Themed collection Single Cell Analysis

Thought leaders Pratip Chattopadhyay and Daniel Chiu introduce the Lab on a Chip single cell analysis thematic collection.

Multimodal single cell analysis provides insights in cellular processes such as cell fate decisions, physiological heterogeneity or genotype–phenotype linkages. This review presents an overview of recent multimodal microfluidic platforms with potential in biomedical research.

We employ real-time image processing in the active control of dielectrophoretic actuation to select, isolate and arrange individual cells in a microfluidic channel.

We developed a single-cell version of the co-immunoprecipitation (co-IP) analysis that examines the amount and protein–protein interactions of target proteins immunoprecipitated from individual cells.

We report on a hand-held multiplexed impedance sensor system and show evidence for impedance-based monitoring of the growth of a single bacterium.

We present a platform for on-chip molecular barcoding that combines high-resolution imaging with genomic analysis, enabling multi-modal phenotypic measurements in single cells.

The upgraded version of intelligent image-activated cell sorting (iIACS) has enabled higher-throughput and more sensitive intelligent image-based sorting of single live cells from heterogeneous populations.

We have developed a novel workflow (sdDE-FACS, ingle roplet ouble mulsion FACS) that allows robust production, screening, and sorting of single double emulsion droplets with complete nucleic acid recovery.

Nanoplasmon-enhanced drop screen is developed to measure single-cell multiple miRNAs with high sensitivity of 0.1 nM, addressing cell nucleocytoplasmic profile in a throughput ∼100 cells per minute.

To uncover the heterogeneity of cellular populations and multicellular constructs we show on-demand isolation of single mammalian cells and 3D cell cultures by coupling bright-field microdroplet imaging with real-time classification and sorting using convolutional neural networks.

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.

In this paper, we present an N-shaped electrode-based microfluidic impedance cytometry for the measurement of the lateral position of single cells and particles in continuous flows.

We present a droplet-based microfluidic platform that permits seamless on-chip droplet sorting and merging, which enables completing multi-step reaction assays within a short time, and demonstrate detection of specific single-cell mRNA expressions.

A multifunctional microfluidic platform combining on-demand aqueous-phase droplet generation, multi-droplet storage, and controlled merging of droplets selected from a storage library in a single integrated microfluidic device is described.