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

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.

This repository contains sequencing data from the publication by Jalkanen and Zhong et al., “Cytoarchitectural Profiling of White Adipose Tissue Depots Links Serum Amyloid A–Expressing Adipocytes to Immune Cell Activation.” It provides transcriptomic resources from five human white adipose tissue depots: subcutaneous, omental, mesenteric, mesocolic, and epiploic.

The repository includes:

1. 10x 3’ single-nucleus RNA-seq data provided by Seurat object.

2. Spatial transcriptomics (Visium) data also provided as Seurat object.

3. Bulk RNA-seq expression matrices from adipose tissue samples under SAA1 and LPS stimulation.

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

This dataset contains raw count data, derivatives (as Seurat objects), and analysis result tables (supplementary tables for upcoming thesis and manuscripts) derived from single-nucleus RNA sequencing and spatial transcriptomics experiments. Tissues were collected from the Sydney Brain Bank and consist of hypothalamus tissues from donors on the frontotemporal dementia (FTD) and motor neuron disease (MND) spectrum. Single-nuclei (Chromium Flex for FFPE) data was derived from 3 donors with MND, 3 donors with behavioural-variant FTD (bvFTD), and 2 donors with FTD-MND. Spatial transcriptomics (Visium HD for FFPE) was conducted on two donors with bvFTD.This dataset contains: derivative data (Seurat objects in .rds format)raw single-nuclei counts from 10x Chromium (CellRanger v7.2.0 output)analysis result tables (.xlsx)segmented counts from Visium HD (SpaceRanger v4 output) The generation of this data is supported by a Royal Brisbane and Women’s Hospital Foundation (private donation), Motor Neurone Disease Research Australia (NTI MND Research Grant and Murray Geale Research Grant), and the Brain Foundation.

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

spatial_transcriptomics_zenodo.RDS - Seurat object RDS file with 10x Visium spatial transcriptomics dataset.

gamma_delta.RDS - Seurat object RDS file with scRNA-Seq 10x dataset from gamma-delta and CD8+ T-Cells.

BD_CD8.RDS - Seurat object RDS file with scRNA-Seq BD Rhapsody dataset from CD8+ T-Cells.

BD_CD4.RDS - Seurat object RDS file with scRNA-Seq BD Rhapsody dataset from CD4+ T-Cells.

BD_CD45.RDS - Seurat object RDS file with scRNA-Seq BD Rhapsody dataset from CD45+ Immune cells.

single_cell_CD45.RDS - Seurat object RDS file with scRNA-Seq 10x dataset from CD45+ Immune cells.

cd8.RDS - Seurat object RDS file with scRNA-Seq 10x dataset from CD8+ T-Cells.

cd4.RDS - Seurat object RDS file with scRNA-Seq 10x dataset from CD4+ T-Cells.

epi.RDS - Seurat object RDS file with scRNA-Seq 10x dataset from epithelial cells.

eecs.RDS - Seurat object RDS file with scRNA-Seq 10x dataset from enteroendocrine cells.

The lacrimal gland (LG) is an exocrine gland that produces the watery part of the tear film that lubricates the ocular surface. Chronic inflammation, such as Sjögren’s syndrome (SS), is one of the leading causes of aqueous-deficiency dry eye (ADDE) disease worldwide. In this study we analyzed the chronic inflammation in the LGs of the NOD.B10Sn-H2b/J (NOD.H-2b) mice, a mouse model of SS, utilizing bulk RNAseq and Visium spatial gene expression. With Seurat we performed unsupervised clustering and analyzed the spatial cell distribution and gene expression changes in all cell clusters within the LG sections. Moreover, for the first time, we analyzed and validated specific pathways defined by bulk RNAseq using Visium technology to determine activation of these pathways within the LG sections. This analysis suggests that altered metabolism and the hallmarks of inflammatory responses from both epithelial and immune cells drive inflammation. The most significant pathway enriched in upregulated DEGs was the “TYROBP Causal Network”, that has not been described previously in SS. We also noted a significant decrease in lipid metabolism in the LG of the NOD.H-2b mice. Our data suggests that modulation of these pathways can provide a therapeutic strategy to treat ADDE.

The glioblastoma spatial transcriptomics dataset was generated from 4 FFPE fixed adult glioblastoma samples, using the 10x Genomics Visium platform, producing raw FASTQ files, spatial feature matrices (.h5, .mtx, .tsv), and paired histology images (.tif). Spot-level transcript counts were aligned to tissue architecture and processed in Seurat, with results stored as .rds objects. Data visualization was performed using SpatialFeaturePlot, and quantitative analyses were supported by custom Matlab scripts. The dataset provides both raw and processed files, offering reproducible spatially resolved transcriptomic profiles of glioblastoma tissue sections

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.

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.

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