Massively Parallel Selection of NanoCluster Beacons.

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 polar opposite twins (POTs), it is difficult to discover new POT-NCBs using the conventional low-throughput characterization approaches. Here, a high-throughput selection method is reportedthat takes advantage of repurposed next-generation-sequencing chips to screen the activation fluorescence of ≈40 000activator sequences. It is found that the nucleobases at positions 7-12of the 18-nucleotide-long activator are critical to creating bright NCBs and positions 4-6and 2-4are hotspots to generate yellow-orange and red POTs, respectively. Based on these findings, a "zipper-bag" model is proposed that can explain how these hotspots facilitate the formation of distinct silver cluster chromophores and alter their chemical yields. Combining high-throughput screening with machine-learning algorithms, a pipeline is established to design bright and multicolor NCBs in silico.