cancer remain poorly understood. Here, we performed a transcriptomic analysis of size-fractionated plasma from lung cancer, liver cancer, multiple myeloma, and healthy donors. Morphology and size distribution analysis showed the successful separation of medium and small EVs and non-vesicular carriers. We developed a strategy to purify and sequence ultra-low amounts of cf-mRNA from vesicular and non-vesicular subpopulations with the implementation of RNA spike-ins to control for technical variability and to normalize for intrinsic drastic differences in the amount of cf-mRNA carried in each plasma fraction. We found that the majority of cf-mRNA was enriched and protected in EVs with remarkable stability in RNase-rich environments. We observed specific enrichment patterns of cancer-associated cf-mRNA in each vesicular and non-vesicular subpopulation. The EV-enriched differentiating genes were associated with specific biological pathways, such as immune systems, liver function, and toxic substance regulation in lung cancer, liver cancer, and multiple myeloma, respectively. Our results suggest that dissecting the complexity of EVs subpopulations illuminates their biological significance and offers a promising liquid biopsy approach.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- approach: experimental assay
- Citations
- 24
- FWCI
- 2.22
- Field percentile
- top 11%
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