Encoding Microreactors with Droplet Chains in Microfluidics.

Abstract

Droplet-based high throughput biomolecular screening and combinatorial synthesis entail a viable indexing strategy to be developed for the identification of each microreactor. Here, we propose a novel indexing scheme based on the generation of droplet sequences on demand to form unique encoding droplet chains in fluidic networks. These codes are represented by multiunit and multilevel droplets packages, with each code unit possessing several distinct signal levels, potentially allowing large encoding capacity. For proof of concept, we use magnetic nanoparticles as the encoding material and a giant magnetoresistance (GMR) sensor-based active sorting system supplemented with an optical detector to generate and decode the sequence of one exemplar sample droplet reactor and a 4-unit quaternary magnetic code. The indexing capacity offered by 4-unit multilevel codes with this indexing strategy is estimated to exceed 104, which holds great promise for large-scale droplet-based screening and synthesis.