Project description:Droplet microfluidic methods have massively increased the throughput of single-cell RNA sequencing campaigns. The benefit of scale-up is, however, accompanied by increased background noise when processing challenging samples as well as lower overall RNA capture efficiency. These drawbacks stem from the lack of strategies to enrich for high-quality material at the moment of cell encapsulation and the lack of implementable multi-step enzymatic processes that increase RNA capture. Here we alleviate both bottlenecks by deploying fluorescence-activated droplet sorting to enrich for droplets that contain single viable cells, intact nuclei or fixed cells and use reagent addition to droplets by picoinjection to perform multi-step lysis and reverse transcription. Our methodology increases gene detection rates fivefold, while reducing background noise by up to half, depending on sample quality. We harness these unique properties to deliver a high-quality molecular atlas of mouse brain development using highly damaged input material. Our method is broadly applicable to other droplet-based workflows to deliver sensitive and accurate single-cell profiling at a reduced cost.
Project description:We introduce and implement a comprehensive single-cell proteomics sample pre-treatment solution based on an active matrix digital microfluidics chip(AM-DMF-SCP). This platform accommodates high-throughput single-cell isolation and seamless, non-destructive sample pre-treatment, characterized by its efficiency, speed, stability, and ease of use, thereby overcoming the longstanding bottleneck in single-cell protein sample preparation.
Project description:Cell-cell interactions are important to numerous biological systems, including tissue microenvironments, the immune system, and cancer. However, current methods for studying cell combinations and interactions are limited in scalability, allowing just hundreds to thousands of multi-cell assays per experiment; this limited throughput makes it difficult to characterize interactions at biologically relevant scales. Here, we describe a new paradigm in cell interaction profiling that allows accurate grouping of cells and characterization of their interactions for tens to hundreds of thousands of combinations. Our approach leverages high throughput droplet microfluidics to construct multicellular combinations in a deterministic process that allows inclusion of programmed reagent mixtures and beads. The combination droplets are compatible with common manipulation and measurement techniques, including imaging, barcode-based genomics, and sorting. We demonstrate the approach by using it to enrich for CAR-T cells that activate upon incubation with target cells, a bottleneck in the therapeutic T cell engineering pipeline. The speed and control of our approach should enable valuable cell interaction studies.
Project description:Triplicate samples (biological replicates) of in vitro culture-derived reticulocytes (derived from CD34+ cells) were analysed through qualitative phosphoproteomics before and after passage through a microfluidics capillary chip to investigate the mechanisms underlying shear stress recognition.