Analysis of microRNA (miRNA), a pivotal primary regulator of fundamental cellular processes, at the single-cell level is essential to elucidate regulated gene expression precisely. Most single-cell gene sequencing methods use the polymerase chain reaction (PCR) to increase the concentration of the target gene for detection, thus requiring a barcoding process for cell identification and creating a challenge for real-time, large-scale screening of sequences in cells to rapidly profile physiological samples. In this study, a rapid, PCR-free, single-cell miRNA assay is developed from a continuous-flow microfluidic process employing a DNA hybridization chain reaction to amplify the target miRNA signal. Individual cells are encapsulated with DNA amplifiers in water-in-oil droplets and then lysed. The released target miRNA interacts with the DNA amplifiers to trigger hybridization reactions, producing fluorescence signals. Afterward, the target sequences are recycled to trigger a cyclic cascade reaction and significantly amplify the fluorescence signals without using PCR thermal cycling. Multiple DNA amplifiers with distinct fluorescence signals can be encapsulated simultaneously in a droplet to measure multiple miRNAs from a single cell simultaneously. Moreover, this process converts the lab bench PCR assay to a real-time droplet assay with the post-reaction fluorescence signal as a readout to allow flow cytometry-like continuous-flow measurement of sequences in a single cell with an ultrahigh throughput (300-500 cells per minute) for rapid biomedical identification.

Light-directed forces have been widely used to pattern micro/nanoscale objects with precise control, forming functional assemblies. However, a substantial laser intensity is required to generate sufficient optical gradient forces to move a small object in a certain direction, causing limited throughput for applications. A high-throughput light-directed assembly is demonstrated as a printing technology by introducing gold nanorods to induce thermal convection flows that move microparticles (diameter = 40 µm to several hundreds of micrometers) to specific light-guided locations, forming desired patterns. With the advantage of effective light-directed assembly, the microfluidic-fabricated monodispersed biocompatible microparticles are used as building blocks to construct a structured assembly (≈10 cm scale) in ≈2 min. The control with microscale precision is approached by changing the size of the laser light spot. After crosslinking assembly of building blocks, a novel soft material with wanted pattern is approached. To demonstrate its application, the mesenchymal stem-cell-seeded hydrogel microparticles are prepared as functional building blocks to construct scaffold-free tissues with desired structures. This light-directed fabrication method can be applied to integrate different building units, enabling the bottom-up formation of materials with precise control over their internal structure for bioprinting, tissue engineering, and advanced manufacturing.

The advent of single-cell research in the recent decade has allowed biological studies at an unprecedented resolution and scale. In particular, single-cell analysis techniques such as Next-Generation Sequencing (NGS) and Fluorescence-Activated Cell Sorting (FACS) have helped show substantial links between cellular heterogeneity and infectious disease progression. The extensive characterization of genomic and phenotypic biomarkers, in addition to host–pathogen interactions at the single-cell level, has resulted in the discovery of previously unknown infection mechanisms as well as potential treatment options. In this article, we review the various single-cell technologies and their applications in the ongoing fight against infectious diseases, as well as discuss the potential opportunities for future development.

Drop-PEARL enables rapid screening and isolation of virus-neutralizing antibody secreting cells for high-throughput therapeutic antibody discovery.

A continuous-flow intelligent optofluidic device using a convolutional neural network (CNN) computational method was developed to enable high-throughput single-bacterium profiling of bacteria cellulose (BC) with a throughput of ∼35 bacteria per second.