Browse
Submit Data
Databases
API
Help

Dataset Information

29 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A Multiplexed Barcodelet Single-Cell RNA-Seq Approach Elucidates Combinatorial Signaling Pathways that Drive ESC Differentiation.

ABSTRACT: Empirical optimization of stem cell differentiation protocols is time consuming, is laborintensive, and typically does not comprehensively interrogate all relevant signaling pathways. Here we describe barcodelet single-cell RNA sequencing (barRNA-seq), which enables systematic exploration of cellular perturbations by tagging individual cells with RNA "barcodelets" to identify them on the basis of the treatments they receive. We apply barRNA-seq to simultaneously manipulate up to seven developmental pathways and study effects on embryonic stem cell (ESC) germ layer specification and mesodermal specification, uncovering combinatorial effects of signaling pathway activation on gene expression. We further develop a data-driven framework for identifying combinatorial signaling perturbations that drive cells toward specific fates, including several annotated in an existing scRNA-seq gastrulation atlas, and use this approach to guide ESC differentiation into a notochord-like population. We expect that barRNA-seq will have broad utility for investigating and understanding how cooperative signaling pathways drive cell fate acquisition.

SUBMITTER: Yeo GHT

PROVIDER: S-EPMC7398619 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

A Multiplexed Barcodelet Single-Cell RNA-Seq Approach Elucidates Combinatorial Signaling Pathways that Drive ESC Differentiation

Project description:Empirical optimization of stem cell differentiation protocols is time consuming, is labor intensive, and typi- cally does not comprehensively interrogate all relevant signaling pathways. Here we describe barcodelet sin- gle-cell RNA sequencing (barRNA-seq), which enables systematic exploration of cellular perturbations by tagging individual cells with RNA ‘‘barcodelets’’ to identify them on the basis of the treatments they receive. We apply barRNA-seq to simultaneously manipulate up to seven developmental pathways and study effects on embryonic stem cell (ESC) germ layer specification and mesodermal specification, uncovering combina- torial effects of signaling pathway activation on gene expression. We further develop a data-driven frame- work for identifying combinatorial signaling perturbations that drive cells toward specific fates, including several annotated in an existing scRNA-seq gastrulation atlas, and use this approach to guide ESC differen- tiation into a notochord-like population. We expect that barRNA-seq will have broad utility for investigating Q2 and understanding how cooperative signaling pathways drive cell fate acquisition.

2020-05-26 | GSE122009 | GEO

A Multiplexed Barcodelet Single-Cell RNA-Seq Approach Elucidates Combinatorial Signaling Pathways that Drive ESC Differentiation

Project description:A Multiplexed Barcodelet Single-Cell RNA-Seq Approach Elucidates Combinatorial Signaling Pathways that Drive ESC Differentiation

| PRJNA503119 | 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 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

Decoding Neuronal Diversification by Multiplexed Single-cell RNA-Seq.

Project description:Cellular reprogramming is driven by a defined set of transcription factors; however, the regulatory logic that underlies cell-type specification and diversification remains elusive. Single-cell RNA-seq provides unprecedented coverage to measure dynamic molecular changes at the single-cell resolution. Here, we multiplex and ectopically express 20 pro-neuronal transcription factors in human dermal fibroblasts and demonstrate a widespread diversification of neurons based on cell morphology and canonical neuronal marker expressions. Single-cell RNA-seq analysis reveals diverse and distinct neuronal subtypes, including reprogramming processes that strongly correlate with the developing brain. Gene mapping of 20 exogenous pro-neuronal transcription factors further unveiled key determinants responsible for neuronal lineage specification and a regulatory logic dictating neuronal diversification, including glutamatergic and cholinergic neurons. The multiplex scRNA-seq approach is a robust and scalable approach to elucidate lineage and cellular specification across various biological systems.

| S-EPMC8072034 | biostudies-literature

Co-LncRNA: investigating the lncRNA combinatorial effects in GO annotations and KEGG pathways based on human RNA-Seq data.

Project description:Long non-coding RNAs (lncRNAs) are emerging as key regulators of diverse biological processes and diseases. However, the combinatorial effects of these molecules in a specific biological function are poorly understood. Identifying co-expressed protein-coding genes of lncRNAs would provide ample insight into lncRNA functions. To facilitate such an effort, we have developed Co-LncRNA, which is a web-based computational tool that allows users to identify GO annotations and KEGG pathways that may be affected by co-expressed protein-coding genes of a single or multiple lncRNAs. LncRNA co-expressed protein-coding genes were first identified in publicly available human RNA-Seq datasets, including 241 datasets across 6560 total individuals representing 28 tissue types/cell lines. Then, the lncRNA combinatorial effects in a given GO annotations or KEGG pathways are taken into account by the simultaneous analysis of multiple lncRNAs in user-selected individual or multiple datasets, which is realized by enrichment analysis. In addition, this software provides a graphical overview of pathways that are modulated by lncRNAs, as well as a specific tool to display the relevant networks between lncRNAs and their co-expressed protein-coding genes. Co-LncRNA also supports users in uploading their own lncRNA and protein-coding gene expression profiles to investigate the lncRNA combinatorial effects. It will be continuously updated with more human RNA-Seq datasets on an annual basis. Taken together, Co-LncRNA provides a web-based application for investigating lncRNA combinatorial effects, which could shed light on their biological roles and could be a valuable resource for this community. Database URL: http://www.bio-bigdata.com/Co-LncRNA/.

| S-EPMC4565967 | biostudies-literature

Efficient combinatorial targeting of RNA transcripts in single cells with Cas13 RNA Perturb-seq.

Project description:Pooled CRISPR screens coupled with single-cell RNA-sequencing have enabled systematic interrogation of gene function and regulatory networks. Here, we introduce Cas13 RNA Perturb-seq (CaRPool-seq), which leverages the RNA-targeting CRISPR-Cas13d system and enables efficient combinatorial perturbations alongside multimodal single-cell profiling. CaRPool-seq encodes multiple perturbations on a cleavable CRISPR array that is associated with a detectable barcode sequence, allowing for the simultaneous targeting of multiple genes. We compared CaRPool-seq to existing Cas9-based methods, highlighting its unique strength to efficiently profile combinatorially perturbed cells. Finally, we apply CaRPool-seq to perform multiplexed combinatorial perturbations of myeloid differentiation regulators in an acute myeloid leukemia (AML) model system and identify extensive interactions between different chromatin regulators that can enhance or suppress AML differentiation phenotypes.

| S-EPMC10030154 | biostudies-literature

Identification of Molecular Biomarkers and Key Pathways for Esophageal Carcinoma (EsC): A Bioinformatics Approach.

Project description:Esophageal carcinoma (EsC) is a member of the cancer group that occurs in the esophagus; globally, it is known as one of the fatal malignancies. In this study, we used gene expression analysis to identify molecular biomarkers to propose therapeutic targets for the development of novel drugs. We consider EsC associated four different microarray datasets from the gene expression omnibus database. Statistical analysis is performed using R language and identified a total of 1083 differentially expressed genes (DEGs) in which 380 are overexpressed and 703 are underexpressed. The functional study is performed with the identified DEGs to screen significant Gene Ontology (GO) terms and associated pathways using the Database for Annotation, Visualization, and Integrated Discovery repository (DAVID). The analysis revealed that the overexpressed DEGs are principally connected with the protein export, axon guidance pathway, and the downexpressed DEGs are principally connected with the L13a-mediated translational silencing of ceruloplasmin expression, formation of a pool of free 40S subunits pathway. The STRING database used to collect protein-protein interaction (PPI) network information and visualize it with the Cytoscape software. We found 10 hub genes from the PPI network considering three methods in which the interleukin 6 (IL6) gene is the top in all methods. From the PPI, we found that identified clusters are associated with the complex I biogenesis, ubiquitination and proteasome degradation, signaling by interleukins, and Notch-HLH transcription pathway. The identified biomarkers and pathways may play an important role in the future for developing drugs for the EsC.

| S-EPMC8769846 | biostudies-literature

ePathOptimize: A Combinatorial Approach for Transcriptional Balancing of Metabolic Pathways.

Project description:The ability to fine tune gene expression has created the field of metabolic pathway optimization and balancing where a variety of factors affecting flux balance are carefully modulated to improve product titers, yields, and productivity. Using a library of isopropyl ?-D-1-thiogalactopyranoside (IPTG)-inducible mutant T7 promoters of varied strength a combinatorial method was developed for transcriptional balancing of the violacein pathway. Violacein biosynthesis involves a complex five-gene pathway that is an excellent model for exploratory metabolic engineering efforts into pathway regulation and control due to many colorful intermediates and side products allowing for easy analysis and strain comparison. Upon screening approximately 4% of the total initial library, several high-titer mutants were discovered that resulted in up to a 63-fold improvement over the control strain. With further fermentation optimization, titers were improved to 1829 ± 46 mg/L; a 2.6-fold improvement in titer and a 30-fold improvement in productivity from previous literature reports.

| S-EPMC4650668 | biostudies-literature

Systems approach elucidates convoluted transcriptional regulation on carbon metabolism [RNA-seq]

Project description:The carbon metabolism in bacteria is regulated by two global TFs, CRP and Cra. While the regulon of CRP has been extensively studied, the complete definition of Cra has been missing. Thus, in vivo TF binding measurement with ChIP-exo and transcriptional expression measurement with RNA-seq for Cra was performed to define the Cra regulon.

2018-04-18 | GSE65642 | GEO

Anatomy and RNA-Seq reveal important gene pathways regulating sex differentiation in a functionally Androdioecious tree, Tapiscia sinensis.

Project description:BACKGROUND:Gametogenesis is a key step in the production of ovules or pollen in higher plants. The sex-determination aspects of gametogenesis have been well characterized in the model plant Arabidopsis. However, little is known about this process in androdioecious plants. Tapiscia sinensis Oliv. is a functionally androdioecious tree, with both male and hermaphroditic individuals. Hermaphroditic flowers (HFs) are female-fertile flowers that can produce functional pollen and set fruits. However, compared with male flowers (MFs), the pollen viability and number of pollen grains per flower are markedly reduced in HFs. MFs are female-sterile flowers that fail to set fruit and that eventually drop. RESULTS:Compared with HF, a notable cause of MF female sterility in T. sinensis is when the early gynoecium meristem is disrupted. During the early stage of HF development (stage 6), the ring meristem begins to form as a ridge around the center of the flower. At this stage, the internal fourth-whorl organ is stem-like rather than carpelloid in MF. A total of 52,945 unigenes were identified as transcribed in MF and HF. A number of differentially expressed genes (DEGs) and metabolic pathways were detected as involved in the development of the gynoecium, especially the ovule, carpel and style. At the early gynoecium development stage, DEGs were shown to function in the metabolic pathways regulating ethylene biosynthesis and signal transduction (upstream regulator), auxin, cytokinin transport and signalling, and sex determination (or flower meristem identity). CONCLUSIONS:Pathways for the female sterility model were initially proposed to shed light on the molecular mechanisms of gynoecium development at early stages in T. sinensis.

| S-EPMC6915933 | 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