Image-based spatial transcriptomics identifies molecular niche dysregulation associated with distal lung remodeling in pulmonary fibrosis
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE276945 | GEO | 2024/09/12
REPOSITORIES: GEO
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Project description:The human lung is structurally complex, with a diversity of specialized epithelial, stromal and immune cells playing specific functional roles in anatomically distinct locations, and large-scale changes in the structure and cellular makeup of this distal lung is a hallmark of pulmonary fibrosis (PF) and other progressive chronic lung diseases. Single-cell transcriptomic studies have revealed numerous disease-emergent/enriched cell types/states in PF lungs, but the spatial contexts wherein these cells contribute to disease pathogenesis has remained uncertain. Using sub-cellular resolution image-based spatial transcriptomics, we analyzed the gene expression of more than 1 million cells from 19 unique lungs. Through complementary cell-based and innovative cell-agnostic analyses, we characterized the localization of PF-emergent cell-types, established the cellular and molecular basis of classical PF histopathologic disease features, and identified a diversity of distinct molecularly-defined spatial niches in control and PF lungs. Using machine-learning and trajectory analysis methods to segment and rank airspaces on a gradient from normal to most severely remodeled, we identified a sequence of compositional and molecular changes that associate with progressive distal lung pathology, beginning with alveolar epithelial dysregulation and culminating with changes in macrophage polarization. Together, these results provide a unique, spatially-resolved characterization of the cellular and molecular programs of PF and control lungs, provide new insights into the heterogeneous pathobiology of PF, and establish analytical approaches which should be broadly applicable to other imaging-based spatial transcriptomic studies.
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Project description:Large-scale changes in the structure and cellular makeup of the distal lung are a hallmark of pulmonary fibrosis (PF), but the spatial contexts that contribute to disease pathogenesis have remained uncertain. Using image-based spatial transcriptomics, we analyzed the gene expression of 1.6 million cells from 35 unique lungs. Through complementary cell-based and innovative cell-agnostic analyses, we characterized the localization of PF-emergent cell types, established the cellular and molecular basis of classical PF histopathologic features and identified a diversity of distinct molecularly defined spatial niches in control and PF lungs. Using machine learning and trajectory analysis to segment and rank airspaces on a gradient of remodeling severity, we identified compositional and molecular changes associated with progressive distal lung pathology, beginning with alveolar epithelial dysregulation and culminating with changes in macrophage polarization. Together, these results provide a unique, spatially resolved view of PF and establish methods that could be applied to other spatial transcriptomic studies.
2024-06-04 | GSE250346 | GEO
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.
2024-09-12 | GSE276934 | GEO
Project description:Large-scale changes in the structure and cellular makeup of the distal lung are a hallmark of pulmonary fibrosis (PF), but the spatial contexts that contribute to disease pathogenesis have remained uncertain. Using image-based spatial transcriptomics, we analyzed the gene expression of 1.6 million cells from 35 unique lungs. Through complementary cell-based and innovative cell-agnostic analyses, we characterized the localization of PF-emergent cell types, established the cellular and molecular basis of classical PF histopathologic features and identified a diversity of distinct molecularly defined spatial niches in control and PF lungs. Using machine learning and trajectory analysis to segment and rank airspaces on a gradient of remodeling severity, we identified compositional and molecular changes associated with progressive distal lung pathology, beginning with alveolar epithelial dysregulation and culminating with changes in macrophage polarization. Together, these results provide a unique, spatially resolved view of PF and establish methods that could be applied to other spatial transcriptomic studies.
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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:Image-based spatial transcriptomics identifies molecular niche dysregulation associated with distal lung remodeling in pulmonary fibrosis [Visium]
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Project description:Given the successful graft-versus-leukemia cell treatment effect observed with allogeneic hematopoietic stem cell transplant for patients with refractory or relapsed acute myeloid leukemia, immunotherapies have also been investigated in the nontransplant setting. Here, we use a multi-omic approach to investigate spatiotemporal interactions in the bone marrow niche between leukemia cells and immune cells in patients with refractory or relapsed acute myeloid leukemia treated with a combination of the immune checkpoint inhibitor pembrolizumab and hypomethylating agent decitabine. We derived precise segmentation data by extensively training nuclear and membrane cell segmentation models, which enabled accurate transcript assignment and deep learning-feature-based image analysis. To overcome read-depth limitations, we integrated the single-cell RNA sequencing data with single-cell-resolution spatial transcriptomic data from the same sample. Quantifying cell-cell distances between cell edges rather than cell centroids allowed us to conduct a more accurate downstream analysis of the tumor microenvironment, revealing that multiple cell types of interest had global enrichment or local enrichment proximal to leukemia cells after pembrolizumab treatment, which could be associated with their clinical responses. Furthermore, ligand-receptor analysis indicated a potential increase in TWEAK signaling between leukemia cells and immune cells after pembrolizumab treatment.
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Project description:Recent progress in image-based spatial RNA profiling enables to spatially resolve tens to hundreds of distinct RNA species with high spatial resolution. It presents new avenues for comprehending tissue organization. In this context, the ability to assign detected RNA transcripts to individual cells is crucial for downstream analyses, such as in-situ cell type calling. Yet, accurate cell segmentation can be challenging in tissue data, in particular in the absence of a high-quality membrane marker. To address this issue, we introduce ComSeg, a segmentation algorithm that operates directly on single RNA positions and that does not come with implicit or explicit priors on cell shape. ComSeg is applicable in complex tissues with arbitrary cell shapes. Through comprehensive evaluations on simulated and experimental datasets, we show that ComSeg outperforms existing state-of-the-art methods for in-situ single-cell RNA profiling and in-situ cell type calling. ComSeg is available as a documented and open source pip package at https://github.com/fish-quant/ComSeg .
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Project description:Idiopathic pulmonary fibrosis (IPF) is a fibroproliferative disorder limited to the lung. New findings, starting from our proteomics studies on IPF, suggest that systemic involvement with altered molecular mechanisms and metabolic disorder is an underlying cause of fibrosis. The role of metabolic dysregulation in the pathogenesis of IPF has not been extensively studied, despite a recent surge of interest. In particular, our studies on bronchoalveolar lavage fluid have shown that the renin-angiotensin-aldosterone system (RAAS), the hypoxia/oxidative stress response, and changes in iron and lipid metabolism are involved in onset of IPF. These processes appear to interact in an intricate manner and to be related to different fibrosing pathologies not directly linked to the lung environment. The disordered metabolism of carbohydrates, lipids, proteins and hormones has been documented in lung, liver, and kidney fibrosis. Correcting these metabolic alterations may offer a new strategy for treating fibrosis. This paper focuses on the role of metabolic dysregulation in the pathogenesis of IPF and is a continuation of our previous studies, investigating metabolic dysregulation as a new target for fibrosis therapy.
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