Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Cell-level metadata are indispensable for documenting single-cell sequencing datasets.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) provides an unprecedented view of cellular diversity of biological systems. However, across the thousands of publications and datasets generated using this technology, we estimate that only a minority (<25%) of studies provide cell-level metadata information containing identified cell types and related findings of the published dataset. Metadata omission hinders reproduction, exploration, validation, and knowledge transfer and is a common problem across journals, data repositories, and publication dates. We encourage investigators, reviewers, journals, and data repositories to improve their standards and ensure proper documentation of these valuable datasets.

SUBMITTER: Puntambekar S

PROVIDER: S-EPMC8121533 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Cell-level metadata are indispensable for documenting single-cell sequencing datasets.

Puntambekar Sidhant S Hesselberth Jay R JR Riemondy Kent A KA Fu Rui R

PLoS biology 20210504 5

Single-cell RNA sequencing (scRNA-seq) provides an unprecedented view of cellular diversity of biological systems. However, across the thousands of publications and datasets generated using this technology, we estimate that only a minority (<25%) of studies provide cell-level metadata information containing identified cell types and related findings of the published dataset. Metadata omission hinders reproduction, exploration, validation, and knowledge transfer and is a common problem across jou ...[more]

PMID: 33945522

Similar Datasets

Single-cell sequencing of neonatal uterus reveals an Misr2+ endometrial progenitor indispensable for fertility.

Project description:The Mullerian ducts are the anlagen of the female reproductive tract, which regress in the male fetus in response to MIS. This process is driven by subluminal mesenchymal cells expressing Misr2, which trigger the regression of the adjacent Mullerian ductal epithelium. In females, these Misr2+ cells are retained, yet their contribution to the development of the uterus remains unknown. Here, we report that subluminal Misr2+ cells persist postnatally in the uterus of rodents, but recede by week 37 of gestation in humans. Using single-cell RNA sequencing, we demonstrate that ectopic postnatal MIS administration inhibits these cells and prevents the formation of endometrial stroma in rodents, suggesting a progenitor function. Exposure to MIS during the first six days of life, by inhibiting specification of the stroma, dysregulates paracrine signals necessary for uterine development, eventually resulting in apoptosis of the Misr2+ cells, uterine hypoplasia, and complete infertility in the adult female.

| S-EPMC6650247 | biostudies-literature

Insights Into Human Intrahepatic NK Cell Function From Single Cell RNA Sequencing Datasets.

Project description:Diverse populations of natural killer (NK) cells have been identified in circulating peripheral blood and a wide variety of different tissues and organs. These tissue-resident NK cell populations are phenotypically distinct from circulating NK cells, however, functional descriptions of their roles within tissues are lacking. Recent advances in single cell RNA sequencing (scRNA-seq) have enabled detailed transcriptional profiling of tissues at the level of single cells and provide the opportunity to explore NK cell diversity within tissues. This review explores potential novel functions of human liver-resident (lr)NK cells identified in human liver scRNA-seq studies. By comparing these datasets we identified up-regulated and down-regulated genes associated with lrNK cells clusters. These genes encode a number of activating and inhibiting receptors, as well as signal transduction molecules, which highlight potential unique pathways that lrNK cells utilize to respond to stimuli within the human liver. This unique receptor repertoire of lrNK cells may confer the ability to regulate a number of immune cell populations, such as circulating monocytes and T cells, while avoiding activation by liver hepatocytes and Kupffer cells. Validating the expression of these receptors on lrNK cells and the proposed cellular interactions within the human liver will expand our understanding of the liver-specific homeostatic roles of this tissue-resident immune cell population.

| S-EPMC8019706 | biostudies-literature

Identification of glioblastoma stem cell-associated lncRNAs using single-cell RNA sequencing datasets.

Project description:Glioblastoma multiforme (GBM) is an aggressive, heterogeneous brain tumor in which glioblastoma stem cells (GSCs) are known culprits of therapy resistance. Long non-coding RNAs (lncRNAs) have been shown to play a critical role in both cancer and normal biology. A few studies have suggested that aberrant expression of lncRNAs is associated with GSCs. However, a comprehensive single-cell analysis of the GSC-associated lncRNA transcriptome has not been carried out. Here, we analyzed recently published single-cell RNA sequencing datasets of adult GBM tumors, GBM organoids, GSC-enriched GBM tumors, and developing human brain samples to identify lncRNAs highly expressed in GSCs. We further revealed that the GSC-specific lncRNAs GIHCG and LINC01563 promote proliferation, migration, and stemness in the GSC population. Together, this study identified a panel of uncharacterized GSC-enriched lncRNAs and set the stage for future in-depth studies to examine their role in GBM pathology and their potential as biomarkers and/or therapeutic targets in GBM.

| S-EPMC10679778 | biostudies-literature

Robust integration of multiple single-cell RNA sequencing datasets using a single reference space.

Project description:In many biological applications of single-cell RNA sequencing (scRNA-seq), an integrated analysis of data from multiple batches or studies is necessary. Current methods typically achieve integration using shared cell types or covariance correlation between datasets, which can distort biological signals. Here we introduce an algorithm that uses the gene eigenvectors from a reference dataset to establish a global frame for integration. Using simulated and real datasets, we demonstrate that this approach, called Reference Principal Component Integration (RPCI), consistently outperforms other methods by multiple metrics, with clear advantages in preserving genuine cross-sample gene expression differences in matching cell types, such as those present in cells at distinct developmental stages or in perturbated versus control studies. Moreover, RPCI maintains this robust performance when multiple datasets are integrated. Finally, we applied RPCI to scRNA-seq data for mouse gut endoderm development and revealed temporal emergence of genetic programs helping establish the anterior-posterior axis in visceral endoderm.

| S-EPMC8456427 | biostudies-literature

Isoform-level quantification for single-cell RNA sequencing.

Project description:MotivationRNA expression at isoform level is biologically more informative than at gene level and can potentially reveal cellular subsets and corresponding biomarkers that are not visible at gene level. However, due to the strong 3' bias sequencing protocol, mRNA quantification for high-throughput single-cell RNA sequencing such as Chromium Single Cell 3' 10× Genomics is currently performed at the gene level.ResultsWe have developed an isoform-level quantification method for high-throughput single-cell RNA sequencing by exploiting the concepts of transcription clusters and isoform paralogs. The method, called Scasa, compares well in simulations against competing approaches including Alevin, Cellranger, Kallisto, Salmon, Terminus and STARsolo at both isoform- and gene-level expression. The reanalysis of a CITE-Seq dataset with isoform-based Scasa reveals a subgroup of CD14 monocytes missed by gene-based methods.Availability and implementationImplementation of Scasa including source code, documentation, tutorials and test data supporting this study is available at Github: https://github.com/eudoraleer/scasa and Zenodo: https://doi.org/10.5281/zenodo.5712503.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC8826380 | biostudies-literature

Millefy: visualizing cell-to-cell heterogeneity in read coverage of single-cell RNA sequencing datasets.

Project description:BACKGROUND:Read coverage of RNA sequencing data reflects gene expression and RNA processing events. Single-cell RNA sequencing (scRNA-seq) methods, particularly "full-length" ones, provide read coverage of many individual cells and have the potential to reveal cellular heterogeneity in RNA transcription and processing. However, visualization tools suited to highlighting cell-to-cell heterogeneity in read coverage are still lacking. RESULTS:Here, we have developed Millefy, a tool for visualizing read coverage of scRNA-seq data in genomic contexts. Millefy is designed to show read coverage of all individual cells at once in genomic contexts and to highlight cell-to-cell heterogeneity in read coverage. By visualizing read coverage of all cells as a heat map and dynamically reordering cells based on diffusion maps, Millefy facilitates discovery of "local" region-specific, cell-to-cell heterogeneity in read coverage. We applied Millefy to scRNA-seq data sets of mouse embryonic stem cells and triple-negative breast cancers and showed variability of transcribed regions including antisense RNAs, 3 ' UTR lengths, and enhancer RNA transcription. CONCLUSIONS:Millefy simplifies the examination of cellular heterogeneity in RNA transcription and processing events using scRNA-seq data. Millefy is available as an R package (https://github.com/yuifu/millefy) and as a Docker image for use with Jupyter Notebook (https://hub.docker.com/r/yuifu/datascience-notebook-millefy).

| S-EPMC7053140 | biostudies-literature

Population-level integration of single-cell datasets enables multi-scale analysis across samples.

Project description:The increasing generation of population-level single-cell atlases has the potential to link sample metadata with cellular data. Constructing such references requires integration of heterogeneous cohorts with varying metadata. Here we present single-cell population level integration (scPoli), an open-world learner that incorporates generative models to learn sample and cell representations for data integration, label transfer and reference mapping. We applied scPoli on population-level atlases of lung and peripheral blood mononuclear cells, the latter consisting of 7.8 million cells across 2,375 samples. We demonstrate that scPoli can explain sample-level biological and technical variations using sample embeddings revealing genes associated with batch effects and biological effects. scPoli is further applicable to single-cell sequencing assay for transposase-accessible chromatin and cross-species datasets, offering insights into chromatin accessibility and comparative genomics. We envision scPoli becoming an important tool for population-level single-cell data integration facilitating atlas use but also interpretation by means of multi-scale analyses.

| S-EPMC10630133 | biostudies-literature

Cell-level somatic mutation detection from single-cell RNA sequencing.

Project description:MotivationBoth single-cell RNA sequencing (scRNA-seq) and DNA sequencing (scDNA-seq) have been applied for cell-level genomic profiling. For mutation profiling, the latter seems more natural. However, the task is highly challenging due to the limited input materials from only two copies of DNA molecules, while whole-genome amplification generates biases and other technical noises. ScRNA-seq starts with a higher input amount, so generally has better data quality. There exists various methods for mutation detection from DNA sequencing, it is not clear whether these methods work for scRNA-seq data.ResultsMutation detection methods developed for either bulk-cell sequencing data or scDNA-seq data do not work well for the scRNA-seq data, as they produce substantial numbers of false positives. We develop a novel and robust statistical method-called SCmut-to identify specific cells that harbor mutations discovered in bulk-cell data. Statistically SCmut controls the false positives using the 2D local false discovery rate method. We apply SCmut to several scRNA-seq datasets. In scRNA-seq breast cancer datasets SCmut identifies a number of highly confident cell-level mutations that are recurrent in many cells and consistent in different samples. In a scRNA-seq glioblastoma dataset, we discover a recurrent cell-level mutation in the PDGFRA gene that is highly correlated with a well-known in-frame deletion in the gene. To conclude, this study contributes a novel method to discover cell-level mutation information from scRNA-seq that can facilitate investigation of cell-to-cell heterogeneity.Availability and implementationThe source codes and bioinformatics pipeline of SCmut are available at https://github.com/nghiavtr/SCmut.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC6853710 | biostudies-literature

Identification of HBEGF+ fibroblasts in the remission of rheumatoid arthritis by integrating single-cell RNA sequencing datasets and bulk RNA sequencing datasets.

Project description: Not available

| S-EPMC9446562 | biostudies-literature

PPMS: A framework to Profile Primary MicroRNAs from Single-cell RNA-sequencing datasets.

Project description:MotivationSingle-cell/nuclei RNA-sequencing (scRNA-seq) technologies can simultaneously quantify gene expression in thousands of cells across the genome. However, the majority of the noncoding RNAs, such as microRNAs (miRNAs), cannot currently be profiled at the same scale. MiRNAs are a class of small noncoding RNAs and play an important role in gene regulation. MiRNAs originate from the processing of primary transcripts, known as primary-microRNAs (pri-miRNAs). The pri-miRNA transcripts, independent of their cognate miRNAs, can also function as long noncoding RNAs, code for micropeptides or even interact with DNA, acting like enhancers. Therefore, it is apparent that the significance of scRNA-seq pri-miRNA profiling expands beyond using pri-miRNA as proxies of mature miRNAs. However, there are no computational methods that allow profiling and quantification of pri-miRNAs at the single-cell-type resolution.ResultsWe have developed a simple yet effective computational framework to profile pri-MiRNAs from single-cell RNA-sequencing datasets (PPMS). Based on user input, PPMS can profile pri-miRNAs at cell-type resolution. PPMS can be applied to both newly produced and publicly available datasets obtained via single cell or single-nuclei RNA-seq. It allows users to (i) investigate the distribution of pri-miRNAs across cell types and cell states and (ii) establish a relationship between the number of cells/reads sequenced and the detection of pri-miRNAs. Here, to demonstrate its efficacy, we have applied PPMS to publicly available scRNA-seq data generated from (i) individual chambers (ventricles and atria) of the human heart, (ii) human pluripotent stem cells during their differentiation into cardiomyocytes (the heart beating cells) and (iii) hiPSCs-derived cardiomyocytes infected with severe acute respiratory syndrome coronavirus 2.

| S-EPMC9677482 | 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