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975 results for “Spatial transcriptomics”
Spatially Resolved Transcriptomics Atlas of Matched Primary and Metastatic Pancreatic Cancer Reveal Principles of Ecological Adaptation
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STCGAN: a novel Cycle-Consistent Generative Adversarial Network for Spatial Transcriptomics Cellular Deconvolution
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IF and SCRINSHOT image data of probe set selection for targeted spatial transcriptomics
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Accurate Spatial Heterogeneity Dissection and Gene Regulation Interpretation for Spatial Transcriptomics using Dual Graph Contrastive Learning
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Datasets collected for benchmarking in spatial transcriptomics
<p>Datasets collected for benchmarking in spatial transcriptomics. In addition, the code for benchmarking (March 2025 version, svg-benchmark-main.zip) is also located here and can be accessed on the GitHub website <a href="https://github.com/XiDsLab/svg-benchmark">https://github.com/XiDsLab/svg-benchmark</a>.</p>
Imaging spatial transcriptomics in a transgenic mouse model of α-synucleinopathy
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10X Genomics Human Visium Spatial Transcriptomics Demo Dataset for Cellxgene VIP
<p>4 Visium Spatial Transcriptomics datasets downloaded 10X Genomics data site ,and organized in the way to be used for Cellxgene VIP input.</p> <p>10X_demo_data_Breast_Cancer_Block_A_Section_1<br> 10X_demo_data_Breast_Cancer_Block_A_Section_2<br> 10X_demo_data_Human_Heart<br> 10X_demo_data_Human_Lymph_Node<br> </p>
Gastric Cancer Spatial Transcriptomics
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Spatial transcriptomic data
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Supporting data for SpatialOne: End-to-End Analysis of Spatial Transcriptomics at Scale
<p>Supplementary data supporting the <em>SpatialOne: End-to-End Analysis of Spatial </em><em>Transcriptomics at Scale</em> publication</p> <p> </p> <blockquote> <p>To showcase the capabilities of SpatialOne, two human lung cancer formalin-fixed, paraffin-embedded (FFPE) samples are analyzed. These samples are prepared following the CG000495 protocol (Figure 1b), sequenced with the 10x Visium CytAssist, and processed using the 10x SpaceRanger version 2. We also present analysis of two adult mouse samples sequenced using 10x Visium samples (one fresh frozen brain tissue section processed using SpaceRanger v2 and one FFPE kidney sample processed using the SpaceRanger v1), and 75 internal samples. </p> <p> For the human lung cancer samples, single-cell data from the the Lung Cancer Atlas (Salcher et al., 2022) is used as reference. This dataset is filtered to include only Chromium-generated data. For the mice samples, the GSE107585 single-cell dataset serves as reference. In the human lung cancer datasets, a pathologist annotated regions of interest corresponding to tumors, blood vessels, and alveolar regions.</p> </blockquote> <p> </p> <p>Changelog:</p> <ul> <li>Added a README file describing the zip content.</li> </ul>
Datasets collected for Masked adversarial neural network for cell type deconvolution in spatial transcriptomics
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Spatial Transcriptomics data (GeoMx) of midbrain dopamine cells in control and PD subjects
<p>The repository includes Spatial Transcriptomic datasets generated by Nanostring GeoMx (Hu WTA) analysis of midbrain TH+ cells from Controls (n=10), Incidental Lewy Body Disease (n=10), early Parkinsons Disease (ePD,n=5) and late Parkinsons Disease (lPD,n=5). A total 348 Regions of Interest were analysed. The raw and processed counts and metadata are provided as an R Seurat object (geomx_edwards_thmask.rds). The scripts used for low level data processing are described in https://github.com/zchatt/ASAP-SpatialTranscriptomics/blob/main/geomx/lowlevel/README.md</p> <p>Tissue samples from pathologically confirmed asymptomatic stage I-II Lewy body disease, stage IV Lewy body PD (early-PD), stage VI Lewy body PD (late-PD)(Braak, Del Tredici et al. 2003) and controls without the neurological or neuropathological disease were obtained from the Sydney Brain Bank. The study was approved by the University of Sydney Human Research Ethics Committee (2021/845). All cases with PD were levodopa-responsive and fulfilled the UK Brain Bank Clinical Criteria for a diagnosis of clinical PD (Hughes, Ben-Shlomo et al. 1992) with no other neurodegenerative conditions. </p> <p>Cells were not extracted. Tissue sections were cut from FFPE blocks of post-mortem human midbrains at 6µm on a rotary microtome (HistoCore MULTICUT, Leica Biosystems) and mounted on Series 2 adhesive microscope slides (Trajan Scientific Medical, AU) for processing for spatial trranscriptomics. To remove the paraffin, slides were incubated in the oven at 60°C for 1hr and then submerged in HistoChoice Clearing Agent (Sigma-Aldrich, H2779) for 2x7mins, followed by rehydration in decreasing ethanol concentrations (100% ethanol for 2x3mins, 95% ethanol for 3mins, 70% ethanol for 3mins) and distilled H2O for 3mins. </p> <p>Tissue sections were immunohistochemically stained for tyrosine hydroxylase and Regions of Interest (ROIs) processed following Nanostring GeoMx® Digital Spatial Profiler using the manufacturer’s instructions. Libraries were sequenced on Illumina Novaseq 6000 platform using NovaSeq SP 100 cycle kit (XP workflow, 27-8-8-27).</p> <p>This research was funded in whole or in part by Aligning Science Across Parkinson’s (ASAP-020529) through the Michael J. Fox Foundation for Parkinson’s Research (MJFF). For the purpose of open access, the author has applied a CC BY 4.0 public copyright license to all Author Accepted Manuscripts arising from this submission.</p>
Spatial Transcriptomics data (GeoMx) of locus coeruleus dopamine cells in control and PD subjects
<p>The repository includes Spatial Transcriptomic datasets generated by Nanostring GeoMx (Hu WTA) analysis of locus coeruleus TH+ cells from Controls (n=9), early Parkinsons Disease (ePD, n=8) and late Parkinsons Disease (lPD, n=2). A total 39 Regions of Interest were analysed. The raw and processed counts and metadata are provided as an R Seurat object (geomx_vila_thmask.rds). The scripts used for low level data processing are described in https://github.com/zchatt/ASAP-SpatialTranscriptomics/blob/main/geomx/lowlevel/README.md.</p> <p>Tissue samples from pathologically confirmed stage IV Lewy body PD (early-PD), stage VI Lewy body PD (late-PD)(Braak, Del Tredici et al. 2003) and controls without the neurological or neuropathological disease were obtained from the Sydney Brain Bank. The study was approved by the University of Sydney Human Research Ethics Committee (2021/845). All cases with PD were levodopa-responsive and fulfilled the UK Brain Bank Clinical Criteria for a diagnosis of clinical PD (Hughes, Ben-Shlomo et al. 1992) with no other neurodegenerative conditions. </p> <p>Cells were not extracted. Tissue sections were cut from FFPE blocks of post-mortem human midbrains at 6µm on a rotary microtome (HistoCore MULTICUT, Leica Biosystems) and mounted on Series 2 adhesive microscope slides (Trajan Scientific Medical, AU) for processing for spatial trranscriptomics. To remove the paraffin, slides were incubated in the oven at 60°C for 1hr and then submerged in HistoChoice Clearing Agent (Sigma-Aldrich, H2779) for 2x7mins, followed by rehydration in decreasing ethanol concentrations (100% ethanol for 2x3mins, 95% ethanol for 3mins, 70% ethanol for 3mins) and distilled H2O for 3mins. </p> <p>Tissue sections were immunohistochemically stained for tyrosine hydroxylase and Regions of Interest (ROIs) processed following Nanostring GeoMx® Digital Spatial Profiler using the manufacturer’s instructions. Libraries were sequenced on Illumina Novaseq 6000 platform using NovaSeq SP 100 cycle kit (XP workflow, 27-8-8-27).</p> <p>This research was funded in whole or in part by Aligning Science Across Parkinson’s (ASAP-020505) through the Michael J. Fox Foundation for Parkinson’s Research (MJFF). For the purpose of open access, the author has applied a CC BY 4.0 public copyright license to all Author Accepted Manuscripts arising from this submission.</p>
Spatial Transcriptomics data (GeoMx) of midbrain tissue in control and PD subjects
<p>he repository includes Spatial Transcriptomic datasets generated by Nanostring GeoMx (Hu WTA) analysis of midbrain unmasked (whole tissue) Regions of Interest from Controls (n=10), Incidental Lewy Body Disease (n=11), early Parkinsons Disease (ePD, n=5) and late Parkinsons Disease (lPD, n=6). A total 515 Regions of Interest were analysed. The raw and processed counts and metadata are provided as an R Seurat object (geomx_vila_unmask.rds). The scripts used for low level data processing are described in https://github.com/zchatt/ASAP-SpatialTranscriptomics/blob/main/geomx/lowlevel/README.md </p> <p>Tissue samples from pathologically confirmed asymptomatic stage I-II Lewy body disease, stage IV Lewy body PD (early-PD), stage VI Lewy body PD (late-PD)(Braak, Del Tredici et al. 2003) and controls without the neurological or neuropathological disease were obtained from the Sydney Brain Bank. The study was approved by the University of Sydney Human Research Ethics Committee (2021/845). All cases with PD were levodopa-responsive and fulfilled the UK Brain Bank Clinical Criteria for a diagnosis of clinical PD (Hughes, Ben-Shlomo et al. 1992) with no other neurodegenerative conditions. </p> <p>Cells were not extracted. Tissue sections were cut from FFPE blocks of post-mortem human midbrains at 6µm on a rotary microtome (HistoCore MULTICUT, Leica Biosystems) and mounted on Series 2 adhesive microscope slides (Trajan Scientific Medical, AU) for processing for spatial trranscriptomics. To remove the paraffin, slides were incubated in the oven at 60°C for 1hr and then submerged in HistoChoice Clearing Agent (Sigma-Aldrich, H2779) for 2x7mins, followed by rehydration in decreasing ethanol concentrations (100% ethanol for 2x3mins, 95% ethanol for 3mins, 70% ethanol for 3mins) and distilled H2O for 3mins. </p> <p>Tissue sections were immunohistochemically stained for tyrosine hydroxylase and Regions of Interest (ROIs) processed following Nanostring GeoMx® Digital Spatial Profiler using the manufacturer’s instructions. Libraries were sequenced on Illumina Novaseq 6000 platform using NovaSeq SP 100 cycle kit (XP workflow, 27-8-8-27).</p> <p>This research was funded in whole or in part by Aligning Science Across Parkinson’s (ASAP-020505) through the Michael J. Fox Foundation for Parkinson’s Research (MJFF). For the purpose of open access, the author has applied a CC BY 4.0 public copyright license to all Author Accepted Manuscripts arising from this submission.</p>
Understanding tumor heterogeneity in melanoma brain metastasis using spatial transcriptomics and multi-regional bulk sequencing
<p>Melanoma brain metastasis (MBM) exhibits extensive inter- and intra-tumor heterogeneity, driven by a complex tumor microenvironment (TME). The aim with this study was to profile the MBMs by using a multi-omics approach, integrating spatial transcriptomics with bulk exome, proteome, and transcriptome profiling. We identified significant patient-specific variations in immune cell infiltration, particularly in B/plasma cells, myeloid cells, and cancer-associated fibroblasts (CAFs). Notably, immunotherapy-treated patients showed enrichedpathways related to EMT, IFN-γ signaling, oxidative phosphorylation, T-cell signaling, inflammation and DNA damage, which aligned with distinct cellular compositions observed in the spatial analysis. We also uncovered considerable intra-tumor heterogeneity, especially at the protein level, revealing differential expression patterns of key tumor and immune-related markers. The correlation between mRNA and protein data highlighted consistent enrichment of critical pathways across multi-omics layers. These findings provide a comprehensive view of MBM's molecular and cellular landscape, emphasizing the importance of addressing tumor heterogeneity in developing effective therapeutic strategies.</p>
Raw Image Data Repository: Repurposing Large-Format Microarrays for Scalable Spatial Transcriptomics
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Spatially resolved transcriptomics and graph-based deep-learning improve accuracy of routine CNS tumor diagnostics
<p><span>The diagnostic landscape of brain tumors has recently evolved to integrate comprehensive molecular markers alongside traditional histopathological evaluation. Foremost, genome-wide DNA methylation profiling and next generation sequencing (NGS) has become a cornerstone in classifying Central Nervous System (CNS) tumors, as recognized by its inclusion into the 2021 WHO classification. Despite its diagnostic precision, a limiting requirement for NGS and methylation profiling is sufficient DNA quality and quantity which restricts its feasibility, especially in cases with small biopsy samples or low tumor cell content, both frequent challenges in specimen of diffusely growing CNS lesions. Addressing these challenges, we demonstrate a application, namely <strong>NePSTA </strong>(<strong>Ne</strong>uro<strong>P</strong>athology <strong><em>S</em></strong><em>patial <strong>T</strong>ranscriptomic<strong> A</strong>nalysis</em>), which is capable of generating comprehensive morphological and molecular neuropathological diagnostics from single 5 µm tissue sections. Our framework employs 10x Visium spatial transcriptomics with graph neural networks for automated histological and molecular evaluations. Trained and evaluated across 130 patients with CNS malignancies and healthy donors across four medical centers, NePSTA<strong> </strong>integrates spatial gene expression data and inferred CNAs to predict tissue histology and methylation-based subclasses with high accuracy. Further, we demonstrate the ability to reconstruct immunohistochemistry and genotype profiling on single thin slides of minute tissue biopsies. Our approach has minimal tissue requirements, often inadequate for conventional molecular diagnostics, demonstrating the potential to transform neuropathological diagnostics and enhance tumor subtype identification with implications for fast and precise diagnostic work-up.</span></p>
Cellular and molecular heterogeneities and signatures, and pathological trajectories of fatal COVID-19 lungs defined by spatial single-cell transcriptome analysis
<p>Spatial in-situ data analysis.</p>
EAGS: efficient and adaptive gaussian smoothing applied to high-resolved spatial transcriptomics
<p>This dataset is used to preserve the mouse brain and mouse olfactory bulb data (in h5ad format) involved in the EAGS study.</p> <p>You can get details of the different datasets from <strong>readme.txt</strong>.</p> <p>Abstract of the EAGS study:</p> <p>The emergence of high-resolved spatial transcriptomics (ST) technology has facilitated the research of novel methods to investigate biological development, growth and other complex biological processes. High-resolution and whole transcriptomics ST datasets require customized imputation methods to improve signal-to-noise ratio and the data quality. We propose an efficient and adaptive gaussian smoothing (EAGS) method for imputation on high-resolved ST. Its adaptive two-factor smoothing creates patterns based on the spatial and expression information of the cells, creates adaptive weights for the smoothing of cells in the same pattern, then utilizes the weights to restore the gene expression profiles. The performance and efficiency of EAGS are verified on high-resolved ST data of mouse brain and olfactory bulb. Compared with other competitive methods, EAGS shows higher clustering accuracy, better biological interpretation and a significant advantage in computational consumption.</p>
Spatial Transcriptomic Experiment of Triple-Negative Breast Cancer PDX Model PIM001-P model treatment naive sample
<p>Spatial Transcriptomic Experiment of Triple-Negative Breast Cancer PDX Model PIM001-P model treatment naive sample</p> <p>10X Genomics Visium platform. </p> <p><strong>Library Preparation and Sequencing of PIM001P</strong></p> <p>Tissue sections of 10µm thickness were mounted onto the capture areas of the Visium Spatial Gene Expression slide and stained using hematoxylin and eosin. Tissue sections were permeabilized on a thermocycler for 24 minutes, as determined by the Tissue Optimization step. Poly-adenlyated mRNA is released and captured by surface-bound primers within each capture area. Reverse transcription, template switching, extension, and second strand synthesis are performed on the slide. Full-length, spatially barcoded cDNA transcripts are then denatured from the slide and amplified via PCR prior to library construction. Approximately 110 to 375 ng of amplified cDNA was carried forward into library construction. During library construction, cDNA is enzymatically fragmented to target amplicon size then undergoes end repair, A-tailing, adapter ligation, and then amplified using between 14 and 16 PCR cycles. The resulting libraries were quantitated using the Invitrogen Qubit 2.0 quantitation assay and fragment size assessed with the Agilent Bioanalyzer. A qPCR quantitation was performed on the libraries to determine the concentration of adapter ligated fragments using Applied Biosystems ViiA7 Real-Time PCR System and a KAPA Library Quant Kit (p/n KK4824). All samples were pooled equimolarly and re-quantitated by qPCR, and also re-assessed on the Bioanalyzer.</p> <p><strong>Sequencing: </strong></p> <p>150 pM of equimolarly pooled library was loaded onto the NovaSeq 6000 S4 flowcell and sequenced at the recommended 28-10-10-50 read configuration. PhiX Control v3 adapter-ligated library (Illumina p/n FC-110-3001) was spiked-in at 2% by weight to ensure balanced diversity and to monitor clustering and sequencing performance. A minimum of 300 million read pairs per sample was sequenced. FastQ file generation was executed using 10X Genomics’ Space Ranger mkfastq software.</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.