Abstract

The power of personalized medicine is based on a deep understanding of cellular and molecular processes underlying disease pathogenesis. Accurately characterizing and analyzing connections between these processes is dependent on our ability to access multiple classes of biomarkers (DNA, RNA, and proteins)—ideally, in a minimally processed state. Here, we characterize a biomarker isolation platform that enables simultaneous isolation and on-chip detection of cell-free DNA (cfDNA), extracellular vesicle RNA (EV-RNA), and EV-associated proteins in unprocessed biological fluids using AC Electrokinetics (ACE). Human biofluid samples were flowed over the ACE microelectrode array (ACE chip) on the Verita platform while an electrical signal was applied, inducing a field that reversibly captured biomarkers onto the microelectrode array. Isolated cfDNA, EV-RNA, and EV-associated proteins were visualized directly on the chip using DNA and RNA specific dyes or antigen-specific, directly conjugated antibodies (CD63, TSG101, PD-L1, GPC-1), respectively. Isolated material was also eluted off the chip and analyzed downstream by multiple methods, including PCR, RT-PCR, next-generation sequencing (NGS), capillary electrophoresis, and nanoparticle size characterization. The detection workflow confirmed the capture of cfDNA, EV-RNA, and EV-associated proteins from human biofluids on the ACE chip. Tumor specific variants and the mRNAs of housekeeping gene PGK1 were detected in cfDNA and RNA isolated directly from chips in PCR, NGS, and RT-PCR assays, demonstrating that high-quality material can be isolated from donor samples using the isolation workflow. Detection of the luminal membrane protein TSG101 with antibodies depended on membrane permeabilization, consistent with the presence of vesicles on the chip. Protein, morphological, and size characterization revealed that these vesicles had the characteristics of EVs. The results demonstrated that unprocessed cfDNA, EV-RNA, and EV-associated proteins can be isolated and simultaneously fluorescently analyzed on the ACE chip. The compatibility with established downstream technologies may also allow the use of the platform as a sample preparation method for workflows that could benefit from access to unprocessed exosomal, genomic, and proteomic biomarkers.

Highlights

  • The goal of personalized medicine is to individualize care pathways, tailor screening, treatment, disease monitoring, and prevention recommendations to one’s genomic and proteomic makeup (Crowley et al, 2013; DiazJr., and Bardelli, 2014; Lewis et al, 2015; Cohen et al, 2017; De Rubis et al, 2019)

  • Biomarker capture and on-chip detection of the biomarkers was performed in plasma samples collected from two donors diagnosed with Non-Small Cell Lung Cancer (NSCLC)

  • Published data showed that the fluorescent intensity is proportional to the input DNA concentration (Krishnan et al, 2011; Turner et al, 2018) and demonstrates the utility of our platform for physiologically relevant cell-free DNA (cfDNA) concentrations in cancer patient plasma (Wu et al, 2002; Volik et al, 2016)

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Summary

Introduction

The goal of personalized medicine is to individualize care pathways, tailor screening, treatment, disease monitoring, and prevention recommendations to one’s genomic and proteomic makeup (Crowley et al, 2013; DiazJr., and Bardelli, 2014; Lewis et al, 2015; Cohen et al, 2017; De Rubis et al, 2019). Scientific discoveries that drive our understanding of an individual’s cellular and molecular processes rely on a combination of insights based on genomic and proteomic technologies. These often require biomarker-specific purification workflows (DNA, RNA, and proteins) that are performed on multiple isolation platforms, use highly pre-processed biological material, and lengthy analytical procedures that limit routine clinical use (Gold et al, 2015; Volik et al, 2016; Cohen et al, 2018; Aggarwal et al, 2019). An important step toward accessible personalized medicine could be the development of a workflow capable of simultaneous isolation and analysis of DNA, RNA, and EVassociated protein biomarkers in minimally processed samples (Vaidyanathan et al, 2018)

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