Project description:We developed cell2location, a principled and versatile Bayesian model that is designed to resolve fine-grained cell types in spatial transcriptomic data and create comprehensive cellular maps of diverse tissues. To validate cell2location in real tissue, we applied the model to data from the mouse brain, which features diverse neural cell types organised in a well characterised spatial architecture across brain areas, thus presenting a canonical use case to test spatial genomics. We generated matched single nucleus (sn, this submission) and Visium spatial RNA-seq (10X Genomics) profiles of adjacent mouse brain sections that contain multiple regions from the telencephalon and diencephalon. To assess the biological and intra-organ technical variation in spatial mapping, we assayed two mouse brains and serial tissue sections from each brain (total of 3 and 2 matched sections from two animals, respectively, and an extra section for snRNA-seq), creating a rich multi-modal and replicated transcriptomic dataset. Tissue processing. Brains of wild-type adult C57BL/6 mice (postnatal day 56, 1 female and 1 male) were dissected, snap frozen, embedded in optimal cutting temperature compound (Tissue-Tek) and stored at -80oC. Brain hemispheres were cryosectioned at -20oC using a cryostat (Leica, CM3050S). To assess tissue quality, RNA was extracted from test tissue sections using the RNeasy Pico Kit (Qiagen) and yielded high RIN values (9.6 and 9.7) on an Agilent Bioanalyser, indicating high RNA quality. For matched single nuclei and Visium RNA-seq experiments, brain hemispheres were cryosectioned to adjacent thick (200 µm) and thin (10 µm) coronal sections, respectively, and processed the same day. In total, four consecutive sets of thick and thin tissue sections were collected from each brain. Five sets of tissue sections yielded both good quality single nuclei and Visium data (three adjacent sections from mouse 1 and two sections from mouse 2) while one additional section from mouse 2 yielded good single nuclei; these were considered for analysis in this study. Visium spatial transcriptomics. Thin (10 µm) mouse brain sections were cryosectioned and mounted directly onto separate capture areas on 10X Visium Spatial Gene Expression slides (beta product version). Processing was done per manufacturer’s protocols. Briefly, sections were methanol-fixed, hematoxylin and eosin (H&E)-stained, and imaged on a NanoZoomer 2.0 slide scanner (Hamamatsu). Sections were then permeabilized and further processed to obtain cDNA libraries that were quality controlled using the Agilent Bioanalyser. The cDNA libraries were sequenced on the Illumina HiSeq 4000 system, aiming at 300 million raw reads per section with read lengths 28cy R1, 8cy i7 index, 0cy i5 index, 91cy read 2. 10X Visium spatial sequencing data was aligned to mouse pre-mRNA genome reference version mm10 using 10X SpaceRanger and mRNA count matrices were generated by adding intronic and exonic reads for each gene in each location. The paired histology H&E images were processed using 10X SpaceRanger to select locations covered by tissue by aligning pre-recorded spot locations with fiducial border spots in the histology image. This allows evaluating the correspondence between cell maps produced using our method and the known brain anatomy. This also allows identifying the number of nuclei in each spot using nuclear segmentation as described in Suppl. Methods and reported in Fig S8A-D. The histology image was used to manually annotate cortical layers in the primary somatosensory cortex (SSp) region using the lasso tool in the 10X Loupe browser.
Project description:These are the Visium spatial transcriptomic data (10x Genomics) from 9 patients with Head and Neck Squamous Cell Carcinoma (oral cavity) treated in Gustave Roussy. Patients are stratified by their tumoral density of multinucleated giant cells (MGC) : 6 patients have high MGC density (patients 1, 2, 3, 4, 5, 8) and 3 have low MGC density (patients 6, 7, 9). There is one data file for each patient, except for one patient that has 3 data files (patient 1). Accordingly, there are 9 patients but 11 samples. The source code of the is available on GitHub (https://github.com/AhmedAmineAnzali/MGC_Paper_Analysis). The results are published in the paper untitled : Trem2-expressing multinucleated giant macrophages are a biomarker of good prognosis in head and neck squamous cell carcinoma (Gessain et al., 2024, Cancer Discovery). Please contact the corresponding author for more information.
Project description:Two primary and two post-radiotherapy recurrent glioblastoma tumors derived in the RCAS/Nestin-Tv-a mouse model were processed using the 10x Genomics Visium Spatial Gene Expression v1 chemistry.
Project description:We generated a tamoxifen-inducible, adipocyte-specific ATGL-KO animals to analyze the role of adipocyte ATGL in cardiac ischemia/reperfusion. KO was induced in 10 weeks old male mice via 7d of consecutive i.p.-injections of 500µg hydroxytamoxifen/day. Pnpla2-flox homozygous, Cre-negative littermates were used as control. After 2 weeks washout phase mice underwent closed chest ischemia with first ligature-induction surgery and 3-7 d later 1 hour closed chest ischemia followed by 24 h reperfusion. Mice were sacrificed and hearts excised to perform spatial transcriptomic analysis on 10X Visium Gene Expression slides. The goal was to identify differences in gene expression between KO and control in the different zones of the heart after ischemia (eg. ischemic, border and remote zone).
Project description:This dataset contains spatial transcriptomics data of four lung neuroendocrine tumours (lung NETs), a rare and understudied type of lung cancer. The dataset consists of raw sequencing data, metadata, and gene expression matrices. It is part of the lungNENomics project. See https://doi.org/10.5281/zenodo.19366762 for processed data for the series, including downstream analyses data for the four samples in this dataset, such as inferred CNVs and aneuploidy status, and spatial domain and cell proportions for each spot. The lungNENomics project also generated other molecular data for a series of more than 200 samples. The raw sequencing data (fastq files for RNA-seq, cram files for WGS, and idat files for methylation arrays) is hosted on the European Genome-Phenome Archive website, study EGAS00001005979. Medical imaging data (Hematoxylin & Eosin stained whole-slide images) from the lungNENomics project is hosted in the EBI bioImage Archive (10.6019/S-BIAD3143).
Project description:Aging is a major, yet unmodifiable, risk factor for cardiovascular disease, leading to vascular alterations, increased cardiac fibrosis, and inflammation, all of which contribute to impaired cardiac function. To investigate the spatial impact of aging, we applied an integrative approach combining single-nucleus RNA sequencing and spatial transcriptomics in 12-week-old and 18-month-old mice. We systematically mapped the aging heart and identified larger vessel-associated niches as key hotspots for activated macrophages and fibroblasts in aged hearts. These niches, surrounding arteries, were also enriched in senescent cells. Our findings suggest that the microenvironment around the vasculature is particularly susceptible to age-related changes and serves as a primary site for inflammation-driven aging so called \"inflammaging\". This study provides new insights into how aging reshapes cardiac cellular architecture, highlighting vessel-associated niches as potential therapeutic targets for age-related cardiac dysfunction.
Project description:Spatial organization of different cell types within prenatal skin across various anatomical sites is not well understood. To address this, here we have generated spatial transcriptomics data from prenatal facial and abdominal skin obtained from a donor at 10 post conception weeks. This in combination with our prenatal skin scRNA-seq dataset has helped us map the location of various identified cell types.
Project description:Specification of primordial germ cells (PGCs) marks the beginning of the totipotent state. However, without a tractable experimental model, the mechanism of human PGC (hPGC) specification remains unclear. Here, we demonstrate specification of hPGC-like cells (hPGCLCs) from germline competent pluripotent stem cells. The characteristics of hPGCLCs are consistent with the embryonic hPGCs and a germline seminoma that share a CD38 cell-surface marker, which collectively defines likely progression of the early human germline. Remarkably, SOX17 is the key regulator of hPGC-like fate, whereas BLIMP1 represses endodermal and other somatic genes during specification of hPGCLCs. Notable mechanistic differences between mouse and human PGC specification could be attributed to their divergent embryonic development and pluripotent states, which might affect other early cell-fate decisions. We have established a foundation for future studies on resetting of the epigenome in hPGCLCs and hPGCs for totipotency and the transmission of genetic and epigenetic information. RNA-Seq analysis to investigate transcriptomes of hPGC-like cells (hPGCLCs), fetal hPGCs, TCam-2 and hESCs
Project description:This project presents a spatial proteomic investigation of vascular smooth muscle cells (VSMCs) and atherosclerotic plaque regions in a diabetic mouse model of atherosclerosis (DMAS). Using ApoE-/- mice fed with a high-fat diet and treated with low-dose streptozotocin (STZ) to induce a diabetic phenotype, we performed laser capture microdissection (LCM) to separately isolate plaque regions and adjacent VSMC-rich areas of the aortic arch. Subsequently, spatially resolved proteomic profiling was carried out using LC-MS/MS, followed by quantitative analysis based on iBAQ values. Differentially expressed proteins between plaque and VSMC regions were identified and enriched pathway analysis revealed significant alterations in oxidative stress response, insulin signaling, mitochondrial function, RNA transport, and extracellular matrix remodeling under diabetic conditions. Of particular interest, the spatial proteomics data highlighted a marked downregulation of the nuclear pore complex protein Nup93 in VSMCs from diabetic mice, implicating impaired nuclear transport and dysregulation of RNA alternative splicing. This finding was supported by transcriptomic integration and functional analyses that revealed aberrant splicing of SerpinE2, a gene associated with cell proliferation and ECM accumulation. This dataset provides region-specific, high-resolution proteomic profiles of vascular tissues in a diabetic atherosclerosis model. It serves as a valuable resource for investigating the spatial heterogeneity of vascular pathology and the molecular mechanisms underlying diabetes-accelerated atherosclerosis. The raw and processed data can be used to explore protein expression dynamics, identify spatiotemporal biomarkers, and validate candidate targets such as Nup93 and SerpinE2 involved in VSMC phenotypic switching.