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.

Skeletal muscle repair is driven by the coordinated self-renewal and fusion of myogenic stem and progenitor cells. Single-cell gene expression analyses of myogenesis have been hampered by the poor sampling of rare and transient cell states that are critical for muscle repair, and do not inform the spatial context that is important for myogenic differentiation. Here, we demonstrate how large-scale integration of single-cell and spatial transcriptomic data can overcome these limitations. We created a single-cell transcriptomic dataset of mouse skeletal muscle by integration, consensus annotation, and analysis of 23 newly collected scRNAseq datasets and 88 publicly available single-cell (scRNAseq) and single-nucleus (snRNAseq) RNA-sequencing datasets. The resulting dataset includes more than 365,000 cells and spans a wide range of ages, injury, and repair conditions. Together, these data enabled identification of the predominant cell types in skeletal muscle, and resolved cell subtypes, in...

Dataset created in the study "A Spatial Transcriptomics Atlas of the Malaria-infected Liver Indicates a Crucial Role for Lipid Metabolism and Hotspots of Inflammatory Cell Infiltration"

Structure

ST_berghei_liver

contains data generated during stpipeline analysis and imaging on 2k arrays Spatial Transcriptomics platform as well as data necessary for and from hepaquery analysis. These samples include 38 sections in total of which 8 are from mice (n=4) infected with sporozoites for 12h, 5 sections from control mice (n=3) at 12h, 7 sections from mice (n=4) infected with sporozoites for 24h and 4 sections from control mice (n=3) for 24 as well as 8 samples of mice (n=2) infected with sporozoites for 38h and control mice (n =2) for 38h.

count contains gene expression matrix output from stpipeline in .tsv format

spotfiles contains coordinate files for count matrices

images contains scaled H&E, Fluorescence (FL) and annotated H&E images (from FL annotations) scaled to 10% of the original image size.

masks contains image masks for hepaquery analysis

distances contains distance measurements from original section sorted by timepoint as well as combined across timepoints

cluster contains clustering information across spatial positions used in spatial enrichment analysis

STUtiility_mus_pb_ST.RDS describes seurat object generated using the STUtility package using ST data of the 38 liver sections of which the data is stored in ST_berghei_liver

visium_berghei_liver

contains data generated with the spaceranger pipeline and imaging using the Visium spatial transcriptomics platform. These samples include 8 sections in total, of which 1 was infected with sporozoites for 12h, 1 control section at 12h, 1 section infected with sporozoites for 24h and 1 control section at 24 as well as 2 sporozoite infected sections, and 2 control sections at 38h.

V10S29-135_A1 contains spaceranger output for section 1 for infected and control sections at 38h post-infection

V10S29-135_B1 contains spaceranger output for section 1 for infected and control sections at 12h post-infection

V10S29-135_C1 contains spaceranger output for section 1 for infected and control sections at 24h post-infection

V10S29-135_D1 contains spaceranger output for section 2 for infected and control sections at 38h post-infection

se_visium.RDS describes seurat object generated using the STUtility package using ST data of the 38 liver sections of which the data is stored in visium_berghei_liver

snSeq_berghei_liver

contains data generated with the cellranger pipeline and imaging using the Visium spatial transcriptomics platform. These samples include single nuclei of 2 infected and control mice after 12h, 2 infected and control mice after 24h, 2 infected and control mice after 38h, and 2 uninfected mice prior to a challenge.

cellranger_cnt_out contains feature count matrix information from cell ranger output

final_merged_curated_annotations_270623.RDS describes seurat object generated using the STUtility package using ST data of the 38 liver sections of which the data is stored in snSeq_berghei_liver.tar.gz

raw images.zip contains raw images for supplementary figures 20-22

adjusted images.zip contains brightness and contrast adjusted images for supplementary figures 20-22

Seurat objects of 10 Visium sections (4 before and 6 after treatment) seurat

This study aims to use spatial transcriptomics to characterize the cell-type-specific expression profile associated with the microscopic features observed in Wooden Breast myopathy. 1 cm3 muscle sample was dissected from the cranial part of the right pectoralis major muscle from three randomly sampled broiler chickens at 23 days post-hatch and processed with Visium Spatial Gene Expression kits (10X Genomics), followed by high-resolution imaging and sequencing on the Illumina Nextseq 2000 system. WB classification was based on histopathologic features identified. Sequence reads were aligned to the chicken reference genome (Galgal6) and mapped to histological images. Unsupervised K-means clustering and Seurat integrative analysis differentiated histologic features and their specific gene expression pattern, including lipid laden macrophages (LLM), unaffected myofibers, myositis and vasculature. In particular, LLM exhibited reprogramming of lipid metabolism with up-regulated lipid transporters and genes in peroxisome proliferator-activated receptors pathway, possibly through P. Moreover, overexpression of fatty acid binding protein 5 could enhance fatty acid uptake in adjacent veins. In myositic regions, increased expression of cathepsins may play a role in muscle homeostasis and repair by mediating lysosomal activity and apoptosis. A better knowledge of different cell-type interactions at early stages of WB is essential in developing a comprehensive understanding.

A mouse brain 10X Visium dataset for spatially proximal cell-cell communication analysis using CellChat v2. We download this dataset from https://www.10xgenomics.com/resources/datasets/mouse-brain-serial-section-1-sagittal-anterior-1-standard-1-0-0. Biological annotations of spots (i.e., cell group information) are predicted using Seurat R package.

CellChat object after running cell-cell communication analysis of the mouse brain 10X Visium dataset using CellChat v2. We download this dataset from https://www.10xgenomics.com/resources/datasets/mouse-brain-serial-section-1-sagittal-anterior-1-standard-1-0-0. Biological annotations of spots (i.e., cell group information) are predicted using Seurat R package.

We acquired 10x Visium spatial transcriptomics (ST) data from 9 patients with invasive adenocarcinomas [1–5] to explore the role of the tumour microenvironment (TME) on intratumor heterogeneity (ITH) and drug response in breast cancer. By leveraging a new version of Beyondcell 6, a tool for identifying tumour cell subpopulations with distinct drug response patterns, we predicted sensitivity to over 1,200 drugs while accounting for the spatial context and interaction between the tumour and TME compartments. Moreover, we also used Beyondcell to compute spot-wise functional enrichment scores and identify niche-specific biological functions.

Here, you can find:

In signatures folder:

SSc breast: Collection of gene signatures used to predict sensitivity to > 1,200 drugs derived from breast cancer cell lines.

Functional signatures: Collection of gene signatures used to compute enrichment in different biological pathways.

In visium folder:

Visium objects: Processed ST Seurat objects with deconvoluted spots, SCTransform-normalised counts, and clonal composition predicted with SCEVAN [7]. These objects, together with the signatures, were used to compute the Beyondcell objects.

In single-cell folder:

Single-cell objects: Raw and filtered merged single-cell RNA-seq (scRNA-seq) Seurat objects with unnormalised counts used as a reference for spot deconvolution.

In beyondcell folder:

Beyondcell sensitivity objects with prediction scores for all drug response signatures in SSc breast.

Beyondcell functional objects with enrichment scores for all functional signatures.

Preprint: https://doi.org/10.1101/2022.03.21.485045

Abstract:

Salamanders are important tetrapod models to study brain organization and regeneration, however the identity and evolutionary conservation of brain cell types is largely unknown. Here, we delineate cell populations in the axolotl telencephalon during homeostasis and regeneration, representing the first single-cell genomic and spatial profiling of an anamniote tetrapod brain. We identify glutamatergic neurons with similarities to amniote neurons of hippocampus, dorsal and lateral cortex, and conserved GABAergic neuron classes. We infer transcriptional dynamics and gene regulatory relationships of postembryonic, region-specific direct and indirect neurogenesis, and unravel conserved signatures. Following brain injury, ependymoglia activate an injury-specific state before reestablishing lost neuron populations and axonal connections. Together, our analyses yield key insights into the organization, evolution, and regeneration of a tetrapod nervous system.

File description:

all_nuclei_clustered_highlevel_anno.rds - Seurat object including all snRNA-seq data from uninjured pallium, both from microdissections and whole pallium multiome.

pallium_metadata_simp.csv - csv file containing a simplified version of the metadata for the uninjured pallium

Edu_1_2_4_6_8_12_fil_highvarfeat.rds - Seurat object containing all Div-seq data for the pallium injury time course

divseq_predicted_metadata.csv - csv file containing a simplified version of the metadata for the pallium injury time course

ep_wpi_srat.rds - Seurat object containing an integrated version of ependymoglia cells from uninjured and injured pallium (see Fig 6 in the preprint).

D1_113_sub_b.rds - Seurat object containing a Visium data for the axolotl pallium

multiome_integATAC_SCT.rds - Signac object containing the data used for multiome analysis of the uninjured whole pallium

predictions_cell2loc.csv - csv file containing cell2location scores for the uninjured pallium cell types in the Visium dataset

Mouse lung tissues were perfused with PBS and fixed with 10% formalin via tracheal instillation at a constant pressure (15 cm H2O) and embedded in paraffin wax. Lung tissues were sectioned into 5 μm sections. Tissue sections were placed within the fiducial frame or the etched frames of the Capture Area on the 10X Genomics Visium Spatial slides. Slides were then deparaffinated, decrosslinked and stained with H & E staining kit (Millipore Sigma). Images were acquired under Keyence BZ-X800E slide scanner. The mouse whole transcriptome probe panel is added to the deparaffinized, stained, and decrosslinked tissues. After hybridization, single stranded ligation products were released and then captured on the Visium slides. Probes are extended by the addition of UMI, Spatial Barcode and partial Read 1, followed by library preparation and sample indexed. The library was sequenced on a Hiseq 4000 with pair-end 150bp (Novogene). The raw sequencing data was analyzed by CellRanger 7.0 (10X Genomics) and Seurat V4. Visium data was integrated with scRNA-seq data. The cell annotation was transferred from scRNA-seq.

Using 23-months old mice of a inducible expression of human a-syn constructs based Parkinson mouse model, we produced a single nucleus RNA dataset by cutting 0mm Bregma to -5mm Bregma. The Chromium 3’ Single Cell Library Kit (10x Genomics) was used and Sequencing was performed on a NovaSeq 6000. From the same model we also used 20-months old mice with the Visium Spatial V1 platform (10x Genomics). Sequencing was performed on a NovaSeq 6000. Both were PE150.

snRNA pipeline: For the alignment of reads, a custom reference was created by adding the sequences of the V1S/SV2 transgene and the Camk2a promoter to the mm10 mouse reference genome. Count matrices generated by cellranger count 7.1 were loaded into an AnnData object and processed using the Python-based framework Scanpy 1.10.2. Integration with R, where needed, was facilitated through the rpy2 package. Raw count matrices were corrected for ambient RNA contamination using the SoupX 1.6.2. To remove potential doublets, scDblFinder 1.18.0 was employed with a fixed seed (123). Nuclei with nUMI and nGenes values exceeding three median absolute deviations (MADs) from the median were excluded. Genes detected in fewer than five nuclei across the dataset were excluded. The resulting dataset was normalized via scanpy.pp.normalize_total and scanpy.pp.log1p. Highly variable genes were identified using the function scanpy.pp.highly_variable_genes with the Seurat v3 flavor, selecting the top 4,000 genes. Dimensionality reduction was performed using principal component analysis (PCA) and batch effects were corrected using the python-implemented version of Harmony via the function scanpy.external.pp.harmony_integrate. Harmony embeddings were then used to construct a k-nearest neighbor (kNN) graph with scanpy.pp.neighbors. Clustering was performed using Leiden clustering with standard parameters via the function scanpy.tl.leiden. Clusters were annotated using literature, the mousebrain.org, and markers identified via the FindConservedMarkers function in Seurat. First, neurons and non-neuronal cells were distinguished using mainly canonical markers, such as but not limited to Rbfox3 (neurons), Mbp (oligodendrocytes), Acsbg1 (astrocytes), Pdgfra (oligodendrocyte precursor cells), Inpp5d (microglia), Colec12 (vascular cells), and Ttr (choroid plexus cells). Neurons were further classified into Vglut1 (Slc17a7), Vglut2 (Slc17a6), GABA (Gad2), cholinergic (Scube1), and dopaminergic (Th) neurons. Vglut1 and GABA neurons were further annotated into subtypes based on subclustering and FindConservedMarkers markers.

visium spatial pipeline: Sequences were fiducially aligned to spots using Loupe Browser ver. 8. All aligned sequences were mapped using spaceranger count 3.0.1 with a custom refence, which included sequences for the promotor and transgene (Camk2aTTA, V1S/SV2) to the mouse genome mm39. We filtered each sample of the Visium Spatial dataset based on the MAD filtering of number of reads (nUMI), number of genes (nGene), and percentage of mitochondrial genes (percent.mt). A spot was filtered out if it was outside of 3x MAD value in at least two metrics. Filtered samples were merged into one Seurat 5.1.0 object and we obtained normalized counts by the SCTransform function of Seurat. Integration was performed using Harmony 1.2.0 on 50 PCA embeddings and clustering was done using Leiden clustering based on 30 harmony embeddings. Integrated clusters were visualized using the UMAP method. Samples that were not successfully integrated (based on similarity measures of the harmony embeddings) and showed high percentage.mt or low nUMI levels compared to other samples, were removed from subsequent analysis. A final integration and clustering were performed after filtering. Regions were first annotated based on a 0.1 resolution clustering to get high level region annotation (Cortex, Hippocampus, Subcortex). Each high-level region was further annotated based on either more granular resolutions or subclustering. Marker genes from mousebrain.org and literature were used in combination with the Allen mouse brain atlas to obtain anatomically relevant annotations.

Datasets for ShinyCell2 Example Applications, which include:

spatial_brain.rds: Example spatial transcriptomics dataset of sagital mouse brain slices generated using the 10x Visium v1 chemistry, processed using the Seurat spatial pipeline (https://satijalab.org/seurat/articles/spatial_vignette)

multimodal_pbmc.rds: Example CITE-seq dataset of PBMC reference containing 162,000 PBMC cells measured with 228 antibodies (https://satijalab.org/seurat/articles/multimodal_reference_mapping.html)

ArchR-ProjHeme.tar.gz: Example scATAC-seq dataset of bone marrow and peripheral blood mononuclear cells, which is used as the tutorial dataset for the ArchR pipeline (https://www.archrproject.com/articles/Articles/tutorial.html). As ArchR objects are stored in a directory containing many files, the entire folder is tarred and compressed here.

signac_pbmc.rds: Example scATAC-seq dataset of PBMC provided by 10x Genomics, which is used as the tutorial dataset for the signac pipeline (https://stuartlab.org/signac/articles/pbmc_vignette.html). Signac objects store the full list of all unique fragments across all single cells in a separate fragment file, uploaded as signac_pbmc_fragments.tsv.gz here

This dataset contains scRNA-Seq (10x 3' v3) and spatial transcriptomics (10x Visium V2 Cytassist) data for 10 patients with oropharyngeal squamous cell carcinoma. The scRNA-Seq .RDS file contains an integrated seurat object containing 82,844 cells with corresponding metadata within the object. Spatial transcriptomics data was read into Seurat using Load10X_Spatial(). The visium data is uploaded both as SpaceRanger output files for each sample and as a Seurat object with deconvoluted spot-level cell type abundance metadata.

In the context of the Human Cell Atlas, we have created a single-cell-driven taxonomy of cell types and states in human tonsils. This repository contains the Seurat objects derived from this effort. In particular, we have datasets for each modality (scRNA-seq, scATAC-seq, CITE-seq, spatial transcriptomics), as well as cell type-specific datasets. Most importantly, this is the input that we used to create the HCATonsilData package, which allows programmatic access to all this datasets within R.

Version 2 of this repository includes cells from 7 additional donors, which we used as a validation cohort to validate the cell types and states defined in the atlas. In addition, in this version we also provide the Seurat object associated with the spatial transcriptomics data (10X Visium), as well as the fragments files for scATAC-seq and Multiome

The dataset includes spatially-resolved gene expression and antigen receptor data from two Tonsil samples (1 and 2). Tissue sections from the tonsil samples were used for spatial transcriptomics (Visium, 10x genomics). Tonsil 2 tissue sections were analyzed by a new method (Spatial VDJ) to spatially resolve antigen receptor sequences (target capture), which was developed in our publication. Nearby or adjacent tissue sections (from Tonsil2) were also analyzed by a bulk antigen receptor sequencing approach (amplicon sequencing), by a method also newly developed by us in the same publication (Bulk SS3 VDJ). For Visium, the data were anonymized (all SNPs removed) using Bamboozle (Ziegenhain and Sandberg, Nature Communications 2021). The deposited data is in the form of fastq files. All remaining data, metadata, micrographs of the tissue sections (of those used for spatial transcriptomics), and scripts used for the analysis are available at Zenodo (DOI: 10.5281/zenodo.7961605). Final libraries were sequenced on NextSeq2000 (Illumina) or NovaSeq6000 (Illumina) and analyzed with Seurat, Space Ranger, and STutility pipelines.

Dataset created in the study "A Spatial Transcriptomics Atlas of the Malaria-infected Liver Indicates a Crucial Role for Lipid Metabolism and Hotspots of Inflammatory Cell Infiltration"

Structure

ST_berghei_liver

contains data generated during stpipeline analysis and imaging on 2k arrays Spatial Transcriptomics platform as well as data necessary for and from hepaquery analysis. These samples include 38 sections in total of which 8 are from mice (n=4) infected with sporozoites for 12h, 5 sections from control mice (n=3) at 12h, 7 sections from mice (n=4) infected with sporozoites for 24h and 4 sections from control mice (n=3) for 24 as well as 8 samples of mice (n=2) infected with sporozoites for 38h and control mice (n =2) for 38h.

• count contains gene expression matrix output from stpipeline in .tsv format
• spotfiles contains coordinate files for count matrices
• images contains scaled H&E, Fluorescence (FL) and annotated H&E images (from FL annotations) scaled to 10% of the original image size.
• masks contains image masks for hepaquery analysis
• distances contains distance measurements from original section sorted by timepoint as well as combined across timepoints
• cluster contains clustering information across spatial positions used in spatial enrichment analysis

STUtiility_mus_pb_ST.RDS describes seurat object generated using the STUtility package using ST data of the 38 liver sections of which the data is stored in ST_berghei_liver

h5ad

contains anndata files of ST data (normalized read counts), spot information, distance measurements, images and masks generated using the hepaquery package.

visium_berghei_liver

contains data generated with the spaceranger pipeline and imaging using the Visium spatial transcriptomics platform. These samples include 8 sections in total, of which 1 was infected with sporozoites for 12h, 1 control section at 12h, 1 section infected with sporozoites for 24h and 1 control section at 24 as well as 2 sporozoite infected sections, and 2 control sections at 38h.

• V10S29-135_A1 contains spaceranger output for section 1 for infected and control sections at 38h post-infection
• V10S29-135_B1 contains spaceranger output for section 1 for infected and control sections at 12h post-infection
• V10S29-135_C1 contains spaceranger output for section 1 for infected and control sections at 24h post-infection
• V10S29-135_D1 contains spaceranger output for section 2 for infected and control sections at 38h post-infection

se_visium.RDS describes seurat object generated using the STUtility package using ST data of the 38 liver sections of which the data is stored in visium_berghei_liver

snSeq_berghei_liver

contains data generated with the cellranger pipeline and imaging using the Visium spatial transcriptomics platform. These samples include single nuclei of 2 infected and control mice after 12h, 2 infected and control mice after 24h, 2 infected and control mice after 38h, and 2 uninfected mice prior to a challenge.

cellranger_cnt_out contains feature count matrix information from cell ranger output

final_merged_curated_annotations_270623.RDS describes seurat object generated using the STUtility package using ST data of the 38 liver sections of which the data is stored in snSeq_berghei_liver.tar.gz

raw images.zip contains raw images for supplementary figures 20-22

adjusted images.zip contains brightness and contrast adjusted images for supplementary figures 20-22