Massively Parallel Selection of NanoCluster Beacons

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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number2204957
JournalAdvanced Materials
Volume34
Issue number41
DOIs
Publication statusPublished - 2022 Oct 13

Keywords

  • fluorescent nanomaterials
  • high-throughput screening
  • NanoCluster Beacons
  • next-generation sequencing
  • silver nanoclusters

ASJC Scopus subject areas

  • Materials Science(all)
  • Mechanics of Materials
  • Mechanical Engineering

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