Browse
Submit Data
Databases
API
Help

Dataset Information

26 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity.

ABSTRACT: Development of a highly reproducible and sensitive single-cell RNA sequencing (RNA-seq) method would facilitate the understanding of the biological roles and underlying mechanisms of non-genetic cellular heterogeneity. In this study, we report a novel single-cell RNA-seq method called Quartz-Seq that has a simpler protocol and higher reproducibility and sensitivity than existing methods. We show that single-cell Quartz-Seq can quantitatively detect various kinds of non-genetic cellular heterogeneity, and can detect different cell types and different cell-cycle phases of a single cell type. Moreover, this method can comprehensively reveal gene-expression heterogeneity between single cells of the same cell type in the same cell-cycle phase.

SUBMITTER: Sasagawa Y

PROVIDER: S-EPMC4054835 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity.

Sasagawa Yohei Y Nikaido Itoshi I Hayashi Tetsutaro T Danno Hiroki H Uno Kenichiro D KD Imai Takeshi T Ueda Hiroki R HR

Genome biology 20130417 4

Development of a highly reproducible and sensitive single-cell RNA sequencing (RNA-seq) method would facilitate the understanding of the biological roles and underlying mechanisms of non-genetic cellular heterogeneity. In this study, we report a novel single-cell RNA-seq method called Quartz-Seq that has a simpler protocol and higher reproducibility and sensitivity than existing methods. We show that single-cell Quartz-Seq can quantitatively detect various kinds of non-genetic cellular heterogen ...[more]

PMID: 23594475

Similar Datasets

Quartz-Seq: a simple and highly quantitative method for single-cell RNA-Seq

Project description:We report novel single-cell RNA-Seq, called Quartz-Seq. Quartz-Seq was simplified method compared with previous methods based on poly-A tailing reaction. RNA-seq by illumina TruSeq, KAPA library preparation kit, single-cell Quartz-Seq and single-cell Smart-Seq by illumina HiSeq 2000/1000

2013-04-04 | E-GEOD-42268 | biostudies-arrayexpress

Quartz-Seq: a simple and highly quantitative method for single-cell RNA-Seq

Project description:We report novel single-cell RNA-Seq, called Quartz-Seq. Quartz-Seq was simplified method compared with previous methods based on poly-A tailing reaction.

2013-04-04 | GSE42268 | GEO

Fast and highly sensitive full-length single-cell RNA sequencing using FLASH-seq.

Project description:We present FLASH-seq (FS), a full-length single-cell RNA sequencing (scRNA-seq) method with increased sensitivity and reduced hands-on time compared to Smart-seq3. The entire FS protocol can be performed in ~4.5 hours, is simple to automate and can be easily miniaturized to decrease resource consumption. The FS protocol can also use unique molecular identifiers (UMIs) for molecule counting while displaying reduced strand-invasion artifacts. FS will be especially useful for characterizing gene expression at high resolution across multiple samples.

| S-EPMC9546769 | biostudies-literature

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 threefold higher sensitivity, lower costs, and less hands-on time. We implemented CEL-Seq2 on Fluidigm's C1 system, providing its first single-cell, on-chip barcoding method, and we 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.

| S-EPMC4848782 | biostudies-literature

Highly sensitive and ultrafast read mapping for RNA-seq analysis.

Project description:As sequencing technologies progress, the amount of data produced grows exponentially, shifting the bottleneck of discovery towards the data analysis phase. In particular, currently available mapping solutions for RNA-seq leave room for improvement in terms of sensitivity and performance, hindering an efficient analysis of transcriptomes by massive sequencing. Here, we present an innovative approach that combines re-engineering, optimization and parallelization. This solution results in a significant increase of mapping sensitivity over a wide range of read lengths and substantial shorter runtimes when compared with current RNA-seq mapping methods available.

| S-EPMC4833417 | biostudies-literature

Reproducible RNA-seq analysis using recount2.

Project description: Not available

| S-EPMC6742427 | biostudies-literature

Sensitive and powerful single-cell RNA sequencing using mcSCRB-seq.

Project description:Single-cell RNA sequencing (scRNA-seq) has emerged as a central genome-wide method to characterize cellular identities and processes. Consequently, improving its sensitivity, flexibility, and cost-efficiency can advance many research questions. Among the flexible plate-based methods, single-cell RNA barcoding and sequencing (SCRB-seq) is highly sensitive and efficient. Here, we systematically evaluate experimental conditions of this protocol and find that adding polyethylene glycol considerably increases sensitivity by enhancing cDNA synthesis. Furthermore, using Terra polymerase increases efficiency due to a more even cDNA amplification that requires less sequencing of libraries. We combined these and other improvements to develop a scRNA-seq library protocol we call molecular crowding SCRB-seq (mcSCRB-seq), which we show to be one of the most sensitive, efficient, and flexible scRNA-seq methods to date.

| S-EPMC6062574 | biostudies-literature

IVT-seq reveals extreme bias in RNA sequencing.

Project description:RNA-seq is a powerful technique for identifying and quantifying transcription and splicing events, both known and novel. However, given its recent development and the proliferation of library construction methods, understanding the bias it introduces is incomplete but critical to realizing its value.We present a method, in vitro transcription sequencing (IVT-seq), for identifying and assessing the technical biases in RNA-seq library generation and sequencing at scale. We created a pool of over 1,000 in vitro transcribed RNAs from a full-length human cDNA library and sequenced them with polyA and total RNA-seq, the most common protocols. Because each cDNA is full length, and we show in vitro transcription is incredibly processive, each base in each transcript should be equivalently represented. However, with common RNA-seq applications and platforms, we find 50% of transcripts have more than two-fold and 10% have more than 10-fold differences in within-transcript sequence coverage. We also find greater than 6% of transcripts have regions of dramatically unpredictable sequencing coverage between samples, confounding accurate determination of their expression. We use a combination of experimental and computational approaches to show rRNA depletion is responsible for the most significant variability in coverage, and several sequence determinants also strongly influence representation.These results show the utility of IVT-seq for promoting better understanding of bias introduced by RNA-seq. We find rRNA depletion is responsible for substantial, unappreciated biases in coverage introduced during library preparation. These biases suggest exon-level expression analysis may be inadvisable, and we recommend caution when interpreting RNA-seq results.

| S-EPMC4197826 | biostudies-literature

Single-cell RNA-Seq reveals cell heterogeneity and hierarchy within mouse mammary epithelia.

Project description:The mammary gland is very intricately and well organized into distinct tissues, including epithelia, endothelia, adipocytes, and stromal and immune cells. Many mammary gland diseases, such as breast cancer, arise from abnormalities in the mammary epithelium, which is mainly composed of two distinct lineages, the basal and luminal cells. Because of the limitation of traditional transcriptome analysis of bulk mammary cells, the hierarchy and heterogeneity of mammary cells within these two lineages remain unclear. To this end, using single-cell RNA-Seq coupled with FACS analysis and principal component analysis, we determined gene expression profiles of mammary epithelial cells of virgin and pregnant mice. These analyses revealed a much higher heterogeneity among the mammary cells than has been previously reported and enabled cell classification into distinct subgroups according to signature gene markers present in each group. We also identified and verified a rare CDH5+ cell subpopulation within a basal cell lineage as quiescent mammary stem cells (MaSCs). Moreover, using pseudo-temporal analysis, we reconstructed the developmental trajectory of mammary epithelia and uncovered distinct changes in gene expression and in biological functions of mammary cells along the developmental process. In conclusion, our work greatly refines the resolution of the cellular hierarchy in developing mammary tissues. The discovery of CDH5+ cells as MaSCs in these tissues may have implications for our understanding of the initiation, development, and pathogenesis of mammary tumors.

| S-EPMC5986215 | biostudies-other

Single-cell RNA-seq reveals early heterogeneity during aging in yeast.

Project description:The budding yeast Saccharomyces cerevisiae (S. cerevisiae) has relatively short lifespan and is genetically tractable, making it a widely used model organism in aging research. Here, we carried out a systematic and quantitative investigation of yeast aging with single-cell resolution through transcriptomic sequencing. We optimized a single-cell RNA sequencing (scRNA-seq) protocol to quantitatively study the whole transcriptome profiles of single yeast cells at different ages, finding increased cell-to-cell transcriptional variability during aging. The single-cell transcriptome analysis also highlighted key biological processes or cellular components, including oxidation-reduction process, oxidative stress response (OSR), translation, ribosome biogenesis and mitochondrion that underlie aging in yeast. We uncovered a molecular marker of FIT3, indicating the early heterogeneity during aging in yeast. We also analyzed the regulation of transcription factors and further characterized the distinctive temporal regulation of the OSR by YAP1 and proteasome activity by RPN4 during aging in yeast. Overall, our data profoundly reveal early heterogeneity during aging in yeast and shed light on the aging dynamics at the single cell level.

| S-EPMC9649600 | 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