Visium (10x Genomics) spatially resolved transcriptomics data generated from normal and Idiopathic Pulmonary Fibrosis (IPF) lung parenchyma tissues collected from human donors. The fresh-frozen tissues that were analyzed were from four healthy control (HC) subjects and from four IPF patients. For each IPF patient, three different tissues were selected representing areas of mild (“B1”), moderate (“B2") or severe (“B3”) fibrosis within the same donor, as determined by histological inspection of Hematoxylin and Eosin (H&E)-stained samples. Data from a total of 25 tissue sections, from 16 unique lung tissue blocks. The lung tissues were collected post-mortem (HC donors) or during lung transplant/resection (IPF patients) after obtaining informed consent. The study protocols were approved by the local human research ethics committee (HC: Lund, permit number Dnr 2016/317; IPF: Gothenburg, permit number 1026-15) and the samples are anonymized and cannot/should not be traced back to individual donors. 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", "web_summary.html", and the "spatial/" folder) for each individual section analysed. Zipped into one folder: "hs_visium_spaceranger_output.zip" - Sample metadata containing information for each sample with linked subject information: "hs_visium_metadata.tsv" - R object 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): "hs_visium_stutility_obj.rds" - Cell2location output files ("*_spot_cell_abundances_5pc.csv"), zipped into one folder: "cell2location_habermann2020.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 GRCh38 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 human IPF lung, published by Habermann et al., 2020 (DOI: 10.1126/sciadv.aba1972, GEO accession: GSE135893). Seurat/STUtility object was 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) . 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).

The Rodent Research-3 (RR-3) mission was sponsored by the pharmaceutical company Eli Lilly and Co. and the Center for the Advancement of Science in Space to study the effectiveness of a potential countermeasure for the loss of muscle and bone mass that occurs during spaceflight. Twenty BALB/c, 12-weeks old female mice (ten controls and ten treated) were flown to the ISS and housed in the Rodent Habitat for 39-42 days. Twenty mice of similar age, and matching sex and strain were used for ground controls housed in identical hardware and matching ISS environmental conditions. Basal controls were housed in standard vivarium cages. Spaceflight, ground controls and basal groups had blood collected, then were euthanized, had one hind limb removed, and finally whole carcasses were stored at -80 C until dissection. All mice in this data set received only the control/sham injection. Brain samples from three flight and three ground control animal groups were cut in half between hemispheres. One hemisphere of each brain was used for generating spatially resolved transcriptional profiling data. Hemispheres were cryosectioned so that 2 consecutive sections from the hippocampus of each brain was placed on Visium Gene Expression arrays. Samples were fixed, stained with Hematoxylin and Eosin and imaged. Imaging was followed by tissue permeabilization to release mRNA molecules from cells for capture onto the array surface. Subsequently, following the 10XGenomics Visium Gene Expression protocol, Spatial Transcriptomics RNA-seq libraries were prepared and sequenced. The other hemisphere of each brain was used for single nuclei RNA-seq and ATAC-seq using the 10X Multiome protocol. In addition, bulk RNA-seq (ribodepleted, target depth of 60 M clusters, PE 150 bp) was performed from a pool of RNA extracted from 10-20 sections from each of 3 flight and 3 ground control samples.

This folder contains the fastq-files that are generated during the Grand Challenge project using 10X Genomics Visium on head&neck squamous cell carcinoma samples. It contains 4 fastq-files (R1 and R2 for each of the two sequencing lanes) per patient (for each patient, 2 samples (biopsy and resection) were collected, and the two samples of 1 patient (HNI40020) was analyzed twice).

Formalin-fixed paraffin-embedded (FFPE) samples are valuable but underutilized in single-cell omics research due to their low DNA and RNA quality. In this study, leveraging a recent advance in single-cell genomic technology, we introduce snPATHO-seq, a versatile method to derive high-quality single-nucleus transcriptomic data from FFPE samples. We benchmarked the performance of the snPATHO-seq workflow against existing 10x 3’ and Flex assays designed for frozen or fresh samples and highlighted the consistency in snRNA-seq data produced by all workflows. The snPATHO-seq workflow also demonstrated high robustness when tested across a wide range of normal and diseased FFPE tissue samples. When combined with FFPE spatial transcriptomic technologies such as FFPE Visium, the snPATHO-seq provides a multi-modal sampling approach for FFPE samples, allowing more comprehensive transcriptomic characterization. Methods Frozen PBMC samples were thawed and processed directly for 10x 3' and Flex chemistry. For frozen breast cancer tissue samples, samples were dissociated into single-nucleus suspension before being processed for 10x 3' and Flex chemistry for gene expression analysis. For FFPE tissue samples, nuclei were extracted using either the snPATHO-seq protocol or the 10x scFFPE protocol followed by 10x Flex chemistry processing for gene expression detection. FFPE Visium data was generated using the 10x FFPE Visium CytAssist workflow according to the manufacturer's recommendation.

This includes a dataset of 10X genomics Visium spatial RNA sequencing of 3 CDS patients, a dataset of whole-transcriptome RNA sequencing of CD-HIFs, and a dataset of RayBiotech serum proteomics of patients. Methods 10X Genomics Visium spatial RNA sequencing was done by UCLA TPCL and TCGB. The ileal tissues were obtained from UCLA Pathology. Whole-transcriptome RNA sequencing was done by UCLA TCGB. CD-HIF RNA samples were obtained from Dr. Koon's laboratory. Serum proteomics was done by RayBiotech. The sera were obtained from UCLA Pathology.