Million spot binding array platform for exploring and optimizing multiple simultaneous detection events.

Large-scale, high-throughput specificity assays to characterize binding properties within a competitive and complex environment of potential binder-target pairs remain challenging and cost prohibitive. Barcode cycle sequencing (BCS) is a molecular binding assay for proteins, peptides, and other small molecules that is built on a next-generation sequencing (NGS) chip. BCS uses a binder library and targets labeled with unique DNA barcodes. Upon binding, binder barcodes are ligated to target barcodes and sequenced to identify encoded binding events. For complete details on the use and execution of this protocol, please refer to Hong et al. (2022).

Target-switch SELEX: Screening with alternating targets to generate aptamers to conserved terminal dipeptides.

Systematic evolution of ligands by exponential enrichment (SELEX) encompasses a wide variety of high-throughput screening techniques for producing nucleic acid binders to molecular targets through directed evolution. We describe here the design and selection steps for discovery of DNA aptamers with specificity for the two consecutive N-terminal amino acids (AAs) of a small peptide (8-10 amino acids). This bead-based method may be adapted for applications requiring binders which recognize a specific portion of the desired target. For complete details on the use and execution of this protocol, please refer to Hong et al. (2022).

Nanocrown electrodes for parallel and robust intracellular recording of cardiomyocytes.

Drug-induced cardiotoxicity arises primarily when a compound alters the electrophysiological properties of cardiomyocytes. Features of intracellular action potentials (iAPs) are powerful biomarkers that predict proarrhythmic risks. In the last decade, a number of vertical nanoelectrodes have been demonstrated to achieve parallel and minimally-invasive iAP recordings. However, the large variability in success rate and signal strength have hindered nanoelectrodes from being broadly adopted for proarrhythmia drug assessment. In this work, we develop vertically-aligned nanocrown electrodes that are mechanically robust and achieve > 99% success rates in obtaining intracellular access through electroporation. We validate the accuracy of nanocrown electrode recordings by simultaneous patch clamp recording from the same cell. Finally, we demonstrate that nanocrown electrodes enable prolonged iAP recording for continual monitoring of the same cells upon the sequential addition of four incremental drug doses. Our technology development provides an advancement towards establishing an iAP screening assay for preclinical evaluation of drug-induced arrhythmogenicity.

ProtSeq: Toward high-throughput, single-molecule protein sequencing via amino acid conversion into DNA barcodes.

We demonstrate early progress toward constructing a high-throughput, single-molecule protein sequencing technology utilizing barcoded DNA aptamers (binders) to recognize terminal amino acids of peptides (targets) tethered on a next-generation sequencing chip. DNA binders deposit unique, amino acid-identifying barcodes on the chip. The end goal is that, over multiple binding cycles, a sequential chain of DNA barcodes will identify the amino acid sequence of a peptide. Toward this, we demonstrate successful target identification with two sets of target-binder pairs: DNA-DNA and Peptide-Protein. For DNA-DNA binding, we show assembly and sequencing of DNA barcodes over six consecutive binding cycles. Intriguingly, our computational simulation predicts that a small set of semi-selective DNA binders offers significant coverage of the human proteome. Toward this end, we introduce a binder discovery pipeline that ultimately could merge with the chip assay into a technology called ProtSeq, for future high-throughput, single-molecule protein sequencing.

Machine learning guided aptamer refinement and discovery.

Aptamers are single-stranded nucleic acid ligands that bind to target molecules with high affinity and specificity. They are typically discovered by searching large libraries for sequences with desirable binding properties. These libraries, however, are practically constrained to a fraction of the theoretical sequence space. Machine learning provides an opportunity to intelligently navigate this space to identify high-performing aptamers. Here, we propose an approach that employs particle display (PD) to partition a library of aptamers by affinity, and uses such data to train machine learning models to predict affinity in silico. Our model predicted high-affinity DNA aptamers from experimental candidates at a rate 11-fold higher than random perturbation and generated novel, high-affinity aptamers at a greater rate than observed by PD alone. Our approach also facilitated the design of truncated aptamers 70% shorter and with higher binding affinity (1.5 nM) than the best experimental candidate. This work demonstrates how combining machine learning and physical approaches can be used to expedite the discovery of better diagnostic and therapeutic agents.