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973 results for “spatial transcriptomics”
Spatial characterization of the motor and non-motor somal and axonal transcriptome in adult healthy and mutant FUS mice
<table> <tbody> <tr> <td> <p>Here we investigated the transcriptome of motor and non-motor axons and cell bodies in the context of mutant FUS-related amyotrophic lateral sclerosis (ALS). We applied Nanostring GeoMX Digital Spatial Profiler platform to profile the transcriptome of subcellular compartments in the lower motor circuitry of a mouse model ricapitulating ALS motor symptoms. This work sheds light for the first time on the transcriptomic alterations in axons and in somas which may contribute to axonal degeneration and neuromuscular junction denervation, early features of ALS.</p> </td> </tr> </tbody> </table>
Spatial transcriptome analysis defines heme as a hemopexin-targetable inflammatoxin in the brain - Datasets and Python notebooks
<p>This dataset and the associated Python notebooks and R-code are related to the publication "Spatial transcriptome analysis defines heme as a hemopexin-targetable inflammatoxin in the brain".</p>
Library size confounds biology in spatial transcriptomics data
<p>This dataset contains annotated sub-cellular localised spatial measurements from the Visium, Xenium and CosMx platforms. Specifically, it includes datasets analysed in the publication Bhuva et. al, 2023 titled "Library size confounds biology in spatial transcriptomics data". Raw transcript detections are presented. Data is best accessed through the accompanying <em>SubcellularSpatialData</em> R/Bioconductor package. Region files used to annotate individual transcript detections are presented in the form of <a href="https://geojson.org/">GeoJSON</a> files. </p>
SpatialMETA: A Novel Framework for Integrating Spatial Transcriptomics and Metabolomics Data
<p>Multimodal analysis of spatial transcriptomics (ST) and spatial metabolomics (SM) has rapidly advanced for characterizing tissue microenvironments. However, integrating ST and SM data remains challenging due to differing morphologies, resolutions, and batch effects. We developed SpatialMETA (Spatial Metabolomics and Transcriptomics Analysis), a novel method for integrating spatial multi-omics data, which aligns ST and SM to a unified resolution, enables both cross-modal and cross-sample integration to identify ST-SM associated spatial patterns, and provides extensive visualization and analysis capabilities. The datasets for SpatialMETA is avaiable. </p>
Spatial transcriptome data from coronal mouse brain sections after striatal injection of heme and heme-hemopexin
<p>This dataset and the associated Python notebooks are related to the publication "Spatial transcriptome data from coronal mouse brain sections after striatal injection of heme and heme-hemopexin".</p>
Spatial transcriptomics of B and T cell receptors uncovers lymphocyte clonal dynamics.
<p>This dataset contains a single zipped folder containing:</p> <ul> <li> <p>data</p> </li> <li> <p>scripts</p> </li> </ul> <p>needed to reproduce the manuscript entitled "Spatial transcriptomics of B and T cell receptors uncovers lymphocyte clonal dynamics". Each folder is organized by tissue type, methodology, and analysis. A readme file accompanies each folder with details on the files/scripts within that folder. Alongside the paper and supplementary materials, it should be possible to reproduce all the figures in the manuscript.</p>
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>
Molecular features of luminal breast cancer defined through spatial and single-cell transcriptomics (codes and data files)
<p>This dataset includes all the relevant codes and data files associated with the paper ("Molecular features of luminal breast cancer defined through spatial and single-cell transcriptomics") in Clinical and Translational Medicine journal.</p>
Clonally resolved spatial transcriptomics data of mouse spleen
<p>The BGI Stereo-seq strategy was applied to a mouse spleen sample containing SPLINTR barcoded AML cells.</p> <p>Data generated with <a href="https://github.com/DaneVass/bartools_manuscript_code/blob/main/spatial-analysis/data_preprocessing_m4_paper.py" target="_blank" rel="noopener">https://github.com/DaneVass/bartools_manuscript_code/blob/main/spatial-analysis/data_preprocessing_m4_paper.py</a>.</p> <p>mouse4_bin*_bc_counts.tsv:<br>Binned barcode counts across whole slide.<br>Can be merged with AnnData file by `cell_id`.<br>Contains all barcodes detected in a bin (`barcode`) and UMI counts summed by bin (`count_binned`).<br>`isin_adata` marks whether the bin is on the manually segmented tissue section.</p> <p>mouse4_bin*_bc_counts_top1.tsv:<br>Binned barcode counts on tissue section, barcode with most UMI per bin is selected. </p> <p>mouse4_bin*_bc.h5ad:<br>Binned stereo-seq data with barcode information.</p> <p>mouse4_bin*_bc_clustered.h5ad:<br>Filtered, log1p transformed, scaled, clustered stereo-seq data.<br>Data is not zero centered for bin10 for memory efficiency.</p>
Comparison of spatial transcriptomics technologies used for tumor cryosections
<p>This repository contains data from a spatial transcriptomics (ST) analysis of brain tumor cryosections (medulloblastoma with extensive nodularity, MBEN). It is associated with the preprint by Rademacher, Huseynov, Bortolomeazzi et al. 2024, <em>bioRxiv</em>, <a href="https://doi.org/10.1101/2024.04.03.586404">https://doi.org/10.1101/2024.04.03.586404</a>, that has a full description of the work. In the study four imaging-based ST methods – RNAscope HiPlex, Molecular Cartography, MERFISH/Merscope, and Xenium – as well as sequencing-based ST (Visium) and single cell RNA sequencing of dissociated nuclei (snRNA-seq) were compared. The files provided here are described in readme.txt and include the transcript count matrices acquired on the Visium platform as well as Seurat objects of the data and analysis results for the comparison of the different technologies. The data for snRNA-seq, RNAscope HiPlex and Molecular Cartography included in the Seurat objects are based on the primary data acquired in a previous study (Ghasemi et al. 2024, Nat Commun, <a href="https://doi.org/10.1038/s41467-023-44117-x">https://doi.org/10.1038/s41467-023-44117-x</a>). </p>
SMART: Spatial transcriptomics deconvolution using marker-gene-assisted topic model
<p>Source code and simulated datasets used in manuscript "SMART: Spatial transcriptomics deconvolution using marker-gene-assisted topic model"</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>
Data for accurate cell type deconvolution in spatial transcriptomics using a batch effect-free strategy
<p>Simulated and experimental data used in the ReSort manuscript. It is necessary and sufficient to reproduce the results in the paper.</p>
Spatially Resolved Transcriptomics Deconvolutes Prognostic Histological Subgroups in Patients with Colorectal Cancer and Synchronous Liver Metastases
<p>Spatial transcriptomic data (counts.csv) derived using the Nanostring GeoMx digital spatial profiler platform to analyse matched colonic primary and liver metastases from 4 patients with metastatic colorectal cancer. 48 AOIs of cancer transcriptome atlas data. Normalised using Q3 normalisation. In addition, normalised data (Counts - ncounter.csv) from ncounter bulk experiment comparing matched colonic primary and liver metastases</p>
Spatial transcriptomics stratifies health and psoriatic disease severity by emergent cellular ecosystems
<p>While human inflammatory skin diseases' cellular and molecular features are well-characterized, their tissue context and systemic impact remain poorly understood. We thus profiled human psoriasis (PsO) as a prototypic immune-mediated condition with a high preference for extra-cutaneous involvement. Spatial transcriptomics (ST) analyses of 25 healthy, active, and clinically uninvolved skin biopsies, and integration with public single-cell transcriptomics data revealed striking differences in immune microniches between healthy and inflamed skin. Tissue scale-cartography further identified core disease features across all active lesions, including the emergence of an inflamed suprabasal epidermal state and the presence of B lymphocytes in lesional skin. Notably, both lesional and distal non-lesional samples were stratified by skin disease severity, and not by the presence of systemic disease. This segregation was driven by macrophage-, fibroblast- and lymphatic-enriched spatial regions with gene signatures associated with metabolic dysfunction. Taken together, these findings suggest that mild and severe forms of PsO have distinct molecular features and that severe PsO may profoundly alter the cellular and metabolic make up of distal unaffected skin sites. Additionally, our study provides an unprecedented resource for the research community to study spatial gene organization of healthy and inflamed human skin. </p>
STGMVA: clustering, imputation, and integration for spatial resolved transcriptomics using spatiotemporal gaussian mixture variational autoencoder
<p> In this study, we present STGMVA, a comprehensive analysis toolkit employs a spatiotemporal gaussian mixture variational autoencoder to tackle these tasks effectively. STGMVA consists of two stages: pretraining the gene expression and spatial location using a gaussian mixture model, and learning the embedding vectors through a variational graph autoencoder. Results demonstrate STGMVA surpasses state-of-the-art approaches on various spatial transcriptomics datasets, exhibiting superior performance across different scales and resolutions. Notably, STGMVA achieves the highest clustering accuracy in human brain, mouse hippocampus, and mouse olfactory bulb tissues. Furthermore, STGMVA enhances and denoises gene expression patterns for gene imputation task. Additionally, STGMVA has the capability to correct batch effects and achieve joint analysis when integrating multiple tissue slices.</p>
Reliable imputation of spatial transcriptome with uncertainty estimation and spatial regularization
<p>Imputation of missing features in spatial transcriptomics is urgently demanded due to technology limitations, while most existing computational methods suffer from moderate accuracy and cannot estimate the reliability of the imputation. <br> To fill the research gaps, we introduce a computational model, TransImp, that imputes the missing feature modality in spatial transcriptomics by mapping it from single-cell reference. Uniquely, we derived a set of attributes that can accurately predict imputation uncertainty, hence enabling us to select reliably imputed genes. Also, we introduced a spatial auto-correlation metric as a regularization to avoid overestimating spatial patterns. Multiple datasets from various platforms have demonstrated that our approach significantly improves the reliability of downstream analyses in detecting spatial variable genes and interacting ligand-receptor pairs. Therefore, TransImp offers a way towards a reliable spatial analysis of missing features for both matched and unseen modalities, e.g., nascent RNAs.</p>
Boosting multiplexing capabilities for error-robust spatial transcriptomic methods using a set exchange approach
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stFormer: a foundation model for spatial transcriptomics
<p>stFormer incorporates ligand genes within the spatial niche into transformer encoder of single-cell transcriptomics, and outputs gene embeddings specific to the intracellular context and spatial niche. These gene representations can serve as input of various downstream applications, including cell clustering, cell type prediction, gene function prediction, and <em>in silico</em> perturbation analysis of ligand-receptor interaction.</p> <p>The model architecture is designed for ST data resolved at the single-cell level. We propose a biased cross-attention method to enable the model to do learning with single-cell resolution on low-resolution, whole-transcriptome Visium data, which is a widely available spatial resource.</p> <p>We assembled a pretraining corpus comprising ~4.1 million spatial samples from public human Visium datasets, spanning diverse tissues, development stages, and disease states. After pretraining, stFormer is compatible with both single-cell and spot resolution ST data.</p>
Data from "FICTURE: Scalable segmentation-free analysis of sub-micron resolution spatial transcriptomics"
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.