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High-content single-cell combinatorial indexing

ABSTRACT: High-content single-cell combinatorial indexing

PROVIDER: PRJNA728882 | ENA |

REPOSITORIES: ENA

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High-content single-cell combinatorial indexing

Project description:We describe a new generalizable library generation chemistry with increased efficiency that is amendable to tagmentation-based split-pool barcoding strategies, such as single-cell combinatorial indexing (sci). Symmetrical strand sci (‘s3’) uses a novel uracil-based adapter switching approach that provides an improved rate of conversion of source DNA into viable sequencing library fragments following tagmentation. We apply this chemistry to assay chromatin accessibility (s3-ATAC) to profile human cortical and mouse whole brain tissues, with mouse datasets demonstrating a 6-to-13-fold improvement in usable reads obtained per cell when compared to other available methods performed on the same sample type. We also demonstrate the generalizability of s3 by applying it to single-cell whole genome sequencing (s3-WGS), and whole genome plus chromatin conformation (s3-GCC), for structural variant calling in a patient-derived cancer cell line model. Using the high-coverage profiles produced by the s3 technologies we characterized preserved clonal structure and identified a putative subclone-specific translocation.

2021-05-28 | GSE174226 | GEO

Ultra-high throughput single-cell RNA sequencing by combinatorial fluidic indexing

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

Ultra-high throughput single-cell RNA sequencing by combinatorial fluidic indexing

Project description:Ultra-high throughput single-cell RNA sequencing by combinatorial fluidic indexing

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Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing

Project description:We developed a combinatorial indexing strategy to profile the transcriptomes of large numbers of single cells or nuclei (Single cell Combinatorial Indexing RNA-seq or sci-RNA-seq). We applied sci-RNA-seq to profile nearly 50,000 cells from C. elegans at the L2 stage, effectively ~56-fold “shotgun cellular coverage” of its somatic cell composition.

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High-throughput combinatorial indexing enables scalable single-cell chromatin accessibility profiling [GM12878]

Project description:High-throughput combinatorial indexing enables scalable single-cell chromatin accessibility profiling [GM12878]

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High-throughput combinatorial indexing enables scalable single-cell chromatin accessibility profiling [PBMC]

Project description:High-throughput combinatorial indexing enables scalable single-cell chromatin accessibility profiling [PBMC]

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High-throughput combinatorial indexing enables scalable single-cell chromatin accessibility profiling [Validation]

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High-throughput combinatorial indexing enables scalable single-cell chromatin accessibility profiling [Blood]

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