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11,105 results for “single cells”
Single-cell transcriptome analysis reveals evolving tumor microenvironment induced by immunochemotherapy in nasopharyngeal carcinoma
<p>18 bulks and 11 single-cell RNA sequencing samples from paired before anti-PD-1 contained treatment and on treatment in patients with treatment-naive high-risk metastatic locally advanced NPCs were obtained. We aim to explore the mechanism of response heterogeneity for locally advanced NPCs underwent immunochemotherapy.</p>
Deep Learning Based Models for Preimplantation Mouse and Human Embryos Based on Single Cell RNA Sequencing
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Single-cell RNA sequencing reveals immunosuppressive pathways associated with metastatic breast cancer
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Single-cell landscape of innate and acquired drug resistance in acute myeloid leukemia: scRNA-seq and CyTOF processed datasets
<p><strong>This data was generated as part of the Tumor Profiler study. If you use it in your research, please cite:</strong></p> <p>Wegmann, R., Bonilla, X., Casanova, R. <em>et al.</em> Single-cell landscape of innate and acquired drug resistance in acute myeloid leukemia. <em>Nat Commun</em> 15, 9402 (2024). https://doi.org/10.1038/s41467-024-53535-4</p> <p><strong>Derived data - scRNA-seq</strong></p> <p>This is an R data set (.RDS) containing a SingleCellExperiment object with the following slots:</p> <div> <ul> <li>Assays: <ul> <li>counts: raw counts</li> </ul> </li> </ul> </div> <div> <ul> <li>colData: Cell-level metadata <ul> <li> barcodes: The cell barcode</li> <li> fractionMT: Fraction mitochondrial genes per cell</li> <li> n_umi: Total number of UMIs per cell</li> <li> n_gene: Total number of genes per cell</li> <li> log_umi: log10 total number of UMIs per cell</li> <li> g2m_score: Cell cycle phase score for G2M</li> <li>s_score: Cell cycle phase score for S</li> <li>cycle_phase: predicted cell cycle phase</li> <li>celltype_major_full_ct_name: Major cell type full name</li> <li>celltype_major: Major cell type short name</li> <li>celltype_final_full_ct_name: Cell subtype full name</li> <li>celltype_final: Cell subtype short name </li> <li>sample_id </li> </ul> </li> </ul> </div> <div> <ul> <li>rowData: Gene-level metadata <ul> <li>gene_ids</li> <li>gene_names</li> </ul> </li> </ul> </div> <p><strong>Derived data - CyTOF</strong></p> <p>This is an R data set (.RDS) containing a SingleCellExperiment object with the following slots:</p> <ul> <li>Assays:<br> <ul> <li>counts_raw: signal intensity based on CyTOF dual counts</li> <li>exprs_raw: arcsinh transformed raw counts (cofactor 5)</li> <li>counts: batch corrected raw counts (linear scaling based on a quantile)</li> <li>exprs: arcsin transformed counts (cofactor 5)</li> <li>scaled: 0-1 normalized exprs (clipped to the 99.95th percentile)</li> </ul> </li> <li>colData (cell metadata) <ul> <li>bc_id: barcode of the sample during staining </li> <li>run: CyTOF experiment batch, named after the first sample of the batch</li> <li>type: Sample type (blood or bone marrow)</li> <li>sample_id: TuPro sample ID</li> <li>pred_id: Predicted cell type [char]</li> <li>pred_n: Predicted cell type [integer]</li> </ul> </li> <li>rowData (marker metadata) <ul> <li>channel_name: Name and isotopic mass of the metal ion corresponding to this marker</li> <li>marker_name: Protein name</li> <li>channel_group, channel_group_integer: Biological processes the channel identifies, e.g. specific cell type, signalling, cell death</li> <li>tsne_channel: Logical - use this channel for dimensionality reduction?</li> <li>channel_order: Define the order of channels for plotting</li> <li>cluster_channel: Logical - use this channel for clustering?</li> </ul> </li> </ul>
scCTS: identifying the cell type-specific marker genes from population-level single-cell RNA-seq
<p>Single cell RNA-sequencing (scRNA-seq) provides gene expression profiles of individual cells from complex samples, facilitating the detection of cell type-specific marker genes. In scRNA-seq experiments with multiple donors, the population level variation brings an extra layer of complexity in cell type-specific gene detection, for example, they may not appear in all donors. Motivated by this observation, we develop a statistical model named scCTS to identify cell type-specific genes from population-level scRNA-seq data. Extensive data analyses demonstrate that the proposed method identifies more biologically meaningful cell type-specific genes compared to traditional methods.</p>
PHLOWER - Single cell trajectory analysis using Decomposition of the Hodge Laplacian
<p>Datasets that PHLOWER used:</p> <ol> <li>benchmarking data</li> <li>multiome kidney organoid data</li> <li>xenium kidney orgnoid data</li> </ol>
Single-cell analysis reveals host S phase drives large T antigen expression during BK polyomavirus infection
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Spatially resolved single-cell atlas unveils A distinct cellular signature of fatal lung COVID-19 in a Malawian population
<p>Postmortem single-cell studies have transformed understanding of lower respiratory tract diseases (LRTD) including Covid19, but there is minimal data from African settings where HIV, malaria and other environmental exposures may affect disease pathobiology and treatment targets. We used histology and high-dimensional imaging to characterise fatal lung disease in Malawian adults with (n=9) and without (n=7) Covid19, and generated single-cell transcriptomics data from lung, blood and nasal cells. Data integration with other cohorts showed a conserved Covid19 histopathological signature, driven by contrasting immune and inflammatory mechanisms: in USA, European and Asian cohorts by type I/III interferon responses, particularly in blood-derived monocytes, in the Malawi cohort, by response to interferon-gamma (IFN-γ) in lung-resident macrophages. HIV status had minimal impact on histology or immunopathology. Our study provides a data resource and highlights the importance of studying the cellular mechanisms of disease in underrepresented populations, indicating shared and distinct targets for treatment. </p> <p>Here, there are scripts containing pre-processing, image denoise, cell segmentation, single-cell and spatial statistics analysis as well as visualisation plots for the Imaging Mass Cytometry of the post-mortem lung sections. </p> <p>Check out the interactive single cell atlas hosted on the cellxgeneVIP platform hosted by the University of Glasgow, by clicking on <a href="https://cellatlas-cxg.mvls.gla.ac.uk/COSMIC/view/COSMIC_IMC_Lung.h5ad/" rel="nofollow">https://cellatlas-cxg.mvls.gla.ac.uk/COSMIC/view/COSMIC_IMC_Lung.h5ad/</a></p>
Single-cell atlas of circulating immune cells over the first two months of age in extremely premature infants
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A longitudinal single-cell atlas of anti-tumour necrosis factor treatment in inflammatory bowel disease
<p>***<strong>These data supersede the previous release. Please disregard data uploaded prior to October 30th as the metadata was incorrect - note the correct data in Supplementary Table 1</strong>***</p> <p>A longitudinal single-cell atlas of anti-tumour necrosis factor treatment in inflammatory bowel disease. </p> <p>GEO ID for bam files and CellRanger pipeline output (filtered and unfiltered): <strong>GSE282122</strong></p> <p>TAURUS_raw_counts_annotated_final.h5ad: This file is the pooled data across all cell types</p> <p>UMAP_combined_objects.txt.gz: This file has the corresponding UMAP for pooled data</p> <p>PC_combined_objects.txt.gz: This file has the corresponding PC for pooled data</p> <p>paired_sample_list.csv: This file has the paired samples used in analysis for baseline/longitudinal analysis. For baseline analyses, please use baseline samples > 6.5 inflammation score to recapitulate the analyses in the paper.</p> <p>Other data to be uploaded to Zenodo include H&E slides. Please access interactive MDV browser here.</p>
Chromatin accessibility landscape of mouse early embryos revealed by single-cell NanoATAC-seq2
<p>The chromatin accessibility assay using scNanoATAC-seq2 was conducted on the Oxford Nanopore (ONT) long-read sequencing platform and focused on samples related to mouse preimplantation development. These samples included preimplantation embryos, ranging from the zygote to the late blastocyst, as well as oocytes, granulosa cells, and naive mouse embryonic stem cells. The embryos were derived from crosses of C57xDBA, DBAxC57, and C57xCAST. Additionally, cell lines from humans and mice were assayed during the technical testing phase.</p> <p> </p> <p><strong>Note: </strong>In this dataset, 'pre-TE' denotes early trophectoderm (early TE), while 'TE' signifies late trophectoderm (late TE), as outlined in the main article.</p>
Single Cell HTAN SCLC
<p>These data include a subset of single-cell samples from the CPTAC Glioblastoma data processed using the following steps:</p> <ol> <li>Ensembl gene ID's were matched to HGNC symbols, chromosome name, starting position, and ending position via Biomart using the <em>scater</em> R package.</li> <li>Size factors were computed using the <em>scran</em> R package.</li> <li>UMAP dimensions were computed using the <em>scater</em> R package.</li> <li>Probes without matching HGNC symbols were removed.</li> <li>Where duplicate HGNC symbols were present, the gene with the maximum normalized range was retained.</li> <li>Cell types were inferred using the <em>scMRMA</em> R package.</li> <li>Cells with less than or equal to 1,000 features were removed.</li> <li>Cells with mitochondrial reads greater than or equal to 50% were removed.</li> <li>Expression data for each cell type were saved separately for each sample.</li> <li>For macrophages for each sample, a differentiation trajectory was estimated using the <em>monocle3</em> R package, and plots were colored by combined expression of the M0 markers CSF1R, CD14, CD68, and CD11B, the M1 markers CD86, MARC0, CXCL9, CXCL10, CXCL11, NOS2, SOCS1, and CD64, and the M2 markers TGM2, CD23, ARG1, CCL22, CD163, and CD206 (from PMC8268869). Pseudotime starting points were annotated in <em>monocle3</em> using visual inspection of plots. Only samples in which a visible trajectory from M0 -> M1 -> M2 was evident using these markers were retained.</li> <li>For B cells and T cells for each sample, a differentiation trajectory was estimated using the <em>monocle3</em> R package, and plots were colored by combined expression of the B cell and T cell differentiation markers TCF3, FOXO1, EBF1, and PAX5 and TCF3, NOTCH1, HES1, TCF7, TLE3, TLE6, BCL11B, and GATA3, respectively (from PM<span><span>24679436</span></span>). Pseudotime starting points were annotated in <em>monocle3</em> using visual inspection of plots. Only samples in which a visible trajectory of dedifferentiation was evident using these markers were retained.</li> <li>For endothelial cells for each sample, a differentiation trajectory was estimated using the <em>monocle3</em> R package, and plots were colored by combined expression of the endothelial cell differentiation markers GATA2, FLI1, SOX7, SOX18 (from PM<span><span>28334937</span></span>). Pseudotime starting points were annotated in <em>monocle3</em> using visual inspection of plots. Only samples in which a visible trajectory of dedifferentiation was evident using these markers were retained.</li> <li>For fibroblasts for each sample, a differentiation trajectory was estimated using the <em>monocle3</em> R package, and plots were colored by combined expression of fibrogenesis markers TGFB1, FGF2, ANGPT1, ANGPT2, PDGFA, PDGFB, PDGFC, PDGFD, VEGFA, and VEGFB (from PM<span><span>17280897</span></span>). Pseudotime starting points were annotated in <em>monocle3</em> using visual inspection of plots. Only samples in which a visible trajectory of dedifferentiation was evident using these markers were retained.</li> <li>For NK cells for each sample, a differentiation trajectory was estimated using the <em>monocle3</em> R package, and plots were colored by combined expression of transmembrane proteins that mediate cytotoxicity, FASLG and TNFSF10 (from PM31107565). Pseudotime starting points were annotated in <em>monocle3</em> using visual inspection of plots. Only samples in which a visible trajectory of dedifferentiation was evident using these markers were retained. These pseudotime assignments (and other cell type assignments) are available from <strong>HTAN_SCLC_pseudotime.zip.</strong></li> <li>For all cell types for each sample, 5-fold cross-validation matrices for expression, PCA, and pseudotime were generated across 1 random split, for a total of 5 files per cell type, per sample. These files are available from <strong>HTAN_SCLC_expression.zip.</strong></li> </ol>
Single cell deletion of the transcription factors Trps1 and Sox9 reveals novel functions in adult cortical astrocytes
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Substrate induced currents through the monoamine transporters and single cell uptake of the fluorescent substrate APP+
<p>The concentrative power of the transporters for dopamine (DAT), norepinephrine (NET) and serotonin (SERT) is thought to be fueled by the transmembrane Na<sup>+</sup> gradient, but it is conceivable that they can also tap other energy sources, e.g. membrane voltage and/or the transmembrane K<sup>+</sup> gradient. We address this by recording uptake of endogenous substrates or the fluorescent substrate APP<sup>+</sup> ((4-(4-dimethylamino)phenyl-1-methylpyridinium) under voltage control in cells expressing DAT, NET or SERT. We show that DAT and NET differ from SERT in intracellular handling of K<sup>+</sup>. In DAT and NET, substrate uptake was voltage-dependent due to the transient nature of intracellular K<sup>+</sup> binding, which precluded K<sup>+</sup> antiport. SERT, however, antiports K<sup>+</sup> and achieves voltage-independent transport. Thus, there is a trade-off between maintaining constant uptake and harvesting membrane potential for concentrative power, which we conclude to occur due to subtle differences in the kinetics of co-substrate ion binding in closely related transporters.</p>
FIGURE 2. Notothylas yunnanensis T.Peng & R.L.Zhu 1. Thalli with mature orange-red sporophytes. 2. Dorsal thallus epidermis, showing each cell containing a single chloroplast with central pyrenoid. 3 in A revision of the genus Notothylas (Notothyladaceae, Anthocerotophyta) in China
FIGURE 2. Notothylas yunnanensis T.Peng & R.L.Zhu 1. Thalli with mature orange-red sporophytes. 2. Dorsal thallus epidermis, showing each cell containing a single chloroplast with central pyrenoid. 3. Two Nostoc colonies within ventral side of solid thallus in transverse section. 4. Four antheridia with orange chromoplasts in irregularly arranged jacket cells. 5. Transverse section of capsule wall. 6. Dehiscing sporophyte with yellowish spores and columella. 7. Epidermis of capsule wall. 8. Three spores and a pseudoelater. 9. Capsule wall showing slightly thick-walled, sub-quadrate to sub-rectangular, irregularly arranged epidermal cells. All from T. Peng et al. 20120715-7 (holotype HSNU).
Spatial reconstruction of the early hepatic transcriptomic landscape after an acetaminophen overdose using single-cell RNA sequencing
<p>We leveraged single-cell RNA sequencing to understand the early molecular events that define the hepatocyte response to acetaminophen exposure at a subpopulation level. We spatially assigned hepatocytes along the portol-central vein axis by using established landmark genes. By spatially assigning the hepatocytes were were able to account for innate differences in gene expression that existed along this gradient. The excel files herein provide the full list of differentially expressed genes between key subpopulations of interest. Additionally, we classified genes as either pericentral zonated, periportal zonated, or non-zonated, the full list of genes and their spatial assignments are included in the appropriate excel file. </p>
Inferelator 3.0 Yeast Single-Cell Benchmarking Data
<p>Yeast single-cell gene expression data, database-derived prior knowledge network, and hand-curated gold standard network. Used to benchmark the Inferelator 3.0, SCENIC, and CellOracle.</p> <p>Expression data (GSE144820_GSE125162.tsv.gz) is an integer count matrix [44343 rows x 6763 columns] with an index column (0) assembled from <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE144820">GSE144820</a> and <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE125162">GSE125162</a>. Included is a paired metadata file (GSE144820_GSE125162_META_DATA.tsv.gz).</p> <p>A database-derived prior knowledge network (YEASTRACT_20190713_BOTH.tsv) is a boolean connectivity matrix [6885 rows x 220 columns] with an index column (0) obtained from the <a href="http://www.yeastract.com/">YEASTRACT</a> database on 07132019. It consists of edges which have both DNA localization evidence and evidence of changes to gene expression after TF perturbation.</p> <p>A curated gold standard network (Tchourine_2018_yeast_gold_standard.tsv) is a signed connectivity matrix [993 rows x 98 columns] with an index column (0). <a href="https://pubmed.ncbi.nlm.nih.gov/29641998/">Details of its construction have been published</a>.</p>
Single-nucleus Transcriptomics of IDH1- and TP53-mutant Glioma Stem Cells Displays Diversified Commitment on Highly Invasive Cancer Progenitors
<p><strong>Fig. S1</strong>. <strong>Marker genes for Seurat clusters.</strong> (<strong>A</strong>) distribution of marker genes for cluster 0 on the 2D-UMAP space. (<strong>B</strong>) distribution of marker genes for cluster 1 on the 2D-UMAP space. (<strong>C</strong>) distribution of marker genes for cluster 2 on the 2D-UMAP space. (<strong>D</strong>) distribution of marker genes for cluster 3 on the 2D-UMAP space. (<strong>E</strong>) distribution of marker genes for cluster 4 on the 2D-UMAP space. (<strong>F</strong>) distribution of marker genes for cluster 5 on the 2D-UMAP space. (<strong>G</strong>) Stuck violin plot of marker gene expression for Seurat clusters (bottom panel) and their annotation (right side panel). The violin shape displays the number of the cells expressing a gene, the continuous color panel defines median expression value of a gene from the absence of expression (white) to high expression (dark blue).</p> <p><strong>Fig. S2</strong>. <strong>Expression of genes marking cell malignization.</strong> (<strong>A</strong>) expression of collagens in Surat clusters (bottom panel) (<strong>B</strong>) expression of genes linked to Migration and ECM in Surat clusters (bottom panel) (<strong>C</strong>) expression of genes classified as Proto-oncogenes in Surat clusters (bottom panel). The violin shape displays the number of the cells expressing a gene, the violin color defines the Seurat cluster. Gene expression displayed in log-transformed normalized expression values.</p> <p><strong>Fig. S3</strong>. <strong>Expression of genes involved in proliferation and survival of cancer cells.</strong> (<strong>A</strong>) Genes involved in Wnt-pathway in Surat clusters (bottom panel). (<strong>B</strong>) Genes involved in Akt-pathway in Surat clusters (bottom panel). (<strong>C</strong>) Genes inducing resistance to cancer therapeutics in Surat clusters (bottom panel). The violin shape displays the number of the cells expressing a gene, the violin color defines the Seurat cluster. Gene expression displayed in log-transformed normalized expression values.\</p> <p><strong>Fig. S4</strong>. <strong>Expression of genes marking CSC profile.</strong> (<strong>A</strong>) Ion channel genes in Surat clusters (bottom panel). (<strong>B</strong>) Antioncogenes in Surat clusters (bottom panel). <strong>C</strong>. Stem-cell genes in Surat clusters (bottom panel). (<strong>D</strong>) Antiapoptotic genes in Surat clusters (bottom panel). The violin shape displays the number of the cells expressing a gene, the violin color defines the Seurat cluster. Gene expression displayed in log-transformed normalized expression values.</p> <p><strong>Fig. S5</strong>. <strong>Genes differentially expressed between UMAP clusters</strong>. (<strong>A</strong>) Heatmap for wt-GSCs. (<strong>B</strong>) Heatmap for mt-GSCs. Upper colour panel in the heatmap designates Seurat clusters. Gene expression is indicated by continuous colour panel starting from the most downregulated (blue) to the most upregulated (red).</p> <p><strong>Fig. S6</strong>. <strong>Marker genes defying cell annotations</strong>. (<strong>A</strong>) Stack violin plot displays marker gene expression in wt-GSC clusters. (<strong>B</strong>) Stack violin plot displays marker gene expression in mt-GSC clusters. Genes grouped by cell annotations (side description) and UMAP clusters (down column bar). The violin shape displays the number of the cells expressing a gene, the continuous color panel defines median expression value of a gene from the absence of expression (white) to high expression (dark blue).</p> <p><strong>Fig. S7</strong>. <strong>Differentially expressed proliferation and adhesion pathways comparing mutant samples to wild type.</strong> (<strong>A</strong>) ERBB signalling pathway. (<strong>B</strong>) Wnt signalling pathway. (<strong>C</strong>) Genes linked to Focal adhesion. (<strong>D</strong>) Genes classified as Cell adhesion molecules. Red rectangles display upregulated genes (proteins), green rectangles define downregulated genes (proteins). Pictures obtained by KEGG pathview.</p> <p><strong>Table S1. Glioma genotyping primers</strong></p> <p><strong>Table S2. Smart-seq2 Primers</strong></p>
Single-cell spatial architectures associated with clinical outcome in head and neck squamous cell carcinoma
<p>Data supporting the findings of the paper "<a href="https://doi.org/10.1038/s41698-022-00253-z">Single-cell spatial architectures associated with clinical outcome in head and neck squamous cell carcinoma</a>." Files include output of multiplex immunohistochemistry computational image processing workflow for each tumor region and survival data for each patient. The code used to produce the results of this study is available at: <a href="https://github.com/kblise/HNSCC_mIHC_paper">https://github.com/kblise/HNSCC_mIHC_paper</a>.</p> <p>Notes about data files:</p> <ul> <li>clinicalData.csv = Contains the following columns for each tumor region: <ul> <li>sample = tumor region ID</li> <li>dtr = Progression free survival (days to recurrence)</li> <li>tnm = TNM stage</li> <li>anatomy = anatomic site of resection</li> <li>tx = therapy administered</li> <li>area = area in mm<sup>2</sup> of tissue region</li> </ul> </li> <li>pt .csv files = Matrix of single cells (rows) and marker expression (columns). One file per tumor region (n=47). Other columns include: <ul> <li>class = Cell phenotype assigned via hierarchical gating strategy (see below for classes)</li> <li>Location_Center_X and Location_Center_Y = Cartesian coordinates of cell center</li> <li>Cellsp_PD1p = PD-1 expression; 1 = PD-1<sup>+</sup>, 0 = PD-1<sup>-</sup></li> <li>Cellsp_PDL1p = PD-L1 expression; 1 = PD-L1<sup>+</sup>, 0 = PD-L1<sup>-</sup></li> <li>Cellsp_KI67p = Ki-67 expression; 1 = Ki-67<sup>+</sup>, 0 = Ki-67<sup>-</sup></li> </ul> </li> </ul> <p>Classes, corresponding cell phenotype, and gating strategy used:</p> <ul> <li>A = CD8<sup>+</sup> T Cell (CD45<sup>+</sup> CD20<sup>-</sup> CD3<sup>+</sup> CD8<sup>+</sup>)</li> <li>B = CD4<sup>+</sup> T Helper (CD45<sup>+</sup> CD20<sup>-</sup> CD3<sup>+</sup> CD8<sup>-</sup> FOXP3<sup>-</sup>)</li> <li>C = B Cell (CD45<sup>+</sup> CD20<sup>+</sup>)</li> <li>D = Macrophage (CD45<sup>+</sup> CD20<sup>-</sup> CD3<sup>-</sup> CD66B<sup>-</sup> CD68<sup>+</sup>)</li> <li>E = Other Immune (CD45<sup>+</sup> CD20<sup>-</sup> CD3<sup>-</sup> CD66B<sup>-</sup> CD68<sup>-</sup> MHCII<sup>- </sup>CD8<sup>-</sup> FOXP3<sup>-</sup>)</li> <li>F = Other Non-Immune (CD45<sup>-</sup> PANCK<sup>- </sup>αSMA<sup>-</sup>) - excluded from analysis</li> <li>G = Noise - excluded from analysis</li> <li>H = Neoplastic Tumor (CD45<sup>-</sup> PANCK<sup>+</sup>)</li> <li>J = Granulocyte (CD45<sup>+</sup> CD20<sup>-</sup> CD3<sup>-</sup> CD66B<sup>+</sup>)</li> <li>K = CD4<sup>+</sup> Regulatory T Cell (CD45<sup>+</sup> CD20<sup>-</sup> CD3<sup>+</sup> CD8<sup>-</sup> FOXP3<sup>+</sup>)</li> <li>N = αSMA<sup>+</sup> Mesenchymal (CD45<sup>-</sup> PANCK<sup>- </sup>αSMA<sup>+</sup>)</li> <li>X = Antigen Presenting Cell (CD45<sup>+</sup> CD20<sup>-</sup> CD3<sup>-</sup> CD66B<sup>-</sup> CD68<sup>-</sup> MHCII<sup>+</sup>)</li> </ul>
Systematic dissection of transcriptional regulatory networks by genome-scale and single-cell CRISPR screens
Millions of putative transcriptional regulatory elements (TREs) have been cataloged in the human genome, yet their functional relevance in specific pathophysiological settings remains to be determined. This is critical to understand how oncogenic transcription factors (TFs) engage specific TREs to impose transcriptional programs underlying malignant phenotypes. Here, we combine cutting edge CRISPR screens and epigenomic profiling to functionally survey ≈15,000 TREs engaged by estrogen receptor (ER). We show that ER exerts its oncogenic role in breast cancer by engaging TREs enriched in GATA3, TFAP2C, and H3K27Ac signal. These TREs control critical downstream TFs, among which TFAP2C plays an essential role in ER-driven cell proliferation. Together, our work reveals novel insights into a critical oncogenic transcription program and provides a framework to map regulatory networks, enabling to dissect the function of the noncoding genome of cancer cells.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.