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

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...

Seurat object for the spatial transcriptomic data of sample HCM1268A

Seurat object for the spatial transcriptomic data of sample HCM1406C

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.

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.

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

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

Seurat object for the spatial transcriptomic data of sample HCM1225D

BC.Rdata: Seurat Object

ST_expr_smooth_out.RData: Pre-computed smooths spatial transcriptomics gene expression using the weighted mean of neighbouring spots in one compartment.

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.

The dataset includes spatially-resolved and single-cell antigen receptor, as well as gene expression, data from two different HER2+ breast cancer patients. The tumor piece obtained during surgery from each patient was divided into several regions and tissue sections were used for spatial transcriptomics (Visium, 10x genomics). As indicated, some tissue sections were analyzed by a new method (Spatial VDJ) to spatially resolve antigen receptor sequences (target capture), which was developed in our publication. In parallel, tissue pieces from the same tumor were dissociated for single-cell gene expression analysis (10x genomics GEX, VDJ, and feature barcoding/Hash Tag Oligonucleotide). The deposited data is in the form of fastq files. All processed data, metadata, micrographs of the tissue sections (of those used for spatial transcriptomics), and scripts used for the analysis are publicly available at Zenodo (DOI: 10.5281/zenodo.7961605). Final libraries were sequenced on NextSeq2000 (Illumina) or NovaSeq6000 (Illumina) and analyzed with Cell Ranger, Seurat, Space Ranger, and STutility pipelines.

Skeletal muscle regeneration relies on the orchestrated interaction of myogenic and non-myogenic cells with spatial and temporal coordination. The regenerative capacity of skeletal muscle declines with aging due to alterations in myogenic stem/progenitor cell states and functions, non-myogenic cell contributions, and systemic changes, all of which accrue with age. A holistic network-level view of the cell-intrinsic and -extrinsic changes influencing muscle stem/progenitor cell contributions to muscle regeneration across the lifespan remains poorly resolved. To provide a comprehensive atlas of regenerative muscle cell states across mouse lifespan, we collected a compendium of 273,923 single-cell transcriptomes from hindlimb muscles of young, old, and geriatric (4-7, 20, and 26 months old, respectively) mice at six closely sampled time-points following myotoxin injury. We identified 29 muscle-resident cell types, eight of which exhibited accelerated or delayed dynamics in their abundances between age groups, including T and NK cells and multiple macrophage subtypes, suggesting that the age-related decline in muscle repair may arise from temporal miscoordination of the inflammatory response. We performed a pseudotime analysis of myogenic cells across the regeneration timespan and found age-specific myogenic stem/progenitor cell trajectories in old and geriatric muscles. Given the critical role that cellular senescence plays in limiting cell contributions in aged tissues, we built a series of tools to bioinformatically identify senescence in these single-cell data and assess their ability to identify senescence within key myogenic stages. By comparing single-cell senescence scores to co-expression of hallmark senescence genes Cdkn2a and Cdkn1a, we found that an experimentally derived gene list derived from a muscle foreign body response (FBR) fibrosis model accurately (receiver-operator curve AUC = 0.82-0.86) identified senescent-like myogenic cells across mouse ages, injury time-points, and cell-cycle states, in a manner comparable to curated gene-lists. Further, this scoring approach in both single-cell and spatial transcriptomic datasets pinpointed transitory senescent-like subsets within the myogenic stem/progenitor cell trajectory that are associated with stalled MuSC self-renewal states across all ages of mice. This new resource on mouse skeletal muscle aging provides a comprehensive portrait of the changing cellular states and interactions underlying skeletal muscle regeneration across the mouse lifespan. Methods Mouse muscle injury and single-cell isolation. The Cornell University Institutional Animal Care and Use Committee (IACUC) approved all animal protocols (approval # 2014-0085), and experiments were performed in compliance with its institutional guidelines. Mice were maintained at 70-73°F on a 14/10-h light/dark with humidity mainly at 40%. Muscle injury was induced in young (4-7 months-old [mo]), old (20 mo), and geriatric (26 mo) C57BL/6J mice (Jackson Laboratory # 000664; NIA Aged Rodent Colonies) by injecting both tibialis anterior (TA) muscles with 10 µl of notexin (10 µg/ml; Latoxan, France). The mice were sacrificed, and TA muscles were collected at 0, 1, 2, 3.5, 5, and 7 days post-injury (dpi) with n = 3-4 biological replicates per sample. Each TA was processed independently to generate single-cell suspensions. At each time point, the young and old samples are biological replicates of TA muscles from distinct mice, and the geriatric samples are biological replicates of two TA muscles from each of the two mice. A mixture of male and female mice was used. See Supplemental Table 1 for additional details. Muscles were digested with 8 mg/ml Collagenase D (Roche, Basel, Switzerland) and 10 U/ml Dispase II (Roche, Basel, Switzerland) and then manually dissociated to generate cell suspensions. Myofiber debris was removed by filtering the cell suspensions through a 100 µm and then a 40 µm filter (Corning Cellgro # 431752 and # 431750). After filtration, erythrocytes were removed by incubating the cell suspension inan erythrocyte lysis buffer (IBI Scientific # 89135-030). Single-cell RNA-sequencing library preparation. After digestion, the single-cell suspensions were washed and resuspended in 0.04% BSA in PBS at a concentration of 106 cells/ml. A hemocytometer was used to manually count the cells to determine the concentration of the suspension. Single-cell RNA-sequencing libraries were prepared using the Chromium Single Cell 3’ reagent kit v3 (10x Genomics, Pleasanton, CA) following the manufacturer’s protocol (10x Genomics: Resolving Biology to Advance Human Health, 2020). Cells were diluted into the Chromium Single Cell A Chip to yield a recovery of 6,000 single-cell transcriptomes with <5% doublet rate. Libraries were sequenced on the NextSeq 500 (Illumina, San Diego, CA) (Illumina | Sequencing and array-based solutions for genetic research, 2020). The sequencing data was aligned to the mouse reference genome (mm10) using CellRanger v5.0.0 (10x Genomics) (10x Genomics: Resolving Biology to Advance Human Health, 2020). Preprocessing single-cell RNA-sequencing data. From the gene expression matrix, the downstream analysis was carried out in R (v3.6.1). First, the ambient RNA signal was removed using the default SoupX (v1.4.5) workflow (autoEstCounts and adjustCounts; github.com/constantAmateur/SoupX) (Young and Behjati, 2020). Samples were then preprocessed using the standard Seurat (v3.2.3) workflow (NormalizeData, ScaleData, FindVariableFeatures, RunPCA, FindNeighbors, FindClusters, and RunUMAP; github.com/satijalab/seurat) (Stuart et al., 2019). Cells with fewer than 200 genes, with fewer than 750 UMIs, and more than 25% of unique transcripts derived from mitochondrial genes were removed. After preprocessing, DoubletFinder (v2.0.3) was used to identify putative doublets in each dataset (McGinnis, Murrow, and Gartner, 2019). The estimated doublet rate was 5% according to the 10x Chromium handbook. The putative doublets were removed from each dataset. Next, the datasets were merged and then batch-corrected with Harmony (github.com/immunogenomics/harmony) (v1.0) (Korsunsky et al., 2019). Seurat was then used to process the integrated data. Dimensions accounting for 95% of the total variance were used to generate SNN graphs (FindNeighbors) and SNN clustering was performed (FindClusters). A clustering resolution of 0.8 was used resulting in 24 initial clusters. Cell type annotation in single-cell RNA-sequencing data. Cell types were determined by expression of canonical genes. Each of the 24 initial clusters received a unique cell type annotation. The nine myeloid clusters were challenging to differentiate between, so these clusters were subset out (Subset) and re-clustered using a resolution of 0.5 (FindNeighbors, FindClusters) resulting in 15 initial clusters. More specific myeloid cell type annotations were assigned based on the expression of canonical myeloid genes. This did not help to clarify the monocyte and macrophage annotations, but it did help to identify more specific dendritic cell and T cell subtypes. These more specific annotations were transferred from the myeloid subset back to the complete integrated object based on the cell barcode. Analysis of cell type dynamics. We generated a table with the number of cells from each sample (n = 65) in each cell type annotation (n = 29). We removed the erythrocytes from this analysis because they are not a native cell type in skeletal muscle. Next, for each sample, we calculated the percent of cells in each cell type annotation. The mean and standard deviation were calculated from each age and time point for every cell type. The solid line is the mean percentage of the given cell type, the ribbon is the standard deviation around the mean, and the points are the values from individual replicates. We evaluated whether there was a significant difference in the cell type dynamics over all six-time points using non-linear modeling. The dynamics for each cell type were fit to some non-linear equation (e.g., quadratic, cubic, quartic) independent and dependent on age. The type of equation used for each cell type was selected based on the confidence interval and significance (p < 0.05) of the leading coefficient. If the leading coefficient was significantly different from zero, it was concluded that the leading coefficient was needed. If the leading coefficient was not significantly different than zero, it was concluded that the leading coefficient was not needed, and the degree of the equation went down one. No modeling equation went below the second degree. The null hypothesis predicted that the coefficients of the non-linear equation were the same across the age groups while the alternative hypothesis predicted that the coefficients of the non-linear equation were different across the age groups. We conducted a One-Way ANOVA to see if the alternative hypothesis fits the data significantly better than the null hypothesis and we used FDR as the multiple comparison test correction (using the ANOVA and p.adjust (method = fdr) functions in R, respectively). T cell exhaustion scoring. We grouped the three T cell populations (this includes Cd3e+ cycling and non-cycling T cells and Cd4+ T cells) and z-scored all genes. The T cell exhaustion score was calculated using a transfer-learning method developed by Cherry et al 2023 and a T cell exhaustion gene list from Bengsch et al 2018 (Bengsch et al., 2018; Cherry et al., 2023). The Mann-Whitney U-test was performed on the T cell exhaustion score between ages. Senescence scoring. We tested two senescence-scoring methods along with fourteen senescence gene lists (Supplemental Table 2) to identify senescent-like cells within the scRNA-seq dataset. The Two-way Senescence Score (Sen Score) was calculated using a transfer-learning method developed by Cherry et al 2023 (Cherry et al., 2023). With this

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

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.

Collection of processed .Robj files (primarily Seurat spatial transcriptomics datasets) and the original publicly available data for reproducibility of the report generated in collaboration between OSR-TIGET and IRCC Candiolo (academic year 2024). For complete reproduction, visit: https://github.com/carloelle/Report_OSR_Candiolo_2024 .

All data provided here is publicly available, and no data leakage has occurred.

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

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

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.

Supplementary_data_tables 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 Vcam1 and Havcr1

AKI_Ctrl_object.rds - Seurat object generated from seqFISH data for the AKI and healthy control mice as detailed in the manuscript.

Counts_raw.csv contains raw gene counts for all cells in the dataset.

coordinates.csv contains the xy coordinates (in um) for every cell in the dataset.

metadata.csv 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 Vcam1 and Havcr1, which were detected using serial probes as detailed in the manuscript.