Massively Parallel Selection of NanoCluster Beacons. Issue 41 (9th September 2022)

• Record Type: Journal Article
• Authors: Kuo, Yu‐An; Jung, Cheulhee; Chen, Yu‐An; Kuo, Hung‐Che; Zhao, Oliver S.; Nguyen, Trung D.; Rybarski, James R.; Hong, Soonwoo; Chen, Yuan‐I; Wylie, Dennis C.; Hawkins, John A.; Walker, Jada N.; Shields, Samuel W. J.; Brodbelt, Jennifer S.; Petty, Jeffrey T.; Finkelstein, Ilya J.; Yeh, Hsin‐Chih
• Is Part Of: Advanced materials. Volume 34:Issue 41(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-09-09
• Subjects: fluorescent nanomaterials -- high‐throughput screening -- NanoCluster Beacons -- next‐generation sequencing -- silver nanoclusters; Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adma.202204957
• Languages: English
• ISSNs: 0935-9648

Abstract

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 reported that takes advantage of repurposed next‐generation‐sequencing chips to screen the activation fluorescence of ≈40 000 activator sequences. It is found that 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–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. Abstract : Approximately 40 000 distinct NanoCluster Beacons (NCBs) are screened on next‐generation sequencing chips and new NCBs that outperform the known best NCBs are identified. Dramatic fluorescence changes are observed by substituting a single nucleotide in an NCB, which can be explained by the proposed zipper‐bag model. Combining high‐throughput screening with machine‐learning algorithms, bright and multicolor NCBs are designed in silico.