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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 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.
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High-throughput activator sequence selection for silver nanocluster beacons
Invented in 2010, NanoCluster Beacons (NCBs) (1) are an emerging class of turn-on probes that show unprecedented capabilities in single-nucleotide polymorphism (2) and DNA methylation (3) detection. As the activation colors of NCBs can be tuned by a near-by, guanine-rich activator strand, NCBs are versatile, multicolor probes suitable for multiplexed detection at low cost. Whereas a variety of NCB designs have been explored and reported, further diversification and optimization of NCBs require a full scan of the ligand composition space. However, the current methods rely on microarray and multi-well plate selection, which only screen tens to hundreds of activator sequences (4, 5). Here we take advantage of the next-generation-sequencing (NGS) platform for high-throughput, large-scale selection of activator strands. We first generated a ~104 activator sequence library on the Illumina MiSeq chip. Hybridizing this activator sequence library with a common nucleation sequence (which carried a nonfluorescent silver cluster) resulted in hundreds of MiSeq chip images with millions of bright spots (i.e. light-up polonies) of various intensities and colors. With a method termed Chip-Hybridized Associated Mapping Platform (CHAMP) (6), we were able to map these bright spots to the original DNA sequencing map, thus recovering the activator sequence behind each bright spot. After assigning an “activation score” to each “light-up polony”, we used a computational algorithm to select the best activator strands and validate these strands using the traditional in-solution preparation and fluorometer measurement method. By exploring a vast ligand composition space and observing the corresponding activation behaviors of silver clusters, we aim to elucidate the design rules of NCBs.
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- Award ID(s):
- 1757885
- PAR ID:
- 10343198
- Editor(s):
- Achilefu, Samuel; Raghavachari, Ramesh
- Date Published:
- Journal Name:
- High-throughput activator sequence selection for silver nanocluster beacons DOI number
- Page Range / eLocation ID:
- 18
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1117/12.2510649
