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31
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Dataset results
31 results for “spatial proteomics”
A proteome-wide quantitative platform for nanoscale spatially resolved extraction of membrane proteins into native nanodiscs
<p><strong>EM Quantitation:</strong></p> <p>Raw data gathered from EM images taken to determine nanodisc population size distribution.</p> <p> </p> <p><strong>NNB TGN46 analysis:</strong></p> <p>Data analysis of the Native Nanobleach experiments of TGN46 in native nanodiscs to determine population distribution of oligomeric organizations.</p> <p> </p> <p><strong>Polymer conditions:</strong></p> <p>Physiochemical characteristic and extraction conditions for all polymers in the library both commercially available and in-house.</p> <p> </p> <p><strong>Protein groups polymer screen original file:</strong></p> <p>Original output of MaxQuant data processing of polymer screen data.</p> <p> </p> <p><strong>Organelle matching:</strong></p> <p>Code used for mathcing proteins identified in the proteomics output to organelle or residence for all organellar annotations.</p> <p> </p> <p><strong>Polymer code:</strong></p> <p>Code used to process and normalize the MaxQuant output and calulate extraction efficiency across all detected proteins.</p> <p> </p> <p><strong>MAP Library Details:</strong></p> <p>Graphic and table explaining chemical details of all polymer used in the screen, both commerically available and in-house synthesized.</p> <p> </p> <p><strong>NNB TGN46:</strong></p> <p>Raw scope files for the TIRF microscopy single molecule step photobleaching experiment with TGN46.</p> <p> </p> <p><strong>Organellar Breakdown Database:</strong></p> <p>Proteins detected in the polymer screen through proteomics experiments stratified into organelle of residence.</p> <p> </p> <p><strong>Human Proteome FASTA:</strong></p> <p>The FASTA file used for proteome searching in processing the proteomics data to build the screening database.</p> <p> </p> <p><strong>Hand Curated Organellar Proteomes:</strong></p> <p>Organellar proteomes used for organellar sorting and identification of proteins detected in the screen.</p> <p> </p> <p><strong>Polymer SEC Superdex75:</strong></p> <p>Size exculsion chromatography traces for chloroSMA series of polymers. Was used to characterize length and population polydispersity.</p> <p> </p> <p><strong>Negative Stain Raw:</strong></p> <p>RAW TEM scope images of purified synaptophysin-vamp2 containing nanodiscs. Populatoin size distribution was determined.</p> <p> </p> <p><strong>FSEC Polymer CS80:</strong></p> <p>Fluoresence size exclusion chromatogram for purified synaptophysin-vamp2 containing nanodiscs to ensure population homogeneity and purity.</p> <p><strong>NMR Raw data:</strong></p> <p>NMR raw files for characterizing the in-house synthesized Chloro-SMA series and AASTY series.</p> <p> </p>
Data for 'Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis'
<p>Additional data for 'Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis'</p>
scProAtlas: an atlas of multiplexed single-cell spatial proteomics imaging in human tissues
<p>All analysis results for the spatial proteomics imaging techniques in the scProAtlas database are stored in compressed files named accordingly. Within each compressed file, the folders are organized in a fixed storage structure in the following order: Analysis module > Imaging Technique > Dataset > Tissue > ROI.</p> <p>Each folder contains the corresponding metadata (including original sample information, cell type annotations, and neighborhood annotations) stored in a file named <code>cells.tsv</code>. Additionally, the module used to identify spatial pattern genes includes an <code>anndata</code> format file, named <code>adata_moran.h5ad</code>, which stores the integrated results of scRNA-seq and spatial proteomics.</p> <p>scProAtlas_analysis_code.tar.gz contains example codes for all analysis modules in scProAtlas. Here, we provide the example using <strong>SCP_CODEX1 - Large intestine. </strong>The codes include all the scripts used for the entire workflow, from image segmentation to scRNA-spatial proteomics integration, and spatial analysis.</p> <p>We have also uploaded the raw protein channel matrices with AnnData format in <strong>version 3 and 4.</strong></p>
Spatial-DC: a robust deep learning-based method for deconvolution of spatial proteomics
<p>The processed reference and spatial proteomics datasets, along with the processed mIHC imaging data of mouse PDAC tissue are available in the repository.</p> <p>Also, the source code for pre-processing, data analysis, and generating figure and tables has been deposited in both GitHub [<a href="https://github.com/TencentAILabHealthcare/Spatial-DC">https://github.com/TencentAILabHealthcare/Spatial-DC</a>] and Zenodo [<a href="https://doi.org/10.5281/zenodo.14386585">https://doi.org/10.5281/zenodo.14386585</a>].</p> <p> </p>
Supplementary code and data for: Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE
<p>This repository contains code and data to reproduce the figures in the manuscript: Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE.</p> <p>Please refer to the readme in the repository. </p>
HDCA fetal lung spatial proteomics example datasets
<p>The data provided in this repository is published alongside <a href="https://doi.org/10.1101/2024.01.25.577163" target="_blank" rel="noopener noreferrer">this preprint</a> titled as 'High-parametric protein maps reveal the spatial organization in early-developing human lung' and <a href="https://github.com/CellProfiling/HDCA-FetalLung-SpatialProteomics" target="_blank" rel="noopener noreferrer">this code repository</a>. The preprint and GitHub repository provide further metadata and analysis information. When using the data in this repository, please cite the preprint under DOI: <a href="https://doi.org/10.1101/2024.01.25.577163" target="_blank" rel="noopener noreferrer">https://doi.org/10.1101/2024.01.25.577163</a>.</p>
Cancer-Associated Fibroblast Classification in Single-Cell and Spatial Proteomics Data
<p>ometiff: Imaging Data</p> <p>Cell Masks: Masks generated with cellprofiler from ilastik segmentation training</p> <p>cp-output_config: All relevant cellprofiler output and additional configuration files (for example clinical data) necessary to generate the single cell experiments.</p> <p>IMC Data Objects: Single cell experiment RDS files.</p> <p> </p> <p>scRNA-seq_dataobjects: .Rds files containing the clustered breast cancer, colon cancer, HNSCC, NSCLC and PDAC datasets as well as the integrated validation dataset.</p>
Spatial-proteomics reveals recombinant human laminin-111 restores adhesion-signaling and metabolic function to laminin-α2 deficient muscle
Open the record for dataset details and reuse information.
Single-cell spatial transcriptomics and proteomics of APOE Christchurch in 5xFAD and PS19 mice
Open the record for dataset details and reuse information.
Data for 'Membrane marker selection for segmenting single cell spatial proteomics data'
<p>Additional data for 'Membrane marker selection for segmenting single cell spatial proteomics data'</p>
Spatial snapshots of amyloid precursor protein intramembrane processing via early endosome proteomics
<p>Source datasets for Western blot quantification in Figures S1d and S5a for Park et al. "Spatial snapshots of amyloid precursor protein intramembrane processing via early endosome proteomics". </p>
Proteomics data for "Untargeted Spatial Metabolomics and Spatial Proteomics on the Same Tissue Section"
Open the record for dataset details and reuse information.
Immune Signature of Chronic Hand Eczema Unveiled by Spatial Transcriptomics and Single-Cell Proteomics
ClinicalTrials.gov study NCT06884163. IPD Sharing: NO. Countries: 1. Publications: 6.
MCMC files for Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE
<p>MCMC data to accompany paper </p>
HDCA fetal lung spatial proteomics images
<p>The images provided in this repository is published alongside <a href="https://doi.org/10.1101/2024.01.25.577163" target="_blank" rel="noopener noreferrer">this preprint</a> and <a href="https://github.com/CellProfiling/HDCA-FetalLung-SpatialProteomics" target="_blank" rel="noopener noreferrer">this code repository</a>. The preprint and github repository provide further metadata and analysis information. When using the images in this repository, please cite the preprint under DOI: <a href="https://doi.org/10.1101/2024.01.25.577163" target="_blank" rel="noopener noreferrer">https://doi.org/10.1101/2024.01.25.577163</a>.<span> </span></p>
Transcriptomic and proteomic spatial profiling of tertiary lymphoid aggregates in head and neck cancer reveal spatial tissue dynamics and profiles associated with immunotherapy response.
GEO Series GSE259279. Homo sapiens. 9 samples. Type: Other.
Spatial Environment Affects HNF4A Mutation-Specific Proteome Signatures and Cellular Morphology in hiPSC-Derived β-Like Cells
GEO Series GSE188827. Homo sapiens. 28 samples. Type: Expression profiling by high throughput sequencing.
Transcriptomic and proteomic spatial profiling of tertiary lymphoid aggregates in head and neck cancer reveal spatial tissue dynamics and profiles associated with immunotherapy response [protein]
GEO Series GSE259280. Homo sapiens. 375 samples. Type: Other.
The development of a high-plex spatial proteomic methodology for the characterisation of the head and neck tumour microenvironment (GeoMx WTA)
GEO Series GSE290057. Homo sapiens. 122 samples. Type: Other.
Visium spatial transcriptomics and proteomics identifies novel hepatic cell populations and transcriptomic signatures of alcohol-associated hepatitis
GEO Series GSE278662. Homo sapiens. 8 samples. Type: Other.
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.