Ultrasensitive quantification of tumor mRNAs in extracellular vesicles with an integrated microfluidic digital...

Extracellular vesicles (EVs) present a promising liquid biopsy for cancer diagnosis. However, it remains a daunting challenge to quantitatively measure molecular contents of EVs including tumor-associated mRNAs. Herein, we report a configurable microwell-patterned microfluidic digital analysis platform combined with a dual-probe hybridization assay for PCR-free, single-molecule detection of specific mRNAs in EVs. The microwell array in our device is configurable between the flow-through assay mode for enhanced hybridization capture and tagging of mRNAs and the digital detection mode based on femtoliter-scale enzymatic signal amplification for single-molecule counting of surface-bound targets. Furthermore, a dual-probe hybridization assay has been developed to enhance the sensitivity of the digital single-molecule detection of EV mRNAs. Combining the merits of the chip design and the dual-probe digital mRNA hybridization assay, the integrated microfluidic system has been demonstrated to afford quantitative detection of synthetic GAPDH mRNA with a LOD as low as 20 aM. Using this technology, we quantified the level of GAPDH and EWS-FLI1 mRNAs in EVs derived from two cell lines of peripheral primitive neuroectodermal tumor (PNET), CHLA-9 and CHLA-258. Our measurements detected 64.6 and 43.5 copies of GAPDH mRNA and 6.5 and 0.277 copies of EWS-FLI1 fusion transcripts per 105 EVs derived from CHLA-9 and CHLA-258 cells, respectively. To our knowledge, this is the first demonstration of quantitative measurement of EWS-FLI1 mRNA copy numbers in Ewing Sarcoma (EWS)-derived EVs. These results highlight the ultralow frequency of tumor-specific mRNA markers in EVs and the necessity of developing highly sensitive methods for analysis of EV mRNAs. The microfluidic digital mRNA analysis platform presented here would provide a useful tool to facilitate quantitative analysis of tumor-associated EV mRNAs for liquid biopsy-based cancer diagnosis and monitoring.