Massively Parallel Selection of NanoCluster Beacans

Yu-An Kuo, Cheulhee Jung, Yu-An Chen, Hung-Che Kuo, Oliver S. Zhao, Truong D. Nguyen, James R. Rybarski, Soonwoo Hong, Yuan-I Chen, Dennis C. Wylie, John A. Hawkins, Jada N. Walker, Samuel W. Shields, Jennifer S. Brodbelt, Jeffrey T. Petty, Ilya J. Finkelstein, and Hsin-Chih Yeh), BioRxiv (2021).
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Abstract

NanoCluster Beacons (NCBs) are multicolor silver nanocluster probes whose fluorescence can be activated or tuned by a proximal DNA strand called the activator. While a single-nucleotide difference in a pair of activators can lead to drastically different activation outcomes, termed the polar opposite twins (POTs), it is difficult to discover new POT-NCBs using the conventional low-throughput characterization approaches. Here we report a high-throughput selection method that takes advantage of repurposed next-generation-sequencing (NGS) chips to screen the activation fluorescence of ~40,000 activator sequences. We find the nucleobases at positions 7-12 of the 18-nucleotide-long activator are critical to creating bright NCBs and positions 4-6 and 2-4 are hotspots to generate yellow and red POTs, respectively. Based on these findings, we propose a “zipper bag model” that explains how these hotspots lead to the creation of distinct silver cluster chromophores and contribute to the difference in chromophore chemical yields. Combining high-throughput screening with machine learning algorithms, we establish a pipeline to rationally design bright and multicolor NCBs.