Project description:A scalable, cost-effective method that combines CRISPR perturbations with a single-cell indexing assay for transposase-accessible chromatin (CRISPR-sciATAC). This method links genome-wide chromatin accessibility to genetic perturbations through simultaneous capture of ATAC-seq fragments and CRISPR guide RNAs from single cells using a 96-well plate combinatorial indexing approach.
Project description:We describe sciMET-ATAC, a combinatorial indexing-based technique that is capable of producing single-cell DNA methylation plus chromatin accessibility datasets.
Project description:We present a novel method: single-cell combinatorial indexing for methylation analysis (sci-MET), which is the first highly scalable assay for whole genome methylation profiling of single cells. We use sci-MET to produce 3,282 total single-cell bisulfite sequencing libraries and achieve read alignment rates of 68± 8%, comparable to those of bulk cell methods. As a proof of concept, we applied sci-MET to deconvolve the cellular identity of a mixture of three human cell lines. Next, we applied sci-MET to mouse cortical tissue, which successfully identified excitatory and inhibitory neuronal populations as well as non-neuronal cell types.
Project description:Cell atlas projects and high-throughput perturbation screens require single-cell sequencing at a scale that is challenging with current technology. To enable cost-effective single-cell sequencing for millions of individual cells, we developed “single-cell combinatorial fluidic indexing” (scifi). The scifi-RNA-seq assay combines one-step combinatorial pre-indexing of entire transcriptomes inside permeabilized cells with subsequent single-cell RNA-seq using microfluidics. Pre-indexing allows us to load multiple cells per droplet and bioinformatically demultiplex their individual expression profiles. Thereby, scifi-RNA-seq massively increases the throughput of droplet-based single-cell RNA-seq, and it provides a straightforward way of multiplexing thousands of samples in a single experiment. Compared to multi-round combinatorial indexing, scifi-RNA-seq provides an easier, faster, and more efficient workflow. In contrast to cell hashing methods, which flag and discard droplets containing more than one cell, scifi-RNA-seq resolves and retains individual transcriptomes from overloaded droplets.