Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA860345 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR20324828_1.fastq.gz | Fastqsanger.gz | |||
| SRR20324828_2.fastq.gz | Fastqsanger.gz | |||
| SRR20324829_1.fastq.gz | Fastqsanger.gz | |||
| SRR20324829_2.fastq.gz | Fastqsanger.gz | |||
| SRR20324830_1.fastq.gz | Fastqsanger.gz |
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Project description:Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that dynamically reconfigure to drive diverse cellular states. Single-cell transcriptomic technologies, such as single cell RNA-sequencing (scRNA-seq) and single cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), can examine the transcriptional state at individual cell resolution, allowing the study of cellular heterogeneity and cell-type specific gene regulation in dynamic processes such as cell fate specification. However, current approaches to infer cell type-specific gene regulatory networks from these datasets are limited in their ability to integrate scRNA-seq and scATACseq measurements and to model network dynamics on a cell lineage. To address this challenge, we have developed single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer the gene regulatory network for each cell type on a lineage from scRNA-seq and scATAC-seq data. Using simulated, published, and newly collected single cell omic datasets, we show that scMTNI is able to accurately infer gene regulatory networks and captures meaningful network dynamics that identify GRN components associated with cell type transitions. Application of our method to mouse cellular reprogramming identified key regulators associated with cell populations that reprogram versus those that are stalled. Taken together, scMTNI is a powerful framework to infer cell type-specific gene regulatory networks and their dynamics from scRNA-seq and scATAC-seq datasets.
2023-04-14 | GSE208620 | GEO
Project description:The integration of lineage tracing with scRNA-seq has transformed our understanding of gene expression dynamics during development, regeneration, and disease. However, lineage tracing is technically demanding and most existing scRNA-seq datasets are devoid of lineage information. By analyzing our own (mouse embryonic stem cells;mESCs) and public lineage-annotated scRNA-seq datastes, we could identify and characterize genes displaying conserved expression levels over cell divisions in multiple cell types. This resulted in the development of Gene Expression Memory-based Lineage Inference (GEMLI), a computational pipeline allowing to predict cell lineages over several cell divisions solely from scRNA-seq datasets.
2024-01-28 | GSE226169 | GEO
Project description:Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Single-cell technologies such as single cell RNA-sequencing (scRNA-seq) and single cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), can examine cell-type specific gene regulation at unprecedented detail. However, current approaches to infer cell type-specific GRNs are limited in their ability to integrate scRNA-seq and scATAC-seq measurements and to model network dynamics on a cell lineage. To address this challenge, we have developed single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer the GRN for each cell type on a lineage from scRNA-seq and scATAC-seq data. Using simulated and real datasets, we show that scMTNI is a broadly applicable framework for linear and branching lineages that accurately infers GRN dynamics and identifies key regulators of fate transitions for diverse processes such as cellular reprogramming and differentiation.
| S-EPMC10224950 | biostudies-literature
Project description:Barcode-free prediction of cell lineages from scRNA-seq datasets
| PRJNA939121 | ENA
Project description:Single-cell multi-omic datasets, in which multiple molecular modalities are profiled within the same cell, provide a unique opportunity to discover the interplay between cellular epigenomic and transcriptomic changes. To realize this potential, we developed MultiVelo, a mechanistic model of gene expression that extends the popular RNA velocity framework by incorporating epigenomic data. MultiVelo uses a probabilistic latent variable model to estimate the switch time and rate parameters of gene regulation, providing a quantitative summary of the temporal relationship between epigenomic and transcriptomic changes. Fitting MultiVelo on single-cell multi-omic datasets revealed two distinct mechanisms of regulation by chromatin accessibility, quantified the degree of concordance or discordance between transcriptomic and epigenomic states within each cell, and inferred the lengths of time lags between transcriptomic and epigenomic changes.
2022-08-01 | GSE209878 | GEO
Project description:Most existing single-cell techniques can only make one type of molecular measurements. Although computational approaches have been developed to integrate single-cell datasets, their efficacy still needs to be determined with reference to authentic single-cell multi-omic profiles. To address this challenge, we devised single-nucleus methylCytosine, Chromatin accessibility and Transcriptome sequencing (snmC2T-seq) and applied the approach to post-mortem human frontal cortex tissue. We developed a computational framework to evaluate the quality of finely defined cell types using multi-modal information and validated the efficacy of computational multi-omic integration methods. Correlation analysis in individual cells revealed gene groups showing distinct relations between methylation and expression. Integration of snmC2T-seq with other multi- and single- modal datasets enabled joint analyses of the methylome, chromatin accessibility, transcriptome, and chromatin architecture for 63 human cortical cell types. We reconstructed the regulatory lineage of these cortical cell types and found pronounced cell-type-specific enrichment of disease risks for neuropsychiatric traits, predicting causal cell types that can be targeted for treatment.
2019-12-12 | GSE140493 | GEO
Project description:Background: Grape cultivars and wines are distinguishable by their color, flavor and aroma profiles. Omic analyses (transcripts, proteins and metabolites) are powerful tools for assessing biochemical differences in biological systems. Results: Berry skins of red- (Cabernet Sauvignon, Merlot, Pinot Noir) and white-skinned (Chardonnay, Semillon) wine grapes were harvested near optimum maturity from the same experimental vineyard and Ë?Brix-to-titratable acidity ratio. Identical sample aliquots were analyzed for transcripts by grapevine whole-genome oligonucleotide microarray and RNA-seq technologies, proteins by nano-liquid chromatography-mass spectroscopy, and metabolites by gas chromatography-mass spectroscopy and liquid chromatography-mass spectroscopy. Principal components analysis of each of five Omic technologies showed similar results across cultivars in all Omic datasets. Comparison of the processed data of genes mapped in RNA-seq and microarray data revealed a strong Pearson's correlation (0.80). The exclusion of probesets associated with genes with potential for cross-hybridization on the microarray improved the correlation to 0.93. The overall concordance of protein with transcript data was low with a Pearson's correlation of 0.27 and 0.24 for the RNA-seq and microarray data, respectively. Integration of metabolite with protein and transcript data produced an expected model of phenylpropanoid biosynthesis, which distinguished red from white grapes, yet provided detail of individual cultivar differences. Conclusions: The five Omic technologies were consistent in distinguishing cultivar variation. There was high concordance between transcriptomic technologies, but generally protein abundance did not correlate well with transcript abundance. The integration of multiple high-throughput Omic datasets revealed complex biochemical variation amongst five cultivars of an ancient and economically important crop species. Vitis vinifera L. cv. Cabernet Sauvignon, Chardonnay, Merlot, Pinot Noir, Semillon berries were harvested from Nevada Agricultural Experiment Station Valley Road Vineyard, Reno, NV, USA. Whole-genome microarray analysis was used to assess the transcriptomic response of berry skins at harvest, approximately 24 °Brix (2011 vintage). Vines were grown under water deficit and well-watered conditions. At least two clusters harvested from non-adjacent vines were used for each of five experimental replicates.
2015-11-12 | E-GEOD-72421 | biostudies-arrayexpress
Project description:Single cell transcriptome datasets of murine B cell commitment
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Project description:<p><strong>BACKGROUND:</strong> Genomic prediction (GP) based on single nucleotide polymorphisms (SNP) has become a broadly used tool to increase the gain of selection in plant breeding. However, using predictors that are biologically closer to the phenotypes such as transcriptome and metabolome may increase the prediction ability in GP. The objectives of this study were to (i) assess the prediction ability for three phenotypic traits using different omic datasets including sequence variants (SV), deleterious SV (dSV), tolerant SV (tSV), expression presence/absence variation (ePAV), gene expression (GE), transcript expression (TE), and metabolites (M) as single predictors in comparison to those using a SNP array; (ii) investigate the improvement in prediction ability when combining multiple omic datasets information to predict phenotypic variation in barley breeding programs; (iii) explore the relationship between genes and metabolites to unravel the metabolic pathway of the three above mentioned phenotypic traits.</p><p><strong>RESULTS:</strong> The prediction ability from genomic best linear unbiased prediction (GBLUP) for the three traits using dSV information was higher than when using tSV, all SV information, or the SNP array. Any predictors from the transcriptome (GE, TE, as well as ePAV) and metabolome provided higher prediction abilities compared to the SNP array and SV on average across the three traits. In addition, some (di)-similarity existed between different omic datasets, and therefore provided complementary biological perspectives to phenotypic variation. Optimal combining the information of dSV, TE, ePAV, as well as metabolites into GP models could improve the prediction ability over that of the single predictors alone.</p><p><strong>CONCLUSIONS:</strong> The use of integrated omic datasets in GP model is highly recommended. Furthermore, we evaluated a cost-effective approach generating 3’end mRNA sequencing with transcriptome data extracted from seedling without losing prediction ability in comparison to the full-length mRNA sequencing, paving the path for the use of such prediction methods in commercial breeding programs.</p>
2022-03-18 | MTBLS1561 | MetaboLights
Project description:Reproducible inference of transcription factor footprints in ATAC-seq and DNase-seq datasets via protocol-specific bias modeling
| PRJNA427530 | ENA