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973 results for “spatial transcriptomics”
Spatial Transcriptomics in HCC
<p>The morbidity of Hepatocellular carcinoma (HCC) is highest in individuals with chronic liver diseases (CLD). However, the effects of cell composition on the progression of CLDs to HCC remain elusive. To gain a better understanding of the spatial distribution of cells and their interactions, we created spatial transcriptome data from two HCC and their normal adjacent FFPE tissues using the 10x visium platform. We processed the data using cellRanger and mapped it to the Human Hg38 reference genome with GRCh38.p3 annotation. All data generated by cellRanger is provided here</p>
STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer
<p>Scripts generating figures of the paper titled "STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer".</p>
Spotiphy enables single-cell spatial whole transcriptomics across the entire section
<p><span>Spatial transcriptomics (ST) has advanced our understanding of tissue regionalization by enabling the visualization of gene expression within whole tissue sections, but the approach remains dogged by the challenge of achieving single-cell resolution without sacrificing whole genome coverage. Here we present Spotiphy (<u>Spot</u> <u>i</u>mager with <u>p</u>seudo single-cell resolution <u>h</u>istolog<u>y</u>), a novel computational toolkit that transforms sequencing-based ST data into single-cell-resolved whole-transcriptome images. In evaluations with Alzheimer’s disease (AD) and normal </span><span>mouse brains, </span><span>Spotiphy</span><span> delivers the most precise cellular compositions. For the first time, </span><span>Spotiphy reveals</span><span> novel astrocyte </span><span>regional specification in mouse brains. It distinguishes sub-populations of DAM (Disease-Associated Microglia) located in different AD mouse brain regions. Spotiphy also identifies multiple spatial domains as well as changes in the patterns of tumor-tumor microenvironment interactions using human breast ST data. Spotiphy enables visualization of cell localization and gene expression in tissue sections, offering key insights into the function of complex biological systems.</span></p>
STalign: Alignment of spatial transcriptomics data using diffeomorphic metric mapping
<p>Spatial transcriptomics (ST) technologies enable high throughput gene expression characterization within thin tissue sections. However, comparing spatial observations across sections, samples, and technologies remains challenging. To address this challenge, we developed STalign to align ST datasets in a manner that accounts for partially matched tissue sections and other local non-linear distortions using diffeomorphic metric mapping. We apply STalign to align ST datasets within and across technologies as well as to align ST datasets to a 3D common coordinate framework. We show that STalign achieves high gene expression and cell-type correspondence across matched spatial locations that is significantly improved over landmark-based affine alignments. Applying STalign to align ST datasets of the mouse brain to the 3D common coordinate framework from the Allen Brain Atlas, we highlight how STalign can be used to lift over brain region annotations and enable the interrogation of compositional heterogeneity across anatomical structures. STalign is available as an open-source Python toolkit at <a href="https://github.com/JEFworks-Lab/STalign">https://github.com/JEFworks-Lab/STalign</a> and as supplementary software with additional documentation and tutorials available at <a href="https://jef.works/STalign">https://jef.works/STalign</a>.</p> <p>Here we have included alignment results that were used in performance analysis of STalign:</p> <p>We aligned Slice 2 Replicate 3 to Slice 2 Replicate 2 of the MERFISH mouse coronal brain sections available from Vizgen Data Release V1.0. May 2021 (<a href="https://info.vizgen.com/mouse-brain-map">https://info.vizgen.com/mouse-brain-map</a>).</p> <ul> <li>STalign_S2R3_to_S2R2.csv.gz contains cell ids, original cell centroid positions of S2R3, cell positions of S2R3 after alignment to S2R2 with STalign, cell positions of S2R3 after supervised affine alignment to S2R2, and counts for genes and blanks.</li> <li>STalign_S2R2.csv.gz contains cell ids, cell centroid positions of S2R2 and counts for genes and blanks.</li> </ul> <p>Additionally, we aligned Slice 2 Replicate 3 to a Visium dataset of an FFPE preserved adult mouse brain were obtained from the 10X Datasets website for <em>Spatial Gene Expression Dataset by Space Ranger 1.3.0</em> (<a href="https://www.10xgenomics.com/resources/datasets/adult-mouse-brain-ffpe-1-standard-1-3-0">https://www.10xgenomics.com/resources/datasets/adult-mouse-brain-ffpe-1-standard-1-3-0</a>).</p> <ul> <li>STalign_S2R3_to_Visium.csv.gz contains cell ids, original cell centroid positions of S2R3, cell positions of S2R3 after alignment to Visium H&E staining with STalign, and counts for genes and blanks.</li> </ul> <p>Furthermore, we performed alignments with the 50um resolution 3D Allen Reference Atlas Nissl common coordinate framework, CCF (<a href="https://help.brain-map.org/display/mouseconnectivity/API">https://help.brain-map.org/display/mouseconnectivity/API</a>). We applied STalign to align the Allen CCF to each of the 9 MERFISH slices (3 slice locations with 3 biological replicates) provided by Vizgen. Because the Allen CCF has annotated brain regions, we were able to lift over those brain region annotations to label all cells in the MERFISH datasets.</p> <p>Also, since the STalign mappings from the Allen CCF to the MERFISH slices are invertible, for each slice we can apply the inverse of the mapping to get cell positions in the Allen CCF coordinates.</p> <ul> <li>STalign_SXRX_with_structure_id_name.csv.gz contains cell ids for Slice X Replicate X, original cell centroid positions, cell xyz-coordinates in Allen CCF, brain structure id per cell, brain structure acronym</li> </ul> <p>To evaluate the 3D CCF alignment, we performed unified transcriptional clustering analysis and cell-type annotation. All MERFISH datasets were combined. Transcriptional clustering analysis and cell type annotation was performed using the SCANPY package [version 1.9.1]. Data were normalized to counts per million (scanpy: normalize_total) and log transformed (scanpy: log1p). PCA (scanpy: pca) was computed on the cell by gene matrix. A neighborhood graph of cells using the top 10 PCs and 10 nearest neighbors was created (scanpy: neighbors), and Leiden clustering was performed on this graph (scanpy: leiden) to identify 29 clusters. Differentially expressed genes were extracted from each cluster (scanpy: rank_genes_groups), and cell-types were annotated based on marker genes in each cluster.</p> <ul> <li>STalign_celltypeannotations_merfishslices_v2.csv.gz contains for all nine slices cell ids and cell type annotations</li> </ul> <p>This updated (v2) cell-type annotation file contains a new column with simplified cell-types. Briefly, we fixed typos, standardized lower case/upper case formats, merged subclasses of each cell-types. For example, subclasses of astrocytes­­, which are originally labeled as “Astrocytes”, “Astrocytes(1)”, “Astrocytes(2)”, “Astrocytes(3)”, are all labeled as “Astrocytes” in the added column.</p> <p>Note: Cell ids may have been mutated from original string of numbers through reading and writing across programming languages that handle numbers with different precision. If using R to read the files shared here, one can find the cells in STalign_celltypeannotations_merfishslices_v2.csv.gz that correspond with STalign_SXRX_with_structure_id_name.csv.gz when cell ids are formatted as a double in scientific notation, which is how R will read the file automatically.</p>
IST-editing: Infinite spatial transcriptomic editing in a generated gigapixel mouse pup
<p>Mouse pup data (Xenium, main results):</p> <ol> <li>Mouse.zip: Training data</li> <li>dapi_raw.tif: The raw dapi WSI</li> <li>dapi_gen.tif: The generated dapi WSI by IST-editing</li> <li>he_raw.tif: The raw H&E WSI</li> <li>he_gen.tif: The generated H&E WSI by IST-editing</li> </ol> <p>Mouse brain data (Vizgen, supplementary results):</p> <ol> <li>brain_60988.zip: Training data from two brain sections </li> <li>609882_raw.tif: The raw dapi WSI for mouse with ID 609882</li> <li>609882_trn.tif: The generated WSI on the training brain section</li> <li>609882_tst.tif: The generated WSI on the test brain section</li> <li>609889_raw.tif: The raw dapi WSI for mouse with ID 609889</li> <li>609889_trn.tif: The generated WSI on the training brain section</li> <li>609889_tst.tif: The generated WSI on the test brain section</li> </ol> <p> </p> <p> </p> <p> </p>
Visium Spatially Resolved Transcriptomics of Glioblastoma Samples
<p>This repository contains samples (Visium Spatially resolved Transcriptomics) of the project entitled: <strong>Epigenetic neural glioblastoma integrates into neuron-to-glioma-networks and predicts therapeutic vulnerability</strong></p>
Regulatory T cell therapy is associated with distinct immune regulatory lymphocytic infiltrates in kidney transplants: Spatial transcriptomic dataset and images
<p>The outputs of the NanoString GeoMx DSP platform were concatenated into three xlsx files, each illustrating a separate experiment along with their sample annotations. This technique analyzes protein or RNA abundance within regions of interest (ROIs) or specific cell segments selected based on histological features and immunofluorescence. In this repository, the concatenated GeoMx output files are presented, along with PowerPoint presentations for each biopsy that show immunofluorescence images of the selected ROIs and/or cell segments.</p> <ul> <li><strong>Protein_Full ROI:</strong> This experiment measured the abundance of 41 proteins in discrete regions of interest (ROIs) within transplant kidney biopsies.</li> <li><strong>Protein_Rare cell:</strong> This experiment measured the abundance of 40 proteins in specific cell segments, such as CD4+FoxP3- cells vs. CD4+FoxP3+ cells, within transplant kidney biopsies.</li> <li><strong>RNA:</strong> This experiment measured the abundance of 90 genes in discrete ROIs within transplant kidney biopsies.</li> </ul>
Mitigating autocorrelation during spatially resolved transcriptomics data analysis
<p>Here we include the marmoset brain and mouse gut STARmap data introduced in the corresponding manuscript, "Mitigating autocorrelation during spatially resolved transcriptomics data analysis". We also include the mouse brain STARmap PLUS data that was used to demonstrate cross-species spatial integration and was previously published in Shi, He, Zhou et al. 2022.</p>
Full-Length Spatial Transcriptomics Reveals the Unexplored Isoform Diversity of the Myocardium Post-MI
<p>We introduce Single-cell Nanopore Spatial Transcriptomics (scNaST), a software suite to facilitate the analysis of spatial gene expression from second- and third-generation sequencing, allowing to generate a full-length near-single-cell transcriptional landscape of the tissue microenvironment. Taking advantage of the Visium Spatial platform, we adapted a strategy recently developed to assign barcodes to long-read single-cell sequencing data for spatial capture technology. Here, we demonstrate our workflow using four short axis sections of the mouse heart following myocardial infarction. We constructed a <em>de novo</em> transcriptome using long-read data, and successfully assigned 19,794 transcript isoforms in total, including clinically-relevant, but yet uncharacterized modes of transcription, such as intron retention or antisense overlapping transcription. We showed a higher transcriptome complexity in the healthy regions, and identified intron retention as a mode of transcription associated with the infarct area. Our data revealed a clear regional isoform switching among differentially used transcripts for genes involved in cardiac muscle contraction and tissue morphogenesis. Molecular signatures involved in cardiac remodeling integrated with morphological context may support the development of new therapeutics towards the treatment of heart failure and the reduction of cardiac complications.</p>
Representation learning for multi-modal spatially resolved transcriptomics data
<p>This folder contains the already unified input used for the models. The raw data is referenced here:</p> <ul> <li>LIBD Human DLPFC dataset is available at <a href="https://github.com/LieberInstitute/HumanPilot">https://github.com/LieberInstitute/HumanPilot</a> and <a href="http://research.libd.org/spatialLIBD" rel="nofollow">http://research.libd.org/spatialLIBD</a>;</li> <li>Human Breast Cancer - Zenodo <a href="https://doi.org/10.5281/zenodo.4739739" rel="nofollow">https://doi.org/10.5281/zenodo.4739739</a>,</li> <li>Human Liver Normal and Cancer - <a href="https://nanostring.com/products/cosmx-spatial-molecular-imager/human-liver-rna-ffpe-dataset/" rel="nofollow">https://nanostring.com/products/cosmx-spatial-molecular-imager/human-liver-rna-ffpe-dataset/</a>.</li> </ul>
Spatial transcriptomics defines injury specific microenvironments and cellular interactions in kidney regeneration and disease
<p>This dataset contains raw and processed seqFISH data quantifying 1300 genes within single cells from three Acute Kidney Injury (AKI) and three control mice kidneys. The dataset also contains the codebook and probe sequences used to create probe libraries for the seqFISH experiments.</p> <p><strong>Supplementary_data_tables</strong> folder contains the supplementary data tables for the manuscript: Data_1 contains DE gene expression, Data_2 and Data_3 contain the codebook and probe sequence information needed to generate the probe libraries used for the seqFISH experiments. Data_4 contains the probe sequence information needed to generate the serial probes against <em>Vcam1</em> and <em>Havcr1</em></p> <p><strong>AKI_Ctrl_object.rds</strong> - Seurat object generated from seqFISH data for the AKI and healthy control mice as detailed in the manuscript.</p> <p><strong>Counts_raw.csv </strong>contains raw gene counts for all cells in the dataset.</p> <p><strong>coordinates.csv</strong> contains the xy coordinates (in um) for every cell in the dataset.</p> <p><strong>metadata.csv</strong> contains the metadata for each cell in the dataset including sample and cell type allocation as well as Microenvironment (ME) assignment. This file also contains expression data of <em>Vcam1</em> and <em>Havcr1, </em>which were detected using serial probes as detailed in the manuscript.</p>
Spatial transcriptomics defines injury specific microenvironments and cellular interactions in kidney regeneration and disease
<p>Kidney injury disrupts the intricate renal architecture and triggers limited regeneration, and injury-invoked inflammation and fibrosis. Deciphering molecular pathways and cellular interactions driving these processes is challenging due to the complex renal architecture. Here, we apply single cell spatial transcriptomics to examine ischemia-reperfusion injury in the mouse kidney. Spatial transcriptomics reveals injury-specific and spatially-dependent gene expression patterns in distinct cellular microenvironments within the kidney and predicts <em>Clcf1-Crfl1</em> in a molecular interplay between persistently injured proximal tubule cells and neighboring fibroblasts. Immune cell types play a critical role in organ repair. Spatial analysis reveals cellular microenvironments resembling early tertiary lymphoid structures and identifies associated molecular pathways. Collectively, this study supports a focus on molecular interactions in cellular microenvironments to enhance understanding of injury, repair and disease.</p>
Analysis and visualization of the Fasciola hepatica spatial transcriptomics dataset
<p>This repository contains various files related to the analysis of the paper: Spatial transcriptomics of a parasitic flatworm provides a molecular map of drug targets and drug-resistance genes.</p>
Celiac-superior mesenteric ganglia (CG-SMG) spatial transcriptomics
<p>We prepared the spatial transcriptomics dataset for the celiac and superior mesenteric ganglia (CG-SMG) with 5 sections of 3 mice per Right (R)-CG, SMG, Left (L)-CG).</p>
Dataset associated with A. Hallou, R. He, et al. A computational pipeline for spatial mechano-transcriptomics. bioRxiv 2023.08.03.551894
<p>Dataset associated with:</p> <p>Adrien Hallou, Ruiyang He, Benjamin David Simons and Bianca Dumitrascu. A computational pipeline for spatial mechano-transcriptomics. bioRxiv 2023.08.03.551894; doi: <a href="https://doi.org/10.1101/2023.08.03.551894">https://doi.org/10.1101/2023.08.03.551894</a></p> <p>Licence</p> <p>This dataset is licensed under the <a href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a>.</p>
Data and Analysis Files Repository: Repurposing Large-Format Microarrays for Scalable Spatial Transcriptomics
<p>Data and Analysis Files from "Repurposing Large-Format Microarrays for Scalable Spatial Transcriptomics"</p> <p>ArraySeq_Method.zip contains the following folder and contents:</p> <ul> <li>STARSolo: All code and count matrix output from fastq spatial barcode demultiplexing. </li> <li>Images: All resolution-downsampled H&E image scans from analyzed tissues</li> <li>Space_Ranger: All 10x Space Ranger output from Visium datasets generated in the paper. </li> <li>Analysis: All scripts for analyzing and plotting Array-seq and Visium datasets generated in this paper. Also contains output h5ad files. </li> </ul> <p>ArraySeq_Barcode_generation_n12.rmd: The script used to generate the Array-seq probes with 12-mer spatial barcodes. </p>
Profiling the Heterogeneity of Colorectal Cancer Consensus Molecular Subtypes using Spatial Transcriptomics: datasets
<p>You can find here the datasets used in the publication: </p> <p><em><strong>Valdeolivas, A., Amberg, B., Giroud, N. et al. Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics. npj Precis. Onc. 8, 10 (2024). https://doi.org/10.1038/s41698-023-00488-4</strong> </em></p> <p>This contents the raw Spatial Transcriptomics data, spot categorization made by pathologist, the results of the deconvolution and intermediary files required to run the analysis described in our manuscript and available in Github: </p> <p><a href="https://github.com/alberto-valdeolivas/ST_CRC_CMS">https://github.com/alberto-valdeolivas/ST_CRC_CMS</a></p> <p>In particular, you will find here several zip compressed files with the following content: </p> <p>- <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/Intermediary_FileObjects.zip?versionId=989cd48d-45f6-46b9-9f90-1927af392a7e">Intermediary_FileObjects.zip</a>: The intermediary files generated in the scripts hosted in the github repo and required to run some later scripts. </p> <p>- <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/IntermediaryFiles_ST_CRC_LiverMetastasis.zip">IntermediaryFiles_ST_CRC_LiverMetastasis.zip</a>: The intermediary files generated in the scripts hosted in the github repo and required to run some of the scripts dealing with the external CRC ST dataset used in our manuscript. </p> <p>- <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/Pathology_SpotAnnotations.zip?versionId=ce657a54-9fec-4633-9d89-31f1479b93b7">Pathology_SpotAnnotations.zip</a>: The categories assigned by the pathologists to all the spots across our set ST samples to a different anatomical category (tumor, stroma, non-neoplastic mucosa...) </p> <p>-<a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A121573_Rep1.zip?versionId=dbfaad0f-784b-44c9-91d1-713f063d64e3">SN048_A121573_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A121573_Rep2.zip?versionId=ae997080-ca69-44c3-86aa-65bc1d5ef120">SN048_A121573_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A416371_Rep1.zip?versionId=e453ed45-22d8-4d60-b7f3-4daa9212cc88">SN048_A416371_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A416371_Rep2.zip?versionId=be395926-eee8-4670-b355-125e72bf6281">SN048_A416371_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A551763_Rep1.zip?versionId=6b7fa01a-a0d9-43e7-8d1d-8c56cb422374">SN123_A551763_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A595688_Rep1.zip?versionId=f625a286-fbc7-48f6-a57d-7d0df67a0574">SN123_A595688_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A798015_Rep1.zip?versionId=3540f1e5-9cf4-412c-887c-b1d0cc4e03c5">SN123_A798015_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A938797_Rep1_X.zip?versionId=de59c354-fea2-4843-a5f9-5e7a8d863e51">SN123_A938797_Rep1_X.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A551763_Rep2.zip?versionId=9da50bec-8ba4-41b0-a29e-4fc778cf12b7">SN124_A551763_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A595688_Rep2.zip?versionId=29c3e99e-7db2-4c02-9004-dc9d8abf3c27">SN124_A595688_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A798015_Rep2.zip?versionId=a0cf2cca-f3c9-4c45-b311-1ddc81371e35">SN124_A798015_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A938797_Rep2.zip?versionId=e6e4e2bc-1593-4c0f-ac37-b00cc2fc1124">SN124_A938797_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN84_A120838_Rep1.zip?versionId=ec31a69e-d0ce-4e4c-82dc-e7f2e617631a">SN84_A120838_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN84_A120838_Rep2.zip?versionId=89c89532-7bb1-47e4-8900-b5c12a7c4ba0">SN84_A120838_Rep2.zip</a>: The output of Space Ranger, including processed count data matrices and histological images, for the ST data generated in this study</p> <p>- <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/DeconvolutionResults_ST_CRC_BelgianCohort.zip">DeconvolutionResults_ST_CRC_BelgianCohort.zip</a>, <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/DeconvolutionResults_ST_CRC_KoreanCohort.zip">DeconvolutionResults_ST_CRC_KoreanCohort.zip</a>, <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/DeconvolutionResults_ST_CRC_LiverMetastasis.zip">DeconvolutionResults_ST_CRC_LiverMetastasis.zip</a>: These files contain the main results obtained when using the Cell2Location deconvolution approach in our samples (with two different references: Korean and Belgian cohorts) and in the external set of CRC ST samples (only Korean cohort)</p> <p> </p> <p>- We have also uploaded the whole slide images (WSI). These are the files with an ndpi extension: </p> <p><br><a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V10B01-048_new%20CRC_2021_02_16.ndpi?versionId=d5c8cbd3-40de-43da-8370-329def9e4f14">Visium Frozen_SN V10B01-048_new CRC_2021_02_16.ndp ...</a> (samples A121573_Rep1, A121573_Rep2, A416371_Rep1 and A416371_Rep2), <a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V19S23-084.ndpi?versionId=6d91b1f9-56e9-45c3-a2e6-4714975678fb">Visium Frozen_SN V19S23-084.ndpi</a> (samples A120838_Rep1 and A120838_Rep2), <a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V19S23-123.ndpi?versionId=c535482c-0a3c-4ba5-a056-f96796c366b0">Visium Frozen_SN V19S23-123.ndpi</a> (samples A551763_Rep1, A595688_Rep1, A798015_Rep1, A938797_Rep1) and <a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V19S23-124.ndpi?versionId=49ce857c-47bb-4930-bb0c-09213e4acf28">Visium Frozen_SN V19S23-124.ndpi</a> (samples A551763_Rep2, A595688_Rep2, A798015_Rep2 and A938797_Rep2)</p> <p>- We have now included the fastq and Bam files for the different samples, excluding replicate 1 of the A938797 sample whose fastq files are missing: </p> <p><strong>IMPORTANT: Fastq files are in version 1, while bam files are in version 2 of the dashboards reported below: </strong></p> <ol> <li>Sample <a href="https://doi.org/10.5281/zenodo.13991781">S1_Cec</a> (A551763)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.14006187">S2_Col_R </a>(A595688)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.13987002">S3_Col_R </a>(A416371) </li> <li>Sample <a href="https://doi.org/10.5281/zenodo.13990328">S4_Col_Sig </a>(A120838)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.13989699">S5_Rec </a>(A121573)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.14008051">S6_Rec </a>(A938797)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.14006810">S7_Rec/Sig</a> (A798015)</li> </ol> <p> </p> <p> </p>
Example Inputs for PIPEFISH Spatial Transcriptomics Pipeline Tool
<p>This repository contains example input data, including raw images, codebooks, parameters, and segmentation labels needed to run the FISH spatial transcriptomics pipeline tool <a href="https://github.com/hubmapconsortium/spatial-transcriptomics-pipeline">PIPEFISH</a>. The datasets contained are:</p> <ul> <li><em>in situ</em> sequencing (ISS) of a whole coronal slice of a mouse brain (50 genes). <a href="https://www.biorxiv.org/content/10.1101/2021.10.12.464086v1">Link to publication</a>.</li> </ul> <p>Gataric, M., Park, J.S., Li, T., Vaskivskyi, V., Svedlund, J., Strell, C., Roberts, K., Nilsson, M., Yates, L.R., Bayraktar, O. and Gerstung, M., 2021. PoSTcode: Probabilistic image-based spatial transcriptomics decoder. <em>bioRxiv</em>, pp.2021-10.</p> <ul> <li>MERFISH of human U2-OS cell cultures (130 genes). <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5047202/">Link to publication</a>.</li> </ul> <p>Moffitt, J.R., Hao, J., Wang, G., Chen, K.H., Babcock, H.P. and Zhuang, X., 2016. High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization. <em>Proceedings of the National Academy of Sciences</em>, <em>113</em>(39), pp.11046-11051.</p> <ul> <li>seqFISH of a developing mouse embryo (351 genes). <a href="https://www.nature.com/articles/s41587-021-01006-2">Link to publication</a>.</li> </ul> <p>Lohoff, T., Ghazanfar, S., Missarova, A., Koulena, N., Pierson, N., Griffiths, J.A., Bardot, E.S., Eng, C.H., Tyser, R.C.V., Argelaguet, R. and Guibentif, C., 2022. Integration of spatial and single-cell transcriptomic data elucidates mouse organogenesis. <em>Nature biotechnology</em>, <em>40</em>(1), pp.74-85.</p> <p>In order to correctly format the inputs, run the prep_input.py script for the dataset you wish to run while in the <strong>same</strong> <strong>directory</strong> as the script.</p>
Supporting data and analysis for," A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease", main figures PART 1
<p>This deposit contains the supporting records of images and image analysis presented in, " A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease". doi: https://doi.org/10.1101/2022.06.22.497218</p> <p>Associated Zenodo repositories:</p> <table> <thead> <tr> <th scope="col">Description</th> <th scope="col">DOI</th> </tr> </thead> <tbody> <tr> <td>Main figures PART 1, Figure 1,2,3,5</td> <td>10.5281/zenodo.7653239</td> </tr> <tr> <td>Main figures PART 2, Figure 6</td> <td>10.5281/zenodo.7900973</td> </tr> <tr> <td>Supplemental 3DTC figures: S1, S4, S5, S7, S8, S9</td> <td>10.5281/zenodo.7894632</td> </tr> </tbody> </table> <p>Contents:1) a collection of .zip files contains the 3D tissue cytometry files for tissue analyzed in the manuscript doi: https://doi.org/10.1101/2022.06.22.497218. This collection includes the individual analyses for figures 2, 3 and 5. Figure 6 analyses are included in a compansion repository: 10.5281/zenodo.7900973. Contents of zip files by figure contain at a minimum the .obx and a .tif file which includes the segmented objects and associated measurements for use by VTEA (https://vtea.wiki/). Additional files may include gate files (.vtg) or max projections (.tif).</p> <p>2) a collection of zip files containing the RNAScope image files shown in: Figure 1 P,Q. The supplemental figure data for RNAScope. Figures S1,S4 and S5 are found in: 10.5281/zenodo.7894633.</p> <p>Please address any concerns or questions to the authors listed in the deposit or manuscript, doi: https://doi.org/10.1101/2022.06.22.497218</p>
Supporting data and analysis for," A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease", Supplemental 3DTC figures
<p>This deposit contains the supporting records of analysis for 3D cytometry presented in, " A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease". doi: https://doi.org/10.1101/2022.06.22.497218 found in supplemental figures.</p> <p>Contents:</p> <p>1) a collection of .zip files contains the 3D tissue cytometry files for tissue analyzed in the manuscript doi: https://doi.org/10.1101/2022.06.22.497218. This collection includes the individual analyses by figures in the supplemental figure data for 3D tissue cytometry. The main figure data is found at: 10.5281/zenodo.7653239 and 10.5281/zenodo.7900973.</p> <p>2) a collection of zip files containing the RNAScope image files shown in: Figures S1,S4 and S5. The main RNAScope figure data is found at: 10.5281/zenodo.7653239</p> <p>Please address any concerns or questions to the authors listed in the deposit or manuscript, doi: https://doi.org/10.1101/2022.06.22.497218</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.