While differential sensing has the potential to discriminate millions to trillions of analytes based on hundreds of sensing elements, most of the current differential sensor arrays have limited scalability, with only a handful of recognition elements demonstrated. Here we demonstrate a novel differential sensor array based on a class of organic/inorganic hybrid fluorophores, DNA-templated silver nanoclusters (DNA/Ag NCs), that not only has great scalability but also combine both recognition element and signal transduction element into one unit. Six DNA/Ag NC sensors were designed to classify the lysates of six breast epithelial and cancer cell lines with distinct metastatic potential. While MDA-MB-231 and MCF7 lysates showed significantly different fluorescence signatures upon interaction with the sensor array, other cell lines were also differentiable. Our method is simple, versatile, low-cost, reliable, and scalable.
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.
To study cancer progression and drug response, researchers have developed methods to derive tumor organoids from primary tissues, which not only have the same proteomic and genetic abnormalities as the malignant disease but also better replicate tumor behaviors than 2-dimensional culture models. It has been shown that tumor organoids can be used to predict treatment response, understand drug resistance, and study tumor heterogeneity at the individual-patient level. Whereas large-scale production of patient-derived organoids in standard flat-bottom 1,536-well plates has recently been demonstrated for cytotoxicity screening of 3,300 approved drugs, no suitable functional imaging tool can provide rapid 3-dimensional (3D) evaluation over a wide range of cellular states in these mass-produced organoids. The traditional two-photon scanning microscopy is too slow, while the conventional light-sheet microscopy is not compatible with microwell plates. Here we propose a microplate-compatible single-objective multiphoton light-sheet microscopy (SOMP- LSM) that can provide high imaging speed for 3D imaging analysis of organoids and deeper imaging depth with sub-cellular resolution. Our simulation of 3D point-spread function simulation for the SO-MP-LSM shows that our imaging system can achieve 270 nm lateral resolution and 800 nm axial resolution deep into organoids.
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