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6,040 results for “Single-cell”
Dataset of single-cell transcriptomic matrix of 10 human glioblastoma tissue
<p>Dataset of single-cell transcriptomic matrix of 10 human glioblastoma tissue. <span>scRNA-seq was performed using the droplet-based 10x Genomics platform</span><span> </span><span>(10x Genomics, Pleasanton, CA, USA)<span>. <span>GBM tissues for single-cell RNA sequencing (<a name="_Hlk147870629"></a>scRNA-seq)</span> were collected from patients admitted to Xiangya Hospital, Central South University.</span></span></p>
Single-cell and spatial transcriptomics delineate molecular traits and immunosuppressive landscape during histological progression of lung adenocarcinoma
<p>Two specimens of lung adenocarcinoma, each corresponding to the lepidic and solid histologic patterns as confirmed through histologic scrutiny, were procured in accordance with standard surgical protocols. These specimens underwent a process of formalin fixation and were subsequently encapsulated within paraffin-embedded tissue blocks. The specimens were then sectioned and subjected to hematoxylin and eosin (H&E) staining to facilitate subsequent imaging at a resolution of 40x (equivalent to 0.25 micron/pixel) via the use of Aperio GT450 scanners. The tissue slides were then conveyed to the Genomics core, where following the decoverslipping of the tissue, the Visium CytAssist device was employed to transfer transcriptomic probes from the original glass slides to capture areas on Visium slides measuring 11mm x 11mm. Comprehensive transcriptomic profiling was achieved post mRNA permeabilization, through poly(A) capture and probe hybridization. The resultant libraries were sequenced utilizing the Illumina Novaseq 6000, using paired-end sequencing with a read length of 150 base pairs.</p>
scPerturb Single-Cell Perturbation Data: RNA and protein h5ad files
<p>Collection of h5ad files for RNA and protein single-cell perturbation datasets on scPerturb.org.</p> <p>For the associated publication, see <a href="https://www.nature.com/articles/s41592-023-02144-y">https://www.nature.com/articles/s41592-023-02144-y</a>.</p> <p>H5ad files were created using scanpy 1.9.1, using gzip compression.</p> <p> </p>
Processed data for "Characterising the evolutionary dynamics of cancer proliferation in single-cell clones with SPRINTER"
<p>This dataset contains the processed data for the figures and analyses performed in the publication "Characterising the evolutionary dynamics of cancer proliferation in single-cell clones with SPRINTER" from Lucas O., Ward S., Zaidi R., Bunkum A., ..., Zaccaria S. Nature genetics, in press, 2024.</p> <p>The processed data are separated into three respective folders:</p> <ul> <li>GT contains all the data related to the analysis of the generated ground truth datasets;</li> <li>NSCLC contains all the data related to the analysis of the NSCLC dataset;</li> <li>TNBC_HGSC contains all the data related to the analysis of the TNBC and HGSC datasets. </li> </ul>
Nonlinear methods for dimensionality reduction and clustering of bacterial single-cell sequencing data - intermediate data and figures (MSc thesis)
<p>Data, intermediate results and figures for analyses of my master's thesis in biostatistics at LMU Munich. I took a look on how to use Nonlinear Matrix Decomposition (NMD) (<a href="https://doi.org/10.1137/21M1405769">Saul, L., 2022</a>) in the context of bacterial scRNA-seq analysis (Heumos, L., et. al. 2023), replacing Principal Component Analysis in the optimized workflow, as outlined in Ostner, J. (2024).</p> <p>My thesis was structured along the following objectives:</p> <ul> <li>implement the algorithms from <a href="https://arxiv.org/abs/2305.08687">Seraghiti, G., et. al. (2023)</a> in the Python module <a href="https://github.com/flatironinstitute/nomad/">nomad</a> in cooperation with <a href="https://www.simonsfoundation.org/flatiron/" rel="nofollow">Flatiron Institute</a></li> <li>code for the simulation study of the algorithms in <a href="https://arxiv.org/abs/2305.08687">Seraghiti, G., et. al. (2023)</a> with varying sparsity can be found in <code>/simulation</code></li> <li>apply NMD in the context of the BacSC workflow (<a href="https://www.biorxiv.org/content/10.1101/2024.06.22.600071v1">Ostner, J., et. al. (2024)</a>) on raw and normalized counts (found in <code>/application/analysis</code>), also for manually set number of latent dimensions</li> <li>explore NMD's potential for imputation of <a href="https://www.nature.com/articles/s41467-021-27729-z" rel="nofollow">sampling zeros</a> (check <code>/application/NMD_zero_imputation /</code>)</li> <li>potential of Poisson-Hurdle model-based clustering (<a href="https://academic.oup.com/bioinformatics/article/39/1/btac782/6873739">Qiao, Z., et. al. (2023)</a>) for scRNA-seq (<code>/application/poisson_hurdle</code>).</li> </ul>
Spatially resolved single-cell atlas unveils a distinct cellular signature of fatal lung COVID-19 in a Malawian population
<p>This record contains all the fully processed single cell RNA sequencing objects included in the paper Nyirenda et al (2024) '<em>Spatially resolved single-cell atlas unveils a distinct cellular signature of fatal lung COVID-19 in a Malawian population</em>'. </p> <h3>Abstract</h3> <p><em>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. </em></p> <h3>Figure 3</h3> <p>COSMIC_Lung_Atlas.h5ad</p> <p>COSMIC_Lung_Stromal_Atlas.h5ad</p> <p>COSMIC_Lung_Immune_Atlas.h5ad</p> <h3>Figure 4</h3> <p>COSMIC_HCLA_Integration.rds</p> <h3>Figure 5</h3> <p>COSMIC_Nasal_Atlas.h5ad</p> <p>COSMIC_Blood_Atlas.h5ad</p> <h3>Total Post-mortem COVID-19 Lung Autopsy Papers Integration</h3> <p>This data object was generated whereby all publicly available fatal COVID-19 lung single cell cohorts were integrated together. We include this analysis to show that the observed interferon responses detailed in the paper are preserved in not only the HLCA, but also existing post-mortem lung cohorts. The codebase for this additional analysis can be found here: <strong><a href="https://github.com/olympiahardy/Malawi_Integration">https://github.com/olympiahardy/Malawi_Integration</a> </strong></p> <p>COSMIC_Delorey_Melms_Integration.rds</p> <p> </p> <p> </p> <h3>Imaging Mass Cytometry Analysis</h3> <p>This Zenodo record can be accessed using the DOI here: <strong><a href="10.5281/zenodo.13899297">10.5281/zenodo.13899297</a> </strong>. The GitHub repository can be accessed here:<strong> <a href="https://github.com/joaolsf/Spatial_Single_Cell_Lung_Atlas_Malawi_COVID">https://github.com/joaolsf/Spatial_Single_Cell_Lung_Atlas_Malawi_COVID</a></strong></p> <p> </p>
Code and data of "Uncovering disease-related multicellular pathway modules on large-scale single-cell transcriptomes with scPAFA"
<p>Code and data to reproduce the analyses and figures presented in "Uncovering disease-related multicellular pathway modules on large-scale single-cell transcriptomes with scPAFA"</p>
Single-Cell Transcriptomics Reveals a Heterogeneous Cellular Response to BK Virus Infection
<p>The files are the Indrops count matrices for BKV and Mock samples corresponding to the 8 experiments and samples described in the Bioproject https://www.ncbi.nlm.nih.gov/bioproject/PRJNA715178</p> <p> </p>
Imaging Mass Cytometry Images (APP1) from: A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially resolved tissue phenotyping at single-cell resolution.
<p>Four µm-thick sections were cut from the APP1 FFPE block with a microtome and used for staining with a panel of 26 antibodies targeting the main immune, stromal and epithelial cell populations of the gastrointestinal tract (Supplementary Table 2). The optimal dilution of each antibody in the panel was identified by staining and ablating FFPE appendix sections. The resulting images were reviewed by a mucosal immunologist (J.S.) and the dilution giving the best signal to background ratio was selected for each antibody (Supplementary Table 2). To perform the staining for IMC, slides were dewaxed after a one-hour incubation at 60°C, rehydrated and heat-induced antigen retrieval was performed with a pressure cooker in Antigen Retrieval Reagent-Basic (R&D Systems). Slides were incubated in a 10% BSA (Sigma), 0.1% Tween (Sigma), and 2% Kiovig (Shire Pharmaceuticals) Superblock Blocking Buffer (Thermo Fisher) blocking solution at room temperature for two hours. Each antibody was added to a primary antibody mix at the selected concentration in blocking solution and incubated overnight at 4°C. After two washes in PBS and PBS-0.1% Tween, the slides were treated with the DNA intercalator Cell-ID™ Intercalator-Ir (Fluidigm) (containing the two iridium isotopes 191Ir and 193Ir) 1.25 mM in a PBS solution. After a 30-minute incubation, the slides were washed once in PBS and once in MilliQ water and air-dried. The stained slides were then loaded in the Hyperion Imaging System (Fluidigm) imaging module to obtain light-contrast high resolution images of approximately four mm<sup>2</sup>. These images were used to select the ROI in each slide. For APP1, a one mm<sup>2</sup> ROI containing a lymphoid follicle in its whole depth alongside a portion of lamina propria and of epithelium was selected. ROIs were ablated at a o µm/pixel resolution and 200 Hz frequency.</p>
A single-cell atlas of multiple myeloma disease evolution
<p>A collection of computationally integrated (batch corrected) scRNA-seq and scTCR-seq data from a large cohort of bone marrow and peripheral blood samples from patients with multiple myeloma, myeloma precursor disease (MGUS and SMM) and non-cancer controls.</p> <h3>Datasets</h3> <ul> <li><strong>dataDescription.xlsx</strong> - Definition of dataset components.</li> <li><strong>panImmune.h5ad</strong> - All cells types integrated gene expression object.</li> <li><strong>Tcell.h5ad</strong> - T cells integrated object.</li> <li><strong>Tcell-scTCR.csv</strong> - Single-cell TCR data.</li> <li><strong>Botta_2023-predicted.h5ad</strong> - Single-cell gene expression data from Botta et al. analysed by label transfer.</li> <li><strong>metadata.donor.csv</strong> - Donor metadata.</li> </ul> <h3>Published data acquisition</h3> <p>Data shared through the gene expression omnibus (GEO) can be accessed for Maura et al. under accession GSE161195, Bailur et al. GSE163278, Oetjen et al. GSE120221, Granja et al. GSE139369, Zavidij et al. GSE124310, Kfoury et al. GSE143791, Botta et al. GSE205393, and Zheng et al. GSE156728. Data shared via dbGaP for Sklavenitis-Pistofidis et al. can be accessed under accession phs002476.v1.p1. Data shared online can be accessed for Stephenson et al. (via <a href="https://covid19cellatlas.org/">https://covid19cellatlas.org/</a>), Conde et al. (via <a href="https://www.tissueimmunecellatlas.org/">https://www.tissueimmunecellatlas.org/</a>), and Liu et al. (via <a href="https://explore.data.humancellatlas.org/projects/2ad191cd-bd7a-409b-9bd1-e72b5e4cce81">https://explore.data.humancellatlas.org/projects/2ad191cd-bd7a-409b-9bd1-e72b5e4cce81</a>). Single-cell data from <a href="https://doi.org/10.1016/j.ccell.2022.10.017" target="_blank" rel="noopener">Sklavenitis-Pistofidis et al.</a> and extended clinical data from <a href="https://doi.org/10.1038/s43018-023-00657-1">Maura et al.</a> (i.e. paraprotein) are omitted and must be acquired through direct contact with these authors.</p> <h3>Contact information </h3> <ul> <li>Kane Foster - kane.foster.web@gmail.com</li> <li>Kwee Yong - kwee.yong@ucl.ac.uk</li> </ul> <p>Please cite <a href="https://www.medrxiv.org/content/10.1101/2024.06.22.24309250v1">our pre-print</a> if you use this resource in your research.</p>
Data for - Tracking one-in-a-million: Large-scale benchmark for microbial single-cell tracking with experiment-aware robustness metrics
<p><strong>Large-scale Corynebacterium glutamicum data set with Segmentation and Tracking Annotation</strong></p> <p>We provide five time-lapse sequences with manually corrected segmentation and tracking annotations of growing <strong><em>C. glutamicum</em></strong> cultivations. The dataset contains more than 1.4 million cell observations in 29k cell tracks and 14k cell divisions. We provide videos of the annotations (videos.zip) and the dataset in <a href="http://celltrackingchallenge.net/datasets/">Cell Tracking Challenge</a> format (ctc_format.zip). In the videos, cell contours are rendered in yellow, cell links between frames are colored red and cell divisions, and their links are colored in blue.</p> <p><strong>Data Acquisition</strong></p> <p><strong><em>Corynebacterium glutamicum</em></strong> ATCC 13032 was cultivated in BHI-medium at 30°C in this study. From and overnight preculture, the main culture was inoculated the next day with a starting OD600 of 0.05 and grown at 120 rpm to a OD600 of 0.25. A chip was fabricated, according to <a href="https://doi.org/10.1039/D0LC00711K">(Täuber et al., 2020)</a>, and fixed to the microscope’s holder. The main culture cells were transferred to monolayer growth chambers (height = 720 nm) on the microfluidic chip. Flow through the microfluidic device was mediated by pressure driven pumps with a pressure of 100 mbar on the medium reservoir.</p> <p>The time-lapse phase contrast images of five monolayer growth chambers were taken every minute using an inverted microscope (Nikon Eclipse Ti2) with a 100x oil emersion objective and a DS-QI2 camera (Nikon) at 15 % relative DIA-illumination intensity and 100 ms exposure time. The spatial image resolution is 0.072 μm/px.</p>
single-cell RNAseq data (data set 1) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset1) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from CRC samples downloaded from the GEO website (<strong>GSE81861). </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
A single-cell transcriptional gradient in human cutaneous memory T cells restricts Th17/Tc17 identity
<p> </p> <p>In our manuscript, we utilized scRNA-seq libraries we generated from:</p> <p>-8 human psoriatic skin samples and 7 healthy control skin samples (ZIST.rds)</p> <p>-3 human psoriatic skin samples before and after tildrakizumab treatment (three_tildra_Trm1.rds) </p> <p>These *rds files are the Seurat objects for these data sets post-filtering and integration. For raw sequencing data corresponding to these samples, access can be found under accession number EGA: S00001005271.</p> <p>We also utilized bulk RNAseq data generated from CRISPR-Cas9 knockout of ZFP36L2 in human CD4 T cells from 3 different donors. The count matrices for each of these individual samples is uploaded here alongside a key explaining what each of the samples are with filenames that also correspond to the raw fastq files submitted at the European Genome-Phenome Archive (EGA), under accession number EGA: S00001005271. There are two replicates for each sample.</p> <p>All methods underlying the generation and analysis of these datasets can be found in the original manuscript: </p> <p>Cook CP, Taylor M, Liu Y, et al. A single-cell transcriptional gradient in human cutaneous memory T cells restricts Th17/Tc17 identity. <em>Cell Rep Med</em>. 2022;3(8):100715. doi:10.1016/j.xcrm.2022.100715</p> <p><br> Any additional questions or information requests can be addressed to Jeffrey.cheng@ucsf.edu or cook.675@berkeley.edu</p> <p> </p>
The adapted Activity-By-Contact model for enhancer-gene assignment and its application to single-cell data
<p>In our work, we implemented the ABC-model and could show that one assay for measuring the openness of enhancers is sufficient. Further, we propose a generalised calculation of the ABC-score, which describes enhancer activity in a gene-specific manner, and which includes all TSS, without requiring any additional data. We combined our implementation of the ABC-score with an approach to quantify TF binding affinity into STARE: a framework to derive TF affinities to genes. STARE was also designed for potential application on single-cell data. You can find the code in our <a href="https://github.com/schulzlab/stare">GitHub repository</a> and more details in our <a href="https://doi.org/10.1093%2Fbioinformatics%2Fbtad062">publication</a>.</p> <p>We provide the data for the validation of our ABC-implementation on two CRISPR-screens. We also provide the results of our analysis of single-cell data of the human heart with STARE. All data is in hg19.</p> <p>Content:</p> <ul> <li>CRISPRi_screens: One file for each CRISPRi-screen with interactions that were used to plot precision-recall curves, containing columns for different ABC scoring versions.</li> <li>Enformer: Similar to the CRISPRi_screens, but containing columns for different calculations for Enformer's predicted expression change upon in silico mutagenesis of the enhancer region.</li> <li>K562_CandidateEnhancer: K562 enhancer with the 4th column for enhancer activity, one file for each activity representation that was measured.</li> <li>K562_ABC_Predictions: Regular ABC-scores and generalised ABC-scores for each activity measurement. The files contain all scored interactions for a 10MB window, without any cut-off. We also included the results of the implementation of the ABC-score of Fulco et al. (2019).</li> <li>STARE_Hocker_*: Whole STARE output for human heart single-cell data, one for regular ABC, generalised ABC, generalised ABC with average Hi-C matrix and one based on co-accessibility analysis. All approaches were run with a 5 MB window (except for GeneralisedABC500kb), the ABC-based runs with a score cut-off of 0.02. Each folder contains two subdirectories, one for the ABC-scoring and one for the Gene-TF affinity matrices. The 'ABC_output' also contains a GeneInfo file for each cell type, summarising different attributes per gene.</li> <li>INVOKE_Hocker_*: Folder with the input and output of INVOKE (see https://github.com/schulzlab/tepic), based on the STARE runs. CS genes stands for cell type-specific genes, defined as genes with a z-score across cell types of ≥ 2 and TPM ≥ 0.5. The INVOKE commands were as follows: <ul> <li>Rscript INVOKE.R --dataDir=<TF-Gene matrix> --outDir=<out_path> --response=Expression --regularization=E --performance=TRUE --outerCV=10 --seed=1234</li> </ul> </li> </ul> <p>Importantly, the results are based on data from the following publications:</p> <ul> <li>CRISPRi-screens: <ul> <li>Gasperini, Molly, Andrew J. Hill, José L. McFaline-Figueroa, Beth Martin, Seungsoo Kim, Melissa D. Zhang, Dana Jackson, et al. “A Genome-Wide Framework for Mapping Gene Regulation via Cellular Genetic Screens.” <em>Cell</em> 176, no. 1–2 (January 2019): 377-390.e19. https://doi.org/10.1016/j.cell.2018.11.029.</li> <li> <p>Schraivogel, Daniel, Andreas R. Gschwind, Jennifer H. Milbank, Daniel R. Leonce, Petra Jakob, Lukas Mathur, Jan O. Korbel, Christoph A. Merten, Lars Velten, and Lars M. Steinmetz. “Targeted Perturb-Seq Enables Genome-Scale Genetic Screens in Single Cells.” <em>Nature Methods</em> 17, no. 6 (June 2020): 629–35. https://doi.org/10.1038/s41592-020-0837-5.</p> </li> <li> <p>Fulco, Charles P., Joseph Nasser, Thouis R. Jones, Glen Munson, Drew T. Bergman, Vidya Subramanian, Sharon R. Grossman, et al. “Activity-by-Contact Model of Enhancer–Promoter Regulation from Thousands of CRISPR Perturbations.” <em>Nature Genetics</em> 51, no. 12 (December 2019): 1664–69. https://doi.org/10.1038/s41588-019-0538-0.</p> </li> </ul> </li> <li>Enformer model: Avsec, Žiga, Vikram Agarwal, Daniel Visentin, Joseph R. Ledsam, Agnieszka Grabska-Barwinska, Kyle R. Taylor, Yannis Assael, John Jumper, Pushmeet Kohli, and David R. Kelley. “Effective Gene Expression Prediction from Sequence by Integrating Long-Range Interactions.” <em>Nature Methods</em> 18, no. 10 (October 2021): 1196–1203. https://doi.org/10.1038/s41592-021-01252-x.</li> <li>K562 predictions and average Hi-C matrix: Fulco, Charles P., Joseph Nasser, Thouis R. Jones, Glen Munson, Drew T. Bergman, Vidya Subramanian, Sharon R. Grossman, et al. “Activity-by-Contact Model of Enhancer–Promoter Regulation from Thousands of CRISPR Perturbations.” <em>Nature Genetics</em> 51, no. 12 (December 2019): 1664–69. https://doi.org/10.1038/s41588-019-0538-0.</li> <li>Hi-C matrix for K562 predictions: Rao, S. et al. (2014). A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping. Cell, 159(7), 1665–1680</li> <li>STARE and INVOKE runs: Hocker, J. D. et al. (2021). Cardiac cell type–specific gene regulatory programs and disease risk association. Science Advances, 7(20), eabf1444</li> <li>H3K27ac HiChIP for STARE runs: Anene-Nzelu, C. G. et al. (2020). Assigning Distal Genomic Enhancers to Cardiac Disease–Causing Genes. Circulation, 142(9), 910–912</li> <li>INVOKE software: Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction Schmidt et al., Nucleic Acids Research 2016; doi: 10.1093/nar/gkw1061</li> </ul> <p> </p>
Single-cell naïve IgM VH:VL sequence data from 22 Kymice
<p>Single-cell VH:VL sequencing data derived from naïve B-cells isolated from 22 Kymice. This dataset is published as part of the review process for the following preprint: https://www.biorxiv.org/content/10.1101/2022.06.27.497709v1. </p>
pSCoPE: Prioritized Single-Cell Proteomics (data for generating publication figures)
<p>Major aims of single-cell proteomics include increasing the consistency, sensitivity, and depth of protein quantification, especially for proteins and modifications of biological interest. To simultaneously advance all these aims, we developed prioritized Single Cell ProtEomics (pSCoPE). pSCoPE consistently analyzes thousands of prioritized peptides across all single cells (thus increasing data completeness) while analyzing identifiable peptides at full duty-cycle, thus increasing proteome depth. These strategies increased the sensitivity, data completeness, and proteome coverage over 2-fold. The gains enabled quantifying protein variation in untreated and lipopolysaccharide-treated primary macrophages. Within each condition, proteins covaried within functional sets, including phagosome maturation and proton transport. This protein covariation within a treatment condition was similar across the treatment conditions and coupled to phenotypic variability in endocytic activity. pSCoPE also enabled quantifying proteolytic products, suggesting a gradient of cathepsin activities within a treatment condition. pSCoPE is freely available and widely applicable, especially for analyzing proteins of interest without sacrificing proteome coverage. Support for pSCoPE is available at: <a href="http://scp.slavovlab.net/pSCoPE">scp.slavovlab.net/pSCoPE</a></p> <p> </p> <p>The files contained in this .zip directory are necessary for replicating the analysis and figures associated with the pSCoPE manuscript.</p> <p> </p>
Single-cell profiling identifies ACE+ granuloma macrophages as a non-permissive niche for intracellular bacteria during persistent Salmonella infection
<p>Macrophages mediate key antimicrobial responses against intracellular bacterial pathogens, such as <em>Salmonella enterica</em>. Yet, they can also act as a permissive niche for these pathogens to persist in infected tissues within granulomas, which are immunological structures comprised of macrophages and other immune cells. We apply single-cell transcriptomics to investigate macrophage functional diversity during persistent <em>Salmonella</em> <em>enterica</em> serovar Typhimurium (<em>S</em>Tm) infection in mice. We identify determinants of macrophage heterogeneity in infected spleens and describe populations of distinct phenotypes, functional programming, and spatial localization. Using a <em>S</em>Tm mutant with impaired ability to polarize macrophage phenotypes, we find that angiotensin converting enzyme (ACE) defines a granuloma macrophage population that is non-permissive for intracellular bacteria and their abundance anticorrelates with tissue bacterial burden. Disruption of pathogen control by neutralizing TNF is linked to preferential depletion of ACE<sup>+</sup> macrophages in infected tissues. Thus ACE<em><sup>+</sup></em> macrophages have limited capacity to serve as cellular niche for intracellular bacteria to establish persistent infection.</p>
Dual-modality imaging of immunofluorescence and imaging mass cytometry for high-resolution whole slide imaging with accurate single-cell segmentation
<p>Imaging mass cytometry (IMC) is a powerful multiplexed tissue imaging technology that allows simultaneous detection of more than 30 makers on a single slide. It has been increasingly used for single-cell based spatial phenotyping in a wide range of samples. However, it only acquires a small, rectangle field of view (FOV) with a low image resolution that hinders downstream analysis. Here, we reported a highly practical dual-modality imaging method that combines high-resolution immunofluorescence (IF) and high-dementional IMC on the same tissue slide. Our computational pipeline uses the whole slide image (WSI) of IF as spatial reference, integrates small FOV IMC into a WSI of IMC. The high-resolution IF images enable accurate single-cell segmentation to extract robust high-dimensional IMC features for downstream analysis. We applied this method in esophageal adenocarcinoma of different stages, identified the single-cell pathology landscape via reconstruction of WSI IMC images and demonstrated the advantage of the dual-modality imaging strategy.</p>
Training material for the mapping and quantification of single-cell ATAC-seq 10X Datasets
<p>The data provided here is part of the Galaxy Training Network tutorial that analyses 10x genomics single-cell ATAC-seq data from the 10x platform. The original data is from 1k Peripheral Blood Mononuclear Cells (PBMCs) from a Healthy Donor.</p> <p>Due to time constraints during training, the datasets were subsampled to reads that map to chromosome 21 only.</p> <p>The 10x Genomics Datasets follow the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution</a> license.</p> <p>There is an additional count matrix in Anndata format created from full datasets.</p>
Microscopy data for the paper: Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise.
<p>Microscopy data for the paper: Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise.</p> <p> </p> <p>List of files used for each dataset.</p> <p> </p> <p>Dataset 0 : MS2-CY5_Cyto543_560_woStim</p> <p> Images in the dataset :</p> <p> ROI001_XY1657814108_Z00_T0_merged.tif - Image Id Number: 0</p> <p> ROI002_XY1657815441_Z00_T0_merged.tif - Image Id Number: 1</p> <p> ROI003_XY1657814110_Z00_T0_merged.tif - Image Id Number: 2</p> <p> ROI004_XY1657814111_Z00_T0_merged.tif - Image Id Number: 3</p> <p> ROI005_XY1657814112_Z00_T0_merged.tif - Image Id Number: 4</p> <p> ROI006_XY1657814113_Z00_T0_merged.tif - Image Id Number: 5</p> <p> ROI007_XY1657814114_Z00_T0_merged.tif - Image Id Number: 6</p> <p> ROI008_XY1657814115_Z00_T0_merged.tif - Image Id Number: 7</p> <p> ROI009_XY1657814116_Z00_T0_merged.tif - Image Id Number: 8</p> <p> ROI010_XY1657814117_Z00_T0_merged.tif - Image Id Number: 9</p> <p> ROI011_XY1657814118_Z00_T0_merged.tif - Image Id Number: 10</p> <p> ROI012_XY1657814119_Z00_T0_merged.tif - Image Id Number: 11</p> <p> </p> <p>Datset 1 : MS2-CY5_Cyto543_560_18minTPL_5uM</p> <p> Images in the dataset :</p> <p> ROI001 - Position 1_XY1657818948_Z00_T0_merged.tif - Image Id Number: 0</p> <p> ROI001 - Position 2_XY1657818949_Z00_T0_merged.tif - Image Id Number: 1</p> <p> ROI001 - Position 4_XY1657818951_Z00_T0_merged.tif - Image Id Number: 2</p> <p> ROI001 - Position 5_XY1657818952_Z00_T0_merged.tif - Image Id Number: 3</p> <p> ROI001 - Position 6_XY1657818953_Z00_T0_merged.tif - Image Id Number: 4</p> <p> ROI001 - Position 7_XY1657818954_Z00_T0_merged.tif - Image Id Number: 5</p> <p> ROI001 - Position 8_XY1657818955_Z00_T0_merged.tif - Image Id Number: 6</p> <p> ROI001 - Position 9_XY1657818956_Z00_T0_merged.tif - Image Id Number: 7</p> <p> ROI001 - Position 10_XY1657818957_Z00_T0_merged.tif - Image Id Number: 8</p> <p> ROI001 - Position 11_XY1657818958_Z00_T0_merged.tif - Image Id Number: 9</p> <p> ROI001 - Position 12_XY1657818959_Z00_T0_merged.tif - Image Id Number: 10</p> <p> </p> <p>Dataset 2: MS2-CY5_Cyto543_560_5hTPL_5uM</p> <p> Images in the datset :</p> <p> ROI001_XY1657822809_Z00_T0_merged.tif - Image Id Number: 0</p> <p> ROI002_XY1657822933_Z00_T0_merged.tif - Image Id Number: 1</p> <p> ROI003_XY1657822934_Z00_T0_merged.tif - Image Id Number: 2</p> <p> ROI005_XY1657822936_Z00_T0_merged.tif - Image Id Number: 3</p> <p> ROI006_XY1657822937_Z00_T0_merged.tif - Image Id Number: 4</p> <p> ROI007_XY1657822938_Z00_T0_merged.tif - Image Id Number: 5</p> <p> ROI008_XY1657822939_Z00_T0_merged.tif - Image Id Number: 6</p> <p> ROI010_XY1657822941_Z00_T0_merged.tif - Image Id Number: 7</p> <p> ROI013_XY1657822944_Z00_T0_merged.tif - Image Id Number: 8</p> <p> ROI014_XY1657822945_Z00_T0_merged.tif - Image Id Number: 9</p> <p> ROI015_XY1657822946_Z00_T0_merged.tif - Image Id Number: 10</p> <p> ROI016_XY1657822947_Z00_T0_merged.tif - Image Id Number: 11</p> <p> ROI017_XY1657822948_Z00_T0_merged.tif - Image Id Number: 12</p> <p> ROI018_XY1657822949_Z00_T0_merged.tif - Image Id Number: 13</p> <p> </p> <p> </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.