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76 results for “Spatial omics”
Development of Multiomics in situ Pairwise Sequencing (MiP-Seq) for Single-cell Resolution Multidimensional Spatial Omics
<p>The original data used in the article: Development of Multiomics in situ Pairwise Sequencing (MiP-Seq) for Single-cell Resolution Multidimensional Spatial Omics</p> <p>Delineating the spatial multiomics landscape will pave the way to understanding the molecular basis of physiology and pathology. However, current spatial omics technology development is still in its infancy. Here, we developed a high-throughput targeted in situ sequencing strategy, multiomics in situ pairwise sequencing (MiP-Seq), to efficiently decipher multiplexed DNAs, RNAs, proteins, and small biomolecules at subcellular resolution. MiP-Seq simultaneously sequenced the dual barcode base of padlock probes, dramatically increasing the detection capacity to 10N by N rounds of sequencing. We delineated spatial gene profiles in the hypothalamus using MiP-Seq. Moreover, MiP-Seq was unitized to detect tumor gene mutations and allele-specific expression of parental genes and to differentiate sites with and without the m6A RNA modification at specific sites. MiP-Seq was combined with in vivo Ca2+ imaging and Raman imaging to obtain a spatial multiomics atlas correlated to neuronal activity and cellular biochemical fingerprints. Importantly, we proposed a “signal dilution strategy” to resolve the crowded signals that challenge the applicability of in situ sequencing. Together, our method improves spatial multiomics and precision diagnostics, and facilitates analyzing cell function in connection with gene profiles.</p>
Characterizing cell-type spatial relationships across length scales in spatially resolved omics data: data repository
<h1>CRAWDAD</h1> <p>Spatially resolved omics (SRO) technologies enable the identification of cell types while preserving their organization within tissues. Application of such technologies offers the opportunity to delineate cell-type spatial relationships, particularly across different length scales, and enhance our understanding of tissue organization and function. To quantify such multi-scale cell-type spatial relationships, we develop CRAWDAD, Cell-type Relationship Analysis Workflow Done Across Distances, as an open-source R package with source code and additional documentation at https://jef.works/CRAWDAD/.</p> <p>During CRAWDAD's development, we generated simulated datasets and new cell-type annotations for human spleen data, provided here. The external datasets such as the mouse cerebellum, mouse embryo, mouse brain, and human breast cancer data used in the paper can be found in their original publication. See more information in CRAWDAD's data availability statement.</p> <h2>Simulated Datasets</h2> <ul> <li>sim.csv: the simulated data. Used in Figure 1 b-g, Supplementary Figure 1 a-c, and Supplementary Figure 9 a-b.</li> <li>ext_sim.csv: the extended simulated data. Used in Supplementary Figure 1 d-f.</li> <li>null_sim_visualization.csv: the null simulated data. Used to generate the plots Supplementary Figure 2 a-d.</li> <li>null_sim_1.csv - null_sim_10.csv: the 10 null simulated datasets. Used to quantitatively compare CRAWDAD, Squidpy’s co-occurrence implementation, and Ripley’s K Cross.</li> </ul> <h2>HuBMAP Datasets</h2> <ul> <li>pkhl.csv: annotated cell types and positions of sample HBM389.PKHL.936 from donor HBM966.VNKN.965. Used in Figure 5 a-h, Supplementary Figure 5 a, Supplementary Figure 7 a-c, and Supplementary Figure 8 c. doi:10.35079/HBM389.PKHL.936</li> <li>xxcd.csv: annotated cell types and positions of sample HBM772.XXCD.697 from donor HBM966.VNKN.965. Used in Figure 5 d-h, Supplementary Figure 5 a-c, and Supplementary Figure 7 a-c. doi:10.35079/HBM772.XXCD.697</li> <li>fsld.csv: annotated cell types and positions of sample HBM342.FSLD.938 from donor HBM245.ZWNT.288. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM342.FSLD.938</li> <li>pbvn.csv: annotated cell types and positions of sample HBM825.PBVN.284 from donor HBM245.ZWNT.288. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM825.PBVN.284</li> <li>ksfb.csv: annotated cell types and positions of sample HBM556.KSFB.592 from donor HBM298.KGNJ.374. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM556.KSFB.592</li> <li>ngpl.csv: annotated cell types and positions of sample HBM568.NGPL.345 from donor HBM298.KGNJ.374. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM568.NGPL.345</li> </ul> <h2>External Datasets</h2> <ul> <li>Mouse cerebellum: Used in Figure 2 a-e, Supplementary Figure 3 a-b, Supplementary Figure 4 a-d, and Supplementary Figure 8 a.</li> <li>Mouse embryo: Used in Figure 2 f-j, Supplementary Figure 3 c-d, Supplementary Figure 4 e-h, and Supplementary Figure 8 b.</li> <li>Human breast cancer: Used in Figure 3 a-c.</li> <li>Mouse brains: Used in Figure 4 a-e.</li> </ul>
Integrative spatial omics reveals distinct tumor-promoting multicellular niches and immunosuppressive mechanisms in African American and European American patients with TNBC (Spatial Transcriptomic 10X Visium portion)
<p>Racial disparities in triple-negative breast cancer (TNBC) outcomes have been reported. However, the biological mechanisms underlying these disparities remain unclear. We integrated imaging mass cytometry and spatial transcriptomics, to characterize the tumor microenvironment (TME) of African American (AA) and European American (EA) patients with TNBC. The TME in AA patients was characterized by interactions between endothelial cells, macrophages, and mesenchymal-like cells, which were associated with poor patient survival. In contrast, the EA TNBC-associated niche is enriched in T-cells and neutrophils suggestive of an exhaustion and suppression of otherwise active T cell responses. Ligand-receptor and pathway analyses of race-associated niches found AA TNBC to be “immune cold” and hence immunotherapy resistant tumors, and EA TNBC as ‘inflamed’ tumors that evolved a distinctive immunosuppressive mechanism. Our study revealed the presence of racially distinct tumor-promoting and immunosuppressive microenvironments in AA and EA patients with TNBC, which may explain the poor clinical outcomes.</p> <p> </p> <p>This dataset contains the 10X Visium Spatial Transcriptomic data of TNBC patients. There are two cohorts.</p> <p> </p> <p><strong>Baylor Scott and White (BSW) cohort</strong>: <strong>10x.visium.tar.gz</strong>, containing 10 patients with TNBC from Baylor Scott and White affiliated Hospital. </p> <p>Each sample is made of Space Ranger processed spot-separated gene expression data (processed to HDF5 AnnData file). There are also H&E images, and spot coordinate files available. </p> <p> </p> <p>For <strong>Georgia validation cohort</strong>, 400 genes used for validation of ESG signatures (associated with BA-Community 1 and WA-Community-1) were obtained and provided by Ritu Aneja's lab. These 400 genes' spot-based expression data across Black and White TNBC patients are provided. See file <strong>georgia.validation.visium.tar.gz</strong>. Expression was normalized by total counts per spot, followed by log-normalization by Giotto.</p> <p> </p> <p>As well in our paper, we integrated a published racial TNBC cohort for deriving some of initial results in the paper. This refers to the Bassiouni et al (Cancer Research) paper in Carpten's group. <strong>GSM_giotto_processed.tar.gz</strong> refers to this dataset, which we deposit here. The data were normalized by Giotto using standard procedure.</p>
Smoother: a unified and modular framework for incorporating structural dependency in spatial omics data: intermediate results
<p>This directory contains all intermediate results generated in the "Smoother: a unified and modular framework for incorporating structural dependency in spatial omics data" for reproducibility purpose. Associated scripts are available at https://github.com/JiayuSuPKU/Smoother_paper. Raw and processed data can be downloaded at https://zenodo.org/records/10223862.</p><p>Note that some intermediate results and figures in the notebooks may not be exactly the same as those in the paper due to the randomness in some analysis steps, but should be very close.</p>
Smoother: a unified and modular framework for incorporating structural dependency in spatial omics data: raw and processed data files
<p>This directory contains all spatial and single-cell omics datasets analyzed in the "Smoother: a unified and modular framework for incorporating structural dependency in spatial omics data". Associated scripts are available at https://github.com/JiayuSuPKU/Smoother_paper. See https://github.com/JiayuSuPKU/Smoother_paper/blob/main/data/README.md for details on folder structure and data sources.</p>
Raw Data - Part 2 : Spatial multi-omic map of human myocardial infarction
<p>We provide here the raw data of snATAC-seq and snRNA-seq for the manuscript: Kuppe, Ramirez Flores, Li et al. "Spatial multi-omic map of human myocardial infarction", 2022</p>
Raw Data - Part 4 : Spatial multi-omic map of human myocardial infarction ---- Raw image
<p>We provide here the raw image for the visium data for the manuscript: Kuppe, Ramirez Flores, Li et al. "Spatial multi-omic map of human myocardial infarction", 2022</p>
Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma
<p><span>Glioblastomas are malignant tumors of the central nervous system hallmarked by subclonal diversity and dynamic adaptation amid developmental hierarchies </span><span>(Couturier et al., 2020; Neftel et al., 2019; Richards et al., 2021)</span><span>. The source of the dynamic reorganization within the spatial context of these tumors remains elusive. Here, we characterized glioblastomas in-depth by spatially resolved transcriptomics, metabolomics, and proteomics. By </span><span>deciphering regionally shared transcriptional programs across patients, </span><span>we infer that glioblastoma is organized by spatial segregation of lineage states and adapt to inflammatory and/or metabolic stimuli, </span><span>reminiscent </span><span>of the reactive transformation in</span> <span>mature astrocytes. Integration of metabolic imaging and imaging mass cytometry uncovered locoregional tumor-host interdependence, resulting in spatially exclusive adaptive transcriptional programs. Inferring copy-number alterations emphasizes a spatially cohesive organization of subclones associated with reactive transcriptional programs, confirming that environmental stress gives rise to selection pressure. A model of glioblastoma stem cells implanted into human and rodent neocortical tissue mimicking various environments confirmed that transcriptional states originate from dynamic adaptation to various environments.</span></p>
Processed CODEX Datasets from - Discovery and Generalization of Tissue Structures from Spatial Omics Data
<p>This entry provides access to processed CODEX data files of four studies analyzed in the article "Discovery and Generalization of Tissue Structures from Spatial Omics Data". Details of datasets can be found in the STAR Methods section of the article.</p> <p>For each dataset, a zip file containing multiple comma-separated values (CSV) files is included.</p> <p>Each region is assigned an unique identifier (e.g., DKD_kidney_001), and its related data files are:</p> <ul> <li>`{region_id}.cell_data.csv`, a table containing three columns: "CELL_ID", "X", and "Y". This table provides centroid locations for all cells segmented in this region.</li> <li>`{region_id}.expression.csv`, a table containing multiple columns: "CELL_ID", "DAPI", "CD45", etc. This table provides detailed protein biomarker expression quantified for all cells in this region.</li> <li>`{region_id}.scgp_annotations.csv`, a table containing two columns: "CELL_ID" and "SCGP". This table provides SCGP/SCGP-Extension annotations for all cells in this region.</li> </ul> <p>Code base for SCGP is also included in this entry. Please refer to <a href="https://gitlab.com/enable-medicine-public/scgp">https://gitlab.com/enable-medicine-public/scgp</a> for the latest codes, questions, and/or issues. Raw CODEX data and images will be accessible through links posted at the code base. Raw data will also be available from lead contact (A.E.T.) upon request.</p>
High-resolution spatial multi-omics datasets
<p>Supplementary raw data. The raw microscopy data are not uploaded owing to their large size (6.8 Tb), but are available upon reasonable request (Long Cai: lcai@caltech.edu, Yodai Takei: ytakei@caltech.edu).</p> <p>These supplementary data contain additional files for RNA seqFISH+, DNA seqFISH+, and sequential immunofluorescence from cell culture and adult mouse cerebellum experiments.</p> <p>DNA seqFISH+ datasets (provided as a tar.gz folder for each replicate): Super-resolved DNA spot locations by DNA seqFISH+ along with sequential immunofluorescence intensity at the rounded voxel location.</p> <p>RNA seqFISH datasets (provided as a zip folder): Super-resolved mRNA or intron spot locations.</p> <p>Sequential immunofluorescence table (provided as a csv file): Mean voxel intensity of each immunofluorescence marker per nucleus for the adult mouse cerebellum datasets.</p> <p>Note that voxel sizes are 103 nm for x and y, and 250 nm for z in our experimental setting.</p> <p>Please find the uploaded readme.txt file for more details.</p>
Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma
Open the record for dataset details and reuse information.
MERFISH+, a large-scale, multi-omics spatial technology resolves the transcriptomic holograms of the 3D human developing heart
Open the record for dataset details and reuse information.
Graph Fourier transform for spatially resolved omics representation and analyses
<p>The file is a CODEX image of a human tonsil. They contain:</p> <ol> <li>Whole image: entire tissue image of human tonsil.</li> <li>Raw image data: six field of view (FOV) are deposited in this figure</li> <li>Processed data: the pixel-level data of six FOVs are deposited in the Seurat object and H5AD file.</li> </ol> <p><span>The following anti-bodies were used for CODEX profiling:</span></p> <p><span>BCL-2 (124, Novus Biologicals), CCR6 (polyclonal, Novus Biologicals), CD11b (EPR1344, Abcam), CD11c (EP1347Y, Abcam), CD15 (MMA, BD Biosciences), CD16 (D1N9L, Cell Signaling Technology), CD162 (HECA-452, Novus Biologicals), CD163 (EDHu-1, Novus Biologicals), CD2 (RPA-2.10, Biolegend), CD20 (rIGEL/773, Novus Biologicals), CD206 (polyclonal, R&D Systems), CD25 (4C9, Cell Marque), CD30 (BerH2, Cell Marque), CD31 (C31.3+C31.7+C31.10, Novus Biologicals), CD4 (EPR6855, Abcam), CD44 (IM-7, Biolegend), CD45 (B11+PD7/26, Novus Biologicals), CD45RA (HI100, Biolegend),<span> </span>CD45RO (UCH-L1, Biolegend), CD5 (UCHT2, Biolegend), CD56 (MRQ-42, Cell Marque), CD57 (HCD57, Biolegend), CD68 (KP-1, Biolegend), CD69 (polyclonal, R&D Systems), CD7 (MRQ-56, Cell Marque), CD8 (C8/144B, Novus Biologicals), collagen IV (polyclonal, Abcam), cytokeratin (C11, Biolegend), EGFR (D38B1, Cell Signaling Technology), FoxP3 (236A/E7, Abcam), granzyme B (EPR20129-217, Abcam), HLA-DR (EPR3692, Abcam), IDO-1 (D5J4E, Cell Signaling Technology), LAG-3 (D2G4O, Cell Signaling Technology), mast cell tryptase (AA1, Abcam), MMP-9 (L51/82, Biolegend), MUC-1 (955, Novus Biologicals), PD-1 (D4W2J, Cell Signaling Technology), PD-L1 (E1L3N, Cell Signaling Technology), podoplanin (D2-40, Biolegend), T-bet (D6N8B, Cell Signaling Technology), TCR β (G11, Santa Cruz Biotechnology), TCR-γ/δ (H-41, Santa Cruz Biotechnology), Tim-3 (polyclonal, Novus Biologicals), Vimentin (RV202, BD Biosciences), VISTA (D1L2G, Cell Signaling Technology), α-SMA (polyclonal, Abcam), and β-catenin (14, BD Biosciences). </span></p>
The raw data of Spatial omics of glioma
<p>Our files include the raw data from single-cell RNA sequencing, spatial transcriptomics sequencing conducted in our study. The raw data of the paper "Deciphering radial glial stem-like cells based on spatial multi-omics guides safe therapy in glioma".</p>
Test dataset for "Spatial Integration of Multi-Omics Data from Serial Sections using the novel Multi-Omics Imaging Integration Toolset"
<p>The uploaded tar file contains anonymized and reduced test data for the paper "Spatial Integration of Multi-Omics Data from Serial Sections using the novel Multi-Omics Imaging Integration Toolset". (doi: https://doi.org/10.1101/2024.06.11.598306; https://github.com/mwess/miit)</p> <p>Dataset description:<br>- 9 serial histology sections with the following stains: (HES, HE, HES, HES, HES, MTS, IHC, IHC, HES)<br>- Sections are indexed in the following way (due to some sections not being part of this project): 1,2,3,6,7,8,9,10,11<br>- Each serial section contains: <br> - landmarks with matching labels across all sections.<br> - semi-manually generated tissue masks <br>- Section 2 contain spatial transcriptomics data and one annotation file in geojson format.<br>- Sections 6 and 7 contain imzml data that were generated with MALDI-MSI in positive ion mode (section 6) and negative ion mode (section 7) and additional histology annotations.<br>- MALDI-MSI is reduced. The positive ion data contains only intensities and spectra for spermine. The negative ion mode data contains only intensities and spectra for citrate and zinc.<br>- ST data contains only locations of spots and scalefactors. (I.e. no count data is included.). Barcode ids are randomly generated. <br>- In addition, for each ST spot histopathological annotations and GSEA scores for the Citrate-Spermine Secretion gene signature are provided.</p> <p>Abbreviations:</p> <p>- HES = Hematoxylin-Erythrosine-Saffron<br>- HE = Hematoxylin-Eosin<br>- MTS = Masson's Trichrome Staining<br>- IHC = Immunohistochemistry<br>- ST = Spatial Transcriptomics, here refers to Visium10X arrays.<br>- MALDI-MSI = Matrix-Assisted Laser Desorption Ionization - Mass Spectrometry Imaging.</p> <p> </p>
Processed Data 1 : Spatial multi-omic map of human myocardial infarction
<p>We provide here the processed snRNA-seq, snATAC-seq, and visium data for the manuscript: Kuppe, Ramirez Flores, Li et al. "Spatial multi-omic map of human myocardial infarction", 2022</p>
Spatially Resolved Multi-Omics Single-Cell Analyses Inform Mechanisms of Immune Dysfunction in Pancreatic Cancer
GEO Series GSE205049. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing.
Spatial multi-omics defines a shared glioblastoma infiltrative signature at the resection margin [10x Spatial]
GEO Series GSE286413. Homo sapiens. 2 samples. Type: Other.
Integrative Spatial Multi-Omics Reveal Niche-Specific Inflammatory Signaling and Differentiation Hierarchies in Acute Myeloid Leukemia
GEO Series GSE279576. Homo sapiens. 6 samples. Type: Other.
Multi-Omics molecular subtyping based on commensal microbiome predicts molecular characteristics and therapeutic response in Triple-Negative Breast Cancer [Spatial Transcriptome seq]
GEO Series GSE252176. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing; Other.
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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.