Project description:We describe sciMETv3, a combinatorial indexing-based technique that is capable of producing single-cell DNA methylation datasets with 100's of thousands of cells in a single experiment by one individual over two days. sciMETv3 is compatible with capture techniques to reduce sequencing costs (sciMET-cap) as well as enzymatic conversion methods. sciMETv3 libraries can be designed for compatibility with both Illumina and Ultima sequencing platforms.
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.
2021-03-11 | GSE168620 | GEO
Project description:Arabidopsis root single-cell ATAC-seq using combinatorial indexing
Project description:Single-cell RNA-seq libraries were generated using two and three level single-cell combinatorial indexing RNA sequencing (sci-RNA-seq) of untreated or small molecule inhibitor exposed HEK293T, NIH3T3, A549, MCF7 and K562 cells. Different cells and different treatment were hashed and pooled prior to sci-RNA-seq using a nuclear barcoding strategy. This nuclear barcoding strategy relies on fixation of barcode containing well-specific oligos that are specific to a given cell type, replicate or treatment condition.
Project description:To better understand how individual cells function within an anatomical space, we developed XYZeq, a novel scRNA-seq workflow that uses combinatorial indexing in microwells to encode spatial metadata into scRNA-seq libraries. We used XYZeq to profile heterotopic mouse liver and spleen tumor models to capture transcriptomes from tens of thousands of cells across a total of eight tissue slices.
