Browse
Submit Data
Databases
API
Help

Dataset Information

10 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

TARGET-Seq: A Protocol for High-Sensitivity Single-Cell Mutational Analysis and Parallel RNA Sequencing.

ABSTRACT: Single-cell RNA-sequencing technologies are ideally placed to resolve intratumoral heterogeneity. However, the lack of coverage across key mutation hotspots has precluded the correlation of genetic and transcriptional readouts from the same single cell. To overcome this, we developed TARGET-seq, a protocol for TARGETed high-sensitivity single-cell mutational analysis with extremely low allelic dropout rates, parallel RNA SEQuencing, and cell-surface proteomics. Here, we present a detailed step-by-step protocol for TARGET-seq, including troubleshooting tips, approaches for automation, and methods for high-throughput multiplexing of libraries. For complete details on the use and execution of this protocol, please refer to Rodriguez-Meira et al. (2019).

SUBMITTER: Rodriguez-Meira A

PROVIDER: S-EPMC7757016 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Unravelling Intratumoral Heterogeneity through High-Sensitivity Single-Cell Mutational Analysis and Parallel RNA Sequencing.

Project description:Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for resolving transcriptional heterogeneity. However, its application to studying cancerous tissues is currently hampered by the lack of coverage across key mutation hotspots in the vast majority of cells; this lack of coverage prevents the correlation of genetic and transcriptional readouts from the same single cell. To overcome this, we developed TARGET-seq, a method for the high-sensitivity detection of multiple mutations within single cells from both genomic and coding DNA, in parallel with unbiased whole-transcriptome analysis. Applying TARGET-seq to 4,559 single cells, we demonstrate how this technique uniquely resolves transcriptional and genetic tumor heterogeneity in myeloproliferative neoplasms (MPN) stem and progenitor cells, providing insights into deregulated pathways of mutant and non-mutant cells. TARGET-seq is a powerful tool for resolving the molecular signatures of genetically distinct subclones of cancer cells.

| S-EPMC6436961 | biostudies-literature

3'-TARGET-seq: a novel method for high-sensitivity single-cell mutational analysis and parallel high throughput RNA-sequencing [Figures 5 and 6, Figure S7]

Project description:We developed 3'-TARGET-seq, a single-cell genotyping and RNA-seq method, which allows accurate detection of multiple mutations within single-cells from genomic and coding DNA in parallel with high throughput 3'-biased whole transcriptome analysis, providing a powerful tool to link transcriptional and genetic tumor heterogeneity. Single cell whole transcriptome sequencing of 2798 Lineage-CD34+ HSPC (Hematopoietic Stem and Progenitor Cells) from patients with JAK2-V617F mutant myelofibrosis and normal controls using 3'-TARGET-seq reveals distinct molecular signatures associated with the presence of somatic mutations in single cells as well as distinct transcriptional profiles of WT cells from patient samples as compared with normal controls.

2019-02-11 | GSE122198 | GEO

TARGET-seq: novel method for single-cell mutational analysis and parallel RNA-sequencing

Project description:This SuperSeries is composed of the SubSeries listed below.

2019-02-11 | GSE105454 | GEO

Massively parallel single-nucleus RNA-seq with DroNc-seq.

Project description:Single-nucleus RNA sequencing (sNuc-seq) profiles RNA from tissues that are preserved or cannot be dissociated, but it does not provide high throughput. Here, we develop DroNc-seq: massively parallel sNuc-seq with droplet technology. We profile 39,111 nuclei from mouse and human archived brain samples to demonstrate sensitive, efficient, and unbiased classification of cell types, paving the way for systematic charting of cell atlases.

| S-EPMC5623139 | biostudies-literature

Massively parallel and time-resolved RNA sequencing in single cells with scNT-seq.

Project description:Single-cell RNA sequencing offers snapshots of whole transcriptomes but obscures the temporal RNA dynamics. Here we present single-cell metabolically labeled new RNA tagging sequencing (scNT-seq), a method for massively parallel analysis of newly transcribed and pre-existing mRNAs from the same cell. This droplet microfluidics-based method enables high-throughput chemical conversion on barcoded beads, efficiently marking newly transcribed mRNAs with T-to-C substitutions. Using scNT-seq, we jointly profiled new and old transcriptomes in ~55,000 single cells. These data revealed time-resolved transcription factor activities and cell-state trajectories at the single-cell level in response to neuronal activation. We further determined rates of RNA biogenesis and decay to uncover RNA regulatory strategies during stepwise conversion between pluripotent and rare totipotent two-cell embryo (2C)-like stem cell states. Finally, integrating scNT-seq with genetic perturbation identifies DNA methylcytosine dioxygenase as an epigenetic barrier into the 2C-like cell state. Time-resolved single-cell transcriptomic analysis thus opens new lines of inquiry regarding cell-type-specific RNA regulatory mechanisms.

| S-EPMC8103797 | biostudies-literature

Ischaemic sensitivity of human tissue by single cell RNA seq

Project description: Not available

| S-SUBS3 | biostudies-other

Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput.

Project description:Single-cell RNA-seq can precisely resolve cellular states, but applying this method to low-input samples is challenging. Here, we present Seq-Well, a portable, low-cost platform for massively parallel single-cell RNA-seq. Barcoded mRNA capture beads and single cells are sealed in an array of subnanoliter wells using a semipermeable membrane, enabling efficient cell lysis and transcript capture. We use Seq-Well to profile thousands of primary human macrophages exposed to Mycobacterium tuberculosis.

| S-EPMC5376227 | biostudies-literature

mDrop-Seq: Massively Parallel Single-Cell RNA-Seq of Saccharomyces cerevisiae and Candida albicans.

Project description:Advances in high-throughput single-cell RNA sequencing (scRNA-seq) have been limited by technical challenges such as tough cell walls and low RNA quantity that prevent transcriptomic profiling of microbial species at throughput. We present microbial Drop-seq or mDrop-seq, a high-throughput scRNA-seq technique that is demonstrated on two yeast species, Saccharomyces cerevisiae, a popular model organism, and Candida albicans, a common opportunistic pathogen. We benchmarked mDrop-seq for sensitivity and specificity and used it to profile 35,109 S. cerevisiae cells to detect variation in mRNA levels between them. As a proof of concept, we quantified expression differences in heat shock S. cerevisiae using mDrop-seq. We detected differential activation of stress response genes within a seemingly homogenous population of S. cerevisiae under heat shock. We also applied mDrop-seq to C. albicans cells, a polymorphic and clinically relevant species of yeast with a thicker cell wall compared to S. cerevisiae. Single-cell transcriptomes in 39,705 C. albicans cells were characterized using mDrop-seq under different conditions, including exposure to fluconazole, a common anti-fungal drug. We noted differential regulation in stress response and drug target pathways between C. albicans cells, changes in cell cycle patterns and marked increases in histone activity when treated with fluconazole. We demonstrate mDrop-seq to be an affordable and scalable technique that can quantify the variability in gene expression in different yeast species. We hope that mDrop-seq will lead to a better understanding of genetic variation in pathogens in response to stimuli and find immediate applications in investigating drug resistance, infection outcome and developing new drugs and treatment strategies.

| S-EPMC8779198 | biostudies-literature

TARGET-seq: novel method for single-cell mutational analysis and parallel RNA-sequencing

Project description:TARGET-seq: novel method for single-cell mutational analysis and parallel RNA-sequencing

| PRJNA415199 | ENA

Hydro-Seq enables contamination-free high-throughput single-cell RNA-sequencing for circulating tumor cells.

Project description:Molecular analysis of circulating tumor cells (CTCs) at single-cell resolution offers great promise for cancer diagnostics and therapeutics from simple liquid biopsy. Recent development of massively parallel single-cell RNA-sequencing (scRNA-seq) provides a powerful method to resolve the cellular heterogeneity from gene expression and pathway regulation analysis. However, the scarcity of CTCs and the massive contamination of blood cells limit the utility of currently available technologies. Here, we present Hydro-Seq, a scalable hydrodynamic scRNA-seq barcoding technique, for high-throughput CTC analysis. High cell-capture efficiency and contamination removal capability of Hydro-Seq enables successful scRNA-seq of 666 CTCs from 21 breast cancer patient samples at high throughput. We identify breast cancer drug targets for hormone and targeted therapies and tracked individual cells that express markers of cancer stem cells (CSCs) as well as of epithelial/mesenchymal cell state transitions. Transcriptome analysis of these cells provides insights into monitoring target therapeutics and processes underlying tumor metastasis.

| S-EPMC6520360 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data