The next frontier in single cell analysis: multimodal studies and clinical translation
Thought leaders Pratip Chattopadhyay and Daniel Chiu introduce the Lab on a Chip single cell analysis thematic collection.
Combining dielectrophoresis and computer vision for precise and fully automated single-cell handling and analysis
We employ real-time image processing in the active control of dielectrophoretic actuation to select, isolate and arrange individual cells in a microfluidic channel.
Profiling protein–protein interactions of single cancer cells with in situ lysis and co-immunoprecipitation
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.
μCB-seq: microfluidic cell barcoding and sequencing for high-resolution imaging and sequencing of single cells
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.
Intelligent image-activated cell sorting 2.0
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.
Double emulsion flow cytometry with high-throughput single droplet isolation and nucleic acid recovery
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 for high throughput single-cell nucleocytoplasmic miRNA profiling
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.
Deep learning guided image-based droplet sorting for on-demand selection and analysis of single cells and 3D cell cultures
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.
Intelligent optofluidic analysis for ultrafast single bacterium profiling of cellulose production and morphology
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.
Microfluidic impedance cytometry device with N-shaped electrodes for lateral position measurement of single cells/particles
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.
Single-cell RT-LAMP mRNA detection by integrated droplet sorting and merging
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.
Microfluidic on-demand droplet generation, storage, retrieval, and merging for single-cell pairing
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.
About this collection
A collection of papers focussed on multimodal single cell analysis. There is an array of tools that have the potential to allow us to comprehensively and accurately characterize the cells involved in a biological process, and we are a step away from using these tools widely and efficiently to impact clinical care, but there are large obstacles we must break down first. The papers in this collection suggest cutting edge solutions to the obstacles that we face in the advancement of multimodal single cell analysis.