Many biochemical events of importance are complex and dynamic. Fluorescence microscopy offers a versatile solution to study the dynamics of biology at the mesoscale. An important challenge in the field is the simultaneous study of several objects of interest, referred to as optical multiplexing. For improved multiplexing, some prior techniques used repeated reporter washing or the geometry of nanostructures; however, these techniques require complex nanostructure assembly, multiple reporters, or advanced multistep drift correction. Here we propose a time-based approach, for improved optical multiplexing, that uses readily available inexpensive reporters and requires minimal preparation efforts. We program short DNA strands, referred hereby as DNA devices, such that they undergo unique conformation changes in the presence of the dye-labeled reporters. The universal fluorescent reporter transiently binds with the devices to report their activity. Since each device is programmed to exhibit different hybridization kinetics, their fluorescent time trace, referred to as the temporal barcode, will be unique. We model our devices using continuous-time Markov chains and use stochastic simulation algorithm to generate their temporal patterns. We first ran simulation experiments with a small number of DNA devices, demonstrating several distinct temporal barcodes, all of which use a single dye color. Later, using nanostructure-based devices, we designed a much larger pool of temporal barcodes and used machine learning for classification of these barcodes. Our simulation experiments and design principles can aid in the experimental demonstration of the DNA devices.
Keywords: DNA barcodes; DNA hybridization; continuous-time Markov chain; machine learning; optical multiplexing; single-molecule imaging.