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5,472 results for “mouse cells”
Spatial organization of the mouse retina at single cell resolution by MERFISH
<p><strong>Abstract:</strong><br> <br> The visual signal processing in the retina requires the precise organization of diverse neuronal types working in concert. While single-cell omics studies have identified more than 120 different neuronal subtypes in the mouse retina1, little is known about their spatial organization. Here, we generated the first single-cell spatial atlas of the mouse retina using multiplexed error-robust fluorescence in situ hybridization (MERFISH). We profiled over 390,000 cells and identified all major cell types and nearly all subtypes through the integration with reference single-cell RNA sequencing (scRNA-seq) data. Our spatial atlas allowed simultaneous examination of nearly all cell subtypes in the retina, revealing 8 previously unknown displaced amacrine cell subtypes and establishing the first connection between the molecular classification of many cell subtypes and their spatial arrangement. Furthermore, we identified spatially dependent differential gene expression between subtypes, suggesting the possibility of functional tuning of neuronal types based on location.<br> <br> <strong>Data description:</strong><br> 1. VZA105a_integrated_368genes.h5ad<br> This file contains the raw MERFISH count matrix for four samples with 368 gene features. The "sampleid" column represents the unique sample ID, while the "region" column corresponds to the tissue section ID. The "majorclass" and "subclass" columns indicate annotated retinal cell types. Finally, the "center_x" and "center_y" columns provide the coordinates of the cell centers.</p> <p><br> 2. VA45_integrated.h5ad<br> This file contains the raw MERFISH count matrix for six samples with 500 gene features. The "sampleid" column represents the unique sample ID, while the "region" column corresponds to the tissue section ID. The "majorclass" and "subclass" columns indicate annotated retinal cell types. Finally, the "center_x" and "center_y" columns provide the coordinates of the cell centers.<br> <br> 3. merfish_impute.h5ad<br> This file contains the imputed count matrix for ten samples. The "sampleid" column represents the unique sample ID, while the "region" column corresponds to the tissue section ID. The "majorclass" and "subclass" columns indicate annotated retinal cell types. Finally, the "center_x" and "center_y" columns provide the coordinates of the cell centers.<br> </p>
Figure 3 in The study of exposure times and dose-escalation of tick saliva on mouse embryonic stem cell proliferation
Figure 3. Effect of D. marginatus SGE (0-160 µg/ml) on mouse embryonic stem cell proliferation and viability. Values represent relative fold change of cell viability normalized to untreated negative control. The Geisser–Greenhouse correction and Dunnett´s test on multiple comparison were used. All experiments have the P value <0.05, on three different time laps. The results are mean ± standard deviation (SD) from a representative experiment carried out in triplicate and were seeded in equal amount in 3 different 96-well cultured plates.
Figure 2 in The study of exposure times and dose-escalation of tick saliva on mouse embryonic stem cell proliferation
Figure 2. Effect of R. bursa SGE (0-160 µg/ml) on mouse embryonic stem cell proliferation and viability. Values represent relative fold change of cell viability normalized to untreated negative control. The Geisser–Greenhouse correction and Dunnett´s test on multiple comparison were used. The results are mean ± standard deviation (SD) from a representative experiment carried out in triplicate and were seeded in equal amount in 3 different 96-well cultured plates.
Tissue-specific features of the T cell repertoire following allogeneic hematopoietic cell transplantation in human and mouse
<p>T cells are the central drivers of many inflammatory diseases, but the repertoire of tissue-resident T cells at sites of pathology in human organs remains poorly understood. We examined the site-specificity of T cell receptor (TCR) repertoires across tissues (5-18 tissues per patient) in prospectively collected autopsies of patients with and without graft-versus-host disease (GVHD), a potentially lethal tissue-targeting complication of allogeneic hematopoietic cell transplantation, as well as in mouse models of GVHD. Anatomic similarity between tissues was a key determinant of TCR repertoire composition within patients, independent of disease or transplant status. The T cells recovered from peripheral blood and spleen in patients and mice captured a limited portion of the TCR repertoire detected in tissues. Whereas few T cell clones were shared across patients, motif-based clustering revealed shared repertoire signatures across patients in a tissue-specific fashion. T cells at disease sites had a tissue-resident phenotype and were of donor origin based on single-cell chimerism analysis. These data demonstrate the complex composition of T cell populations that persist in human tissues at the end-stage of an inflammatory disorder following lymphocyte-directed therapy. These findings also underscore the importance of studying T cells in tissues rather than blood for tissue-based pathologies and suggest the tissue-specific nature of both the endogenous and post-transplant T cell landscape.</p>
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>
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>
Data for Center-surround interactions underlie bipolar cell motion sensitivity in the mouse retina
<p>Data from the publication "Center-surround interactions underlie bipolar cell motion sensitivity in the mouse retina" published in <em>Nature Communications</em> (2022) https://doi.org/10.1038/s41467-022-32762-7.</p> <p>Code for working with this dataset may be found at https://github.com/eulerlab/bc-motion</p>
Vizgen MERFISH files for Single-cell analysis reveals M. tuberculosis ESX-1-mediated accumulation of anti-inflammatory macrophages in infected mouse lungs
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Single-cell analysis reveals M. tuberculosis ESX-1-mediated accumulation of anti-inflammatory macrophages in infected mouse lungs
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Data from: Cell type-specific dysregulation of gene expression due to Chd8 haploinsufficiency during mouse cortical development
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Contributions of mirror-image hair cell orientation to mouse otolith organ and zebrafish neuromast function: Part 2/2, Hair cell and afferent physiology from mouse utricle
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Data from: Contributions of mirror-image hair cell orientation to mouse otolith organ and zebrafish neuromast function: Part 1/2, Zebrafish data
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Tissue-specific features of the T cell repertoire following allogeneic hematopoietic cell transplantation in human and mouse
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Data and code for: Spatial cell type enrichment predicts mouse brain connectivity
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Gut bacteria from multiple sclerosis patients modulate human T cells and exacerbate symptoms in mouse models
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Data sets comparing Ca(2+) dynamics in whole-cell and β-escin-based perforated patch clamp recordings in adult mouse brain slices.
<p># Hess-et-al-beta-escin-2020<br> Data of the "Data in Brief" article concerning the added buffer approach with beta-escin perforated patch.</p> <p>A joint work by: Simon Hess (`simon.hess@uni-koeln.de`), Christophe Pouzat (`christophe.pouzat@math.unistra.fr`), and Peter Kloppenburg (`peter.kloppenburg@uni-koeln.de`).</p> <p>## Content</p> <p>This repository contains:</p> <p>- Directory `data_whole_cell` contains the experimental data in [HDF5](https://en.wikipedia.org/wiki/Hierarchical_Data_Format) format. The data contains recordings of _Substantia nigra_ dopaminergic neurons recorded in the whole-cell configuration.<br> - Directory `data_beta_escin` contains the experimental data in [HDF5](https://en.wikipedia.org/wiki/Hierarchical_Data_Format) format. The data contains recordings of _Substantia nigra_ dopaminergic neurons recorded in the β-escin perforated patch clamp configuration.</p>
Classification of pseudocalcium visual responses from mouse retinal ganglion cells
<p>The dataset includes spike data of 9 different recordings from mouse retina ganglion cells and the <em>Pseudocalcium </em>traces after convolving spikes with OGB kernel.</p>
Data from: Intrinsic growth heterogeneity of mouse leukemia cells underlies differential susceptibility to a growth-inhibiting anticancer drug
<p>Cancer cell populations consist of phenotypically heterogeneous cells. Growing evidence suggests that pre-existing phenotypic differences among cancer cells correlate with differential susceptibility to anticancer drugs and eventually lead to a relapse. Such phenotypic differences can arise not only externally driven by the environmental heterogeneity around individual cells but also internally by the intrinsic fluctuation of cells. However, the quantitative characteristics of intrinsic phenotypic heterogeneity emerging even under constant environments and their relevance to drug susceptibility remain elusive. Here we employed a microfluidic device, mammalian mother machine, for studying the intrinsic heterogeneity of growth dynamics of mouse lymphocytic leukemia cells (L1210) across tens of generations. The generation time of this cancer cell line had a distribution with a long tail and a heritability across generations. We determined that a minority of cell lineages exist in a slow-cycling state for multiple generations. These slow-cycling cell lineages had a higher chance of survival than the fast-cycling lineages under continuous exposure to the anticancer drug Mitomycin C. This result suggests that heritable heterogeneity in cancer cells' growth in a population influences their susceptibility to anticancer drugs.</p>
Metabolomics of KP mouse LUAD cell lines
<p>The goal of the experiment was to characterize the changes in metabolic pathway flux in these cells in response to HDAC and glutaminase inhibition. This dataset includes metabolomics of U-C13 glucose tracing (1h and 24h) and U-C13 glutamine (8h) of mouse LUAD cell lines with Kras overexpression and p53 knock-out (KP), carrying empty vector (EV) or overexpression of NRF2dNeh2 (NRF2) and treated with DMSO, Romidepsin or CB-839. 3 technical replicates were done per condition. The full methodology is described in the accompanying text file.</p>
Data from: Mouse gingival single cell transcriptomic atlas identified a novel fibroblast subpopulation activated to guide oral barrier immunity in periodontitis
<p>Periodontitis, one of the most common non-communicable diseases, is characterized by chronic oral inflammation and uncontrolled tooth supporting alveolar bone resorption. Its underlying mechanism to initiate aberrant oral barrier immunity has yet to be delineated. Here, we report a unique fibroblast subpopulation activated to guide oral inflammation (AG fibroblasts) identified in a single-cell RNA sequencing gingival cell atlas constructed from the mouse periodontitis models. AG fibroblasts localized beneath the gingival epithelium and in the cervical periodontal ligament responded to the ligature placement and to the discrete topical application of Toll-like receptor stimulants to mouse maxillary tissue. The upregulated chemokines and ligands of AG fibroblasts linked to the putative receptors of neutrophils in the early stages of periodontitis. In the established chronic inflammation, neutrophils together with AG fibroblasts appeared to induce type 3 innate lymphoid cells (ILC3s) that were the primary source of interleukin-17 cytokines. The comparative analysis of <em>Rag2-/-</em> and <em>Rag2gc-/-</em> mice suggested that ILC3 contributed to the cervical alveolar bone resorption interfacing the gingival inflammation. We propose that the AG fibroblast–neutrophil–ILC3 axis as a previously unrecognized mechanism which could be involved in the complex interplay between oral barrier immune cells contributing to pathological inflammation in periodontitis.</p>
ScienceDex guides
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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.