Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

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

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

OTHER RELATED OMICS DATASETS IN: GSE98561

PROVIDER: PRJNA385514 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR5509290_1.fastq.gz	Fastqsanger.gz
	SRR5509290_2.fastq.gz	Fastqsanger.gz
	SRR5509300_1.fastq.gz	Fastqsanger.gz
	SRR5509300_2.fastq.gz	Fastqsanger.gz
	SRR5509310_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 3436

Similar Datasets

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.

2017-08-17 | GSE98561 | GEO

High-content single-cell combinatorial indexing

Project description:High-content single-cell combinatorial indexing

| PRJNA728882 | ENA

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

Single-cell combinatorial indexing ATAC-seq of HCC1143 with MEK, PI3K, and BET inhibition

Project description:We performed single-cell combinatorial indexing ATAC-seq on the basal-like TNBC cell line HCC1143 under MEK, PI3K, BET and combination treatments as well as DMSO controls

2019-02-08 | GSE126261 | GEO

An optimized protocol for single cell transcriptional profiling by combinatorial indexing

Project description:Single cell combinatorial indexing RNA sequencing (sci-RNA-seq) is a powerful method for recovering gene expression data from an exponentially scalable number of individual cells or nuclei. However, sci-RNA-seq is a complex protocol that has historically exhibited variable performance on different tissues, as well as lower sensitivity than alternative methods. Here we report a simplified, optimized version of the three-level sci-RNA-seq protocol that is faster, higher yield, more robust, and more sensitive, than the original sci-RNA-seq3 protocol, with reagent costs on the order of 1 cent per cell or less. We showcase the optimized protocol via whole organism analysis of an E16.5 mouse embryo, profiling ~380,000 nuclei in a single experiment. Finally, we introduce a “tiny sci-*” protocol for experiments where input is extremely limited.

2021-11-05 | GSE186824 | GEO

The complete cell atlas of an aging multicellular organism

Project description:The complete cell atlas of an aging multicellular organism

| PRJNA858484 | ENA

Multiplex Single Cell Profiling of Chromatin Accessibility by Combinatorial Cellular Indexing [RNA-seq]

Project description:Technical advances have enabled the collection of genome and transcriptome data sets with single-cell resolution. However, single-cell characterization of the epigenome has remained challenging. Furthermore, because cells must be physically separated prior to biochemical processing, conventional single-cell preparatory methods scale linearly. We applied combinatorial cellular indexing to measure chromatin accessibility in thousands of single cells per assay, circumventing the need for compartmentalization of individual cells. We report chromatin accessibility profiles from over 15,000 single cells and use these data to cluster cells on the basis of chromatin accessibility landscapes. We identify modules of coordinately regulated chromatin accessibility at the level of single cells both between and within cell types, with a scalable method that may accelerate progress toward a human cell atlas. 3 replicates from GM12878 and HL-60 cell lines collected for differential gene expression analysis.

2015-05-07 | E-GEOD-68102 | biostudies-arrayexpress

Prokaryotic Single-Cell RNA Sequencing by In Situ Combinatorial Indexing

Project description:Prokaryotic Single-Cell RNA Sequencing by In Situ Combinatorial Indexing

| PRJNA591783 | ENA

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

An optimized protocol for single cell transcriptional profiling by combinatorial indexing

Project description:An optimized protocol for single cell transcriptional profiling by combinatorial indexing

| PRJNA776254 | ENA