SCENIC+: identification of enhancers and gene regulatory networks using single-cell multiomics
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ABSTRACT:
PROVIDER: PRJNA867353 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR20963900_1.fastq.gz | Fastqsanger.gz | |||
| SRR20963900_2.fastq.gz | Fastqsanger.gz | |||
| SRR20963901_1.fastq.gz | Fastqsanger.gz | |||
| SRR20963901_2.fastq.gz | Fastqsanger.gz | |||
| SRR20963902_1.fastq.gz | Fastqsanger.gz |
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Project description:SCENIC+: identification of enhancers and gene regulatory networks using single-cell multiomics (Cortex)
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Project description:Joint profiling of chromatin accessibility and gene expression of individual cells provides an opportunity to decipher enhancer-driven gene regulatory networks (eGRN). Here we present a new method for the inference of eGRNs, called SCENIC+. SCENIC+ predicts genomic enhancers along with candidate upstream transcription factors (TF) and links these enhancers to candidate target genes. Specific TFs for each cell type or cell state are predicted based on the concordance of TF binding site accessibility, TF expression, and target gene expression. To improve both recall and precision of TF identification, we curated and clustered more than 40,000 position weight matrices that we could associate with ~1,500 human TFs. We validated and benchmarked each of the SCENIC+ components on diverse data sets from different species, including human peripheral blood mononuclear cell types, human ENCODE cell lines, human melanoma cell states, and Drosophila retinal development. Next, we exploit SCENIC+ predictions to study conserved TFs, enhancers, and GRNs between human and mouse cell types in the cerebral cortex. Finally, we provide new capabilities that exploit the inferred eGRNs to study the dynamics of gene regulation along differentiation trajectories; to map regulatory activities onto tissues using spatial omics data; and to predict the effect of TF perturbations on cell state. SCENIC+ provides critical insight into gene regulation, starting from multi-ome atlases of scATAC-seq and scRNA-seq. The SCENIC+ suite is available as a set of Python modules at scenicplus.readthedocs.io.
2023-04-18 | GSE210744 | GEO
Project description:Joint profiling of chromatin accessibility and gene expression of individual cells provides an opportunity to decipher enhancer-driven gene regulatory networks (eGRN). Here we present a new method for the inference of eGRNs, called SCENIC+. SCENIC+ predicts genomic enhancers along with candidate upstream transcription factors (TF) and links these enhancers to candidate target genes. Specific TFs for each cell type or cell state are predicted based on the concordance of TF binding site accessibility, TF expression, and target gene expression. To improve both recall and precision of TF identification, we curated and clustered more than 40,000 position weight matrices that we could associate with ~1,500 human TFs. We validated and benchmarked each of the SCENIC+ components on diverse data sets from different species, including human peripheral blood mononuclear cell types, human ENCODE cell lines, human melanoma cell states, and Drosophila retinal development. Next, we exploit SCENIC+ predictions to study conserved TFs, enhancers, and GRNs between human and mouse cell types in the cerebral cortex. Finally, we provide new capabilities that exploit the inferred eGRNs to study the dynamics of gene regulation along differentiation trajectories; to map regulatory activities onto tissues using spatial omics data; and to predict the effect of TF perturbations on cell state. SCENIC+ provides critical insight into gene regulation, starting from multi-ome atlases of scATAC-seq and scRNA-seq. The SCENIC+ suite is available as a set of Python modules at scenicplus.readthedocs.io.
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Project description:Joint profiling of chromatin accessibility and gene expression of individual cells provides an opportunity to decipher enhancer-driven gene regulatory networks (eGRN). Here we present a new method for the inference of eGRNs, called SCENIC+. SCENIC+ predicts genomic enhancers along with candidate upstream transcription factors (TF) and links these enhancers to candidate target genes. Specific TFs for each cell type or cell state are predicted based on the concordance of TF binding site accessibility, TF expression, and target gene expression. To improve both recall and precision of TF identification, we curated and clustered more than 40,000 position weight matrices that we could associate with ~1,500 human TFs. We validated and benchmarked each of the SCENIC+ components on diverse data sets from different species, including human peripheral blood mononuclear cell types, human ENCODE cell lines, human melanoma cell states, and Drosophila retinal development. Next, we exploit SCENIC+ predictions to study conserved TFs, enhancers, and GRNs between human and mouse cell types in the cerebral cortex. Finally, we provide new capabilities that exploit the inferred eGRNs to study the dynamics of gene regulation along differentiation trajectories; to map regulatory activities onto tissues using spatial omics data; and to predict the effect of TF perturbations on cell state. SCENIC+ provides critical insight into gene regulation, starting from multi-ome atlases of scATAC-seq and scRNA-seq. The SCENIC+ suite is available as a set of Python modules at scenicplus.readthedocs.io.
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Project description:SCENIC+: identification of enhancers and gene regulatory networks using single-cell multiomics (cell lines)
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Project description:This SuperSeries is composed of the SubSeries listed below.
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Project description:Although technological advances now allow increased tumor profiling, a detailed understanding of the mechanisms leading to the development of different cancers remains elusive. Our approach towards understanding the molecular events that lead to cancer is to characterize changes in transcriptional regulatory networks between normal and tumor tissue. Because enhancer activity is thought to be critical in regulating cell fate decisions, we have focused our studies on distal regulatory elements and transcription factors that bind to these elements. Using DNA methylation data, we identified more than 25,000 enhancers that are differentially activated in breast, prostate, and kidney tumor tissues, as compared to normal tissues. We then developed an analytical approach called TENET (Tracing Enhancer Networks using Epigenetic Traits) that correlates DNA methylation levels at enhancers with gene expression to identify more than 800,000 genome-wide links from enhancers to genes and from genes to enhancers. We found more than 1,200 transcription factors to be involved in these tumor-specific enhancer networks. We further characterized several transcription factors linked to a large number of enhancers in each tumor type, including GATA3 in non-basal breast tumors, HOXC6 and DLX1 in prostate tumors, and ZNF395 in kidney tumors. We showed that HOXC6 and DLX1 are associated with different clusters of prostate tumor-specific enhancers and confer distinct transcriptomic changes upon knockdown in C42B prostate cancer cells. We also discovered de novo motifs enriched in enhancers linked to ZNF395 in kidney tumors. Our studies characterized tumor-specific enhancers and revealed key transcription factors involved in enhancer networks for specific tumor types and subgroups. Our findings, which include a large set of identified enhancers and transcription factors linked to those enhancers in breast, prostate, and kidney cancers, will facilitate understanding of enhancer networks and mechanisms leading to the development of these cancers. Examination of FAIRE and ChIP assays in prostate and breast epithelial cells. RNA-seq assays in C42B, prostate cancer cell line after knocking down of TFs (HOXC6, DLX1).
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Project description:Although technological advances now allow increased tumor profiling, a detailed understanding of the mechanisms leading to the development of different cancers remains elusive. Our approach towards understanding the molecular events that lead to cancer is to characterize changes in transcriptional regulatory networks between normal and tumor tissue. Because enhancer activity is thought to be critical in regulating cell fate decisions, we have focused our studies on distal regulatory elements and transcription factors that bind to these elements. Using DNA methylation data, we identified more than 25,000 enhancers that are differentially activated in breast, prostate, and kidney tumor tissues, as compared to normal tissues. We then developed an analytical approach called TENET (Tracing Enhancer Networks using Epigenetic Traits) that correlates DNA methylation levels at enhancers with gene expression to identify more than 800,000 genome-wide links from enhancers to genes and from genes to enhancers. We found more than 1,200 transcription factors to be involved in these tumor-specific enhancer networks. We further characterized several transcription factors linked to a large number of enhancers in each tumor type, including GATA3 in non-basal breast tumors, HOXC6 and DLX1 in prostate tumors, and ZNF395 in kidney tumors. We showed that HOXC6 and DLX1 are associated with different clusters of prostate tumor-specific enhancers and confer distinct transcriptomic changes upon knockdown in C42B prostate cancer cells. We also discovered de novo motifs enriched in enhancers linked to ZNF395 in kidney tumors. Our studies characterized tumor-specific enhancers and revealed key transcription factors involved in enhancer networks for specific tumor types and subgroups. Our findings, which include a large set of identified enhancers and transcription factors linked to those enhancers in breast, prostate, and kidney cancers, will facilitate understanding of enhancer networks and mechanisms leading to the development of these cancers.
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