Engineered nanointerfaces for microfluidic isolation and molecular profiling of tumor-specific extracellular vesicles

Eduardo Reátegui, Kristan E. Vos, Charles P. Lai, Mahnaz Zeinali, Nadia A. Atai, Berent Aldikacti, Frederick P. Floyd, Aimal Khankhel, Vishal Thapar, Fred H. Hochberg, Lecia V. Sequist, Brian V. Nahed, Bob Carter, Mehmet Toner, Leonora Balaj, David Ting, Xandra O. Breakefield and Shannon L. Stott ()
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Eduardo Reátegui: Harvard Medical School
Kristan E. Vos: Harvard Medical School
Charles P. Lai: Harvard Medical School
Mahnaz Zeinali: Harvard Medical School
Nadia A. Atai: Harvard Medical School
Berent Aldikacti: Harvard Medical School
Frederick P. Floyd: Harvard Medical School
Aimal Khankhel: Harvard Medical School
Vishal Thapar: Harvard Medical School
Fred H. Hochberg: University of California San Diego
Lecia V. Sequist: Harvard Medical School
Brian V. Nahed: Harvard Medical School
Bob Carter: University of California San Diego
Mehmet Toner: Harvard Medical School
Leonora Balaj: Harvard Medical School
David Ting: Harvard Medical School
Xandra O. Breakefield: Harvard Medical School
Shannon L. Stott: Harvard Medical School

Nature Communications, 2018, vol. 9, issue 1, 1-11

Abstract: Abstract Extracellular vesicles (EVs) carry RNA, DNA, proteins, and lipids. Specifically, tumor-derived EVs have the potential to be utilized as disease-specific biomarkers. However, a lack of methods to isolate tumor-specific EVs has limited their use in clinical settings. Here we report a sensitive analytical microfluidic platform (EVHB-Chip) that enables tumor-specific EV-RNA isolation within 3 h. Using the EVHB-Chip, we achieve 94% tumor-EV specificity, a limit of detection of 100 EVs per μL, and a 10-fold increase in tumor RNA enrichment in comparison to other methods. Our approach allows for the subsequent release of captured tumor EVs, enabling downstream characterization and functional studies. Processing serum and plasma samples from glioblastoma multiforme (GBM) patients, we can detect the mutant EGFRvIII mRNA. Moreover, using next-generation RNA sequencing, we identify genes specific to GBM as well as transcripts that are hallmarks for the four genetic subtypes of the disease.

Date: 2018
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DOI: 10.1038/s41467-017-02261-1

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