This project contains the datasets used to evaluate and reproduce the results of ENACT (End-to-End Analysis and Cell Type
Annotation for Visium HD Slides). The dataset consists of:

• a sample Visium HD sample of human colorectal cancer, courtesy of 10X Genomics (Visium HD Spatial Gene Expression Library, Human Colorectal Cancer (FFPE) - 10x Genomics). All credit goes to 10X Genomics.
• configuration files to be used to reproduce the results provided in the ENACT publication,
• evaluation plots used in the ENACT publication, and
• results obtained after running ENACT on the human colorectal cancer sample using the four bin-to-cell assignment methods (naive, weighted_by_area, weighted_by_transcript, and weighted_by_cluster) and the three cell annotation methods (Sargent, CellAssign, CellTypist)

Additionally, results from running ENACT on the following three public VisiumHD samples are provided to showcase ENACT’s tissue-agnostic nature:

Recent developments include: In March 2024, 10x Genomics, Inc. launched its Visium HD Spatial Gene Expression product, allowing researchers to analyze the entire transcriptome from FFPE tissue sections at a single cell-scale resolution. This innovative assay provides a comprehensive understanding of gene expression patterns within tissues, offering researchers a powerful tool to explore cellular biology in detail. , In March 2024, Johns Hopkins biomedical engineers invented an innovative computational technique to accurately align ST data across various samples, resolutions, and technologies, empowering researchers to delve deeper into cellular biology. This novel method, known as STalign, enhances their capacity to effectively compare spatial single-cell data, facilitating comprehensive insights into cellular organization and function. .

This dataset contains Spatial Transcriptomics (ST) data matching with Matrix Assisted Laser Desorption/Ionization - Mass Spetrometry Imaging (MALDI-MSI). This data is complementary to data contained in the same project. FIles with the same identifiers in the two datasets originated from the very same tissue section and can be combined in a multimodal ST-MSI object. For more information about the dataset please see our manuscript posted on BioRxiv (doi: https://doi.org/10.1101/2023.01.26.525195). This dataset includes ST data from 19 tissue sections, including human post-mortem and mouse samples. The spatial transcriptomics data was generated using the Visium protocol (10x Genomics). The murine tissue sections come from three different mice unilaterally injected with 6-OHDA. 6-OHDA is a neurotoxin that when injected in the brain can selectively destroy dopaminergic neurons. We used this mouse model to show the applicability of the technology that we developed, named Spatial Multimodal Analysis (SMA). Using our technology on these mouse brain tissue sections we were able to detect both dopamine with MALDI-MSI and the corresponding gene expression with ST. This dataset includes also one human post-mortem striatum sample that was placed on one Visium slide across the four capture areas. This sample was analyzed with a different ST protocol named RRST (Mirzazadeh, R., Andrusivova, Z., Larsson, L. et al. Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples. Nat Commun 14, 509 (2023). https://doi.org/10.1038/s41467-023-36071-5), where probes capturing the whole transcriptome are first hybridized in the tissue section and then spatially detected. Each tissue section contained in the dataset has been given a unique identifier that is composed of the Visium array ID and capture area ID of the Visium slide that the tissue section was placed on. This unique identifier is included in the file names of all the files relative to the same tissue section, including the MALDI-MSI files published in the other dataset included in this project. In this dataset you will find the following files for each tissue section: - raw files: these are the read one fastq files (containing the pattern *R1*fastq.gz in the file name), read two fastq files (containing the pattern *R1*fastq.gz in the file name) and the raw microscope images (containing the pattern Spot.jpg in the file name). These are the only files needed to run the Space Ranger pipeline, which is freely available for any user (please see the 10x Genomics website for information on how to install and run Space Ranger); - processed data files: we provide processed data files of two types: a) Space Ranger outputs that were used to produce the figures in our publication; b) manual annotation tables in csv format produced using Loupe Browser 6 (csv tables with file names ending _RegionLoupe.csv, _filter.csv, _dopamine.csv, _lesion.csv, _region.csv patterns); c) json files that we used as input for Space Ranger in the cases where the automatic tissue detection included in the pipeline failed to recognize the tissue or the fiducials. Using these processed files the user can reproduce the figures of our publication without having to restart from the raw data files. The MALDI-MSI analyses preceding ST was performed with different matrices in different tissue section. We used 1) 9-aminoacridine (9-AA) for detection of metabolites in negative ionization mode, 2) 2,5-dihydroxybenzoic acid (DHB) for detection of metabolites in positive ionization mode, 3) 4-(anthracen-9-yl)-2-fluoro-1-ethylpyridin-1-ium iodide (FMP-10), which charge-tags molecules with phenolic hydroxyls and/or primary amines, including neurotransmitters. The information about which matrix was sprayed on the tissue sections and other information about the samples is included in the metadata table. We also used three types of control samples: - standard Visium: samples processed with standard Visium (i.e. no matrix spraying, no MALDI-MSI, protocol as recommended by 10x Gemomics with no exeptions) - internal controls (iCTRL): samples not sprayed with any matrix, neither processed with MALDI-MSI, but located on the same Visium slide were other samples were processed with MALDI-MSI - FMP-10-iCTRL: sample sprayed with FMP-10, and then processed as an iCTRL. This and other information is provided in the metadata table.

Supplementary data supporting the SpatialOne: End-to-End Analysis of Spatial Transcriptomics at Scale publication

To showcase the capabilities of SpatialOne, two human lung cancer formalin-fixed, paraffin-embedded (FFPE) samples are analyzed. These samples are prepared following the CG000495 protocol (Figure 1b), sequenced with the 10x Visium CytAssist, and processed using the 10x SpaceRanger version 2. We also present analysis of two adult mouse samples sequenced using 10x Visium samples (one fresh frozen brain tissue section processed using SpaceRanger v2 and one FFPE kidney sample processed using the SpaceRanger v1), and 75 internal samples.

For the human lung cancer samples, single-cell data from the the Lung Cancer Atlas (Salcher et al., 2022) is used as reference. This dataset is filtered to include only Chromium-generated data. For the mice samples, the GSE107585 single-cell dataset serves as reference. In the human lung cancer datasets, a pathologist annotated regions of interest corresponding to tumors, blood vessels, and alveolar regions.

Changelog:

Added a README file describing the zip content.

Sample Group Description

The dataset includes the following human & non-human samples:

Human samples:

-Live healthy donors (LHD; n = 8), labeled as M

-Patients with liver pathology (adjacent normal tissue sampled; n = 8), labeled as P

Non-human samples:

-Wild boar (n = 2), labeled as non_human_P

-Cow (n = 2), labeled as non_human_C

-Domesticated pig (n = 3), labeled as non_human_PD

Visium:

LHDs and adjacent normal samples:

Human loupe files:

This folder includes the Loupe Browser-compatible files (16 total) corresponding to the human samples (M1–M8, P2, P3, P6, P7, P14, P17, P18, P21) for downstream visualization and exploration.

Human_h5_files

This folder contains .h5 formatted output files from Space Ranger for the 16 human samples.

Human_Spatial_transcriptomics_data:

This folder contains spatial transcriptomics data from 16 human samples.
For each sample the following files are included:

counts_ALL.csv – full gene expression matrix

counts_UTT.csv – filtered matrix (UTT: under the tissue)

tissue_positions_list.csv – spatial barcode coordinates

scalefactors_json.json – image scaling information

tissue_hires_image.png – high-resolution histology image

Non_Human_Loupe_files:

This folder contains Loupe Browser-compatible files for the 7 non-human samples (C1, C2, P1, P2, PD1, PD2, PD3).

Non_human_h5_files

This folder contains.h5 formatted output files from Space Ranger for the 7 non-human samples

Non_Human_Spatial_transcriptomics_data:

This folder includes spatial transcriptomics data from 7 non-human samples (C1, C2, P1,P2, PD1, PD2, PD3).

counts_ALL.csv – full gene expression matrix

counts_UTT.csv – filtered matrix (UTT: under the tissue)

tissue_positions_list.csv – spatial barcode coordinates

scalefactors_json.json – image scaling information

tissue_hires_image.png – high-resolution histology image

VisumHD:

This folder contains spatial transcriptomics data from 10x Genomics Visium HD for human liver samples:

M1_VisiumHD.cloupe – Loupe Browser visualization file for patient M1, showing spatial transcriptomics data at 8 μm bin resolution.

M2_VisiumHD.cloupe – Loupe Browser visualization file for patient M2, showing spatial transcriptomics data at 8 μm bin resolution.

M6_VisiumHD.cloupe – Loupe Browser file for a Visium HD slide that includes two tissue sections. The tissue at the bottom of the slide corresponds to patient M6, which is the one analyzed in the downstream dataset (marked under ‘patients’ as ‘M6-high quality’). Data is shown at 8 μm bin resolution.

visiumHD_data_M2_M6.h5ad – A filtered and integrated .h5ad file containing single-cell–resolved spatial gene expression data from both M2 and M6 samples.

-Resolution: Cells. Based on single-cell segmentation (see “Liver Cell Atlas using Visium HD” method).

-Cell filtering: Only cells detected via segmentation and that have passed quality filters were included.

-UMI threshold: Cells with fewer than 200 UMIs were excluded.

-Batch correction: Harmony was applied to correct sample-specific effects prior to UMAP visualization.

-Format: .h5ad (AnnData format, compatible with Scanpy).

-Includes: Single-cell expression matrix, spatial coordinates, Harmony-corrected UMAP, cluster identity, and metadata.

M6:

This folder contains spatial transcriptomics data (8*8 μm) for sample M6, generated using the 10x Genomics Visium HD platform.

NOTES:

-The gene expression matrices (*.h5) come from the full slide output of Space Ranger, including both tissue sections (like the M6 Loupe file ).

-The spatial metadata files (*.json, *.tif, .csv) refer to the cropped region, corresponding to the bottom tissue, which is the actual M6 sample used in downstream analysis.

-This is the raw Space Ranger output, prior to cell segmentation or high-level filtering (apart from the default filtered feature matrix).

-This data reflects raw 8 μm resolution bins, not single-cell segmentations.

-For downstream analysis based on cell segmentation, refer to the visiumHD_data_M2_M6.h5ad file in the top-level VisiumHD folder.

Gene Expression Matrices (uncropped – both tissues included):

filtered_feature_bc_matrix_8um.h5
raw_feature_bc_matrix_8um.h5

Spatial Metadata (cropped – M6 tissue only):

scalefactors_json.json
Images: tissue_hires_image.tif / tissue_lowres_image.tif / tissue_fullres_image.tif
tissue_positions.csv - Barcode-to-position table corresponding only to the cropped region, i.e., the M6 tissue. Only the barcodes listed in this file are relevant to M6 and should be used to extract or analyze this tissue’s expression data from the full matrix.

M2:

This folder contains the full, unmodified output of the 10x Genomics Visium HD Space Ranger for the M2 liver tissue sample (8X8 um resolution).

filtered_feature_bc_matrix_8um.h5
raw_feature_bc_matrix_8um.h5
scalefactors_json.json
Images: tissue_hires_image.tif / tissue_lowres_image.tif
tissue_positions_orig.csv

M1:

This folder contains the full, unmodified output of the 10x Genomics Visium HD Space Ranger for the M1 liver tissue sample (8X8 um resolution)

filtered_feature_bc_matrix_8um.h5
raw_feature_bc_matrix_8um.h5
scalefactors_json.json
Images: tissue_hires_image.tif / tissue_lowres_image.tif
tissue_positions_orig.csv

snRNAseq:

This folder contains single-nucleus RNA-seq (snRNA-seq) data from four human liver samples (M5, M6, M7, M8). Data was generated using Cell Ranger multi.

single_nuc_RNAseq.cloupe - Output from Cell Ranger multi. data from all four samples.

snRNAseq.h5ad - Processed and filtered .h5ad file containing single-nucleus expression data from M5–M8, integrated into one dataset.

-Filtering includes standard QC (e.g., low-gene/UMI exclusion, mitochondrial content, etc.)

-Batch correction: Harmony was applied to correct sample-specific effects prior to UMAP visualization.

-Format: .h5ad (AnnData format, compatible with Scanpy).

-Includes: expression matrix, spatial coordinates, Harmony-corrected UMAP, cluster identity and metadata.

M5, M6, M7, M8

Each sample folder contains raw and filtered matrices generated by Cell Ranger:

-sample_filtered_feature_bc_matrix

-sample_raw_feature_bc_matrix

MERFISH:

For both samples- M5 and M8, each sample folder contains:

-cell_by_gene.csv

-cell_metadata.csv

-detected_transcripts.csv

The Spatial Omics market, valued at $335.60 million in 2025, is poised for significant growth, exhibiting a Compound Annual Growth Rate (CAGR) of 10.60% from 2025 to 2033. This robust expansion is driven by several key factors. Advancements in technologies like spatial transcriptomics, spatial genomics, and spatial proteomics are providing researchers with unprecedented insights into the spatial organization of biological systems. This detailed understanding is crucial for accelerating drug discovery and development, enabling more precise diagnostics, and advancing translational research. The increasing adoption of single-cell analysis techniques further fuels market growth, as researchers strive to understand cellular heterogeneity and interactions within tissues and organs. Furthermore, the growing demand for high-throughput screening and personalized medicine is creating a strong pull for these technologies across pharmaceutical and biotechnology companies as well as academic research institutions. The availability of comprehensive software solutions for data analysis and interpretation is also a significant contributing factor, simplifying the complex data generated by these technologies. The market segmentation reveals a diverse landscape. Instruments currently dominate the product segment, reflecting the high capital expenditure associated with acquiring advanced spatial omics platforms. However, consumables and software are expected to experience substantial growth, driven by increasing research activity and the continuous need for reagents and analytical tools. Formalin-Fixed Paraffin-Embedded (FFPE) samples currently hold a significant share, leveraging existing pathology workflows. However, fresh frozen samples are gaining traction due to their superior preservation quality for certain applications. In terms of applications, diagnostics is a rapidly growing segment, promising to revolutionize personalized medicine by allowing for spatially-resolved diagnoses of diseases. North America currently holds a major share of the market, benefiting from a strong presence of key players and robust funding for biomedical research. However, Asia Pacific is projected to witness significant growth driven by increasing investment in life sciences and healthcare infrastructure. The competitive landscape is marked by the presence of established players such as 10x Genomics and NanoString Technologies, alongside emerging companies developing innovative spatial omics technologies. This dynamic ecosystem is indicative of a field ripe for continued innovation and expansion. Recent developments include: March 2024: 10x Genomics initiated commercial distribution of its eagerly anticipated Visium HD Spatial Gene Expression instrument. Visium HD is engineered to empower researchers with the ability to thoroughly analyze FFPE tissue samples by quantifying spatial gene expression across the entire transcriptome at a single-cell resolution., January 2024: SimBioSys obtained 510(k) clearance from the US Food and Drug Administration (FDA). This clearance permits the marketing of TumorSight Viz, a software application designed to generate three-dimensional spatial visualizations of breast tumors.. Key drivers for this market are: High Burden of Cancer and Genetic Diseases, Advancement in Omics Technologies and Increasing Demand for Personalized Medicine; Rising Government Initiatives and Funding Activities. Potential restraints include: High Burden of Cancer and Genetic Diseases, Advancement in Omics Technologies and Increasing Demand for Personalized Medicine; Rising Government Initiatives and Funding Activities. Notable trends are: The Spatial Transcriptomics Segment is Expected to Hold a Significant Market Share Over the Forecast Period.

FineST (Fine-grained Spatial Transcriptomic) is a statistical model and toolbox to identify the super-resolved spatial co-expression (i.e., spatial association) between a pair of ligand and receptor.

The global spatial OMICS market is poised to witness significant growth, with a market size of USD 335.60 million in 2025 and projected to reach USD 884.07 million by 2033, exhibiting a CAGR of 10.60% during the forecast period. This growth is attributed to increasing demand for precision medicine, advancements in spatial technologies, and rising adoption of spatial OMICS in drug discovery and development. Key drivers of the spatial OMICS market include the rise of single-cell analysis, the need for a deeper understanding of tissue heterogeneity, and the increasing availability of spatial data. Furthermore, the development of novel spatial technologies such as spatial transcriptomics and spatial genomics is expected to further fuel market growth. However, factors such as high costs associated with spatial OMICS and a lack of skilled professionals may restrain market expansion. Recent developments include: March 2024: 10x Genomics initiated commercial distribution of its eagerly anticipated Visium HD Spatial Gene Expression instrument. Visium HD is engineered to empower researchers with the ability to thoroughly analyze FFPE tissue samples by quantifying spatial gene expression across the entire transcriptome at a single-cell resolution., January 2024: SimBioSys obtained 510(k) clearance from the US Food and Drug Administration (FDA). This clearance permits the marketing of TumorSight Viz, a software application designed to generate three-dimensional spatial visualizations of breast tumors.. Key drivers for this market are: High Burden of Cancer and Genetic Diseases, Advancement in Omics Technologies and Increasing Demand for Personalized Medicine; Rising Government Initiatives and Funding Activities. Potential restraints include: High Cost of Instruments and Data Storage, Complex Regulatory Requirement and Standardization Issues. Notable trends are: The Spatial Transcriptomics Segment is Expected to Hold a Significant Market Share Over the Forecast Period.

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.

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

Cancer cells display heterogeneous and dynamic states in glioblastoma, but how these malignant states arise and whether they follow a tractable cellular trajectory across tumours is poorly understood. Here, we generate a deep single cell and spatial multi-region atlas of 12 isocitrate dehydrogenase wild-type (IDH-wt) primary glioblastomas that integrates transcriptomic, epigenomic and genomic analysis to comprehensively characterise their tumour heterogeneity. The datasets in this study include sequencing data from Visium spatial transcriptomic (10x Genomics) profiling of these tumours.

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...

This repository contains all the supplementary data required for the Pseudovisium pre-print.

H5AD file created by following the squidpy Visium fluorescence and H&E tutorials

Here, we summarise available data and source code regarding the publication "Spatially Resolved Transcriptomics Mining in 3D and Virtual Reality Environments with VR-Omics". Abstract Spatially resolved transcriptomics (SRT) technologies produce complex, multi-dimensional data sets of gene expression information that can be obtained at subcellular spatial resolution. While several computational tools are available to process and analyse SRT data, no platforms facilitate the visualisation and interaction with SRT data in an immersive manner. Here we present VR-Omics, a computational platform that supports the analysis, visualisation, exploration, and interpretation SRT data compatible with any SRT technology. VR-Omics is the first tool capable of analysing and visualising data generated by multiple SRT platforms in both 2D desktop and virtual reality environments. It incorporates an in-built workflow to automatically pre-process and spatially mine the data within a user-friendly graphical user interface. Benchmarking VR-Omics against other comparable software demonstrates its seamless end-to-end analysis of SRT data, hence making SRT data processing and mining universally accessible. VR-Omics is an open-source software freely available at: https://ramialison-lab.github.io/pages/vromics.html or below. For development of VR-Omics publicly available data was used. The Visium data from 10XGenomics is available at the 10X Genomics website: https://www.10xgenomics.com/resources/datasets. The 10X Genomics Xenium dataset is available under: https://www.10xgenomics.com/products/xenium-in-situ/preview-dataset-human-breast. The STOmics database is available at: https://db.cngb.org/stomics. The Vizgen MERFISH data release program can be accessed via: https://vizgen.com/data-release-program/. The Tomo-seq data is available via their publication https://doi.org/10.1016/j.cell.2014.09.038 which also contains the MATLAB code for the 3D data reconstruction. The Visium demo was adapted from Asp et al. and can be accessed via the related publication https://doi.org/10.1016/j.cell.2019.11.025 or at https://data.mendeley.com/datasets/zkzvyprd5z/1. The demo datasets generated for VR-Omics can be found at: https://doi.org/10.26180/22207579.v1 or below for download. The 3D Visium data set of the human developing heart adapted from Asp et al. can be found within the application and can be accessed from the main menu following the Visium, Demo context menu. The complete standalone version of VR-Omics (containing Python AW and Visualiser) can be downloaded at https://ramialison-lab.github.io/pages/vromics.html or at https://doi.org/10.26180/20220312.v1 or below for download. Alternatively, the code is available at GitHub (https://github.com/Ramialison-Lab/VR-Omics). To use the GitHub version an installation of Unity Gaming Engine (version 2021.3.11f1) is required. This version does not include the Python AW. The Python AW can be accessed at: https://doi.org/10.26180/22207903.v1. More information of run VR-Omics via Unity can be found in the full documentation accessible at https://ramialison-lab.github.io/pages/vromics.html.

Data and Analysis Files from "Repurposing Large-Format Microarrays for Scalable Spatial Transcriptomics"

ArraySeq_Method.zip contains the following folder and contents:

• STARSolo: All code and count matrix output from fastq spatial barcode demultiplexing.
• Images: All resolution-downsampled H&E image scans from analyzed tissues
• Space_Ranger: All 10x Space Ranger output from Visium datasets generated in the paper.
• Analysis: All scripts for analyzing and plotting Array-seq and Visium datasets generated in this paper. Also contains output h5ad files.

ArraySeq_Barcode_generation_n12.rmd: The script used to generate the Array-seq probes with 12-mer spatial barcodes.

Glioblastomas are malignant tumors of the central nervous system hallmarked by subclonal diversity and dynamic adaptation amid developmental hierarchies (Couturier et al., 2020; Neftel et al., 2019; Richards et al., 2021). The source of the dynamic reorganization within the spatial context of these tumors remains elusive. Here, we characterized glioblastomas in-depth by spatially resolved transcriptomics, metabolomics, and proteomics. By deciphering regionally shared transcriptional programs across patients, we infer that glioblastoma is organized by spatial segregation of lineage states and adapt to inflammatory and/or metabolic stimuli, reminiscent of the reactive transformation in mature astrocytes. Integration of metabolic imaging and imaging mass cytometry uncovered locoregional tumor-host interdependence, resulting in spatially exclusive adaptive transcriptional programs. Inferring copy-number alterations emphasizes a spatially cohesive organization of subclones associated with re..., The dataset was collected using: 1) 10X Visium spatila gene expression kit: And all the instructions for Tissue Optimization and Library preparation were followed according to manufacturer’s protocol. Data were analyzed and quality controlled by the cell ranger pipeline provided by 10X. For further analysis we developed a framework for spatial data analysis. The cell ranger output can be imported into SPATA by either a direct import function (SPATA:: initiateSpataObject_10X) or manually imported using count matrix and barcode-coordinate matrix as well the H&E staining.2) MALDI-FTICR-MSI: Tissue preparation steps for MALDI imaging mass spectrometry (MALDI-MSI) analysis was performed as previously described (Aichler et al., 2017; Sun et al., 2018). We imported the files into R using the readImzML function from the cardinal package(Bemis et al., 2015). We reshaped the pixel data matrix into an intensity matrix and a matrix of coordinates for each tumor separately. We filtered the m/z m..., , # Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma

Author Information

• Name: Dr. Vidhya M. Ravi
  Institution: University Clinic of Freiburg
  Email: vidhya.ravi@uniklinik-freiburg.de
• Name: PD. Dr. Dieter Henrik Heiland
  Institution: University Clinic of Freiburg
  Email: dieter.henrik.heiland@uniklinik-freiburg.de

Date of Data Collection

2020–2021

Geographic Location of Data Collection

Freiburg, Germany

Recommended Citation for This Dataset

Ravi, Vidhya; Will, Paulina; Kueckelhaus, Jan et al. (2022). Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma [Dataset]. Dryad. https://doi.org/10.5061/dryad.h70rxwdmj

1. Spatial Transcriptomics (10X Visium)

File/folder name: 10XVISIUM.zip ...

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 dataset is accessible under request because it includes sensitive data.

Users wishing to access the dataset will need to sign a Data Sharing Agreement document that will be provided at the time of the request submission.

Please write your request at mailto:human-biology@crick.ac.uk" href="mailto:human-biology@crick.ac.uk">human-biology@crick.ac.uk.

Visium spatial transcriptomics was performed on one CRC FFPE sample according to the manufacturer’s instructions (protocol CG000408 Revision D and CG000409 Revision C). The region of interest was scored on the FFPE block and a 5µm section was cut and placed on the Visium slide inside the 6x6mm2 fiducial frame. The slide was then incubated at 42 °C for 3h, deparaffinized, H&E stained and imaged using an Olympus VS200 slide scanner. Once imaged, the coverslip was removed, and the slide was decrosslinked. Visium Human Transcriptome Probe kit (v1, PN-1000363) was used for transcript hybridisation. Hybridised RNA molecules were released after tissue permeabilization and captured within each spot by barcoded oligonucleotides. Captured RNA molecules were used for sequencing library preparation and sequenced using NextSeq2000. Sequencing depth was calculated to ensure at least 25000 reads for each tissue covered spot. Visium fastq files were processed using spaceranger v2.0 to produce raw gene expression count data.

In addition to standard output files generated with spaceranger, the dataset includes:

• A1_CR48_TissueType_Anno.csv: spot annotation created via Loupe Browser v6.2. Each spot is classified according to underlying tissue type. Annotation is left empty for spots matching with empty tissue regions.
• V12D05-285_CR48_20x_A1.tif: full-resolution Visium H&E image (tif format)
• CRC_LCM_Ext_sigs.rds: list object in rds format containing immune (extrinsic) gene signatures described in Acha-Sagredo et al. and used for downstream analysis

All zipped(.zip) folders contained in the dataset (spatial/raw_feature_bc_matrix/filtered_feature_bc_matrix) should be unzipped before trying to load the dataset in R/python with packages like Kandinsky/Seurat/scannpy/squidpy etc.

Table 1: Dataset summary. Columns are: dataname (name of dataset), num_spots (the number of Visium spots with tissue information), med_reads_per_spot (median number of reads per spot), SpliZ_med_per_spot (median number of genes with SpliZ values per spot), ReadZS_med_per_spot (median number of 5000-bp windows with ReadZS values per spot), ge_med_per_spot (median number of genes with nonnegative gene expression per spot), and ReadZS_ge_med_per_spot (median number of 5000-bp windows with nonnegative gene expression per spot)

Table 2: Summary of discoveries. Columns are: dataname (name of dataset), score (either ReadZS_norm, SpliZ_norm, ge_norm, which is gene expression, or ReadZS_ge_norm, which is 5000-bp-window expression), num_genes (the number of genes tested, or windows in the case of ReadZS_norm or ReadZS_ge_norm), num_sig (the number with significant spatial patterns by Moran’s I), top (the top 10 genes/windows found to be significant in the dataset for the given score), and frac_sig (the fraction of significant genes or windows).

Table 3: Spatial score (Moran’s I) for each gene/window in each dataset. A) Scores for the SpliZ. B) Scores for the ReadZS. C) Scores for gene expression. D) Scores for gene expression by genomic window. Columns are: gene (gene name), window (genomic window identifier; use Table 4 to decode), score_cont (Moran’s I score), num_pairs (number of neighboring pairs with non-NA values for this gene/window in this dataset), perm_pvals_emp_adj (empirical permutation p value, adjusted by Benjamini Hochberg correction), dataname (name of the dataset).

Table 4: Mapping of genomic windows to gene names. A) Mapping for mouse data. B) Mapping for human data. Columns are: chr (chromosome), start (beginning coordinate of the window), end (ending coordinate of the window), window (the name assigned to the genomic window), strand (the genomic strand), gene (the gene name(s) assigned to this window and strand).