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3,929 results for “cell type”
Source data for paper "Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data"
<p>Sample-paired scRNA-seq and scATAC-seq data collected from human peripheral blood mononuclear cells with <em>Staphylococcus aureus</em><em> </em>infection. ScATAC-seq data collected from human peripheral blood mononuclear cells with <em>COVID-19</em> infection. </p>
Data for investigation of cell-type-specific expression and regulation in Setbp1 (p.S858R) atypical SGS male mice
<p><strong>processed.tar.gz, contains all files from the data directory associated with this projects and includes the following:</strong></p> <ul> <li><strong>setbp1_targets.csv</strong> : list of human SETP1 gene targets from ChIP-seq experiments converted to mouse and enriched for additional interactions from literature references (https://pubmed.ncbi.nlm.nih.gov/35685777/ and https://www.nature.com/articles/s41467-018-04462-8, </li> <li><strong>seurat_objects/</strong> <ul> <li><strong>kidney_integrated_celltypes.Rdata </strong>: filtered and preprocessed seurat object with cell type assignments for input to SoupX processing</li> <li><strong>kidney_integrated_celltypes_postSoup.Rdata</strong> : preprocessed and quality controlled seurat object with final cell type assignments after SoupX processing</li> <li><strong>setbp1_cerebralintcelltypes.Rdata</strong> : preprocessed and quality controlled seurat object with final cell type assignments</li> </ul> </li> <li><strong>decoupleR_CollecTRI/</strong> <ul> <li><strong>mouse_prior_tri.csv</strong> : accessed May 2023, it is unclear how versioning or accessions are used so we provided the Mouse CollecTRI (https://github.com/saezlab/CollecTRI) used in this study to calculate cell-type-specific TF activity</li> </ul> </li> <li><strong>motif_inputs/</strong> <ul> <li><strong>Mus_motif_all.txt </strong>: TF-motif PANDA input for mm10 reference genome enriched for</li> </ul> </li> <li><strong>expression_inputs/</strong> : expression matrices for all cell types for both tissues and conditions (n = 50) used to construct cell-type-specific PANDA networks</li> </ul> <p><strong>The below files are from the data/results directory of this associated project and include the following:</strong></p> <ul> <li><strong>alpaca.tar.gz</strong> : directory containing differential community analysis outputs from ALPACA</li> <li><strong>seurat.tar.gz </strong>: directory containing SoupX intermediates, as well as the</li> </ul>
Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses
<p>Raw data and code to reproduce figures in the manuscript "Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses"</p> <p># README</p> <p>## Introduction</p> <p>This README provides essential information about the codebase for the manuscript titled "Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses." The code in this repository is self-contained and is expected to run smoothly given the appropriate versions of the required libraries/packages.</p> <p>## Directory structure and execution details</p> <p>### R code</p> <p>- Main Figures 2A-2D, 3A-3C, and 4A-4E, as well as supplemental figures S2A-S2H, S3A-S3E, S4L, and S5A-S5I, were generated using R. Execute the `R_figs_master.r` script located in the `r_code` directory.<br> - All figures will be saved within the `r_code/code_generated_figures` directory.<br> - Note: Exact UMAP representations might vary across different hardware and operating systems, likely due to an issue with the UWOT package ([Reference Issue](https://github.com/satijalab/seurat/issues/5514)). If figures appear outside their designated plot ranges, set "FixAxes" to 'FALSE' in the `single_cell_variables.r` script.</p> <p>### MATLAB code</p> <p>- Main figures 1B, 1D-1F, and 6A-6H, as well as supplemental figures S1A-S1J and S6A-S6I, were generated using MATLAB (version 9.11.0.1809720 (R2021b) Update 1). Execute the `get_the_figs_matlab.m` script located in the `matlab_code` directory.<br> - All figures will be saved within the `matlab_code/code_generated_figures` directory.<br> - Required: [fca_readfcs, version 2020.06.22](https://ch.mathworks.com/matlabcentral/fileexchange/9608-fca_readfcs).</p> <p>### Python code</p> <p>- Figures 5B-5F panels were generated using Python (version 3.6.8). Run the `fig_5_analysis_code.py` script located in the `python_code` directory.<br> - All figures will be saved within the `python_code/code_generated_figures` directory.<br> - The preprocessed images located in `python_code/data_repository/Adamts2_processed`, `python_code/data_repository/Agmat_processed`, and `python_code/data_repository/Baz1a_processed` were generated using the ImageJ macro `python_code/cropped_to_processed_macro.ijm` from the raw images in `python_code/data_repository/Adamts2_cropped`, `python_code/data_repository/Agmat_cropped`, and `python_code/data_repository/Baz1a_cropped`.</p> <p>## Supplementary code (for reference only as raw data is not included)</p> <p>### Mapping code and genome construction code</p> <p>- Initial processing of Single-cell RNA-sequencing was performed with Cell Ranger, coordinated by the Python script:<br> `python_code/mapping_and_genome_construction/single_cell_mapping_pipeline.py`. Some components of this script are deprecated and were primarily used to pass .fastq files to Cell Ranger and organize the outputs.<br> - A custom genome was constructed to account for the expression of CaMPARI2 in the single-cell RNA-sequencing dataset:<br> `python_code/mapping_and_genome_construction/campari2_genome_construction.py`.<br> - Processing of Bulk RNA-sequencing, either single or paired-end, was executed through Python:<br> `python_code/mapping_and_genome_construction/bulk_single_end_mapping.py` and `python_code/mapping_and_genome_construction/bulk_paired_end_mapping.py`.<br> - A custom genome was constructed to account for the expression of various artificial promoter viruses:<br> `python_code/mapping_and_genome_construction/bulk_seq_genome_construction.py`.</p>
Automatic Identification of Kidney Cell Types in scRNA-seq and snRNA-seq Data Using Machine Learning Algorithms - Datasets
<p>Datasets for reproducibility of the results found in Automatic Identification of Kidney Cell Types in scRNA-seq and snRNA-seq Data Using Machine Learning Algorithms. This study utilized data from the following 4 journals:</p> <p>Lake, B.B. et al. A single-nucleus RNA-sequencing pipeline to decipher the molecular anatomy and pathophysiology of human kidneys. Nat Commun 10, 2832 (2019).</p> <p>Liao, J., Yu, Z., Chen, Y. et al. Single-cell RNA sequencing of human kidney. Sci Data 7, 4 (2020).</p> <p>Menon, R. et al. Single cell transcriptomics identifies focal segmental glomerulosclerosis remission endothelial biomarker. JCI Insight 5, e133267 (2020).</p> <p>Wu, H. et al. Single-cell transcriptomics of a human kidney allograft biopsy specimen defines a diverse inflammatory response. J Am Soc Nephrol 29: 2069–2080 (2018).</p> <p>Young, M. D. et al. Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science 361, 594–599 (2018).</p>
Data and code for: Spatial cell type enrichment predicts mouse brain connectivity
<p>A fundamental neuroscience topic is the link between the brain's molecular, cellular and cytoarchitectonic properties and structural connectivity (SC). Recent studies relate inter-regional connectivity to gene expression, but the relationship to regional cell-type distributions remains understudied. Here, we utilize whole-brain mapping of neuronal and non-neuronal subtypes via the Matrix Inversion and Subset Selection (MISS) algorithm to model inter-regional connectivity as a function of regional cell-type composition with machine learning. We deployed random forest algorithms for predicting connectivity from cell type densities, demonstrating surprisingly strong prediction accuracy of cell types in general and particular cells like oligodendrocytes. We found evidence of a strong distance-dependency in the cell-connectivity relationship, with layer-specific excitatory neurons contributing the most for long-range connectivity, while vascular and astroglia are salient for short-range connections. Our results demonstrate a link between cell types and connectivity, providing a roadmap for examining this relationship in other species, including humans.</p>
A telencephalon cell type atlas for goldfish reveals diversity in the evolution of spatial structure and cell types
<p class="MsoNormal"><span>Teleost fish form the largest group of vertebrates, making them critically important for the study on the</span> <span>mechanisms of brain evolution. In fact, teleosts show a tremendous variety of adaptive behaviors similar</span> <span>to birds and mammals, however, the neural basis mediating these behaviors remains elusive. We</span> <span>performed a systematic comparative survey of the goldfish telencephalon: the seat of plastic behavior,</span> <span>learning, and memory in vertebrates. We delineated and mapped goldfish telencephalon cell types using</span> <span>single-cell RNA-seq and spatial transcriptomics, resulting in de novo molecular neuroanatomy</span> <span>parcellation. Glial cells were highly conserved across 450 million years of evolution separating mouse</span> <span>and goldfish, while neurons showed diversity and modularity in gene expression. Specifically,</span> <span>somatostatin (SST) interneurons, famously interspersed in the mammalian isocortex for local inhibitory</span> <span>input, were curiously aggregated in a single goldfish telencephalon nucleus, but molecularly conserved.</span> <span>Cerebral nuclei including the striatum, a hub for motivated behavior in amniotes, had molecularly conserved goldfish homologs. We further suggest different elements of a hippocampal formation across</span> <span>the goldfish pallium. Finally, aiding study of the teleostan everted telencephalon, we describe substantial</span> <span>molecular similarities between the goldfish and zebrafish neuronal taxonomies. Together, our atlas</span> <span>provides new insights into organization and evolution of vertebrate forebrains and may serve as a resource</span> <span>for the functional study underlying cognition in teleost fish.</span></p>
Progenitor like cell type of an MLL-EDC4 fusion in acute myeloid leukemia
<p>Transcriptome analysis by single cell sequencing provides valuable information on intratumor heterogeneity and developmental stages of acute myeloid leukemia (AML) as well as interactions of tumor cells with the microenvironment. However, it has been hardly applied to the subgroup of cases with translocations of the mixed lineage leukemia (<i>MLL</i>) gene for which the enhancer of mRNA decapping 4 (<i>EDC4</i>) gene was recently identified as a novel fusion partner <i>(MLL-EDC4</i>). In our study published in Blood Advances (Schuster et al., 2023; https://doi.org/10.1182/bloodadvances.2022009096), we compared different <i>MLL</i> translocations by single cell RNA sequencing of cells derived from peripheral blood or bone marrow. The <i>MLL</i>-<i>EDC4</i> positive cells almost exclusively showed a transcriptional profile of hematopoietic progenitor cells while leukemic cells of <i>MLL-MLLT3</i> and <i>MLL-ELL</i> fusions exhibited a more differentiated phenotype. The <i>MLL</i>-<i>EDC4</i> progenitor state was characterized by the upregulation of key transcriptional regulators in AML (<i>RUNX1, SOX4, HOPX</i>), target genes of <i>MYC</i> and interferon signaling as well as other genes known to play a critical role in hematopoiesis or leukemic stem cell activation (<i>CDK6, FLT3, NPM1</i>). Here, the scRNA-seq dataset of our study is provided. It contains six scRNA-seq read count matrices of the AML samples with MLL fusions as described in the table scRNA-seq_samples.xlsx. Further details are given in the publication associated with this dataset. </p>
Study of Cemiplimab in Patients With Type of Skin Cancer Stage II to IV Cutaneous Squamous Cell Carcinoma
ClinicalTrials.gov study NCT04154943. IPD Sharing: YES. Countries: 3. Publications: 2.
Study of Oral cMET Inhibitor INC280 in Patients With EGFR Wild-type (wt), Advanced Non-small Cell Lung Cancer (NSCLC) (Geometry Mono-1)
ClinicalTrials.gov study NCT02414139. IPD Sharing: YES. Countries: 20. Publications: 3.
Data from: Cell type-specific dysregulation of gene expression due to Chd8 haploinsufficiency during mouse cortical development
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An ON-type direction selective ganglion cell in primate retina
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Raw differential gene expression data, data S1, from: Molecular cascades and cell type-specific signatures in ASD revealed by single cell genomics
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Complementary cortical and thalamic contributions to cell-type-specific striatal activity dynamics during movement
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A telencephalon cell type atlas for goldfish reveals diversity in the evolution of spatial structure and cell types
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Data and code for: Spatial cell type enrichment predicts mouse brain connectivity
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Prioritization of cell types responsive to biological perturbations in single-cell data with Augur
<p>Processed and analysis-ready input data discussed in our Augur procedures.</p>
Simulated data for testing cell type adjustment methods
<p>***NOTE: An updated version of this dataset is available at https://zenodo.org/record/46746#.VtW7MmSAOko</p> <p>Different simulation scenarios on which to test cell type adjustment methods for epigenome-wide association studies. Each .RData file contains a matrix of methylation beta-values from a simulated blood cell mixture "sim_beta", a list of simulated differentially methylated positions "dmr", and a phenotype "disease_status".</p>
Updated simulated data for testing cell type adjustment methods
<p>Different simulation scenarios on which to test cell type adjustment methods for epigenome-wide association studies. Each .RData file contains a matrix of methylation beta-values from a simulated blood cell mixture "sim_beta", a list of simulated differentially methylated positions "dmr", and a phenotype "disease_status". Updated version with new simulation scenarios. Each of "simulated_data_many_assoc.tar.gz" and "simulated_data_few_assoc.tar.gz" contain 10 replications of those simulation scenarios. The other .RData files represent only one replication of each scenario.</p>
Supplementary data for ENACT: End-to-End Analysis and Cell Type Annotation for Visium High Definition (HD) Slides
<p>This project contains the datasets used to evaluate and reproduce the results of ENACT (End-to-End Analysis and Cell Type<br>Annotation for Visium HD Slides). The dataset consists of:</p> <ul> <li>a sample Visium HD sample of human colorectal cancer, courtesy of 10X Genomics (<a href="https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-human-crc">Visium HD Spatial Gene Expression Library, Human Colorectal Cancer (FFPE) - 10x Genomics</a>). All credit goes to 10X Genomics.</li> <li>configuration files to be used to reproduce the results provided in the ENACT publication, </li> <li>evaluation plots used in the ENACT publication, and</li> <li>results obtained after running ENACT on the human colorectal cancer sample using the four bin-to-cell assignment methods (naive, weighted_by_area, weighted_by_transcript, and weighted_by_cluster) and the three cell annotation methods (Sargent, CellAssign, CellTypist)</li> </ul> <p>Additionally, results from running ENACT on the following three public VisiumHD samples are provided to showcase ENACT’s tissue-agnostic nature:</p> <div> <div> <div> <ul> <li> <p><a href="https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-human-lung-cancer-post-xenium-expt">Human Lung FFPE</a> sample from a subject with Adenocarcinoma (age and gender unspecified),</p> </li> <li> <p><a href="https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-human-tonsil-fresh-frozen">Human Tonsil Fresh Frozen</a> sample from a 21 year old male subject with Reactive Follicular Hyperplasia,</p> </li> <li> <p><a href="https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-human-breast-cancer-fresh-frozen">Human Breast Fresh Frozen</a> sample from a 58 year old female subject with Ductal Carcinoma in Situ (DCIS).</p> </li> </ul> </div> </div> </div> <p> </p>
The potassium channel subunit Kv1.8 (Kcna10) is essential for the distinctive outwardly rectifying conductances of type I and II vestibular hair cells
<p>In amniotes, head motions and tilt are detected by two types of vestibular hair cells (HCs) with strikingly different morphology and physiology. Mature type I HCs express a large and very unusual potassium conductance, g<sub>K,L</sub>, which activates negative to resting potential, confers very negative resting potentials and low input resistances, and enhances an unusual non-quantal transmission from type I cells onto their calyceal afferent terminals. Following clues pointing to K<sub>V</sub>1.8 (KCNA10) in the Shaker K channel family as a candidate g<sub>K,L</sub> subunit, we compared whole-cell voltage-dependent currents from utricular hair cells of K<sub>V</sub>1.8-null mice and littermate controls. We found that K<sub>V</sub>1.8 is necessary not just for g<sub>K,L</sub> but also for fast-inactivating and delayed rectifier currents in type II HCs, which activate positive to resting potential. The distinct properties of the three K<sub>V</sub>1.8-dependent conductances may reflect different mixing with other K<sub>V</sub>1 subunits, such as K<sub>V</sub>1.4 (KCNA4). In K<sub>V</sub>1.8-null HCs of both types, residual outwardly rectifying conductances include K<sub>V</sub>7 (KCNQ) channels. </p> <p>Current clamp records show that in both HC types, K<sub>V</sub>1.8-dependent conductances increase the speed and damping of voltage responses. Features that speed up vestibular receptor potentials and non-quantal afferent transmission may have helped stabilize locomotion as tetrapods moved from water to land.</p>
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.