Image-based spatial transcriptomics identifies molecular niche dysregulation associated with distal lung remodeling in pulmonary fibrosis [Visium]
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ABSTRACT:
PROVIDER: PRJNA1160022 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR30635153_1.fastq.gz | Fastqsanger.gz | |||
| SRR30635153_2.fastq.gz | Fastqsanger.gz |
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Project description:Pulmonary fibrosis (PF) is a chronic, progressive condition that represents the end-stage of many interstitial lung diseases (ILDs). Single-cell transcriptomic studies have revealed disease-emergent epithelial, fibroblast, and macrophage cell types/states in PF lungs, but the spatial contexts wherein these cells contribute to disease pathogenesis has remained uncertain. Using image-based spatial transcriptomics to profile gene expression changes in-situ across 28 lung samples from control and PF lungs, we characterized the expression of 343 genes in over 1 million nuclei at subcellular resolution. Using both cell-based and cell-agnostic approaches, we observed a diversity of distinct molecularly-defined spatial niches in control and PF lungs. Overlaying these computationally-defined niches with disease-associated histopathologic features, we identified novel patterns of dysregulation in alveoli informed by spatial context. We computationally segmented individual air spaces and using cell composition, we ordered airspaces from homeostatic to most dysregulated. Using this ordering we identified a series of stepwise molecular changes associated with progressive distal lung remodeling. Together, these results advance our understanding of the molecular programs underlying progressive PF.
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Project description:Image-based spatial transcriptomics identifies molecular niche dysregulation associated with distal lung remodeling in pulmonary fibrosis
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Project description:Image-based spatial transcriptomics identifies molecular niche dysregulation associated with distal lung remodeling in pulmonary fibrosis [Xenium]
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Project description:Pulmonary fibrosis (PF) is a chronic, progressive condition that represents the end-stage of many interstitial lung diseases (ILDs). Single-cell transcriptomic studies have revealed disease-emergent epithelial, fibroblast, and macrophage cell types/states in PF lungs, but the spatial contexts wherein these cells contribute to disease pathogenesis has remained uncertain. Using image-based spatial transcriptomics to profile gene expression changes in-situ across 28 lung samples from control and PF lungs, we characterized the expression of 343 genes in over 1 million nuclei at subcellular resolution. Using both cell-based and cell-agnostic approaches, we observed a diversity of distinct molecularly-defined spatial niches in control and PF lungs. Overlaying these computationally-defined niches with disease-associated histopathologic features, we identified novel patterns of dysregulation in alveoli informed by spatial context. We computationally segmented individual air spaces and using cell composition, we ordered airspaces from homeostatic to most dysregulated. Using this ordering we identified a series of stepwise molecular changes associated with progressive distal lung remodeling. Together, these results advance our understanding of the molecular programs underlying progressive PF.
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Project description:We designed a Nextflow DSL2-based pipeline, Spatial Transcriptomics Quantification (STQ), for simultaneous processing of 10x Genomics Visium spatial transcriptomics data and a matched hematoxylin and eosin (H&E)-stained whole slide image (WSI), optimized for Patient-Derived Xenograft (PDX) cancer specimens. Our pipeline enables the classification of sequenced transcripts for deconvolving the mouse and human species and mapping the transcripts to reference transcriptomes. We align the H&E WSI with the spatial layout of the Visium slide and generate imaging and quantitative morphology features for each Visium spot. The pipeline design enables multiple analysis workflows, including single or dual reference genomes input and stand-alone image analysis. We show the utility of our pipeline on a dataset from Visium profiling of four melanoma PDX samples. The clustering of Visium spots and clustering of H&E imaging features reveal similar patterns arising from the two data modalities.
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Project description:Multiple distinct cell types of the human lung and airways have been defined by single cell RNA sequencing (scRNAseq). Here we present a multi-omics spatial lung atlas to define novel cell types which we map back into the macro- and micro-anatomical tissue context to define functional tissue microenvironments. Firstly, we have generated single cell and nuclei RNA sequencing, VDJ-sequencing and Visium Spatial Transcriptomics data sets from 5 different locations of the human lung and airways. Secondly, we define additional cell types/states, as well as spatially map novel and known human airway cell types, such as adult lung chondrocytes, submucosal gland (SMG) duct cells, distinct pericyte and smooth muscle subtypes, immune-recruiting fibroblasts, peribronchial and perichondrial fibroblasts, peripheral nerve associated fibroblasts and Schwann cells. Finally, we define a survival niche for IgA-secreting plasma cells at the SMG, comprising the newly defined epithelial SMG-Duct cells, and B and T lineage immune cells. Using our transcriptomic data for cell-cell interaction analysis, we propose a signalling circuit that establishes and supports this niche. Overall, we provide a transcriptional and spatial lung atlas with multiple novel cell types that allows for the study of specific tissue microenvironments such as the newly defined gland-associated lymphoid niche (GALN).
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Project description:This SuperSeries is composed of the SubSeries listed below.
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Project description:The limitation of single-cell or bulk transcriptomic profiling is the lack of spatial topographical context. Spatial transcriptomics (ST) allows sequencing of polyadenylated transcripts from a tissue section which can be spatially mapped onto the histological brightfield image using an array of barcoded oligo-dT capturing probes. Using the 10X Visium platform, here, we unbiasedly characterized the spatial transcriptomic landscape of murine colon during mucosal healing upon dextran sodium sulfate (DSS) induced injury in mice in which B cell has been depleted and control.
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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.
2021-11-12 | E-MTAB-11114 | biostudies-arrayexpress