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| Status |
Public on Apr 18, 2023 |
| Title |
SCENIC+: identification of enhancers and gene regulatory networks using single-cell multiomics (cell lines) |
| Organism |
Homo sapiens |
| Experiment type |
Genome binding/occupancy profiling by high throughput sequencing
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| Summary |
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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| Overall design |
A mix of 11 wild type melanoma cell lines (MM050, MM099, MM116, MM001, MM011, MM057, MM087, MM031, MM047 and MM029) were analysed using 10x scATAC-seq experiments
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| Contributor(s) |
González-Blas CB, De Winter S, Aerts S |
| Citation(s) |
37443338 |
| Submission date |
Aug 08, 2022 |
| Last update date |
Jul 18, 2023 |
| Contact name |
Gert Hulselmans |
| E-mail(s) |
gert.hulselmans@kuleuven.be
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| Organization name |
VIB
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| Department |
Center for Brain and Disease Research
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| Lab |
Laboratory of Computational Biology
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| Street address |
Herestraat 49 PO Box 602
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| City |
Leuven |
| ZIP/Postal code |
3000 |
| Country |
Belgium |
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| Platforms (2) |
| GPL24676 |
Illumina NovaSeq 6000 (Homo sapiens) |
| GPL30173 |
NextSeq 2000 (Homo sapiens) |
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| Samples (2) |
| GSM6436448 |
10x scATAC-seq on mix of MM031, MM074, MM047 and MM029 |
| GSM6436449 |
10x scATAC-seq on mix of MM050, MM099, MM116, MM001, MM011, MM057 and MM087 |
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| This SubSeries is part of SuperSeries: |
| GSE210749 |
SCENIC+: identification of enhancers and gene regulatory networks using single-cell multiomics |
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| Relations |
| BioProject |
PRJNA867358 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSE210745_CRM_34940b_fragments.tsv.gz |
764.8 Mb |
(ftp)(http) |
TSV |
| GSE210745_NML_a56c4b_fragments.tsv.gz |
549.1 Mb |
(ftp)(http) |
TSV |
| GSE210745_mm_lines_cell_metadata.tsv.gz |
1.9 Kb |
(ftp)(http) |
TSV |
| GSE210745_mm_lines_fragment_counts.tsv.gz |
71.0 Mb |
(ftp)(http) |
TSV |
| GSE210745_mm_lines_gene_expression_matrix.tsv.gz |
8.0 Mb |
(ftp)(http) |
TSV |
| GSE210745_mm_lines_region_accessibility_matrix.tsv.gz |
83.7 Mb |
(ftp)(http) |
TSV |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data are available on Series record |
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