Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Scalable Dual-omic Profiling with Single-nucleus Chromatin Accessibility and mRNA Expression Sequencing 2 (SNARE-seq2)

ABSTRACT: Scalable Dual-omic Profiling with Single-nucleus Chromatin Accessibility and mRNA Expression Sequencing 2 (SNARE-seq2)

PROVIDER: PRJNA662341 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR12609300_1.fastq.gz	Fastqsanger.gz
	SRR12609300_2.fastq.gz	Fastqsanger.gz
	SRR12609301_1.fastq.gz	Fastqsanger.gz
	SRR12609301_2.fastq.gz	Fastqsanger.gz
	SRR12609302_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 200

Similar Datasets

Scalable Dual-omic Profiling with Single-nucleus Chromatin Accessibility and mRNA Expression Sequencing 2 (SNARE-seq2)

Project description:We perform benchmark experiments using cell lines to develop and assess the performance of a scalable method for generating matched RNA and chromatin accessibility profiles

2020-09-09 | GSE157660 | GEO

Integrating single-cell RNA-seq and imaging with SCOPE-seq2

Project description:Live cell imaging allows direct observation and monitoring of phenotypes that are difficult to infer from the transcriptome. However, existing methods for linking microscopy and single-cell RNA-seq (scRNA-seq) have limited scalability. Here, we describe an upgraded version of Single Cell Optical Phenotyping and Expression (SCOPE-seq2), which builds on our earlier efforts to combine single-cell imaging and expression profiling, with substantial improvements in throughput, molecular capture efficiency, linking accuracy, and compatibility with standard microscopy instrumentation. We introduce improved optically decodable mRNA capture beads and implement a more scalable and simplified optical decoding process. We demonstrated the utility of SCOPE-seq2 for fluorescence, morphological, and expression profiling of individual primary cells from a human glioblastoma (GBM) surgical sample, revealing relationships between simple imaging features and cellular identity, particularly among malignantly transformed tumor cells.

2020-06-28 | GSE151137 | GEO

A scalable, low cost, multi-omic interrogation and sample hashing workflow for single-cell analysis using the Seq-Well S3 platform

Project description:Clinical samples promise unparalleled insights into the cellular mechanisms that underlie pathological conditions and therapeutic responses. However, they often can be precious, with few cells available for single-cell analysis, necessitating effort to maximize the amount of information that can be garnered from each. Here, we introduce a low material input, cost-effective protocol for conducting multi-omic analyses and sample hashing on single-cell suspensions using the Seq-Well S3 platform. Our protocol, designed to be both accessible and affordable, leverages readily available reagents and standard laboratory equipment, significantly lowering barriers to entry for researchers in low- and middle-income countries. The method detailed herein offers a streamlined and efficient workflow for: (1) precise staining of single-cell suspensions with antibody-oligo conjugates for accurate cell surface protein identification and effective sample multiplexing; (2) reliable generation of Seq-Well S3 sequencing libraries; (3) optional generation of bulk-RNA sequencing libraries through an optimized SMART-seq2 protocol; and, (4) robust computational pipelines for in-depth multi-omic data analysis. This protocol works with fragile and limited cell inputs (here, outlined for 15,000 cells per 200 µL - a fraction of the input required by most commercial methods). It also offers significant cost and time savings, with the entire process from cell isolation to sequencing taking only 3-6 days, plus an additional 1-2 days for data processing. In sum, this generally applicable pipeline empowers researchers around the globe to apply single-cell multiomics to advance their own research agendas.

2024-05-02 | GSE266385 | GEO

An atlas of healthy and injured cell states and niches in the human kidney [SNARE-Seq2]

Project description:An atlas of healthy and injured cell states and niches in the human kidney [SNARE-Seq2]

| PRJNA759872 | ENA

CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq

Project description:Single-cell transcriptomics requires a method that is sensitive, accurate, and reproducible. Here, we present CEL-Seq2, a modified version of our CEL-Seq method, with three-fold higher sensitivity, lower costs, and less hands-on time. We also implemented CEL-Seq2 on Fluidigm’s C1 system, thereby providing its first single-cell, on-chip barcoding method, and detected gene expression changes accompanying the progression through the cell cycle in mouse fibroblast cells. We also compare with Smart-Seq to demonstrate CEL-Seq2’s increased sensitivity relative to other available methods. Collectively, the improvements make CEL-Seq2 uniquely suited to single-cell RNA-Seq analysis in terms of economics, resolution, and ease of use

2016-04-08 | GSE78779 | GEO

A Scalable Epitope Tagging Approach for High Throughput ChIP-seq Analysis

Project description:A Scalable Epitope Tagging Approach for High Throughput ChIP-seq Analysis ChIP-seq comparison between CRISPR editing cells using epitope antibody and non-editing cells using endogeneous TF antibody

2016-09-01 | E-GEOD-78064 | biostudies-arrayexpress

multi-omic analysis

Project description:multi-omic analysis

| PRJNA790956 | ENA

A generic, cost-effective and scalable cell lineage analysis platform

Project description:Advances in single-cell genomics enable commensurate improvements in methods for uncovering lineage relations among individual cells. Current sequencing based methods for cell lineage analysis depend on low resolution bulk analysis or rely on extensive single cell sequencing which is not scalable and could be biased by functional dependencies. Here we show an integrated biochemical-computational platform for generic single-cell lineage analysis that is retrospective, cost-effective and scalable. It consists of a biochemical-computational pipeline that inputs individual cells, produces targeted single-cell sequencing data and uses it to generate a lineage tree of the input cells. We validated the platform by applying it to cells sampled from an ex vivo grown tree and analyzed its feasibility landscape by computer simulations. We conclude that the platform may serve as a generic tool for lineage analysis and thus pave the way towards large-scale human cell lineage discovery.

2016-04-18 | E-MTAB-4553 | biostudies-arrayexpress

Structure-seq2: Sensitive and accurate genome-wide profiling of RNA structure in vivo

Project description:We present Structure-seq2, which provides nucleotide-resolution RNA structural information in vivo and genome-wide. This optimized version of our original Structure-seq method increases sensitivity and data quality by minimizing formation of a deleterious by-product, reducing ligation bias, and improving read coverage. Structure-seq2 can employ a biotinylated nucleotide to facilitate the protocol. We have benchmarked Structure-seq2 on both mRNA and rRNA structure in rice (Oryza sativa) and apply Structure-seq2 to provide evidence of hidden breaks in chloroplast rRNA and a previously unreported N1-methyladenosine (m1A) in a nuclear-encoded rRNA.

2017-06-08 | GSE95514 | GEO

Smart-seq2 of spinal cord motoneurons in spinal cord injury model after dual electrical stimulation with specific frequencies

Project description:Smart-seq2 of spinal cord motoneurons in spinal cord injury model after dual electrical stimulation with specific frequencies

| PRJNA1016263 | ENA