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Self-Assembled Pico-Liter Droplet Microarray for Ultrasensitive Nucleic Acid Quantification

Published Web Location

https://doi.org/10.1021/acsnano.5b03848
The data associated with this publication are within the manuscript.
Abstract

Nucleic acid detection and quantification technologies have made remarkable progress in recent years.  Among existing platforms, hybridization-based assays have the advantages of being amplification free, low instrument cost, and high throughput, but are generally less sensitive compared to sequencing and PCR assays. To bridge this performance gap, we developed a quantitative physical model for the hybridization-based assay to guide the experimental design, which leads to a pico-liter droplet environment for superior performance orders of magnitude above the state of the art in any amplification-free microarray platform. The pico-liter droplet hybridization platform is further coupled with the on-chip enrichment technique to yield ultra high sensitivity both in terms of target concentration and copy number.  Our physical model, taking into account of molecular transport, electrostatic intermolecular interactions, reaction kinetics, suggests that reducing liquid height and optimizing target concentration will maximize the hybridization efficiency, and both conditions can be satisfied in a highly parallel, self-assembled pico-liter droplet microarray that produces a detection limit as low as 570 copies and 50 aM. The pico-liter droplet array device is realized with a micro-patterned super-hydrophobic black silicon surface that allows enrichment of nucleic acid samples by position-defined evaporation.  With on-chip enrichment and oil encapsulated pico-liter droplet arrays, we have demonstrated a record high sensitivity, wide dynamic range (6 orders of magnitude), and marked reduction of hybridization time from >10 hours to less than 5 minutes in a highly repeatable fashion, benefiting from the physics-driven design and nanofeatures of the device. The design principle and technology can contribute to biomedical sensing and point-of-care clinical applications including pathogen detection and cancer diagnosis and prognosis.

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