Seurat object for the spatial transcriptomic data of sample HCM1268A

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

Clinical interventions and inflammatory signaling shape the transcriptional and cellular architecture of the early postnatal lung

Spatial Transcriptomics was performed using the 10X Xenium Platform with a 480 custom-designed probe set on 1 tissue section from 5 distinct early postnatal lung specimens. CSV files contain cell type identities as determined by label transfer.

.zip files should be unzipped to the same directory and can be viewed with Xenium Explorer.

.csv files contain cell type annotations as determined by label transfer to hand annotated single nuclei RNA-sequencing data from early postnatal lung. They can be added as a custom cell group in Xenium Explorer.

Code used in analysis of this data is available at: http://github.com/jason-spence-lab/Frum-et-al.-2025a.git

METHODS
Tissue Preparation for Xenium Spatial Transcriptomics Analysis
Xenium slides were removed from -20°C storage and allowed to come to room temperature for 30 minutes and then were placed on a 42ºC slide warmed and coated with DNAse/RNAse free water (Corning, Cat# 46000CM). Small sections from multiple specimens were carefully placed within the sample placement area. Most of the water was removed when sections had completely flattened. Slides dried on the slide warmer for three hours before transport to the Advanced Genomics Core. Xenium slides were processed by the Advanced Genomics Core using the Xenium In SituGene Expression with Cell Segmentation workflow (10X, #CG000749).

Xenium Data Analysis
Preprocessing/QC Filtering
Centroids and Segmentation coordinates and Gene Expression counts were determined by Xenium Onboard Analysis v4.0 and imported into R using Seurat::ReadXenium(). Gene Expression counts were converted to a Seurat object using Seurat::CreateSeuratObject(). Coordinates for centroids and segmentations were first converted into a field of view using Seurat::CreateFOV() and then appended to the Seurat object. Segmentations with less than 25 gene expression counts were excluded from the analysis.

Label Transfer
To align low-complexity 480 probe Xenium data with higher complexity snRNA-seq data the reference data was transformed using Seurat::SCTransform() with 3000 variable features. Each specimen was processed individually, also undergoing SCTransformation using 250 variable features. Any Xenium probes expressed in over 95% of cells were excluded from analysis. Anchors between each specimen and the snRNA-seq reference were calculated using FindTransferAnchors() using the SCT assay of both datasets, 20 dimensions, k.filter = 200, and considering only the variable features from the Xenium specimen. Cell type annotations from the snRNA-seq data were then transferred to the Xenium specimen using TransferData(), with anchors weighted by the PCs of the Xenium specimen.

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 HCM1220B

Seurat object for the spatial transcriptomic data of sample HCM1406C

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

The repository includes:

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

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

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

Single-cell RNA-seq, ATAC-seq, and Spatial Transcriptomics (Resolve BioSciences' Molecular Cartography) of the Mouse Liver in Aging and Insulin Resistance

liver_spatial.rds: Seurat object including processed data for all samples, for Spatial Transcriptomics

liver_scRNAseq.rds: Seurat object including processed data for all samples, for scRNA-seq data

liver_scATAC.rds: Seurat object including processed data for all samples, for scATAC-seq data

*.txt: coordinates for all target molecules detected; Unit: 138 nm/pixel

*.tiff: DAPI images

Sample information for Spatial Transcriptomics:

A1, B2: Young;

A2, C1: Old insulin sensitive;

B1, C2: Old insulin resistant.

In the context of the Human Cell Atlas, we have created a single-cell 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.

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 comprises results from one single-cell spatial experiment conducted on mouse brains. This experiment was performed using the Bruker Nanostring CosMx technology on 10 µm coronal brain sections from 8-month-old female WT, WT ACAN cKO, 5xFAD, and 5x ACAN cKO mice. The dataset is provided as two separate RDS files split by flowcell which include raw and corrected counts for the RNA data, along with comprehensive metadata. Metadata includes mouse genotype, sample ID, cell type annotations, and X-Y coordinates of each cell., Sample preparation: Isopentane fresh-frozen brain hemispheres were embedded in optimal cutting temperature (OCT) compound (Tissue-Tek, Sakura Fintek, Torrance, CA), and 10 µm thick coronal sections were prepared using a cryostat (CM1950, LeicaBiosystems, Deer Park, IL). Six hemibrains were mounted onto each VWR Superfrost Plus microscope slide (Avantor, 48311-703) and kept at -80°C until fixation. For transcriptomic analysis, n=3 mice per genotype were used for both the 5xFAD and 5xFAD ACAN cKO groups, while n=4 for WT and n=2 for WT ACAN cKO. Tissues were processed according to the Nanostring CosMx fresh-frozen slide preparation manual for RNA assay (NanoString University). Data processing: Spatial transcriptomics datasets were filtered using the AtoMx RNA Quality Control module to flag outlier negative probes (control probes targeting non-existent sequences to quantify non-specific hybridization), lowly-expressing cells, FOVs, and target genes. Datasets were then normal..., # Single-cell spatial transcriptomics of ACAN cKO in WT and 5xFAD mice

Dataset DOI: 10.5061/dryad.z612jm6pw

Description of the data and file structure

Due to the large file size, the R object has been split by flowcell into two separate files (5xACANcKO_RNA_slide1.rds and 5xACANcKO_RNA_slide2.rds). The two files should be loaded into the R workspace and combined using the merge() function. For merging and downstream analysis, we recommend using a high performance computing system and at least 64GB of RAM for optimal performance. Data were analyzed using the R package Seurat. Sample metadata are stored in seurat@meta.data.

Files and variables

Single-cell spatial transcriptomics dataset

Rownames of metadata (accessed using rownames(seurat@meta.data) contain unique identifiers for each single cell, formatted as c_[slide][fov][cell]. Additional metadata columns are described below:

• fov: Field Of View (FOV) the cell is in
• Area: Number of pi...,

Understanding human cardiac development is essential to improving the diagnosis and treatment of congenital heart defects. This dataset is part of a multi-modal atlas of the developing human fetal heart during the critical first trimester. Using single-nucleus RNA sequencing, we sampled nearly 50,000 cardiac nuclei from three human fetuses at 8.6, 9.0, and 10.7 post-conceptional weeks (pcw). This dataset enabled distinction of 21 cell types, including novel contractile, conductive, and stromal cells. Lymphatic endothelial, epicardial and autonomic neural and glial cells were among the new, smaller populations for which we established high-resolution transcriptional profiles. This is the *.rds file of the cells from all three hearts generated after Seurat-based integration and analysis in R.This dataset is part of a greater project also comprising spatial transcriptomics, anatomy, histology, in situ hybridization and 2D and 3D immunofluorescence, most of which is also present on Figshare. The count matrices and the raw sequencing data for the spatial transcriptomics and this dataset will be available on GEO shortly.This publication is part of the Human Cell Atlas. https://www.humancellatlas.org/publications/

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

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

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

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

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

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

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

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

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

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

Single-cell spatial transcriptomics dataset of basal-cell carcinoma (BCC), collected using in situ analysis platform 6k-plex CosMx SMI (Bruker Nanostring). The dataset is provided as a Seurat object in .rds format, and consists of in situ transcripts for 6075 genes in 232,802 cells + metadata (see Metadata section below).

Experimental conditions:

The dataset is derived from tumor sections of four patients. For 2 patients (A and B), nodular-ulcerated BCC samples (as determined by histopathological reports) and of mixed morphology (i.e. consisting of nodular and infiltrative areas). For 2 patients (C and D), we collected samples in three different conditions: i) from baseline (first, diagnostic biopsy); ii) 1 week later from the previously biopsied, wounded site; iii) 1 week after initial biopsy from a distant, unwounded site in the same tumor.

Data acquisition and annotation:

Tumor sections from four different patients were arranged on 2 slides. For each slide, 45 Fields-of-views (FOVs) (0.51mm by 0.51mm) were selected based on the immunofluorescent staining with morphological markers (DAPI, PanCK, CD45, CD68). Tissue slides were subjected to in situ chemistry and imaging using the CosMx SMI instrument. CosMx scan data were uploaded to Nanostring’s AtoMx spatial analysis platform, where pre-processing steps, imaging-barcode decoding and cell segmentation were performed. The pre-processed spatial transcriptomics data were exported to .rds files, and subsequent analyses were performed in R and Seurat.

To predict cell type identities from CosMx in situ transcripts, we applied the InSituType algorithm, using a set of 7 confidently-annotated scRNA-seq samples (Yerly et al. 2022 cohort) as a reference for label transfer, to which we manually added an average expression profile for neutrophils derived from Zilionis et al. (2019). Cells with < 100 detected transcripts and with >2% transcripts coming from negative control probes were labeled as “Low quality” cells. To define cancer cells that participate in homotypic or heterotypic interactions in our CosMx spatial datasets, we first calculated nearest neighbor graphs based on spatial coordinates using the BiocNeighbors package. We limited the neighbor search between cell centroids to a maximum distance D, corresponding to 2.5 times the average cell diameter in the CosMx dataset. If >90% of the cells within distance D of a given cancer cells were also cancer cells, the cancer cell was labeled as “homotypic”; otherwise it was labeled as “heterotypic”.

Metadata:

The dataset contains in situ transcripts for 6075 genes in 232,802 cells, as well as the following cell metadata:

- pat_fov: identifies unique tissue slide and FOV combinations. Note: Run identifier "Run6057_Patient1" includes samples from Patient A and Patient C, run identifier "Run6057_Patient4" includes samples from Patient B and Patient D. See Patient_ID metadata column.

- nCount_Nanostring: number of transcripts detected per cell

- nFeature_Nanostring: number of unique genes detected per cell

- cell Area, Aspect ratio, Height and Width

- mean and max readouts for a panel of antibodies (PanCK, CD68, membrane staining, CD45 and DAPI)

- Patient_ID, for four patients (A to D)

- Area_ID: identifying 8 areas of BCC tissue

- Condition: indicating the experimental condition of the sample

- Ulcerated_area: wheher the FOV was annotated in an ulcerated BCC area

- Dist_from_wound: for wounded samples, whether the FOV is located close or far from the wound

- Condition2: concatenates Condition (see above) with distance from wound

- Wound_direction: cardinal coordinates for the directionality of the wound with respect to the FOV

- Morphology: whether the FOV was annotated as nodular or infiltrative in terms of H&E morphology

- celltype: predicted cell type annotation based on inSituType label transfer

- celltype_prob: confidence score for cell type annotion

- Meta-programs signature scores for MP1 to MP7, calculated with UCell

- CAF signature scores (for wound-responding CAFs and baseline CAFs)

- Invasiveness score: corresponds to MP7 minus MP2 scores

- Invasive CAF score: corresponds to wound-responding CAF score minus baseline/unwounded CAF score

- homotypic: for cancer cells, whether cell-cell interactions are homotypic (only with other cancer cells) or heterotypic (with stromal components).

- X and Y coordinates of the centroid of each cell

- neighbors vector: lists the IDs of the nearest neighbors of each cell

- neighbors_encoding: the cell type distribution of the nearest neighbors; the vector is order according to the factor of cell types.

snRNAseq_merged_seurat.rds: Merged Seurat object of 4 single-nucleus RNAseq (snRNAseq, 10x Genomics) from 4 patients non-tumor livers (3 with and 1 without 11p15.5 mosaicism). spatial_transcriptomics_merged_seurat.rds: Merged Seurat object of 3 spatial transcriptomics assays (10x genomics) from 3 patients non-tumor livers with 11p15.5 mosaicism.

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

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.

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

Visium (10x Genomics) spatially resolved transcriptomics data generated from the mouse model of bleomycin (BLM)-induced lung fibrosis, with lung tissue collected at 7 or 21 days post single-dose BLM (40 µg/mouse, o.p) or saline vehicle challenge. Data from a total of 24 tissue sections, from 18 unique mouse left lung lobes. The mice were all female C57BL/6NCrl, purchased from Charles River (Germany), and were 8 weeks + 5 days old at the time of study initiation. Animal handling conformed to standards established by the Council of Europe ETS123 AppA, the Helsinki Convention for the Use and Care of Animals, Swedish legislation, and AstraZeneca global internal standards. All mouse experiments were approved by the Gothenburg Ethics Committee for Experimental Animals in Sweden and conformed to Directive 2010/63/EU. The present study was approved by the local Ethical committee in Gothenburg (EA000680-2017) and the approved site number is 31-5373/11. Data included in this repository: - Visium data in the format of selected Space Ranger output files ("filtered_feature_bc_matrix.h5", "raw_feature_bc_matrix.h5", "metrics_summary.csv", and the "spatial/" folder) for each individual section analysed. Zipped into one folder: "mm_visium_spaceranger_output.zip" - Sample metadata containing information for each sample with linked subject information: "mm_visium_metadata.tsv" - R objects produced using STUtility and contains the processed data used for downstream analyses, most importantly all spot metadata with assigned data and deconvolution results (NMF, cell2location): "mm_visium_all_stutility_obj.rds" (all), "mm_visium_d7_stutility_obj.rds " (day 7 data), "mm_visium_d21_stutility_obj.rds" (day 21 data) - Cell2location output files ("*_spot_cell_abundances_5pc.csv"), zipped into one folder: "cell2location_strunz2020.zip" - Full resolution H&E images ("*.jpg") of each tissue section that was used as input for spaceranger together with alignment json and sequencing fastq files. Zipped into one folder: "he_fullres_jpgs.zip" - Spot alignment files ("*.json") created in Loupe Browser using the corresponding full resolution H&E image in which spots under the tissue was identified. Zipped into one folder: "loupe_alignment_jsons.zip" Space Ranger output found within the zipped files in folders named "V*****-***-*1". To generate these files, raw FastQ files from the NovaSeq sequencing were processed with the Space Ranger pipeline (v. 1.2.2, 10x Genomics), where the reads were mapped to the mm10 reference genome. Manual spot alignment was performed in the Loupe Browser (v. 6, 10x Genomics) software. Cell type mapping results were obtained using the cell2location (v. 0.1) method, integrating the Space Ranger output data with annotated single cell RNA-seq data produced from mouse BLM injured lungs collected at multiple time points, published by Strunz et al., 2020 (DOI: 10.1038/s41467-020-17358-3, GEO accession: GSE141259). Seurat/STUtility objects were generated from the Space Ranger output files, using the R packages STUtility (v. 1.1.1) and Seurat (v. 4.1.1) in R (v. 4.0.5). The d7 and d21 subset were derived from the object with all samples processed jointly, and thereafter NMF deconvolution and subsequent clustering (d21) was performed. All R scripts used for the data analyses can be found at https://github.com/lfranzen/spatial-lung-fibrosis. The deposited data is presented in the article "Mapping spatially resolved transcriptomes in human and mouse pulmonary fibrosis" by Franzén L & Olsson Lindvall M, et al. (preprint: "Translational mapping of spatially resolved transcriptomes in human and mouse pulmonary fibrosis", bioRxiv, https://doi.org/10.1101/2023.12.21.572330).

Integrated IBD scRNASeq reference: Seurat object and cell type markers.

Molecular Cartography (Resolve) data data of human colon samples. 6 ulcerative colitis colon resections: 3 inflamed, 3 non-inflamed areas (4 patients in total). Segmented object (Seurat).

Visium (10X) spatial transcriptomics data of human colon samples. 4 ulcerative colitis colon resections: 2 inflamed, 2 non-inflamed areas (3 patients in total).

Supplementary Table 1: positively co-occurring lignads and receptors in composition class 5.