Classification of Epigenetic Biomarkers with Atomically Thin Nanopores
Abstract
We use the electronic properties of 2D solid-state nanopore materials to propose a versatile and generally applicable biosensor technology by using a combination of molecular dynamics, nanoscale device simulations, and statistical signal processing algorithms. As a case study, we explore the classification of three epigenetic biomarkers, the methyl-CpG binding domain 1 (MBD-1), MeCP2, and γ-cyclodextrin, attached to double-stranded DNA to identify regions of hyper- or hypomethylations by utilizing a matched filter. We assess the sensing ability of the nanopore device to identify the biomarkers based on their characteristic electronic current signatures. Such a matched filter-based classifier enables real-time identification of the biomarkers that can be easily implemented on chip. This integration of a sensor with signal processing architectures could pave the way toward the development of a multipurpose technology for early disease detection.
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