Dataset folders from "TISSUE: uncertainty-calibrated prediction of single-cell spatial transcriptomics improves downstream analyses". If using the processed data or TISSUE algorithm, please cite: https://doi.org/10.1101/2023.04.25.538326.

The directory of datasets are compressed in tar gzip format. The top level contains folders with dataset names and within each of those folders, there are the relevant data files which include:

• Spatial_count.txt --- a tab-delimited file containing spatial transcriptomics counts matrix

• scRNA_count.txt --- a tab-delimited file containing RNAseq counts matrix

• Locations.txt --- a tab-delimited file containing the (x,y) spatial coordinates of cells in the spatial transcriptomics data

• Metadata.txt --- for some datasets, this is a comma-separated file containing the metadata table for the spatial transcriptomics data

These files are formatted and organized to be read into AnnData objects using the native loading functions in the TISSUE package (https://github.com/sunericd/TISSUE). Some folders will also have additional accessory files such as gene lists corresponding to some experiments present in our manuscript and/or adjacency matrix objects.

Also included are the two simulated spatial transcriptomics datasets that we generated using SRTsim.

The SVZ folders contain our processed MERFISH spatial transcriptomics dataset on the adult mouse subventricular zone. Refer to the SVZFullFinal folder for the full dataset with TISSUE-informed cell labels. All other folders are processed data accessed from publicly available sources. The identity of numbered folders can be found in the Data Availability statement of the benchmarking paper from which they were retrieved: https://doi.org/10.1038/s41592-022-01480-9

"svz_merfish_data.zip" includes the raw MERFISH dataset on the adult mouse subventricular zone.

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.

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 dataset contains the code and data used to perform the analysis, and produce the figures used in the accompanying SpaNorm manuscript that introduces the first and only spatially-aware library size normalisation method.

Additional file 5: RMarkdown on how to use the RegionNeighbours function.

The widespread application of spatial resolved transcriptomics (SRT) has provided a wealth of data for characterizing gene expression patterns within the spatial microenvironments of various tissues. However, the inherent resolution limitations of SRT in most published data restrict deeper biological insights. To overcome this resolution limitation, we propose STRESS, the first deep learning method designed specifically for resolution enhancement tasks using only SRT data. By constructing a 3D structure that integrates spatial location information and gene expression levels, STRESS identifies interaction relationships between different locations and genes, predicts the gene expression profiles in the gaps surrounding each spot, and achieves resolution enhancement based on these predictions. STRESS has been trained and tested on datasets from multiple platforms and tissue types, and its utility in downstream analyses has been validated using independent datasets. We demonstrate that this resolution enhancement facilitates the identification and delineation of spatial domains. STRESS holds significant potential for advancing data mining in spatial transcriptomics.

This dataset contains annotated sub-cellular localised spatial measurements from the Visium, Xenium and CosMx platforms. Specifically, it includes datasets analysed in the publication Bhuva et. al, 2023 titled "Library size confounds biology in spatial transcriptomics data". Raw transcript detections are presented. Data is best accessed through the accompanying SubcellularSpatialData R/Bioconductor package. Region files used to annotate individual transcript detections are presented in the form of GeoJSON files.

Visium spatial transcriptomics (10X Genomics) performed on 4 CCA samples. Each sample has two paired-end sequencing runs: the first (I1 & I2) are a pair reading indexes; the second (R1 & R2) are a pair reading inserts, with R1 additionally reading 10X barcodes. For histology images, please contact authors.

Single-cell RNA-seq and spatial transcriptomics data for 12 patients with sarcoidosis. From each patient, we analyzed skin biopsies of both lesional and non-lesional skin and we performed spatial transcriptomics for lesional skin samples. The data are provided as aligned BAM files.

We provided the datasets used in the paper "Querying functional and structural niches on spatial transcriptomics data".

Here we include the marmoset brain STARmap data introduced in the corresponding manuscript, "Mitigating autocorrelation during spatially resolved transcriptomics data analysis". We also include the mouse brain STARmap PLUS data that was used to demonstrate cross-species spatial integration. The mouse data was previously published in Shi, He, Zhou et al. 2022. The data was downloaded from Spatial Omics DataBase (SODB) and formatted to include here for demonstration.

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.

This item contains the raw 10X Visium data for patient 5 from the manuscript "Niche-DE: niche differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions"

The data for training the GAN (Inversion) model and reproduce the results reported in the paper

Spatial transcriptomics defines injury-specific microenvironments in the adult mouse kidney and novel cellular interactions in regeneration and disease

https://doi.org/10.5061/dryad.bnzs7h4hj

This dataset contains processed RDS object used to generate the figures in the manuscript as well as the metadata, raw gene counts and cell locations in csv format.

Description of the data and file structure:

'**AKI_Ctrl_object.rds'** : This file contains the .Rds object which has been processed by Suerat as described in the manuscript.

• ‘CellID’: Unique identifier for each cell in the dataset ordered as [sampleID]_[Position in Sample]_cell_[numerical ID]
• ‘orig.ident’: Original ID for each sample
• 'SampleID': Identifier of each samle (3 control mice and 3 mice which undewent IRI treatment as detailed in the manuscript)
• ‘nCount_Spatial’: Total number of RNA transcripts detected in each cell for the Spatial assay
• 'nFeature_Spatial': Tot...

In the last decades, image-based transcriptomic and proteomic experiments have moved from single-target probes to multiplexed experiments, allowing researchers to study hundreds or even thousands of mRNA and protein targets simultaneously. This large increase in scope necessitates methods in either increased specificity or in error-correction, such as the Hamming Codes utilized in the imaging-based spatial transcriptomic method MERFISH. For some experimental conditions, Hamming Codes are efficient in encoding the highest possible number of genes for spatial analysis. However, for most experimental parameters, the optimal generation of error-robust codebooks is an unsolved mathematical problem. Here we present a method to generate highly optimized Extended Hamming Codebooks compatible with established error-correctable methodologies such as MERFISH. Our method uses an iterative set-exchange approach and generally reaches over 90% of the theoretical maximum limit of gene set complexity. W..., , , # Boosting multiplexing capabilities for error-robust spatial transcriptomic methods using a set exchange approach

https://doi.org/10.5061/dryad.zkh1893m5

We deposit a host of error-robust constant weight binary codebooks for Hamming Weights (HW) 4, 5, and 6, that are ready-to-use with current MERFISH (Multiplexed-Error-Robust-Fluorescence-In-Situ-Hybridization) methodologies. Each codebook is supplied both as a binary version and as a set-based version, and is accompanied by a file with metadata and a reordered version that maximizes the Hamming Distance of the earliest appearing rows. See below for full details of file organization.

Description of the data and file structure

The data is associated with a manuscript in Science Advances, intending to create optimized error-robust constant-weight binary codebooks for given parameter combinations.Â

The manuscript is titled: Boosting Multiplexing Capabilities for Error-Robust Spatial Trans...,

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. .

Xenium platform was used for the spatial transcriptomic analysis of human DRG neurons, 100 marker genes were selected as the customized probe panel and hybridized to fresh frozen hDRG sections. Manual segmentation of each neuron soma was performed, based on expressions of pan-neuronal marker gene PGP9.5, satellite glia cell marker FAB7B, and the corresponding H.E. staining. In total, 1340 neurons were identified (excluding 75 region-of-interest with poor or unclear neuronal soma morphology in H & E staining) and clustered into 16 groups. The 16 clusters were assigned as different cell types based on marker genes expression. Methods In the study presented here, four dorsoal root ganglia tissues from two healthy donors were used for Xenium spatial transcriptomics analysis, A hundred gene panel (including 87 neuronal genes from our single-soma sequencing dataset and 13 non-neuronal cell marker genes) were selected to perform spatial transcriptomics. The spatial distribution of these genes in neurons and non-neuronal cells was successfully profiled and quantified.

Snapshot img

Spatial Genomics And Transcriptomics Market Size 2025-2029

The spatial genomics and transcriptomics market size is forecast to increase by USD 732.3 million, at a CAGR of 12% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing adoption of spatial genomics in drug discovery. This innovative approach allows for a more precise understanding of the spatial organization of cells, enabling the identification of new targets and biomarkers for disease diagnosis and treatment. Furthermore, the use of spatial omics is gaining traction in biomarker identification, offering potential for personalized medicine and improved patient outcomes and in therapeutic areas like neurological disorders, infectious diseases, neuroscience, immunology, genomics, and proteomics. However, the market faces challenges, including the lack of workforce expertise in spatial genomics. As this field continues to evolve, there is a pressing need for skilled professionals to drive research and development efforts.
Companies seeking to capitalize on the opportunities in this market must invest in workforce development and collaborate with academic institutions and industry partners to build a strong foundation for future success. The ability to navigate these challenges and harness the power of spatial genomics will be crucial for companies looking to gain a competitive edge in the life sciences industry.

What will be the Size of the Spatial Genomics And Transcriptomics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free Sample

The market continues to evolve, driven by advancements in technologies and applications across various sectors. Cell signaling, confocal microscopy, RNA extraction, and sample preparation are integral components of this dynamic landscape. Ethical considerations are increasingly becoming a focus, as the use of high-throughput sequencing and data visualization tools uncovers new insights into genomic data. In situ sequencing and software solutions facilitate pathway analysis and data integration, enabling a more comprehensive understanding of biological processes. RNA extraction and sample preparation techniques play a crucial role in the market, ensuring accurate and reliable data. High-throughput sequencing technologies, such as next-generation sequencing (NGS), have revolutionized genome editing and disease modeling by providing vast amounts of genomic data.

Data repositories and machine learning algorithms facilitate data interpretation and gene regulatory network analysis. The continuous unfolding of market activities includes the development of spatial transcriptomics platforms, which offer three-dimensional genome organization insights. Microfluidic devices and protein-DNA interactions are also gaining attention, as they enable precise manipulation of biological samples. Quantitative PCR (qPCR) and chromatin conformation capture techniques complement these advancements, providing additional layers of information. The integration of various technologies, such as microarray technology, fluorescence microscopy, and data visualization tools, offers a more holistic approach to understanding complex biological systems. Spatial genomics and transcriptomics applications extend to drug discovery and gene expression analysis, providing valuable insights into cellular processes and biological pathways.

In conclusion, the market is characterized by continuous innovation and evolving patterns. The integration of various technologies, including cell signaling, confocal microscopy, RNA extraction, sample preparation, ethical considerations, high-throughput sequencing, data visualization, in situ sequencing, software solutions, pathway analysis, data integration, microfluidic devices, protein-DNA interactions, next-generation sequencing, gene regulatory networks, and more, offers a more comprehensive understanding of biological systems. This knowledge drives progress in personalized medicine, biomarker discovery, genome editing, disease modeling, and other sectors.

How is this Spatial Genomics And Transcriptomics Industry segmented?

The spatial genomics and transcriptomics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.ProductConsumablesInstrumentsEnd-userTranslational researchAcademic customersDiagnostic customersPharmaceutical manufacturerApplicationDrug Discovery & DevelopmentDisease Research (Oncology, Neuroscience)Biomarker IdentificationTechniqueSpatial Transcriptomics (e.g., Visium, MERFISH)Spatial GenomicsProteomics (Spatial Proteomics)GeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKMiddle East and Afr

Manuscript link: pending

This repository contains, or provides links to, the code and associated data used to generate figures 3 and 4 of the manuscript. Due to limited storage space here, the experimental documentation, raw FASTQs, Space Ranger Inputs, and Space Ranger (v3.1.3) Outputs for the H&E dataset are available through Gene Expression Omnibus series accession number GSE296623 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE296623. The Loupe files, .csv annotation exports from Loupe, and code are provided here. Please note the cell type annotations are incomplete; PrintPattern "Random_TME_ALL" was annotated for the manuscript.

Space Ranger was run again using an IF image, and the data is deposited here. *NOTE: The IF image is a live cell image (cells are transduced) taken right before fixation. The cells have migrated slightly between the image and when they were fixed (long scan/image acquisition time) so please proceed with extreme caution if using that version of the data since the image is slightly different than what was actually transferred to the capture area. This data was used only for illustrative purposes. I did not analyze any of the IF outs.