A major drawback of single-cell ATAC-seq (scATAC-seq) is its sparsity, i.e., open chromatin regions with no reads due to loss of DNA material during the scATAC-seq protocol. Here, we propose scOpen, ...a computational method based on regularized non-negative matrix factorization for imputing and quantifying the open chromatin status of regulatory regions from sparse scATAC-seq experiments. We show that scOpen improves crucial downstream analysis steps of scATAC-seq data as clustering, visualization, cis-regulatory DNA interactions, and delineation of regulatory features. We demonstrate the power of scOpen to dissect regulatory changes in the development of fibrosis in the kidney. This identifies a role of Runx1 and target genes by promoting fibroblast to myofibroblast differentiation driving kidney fibrosis.
Spatial multi-omic studies have emerged as a promising approach to comprehensively analyze cells in tissues, enabling the joint analysis of multiple data modalities like transcriptome, epigenome, ...proteome, and metabolome in parallel or even the same tissue section. This review focuses on the recent advancements in spatial multi-omics technologies, including novel data modalities and computational approaches. We discuss the advancements in low-resolution and high-resolution spatial multi-omics methods which can resolve up to 10,000 of individual molecules at subcellular level. By applying and integrating these techniques, researchers have recently gained valuable insights into the molecular circuits and mechanisms which govern cell biology along the cardiovascular disease spectrum. We provide an overview of current data analysis approaches, with a focus on data integration of multi-omic datasets, highlighting strengths and weaknesses of various computational pipelines. These tools play a crucial role in analyzing and interpreting spatial multi-omics datasets, facilitating the discovery of new findings, and enhancing translational cardiovascular research. Despite nontrivial challenges, such as the need for standardization of experimental setups, data analysis, and improved computational tools, the application of spatial multi-omics holds tremendous potential in revolutionizing our understanding of human disease processes and the identification of novel biomarkers and therapeutic targets. Exciting opportunities lie ahead for the spatial multi-omics field and will likely contribute to the advancement of personalized medicine for cardiovascular diseases.
The cardiac vascular and perivascular niche are of major importance in homeostasis and during disease, but we lack a complete understanding of its cellular heterogeneity and alteration in response to ...injury as a major driver of heart failure. Using combined genetic fate tracing with confocal imaging and single-cell RNA sequencing of this niche in homeostasis and during heart failure, we unravel cell type specific transcriptomic changes in fibroblast, endothelial, pericyte and vascular smooth muscle cell subtypes. We characterize a specific fibroblast subpopulation that exists during homeostasis, acquires Thbs4 expression and expands after injury driving cardiac fibrosis, and identify the transcription factor TEAD1 as a regulator of fibroblast activation. Endothelial cells display a proliferative response after injury, which is not sustained in later remodeling, together with transcriptional changes related to hypoxia, angiogenesis, and migration. Collectively, our data provides an extensive resource of transcriptomic changes in the vascular niche in hypertrophic cardiac remodeling.
Multi‐omics datasets can provide molecular insights beyond the sum of individual omics. Various tools have been recently developed to integrate such datasets, but there are limited strategies to ...systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi‐Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolomics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network‐level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi‐omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from eleven clear cell renal cell carcinoma (ccRCC) patients. COSMOS was able to capture relevant crosstalks within and between multiple omics layers, such as known ccRCC drug targets. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi‐omics studies.
SYNOPSIS
A new approach integrates multi‐omics datasets with a prior knowledge network spanning signaling, metabolism and allosteric regulations. Application to a kidney cancer patient cohort captures relevant cross‐talks among deregulated processes.
A causal multi‐omics network is built by integrating multiple ressources spanning signaling, metabolism and allosteric regulations.
Transcriptomics, phosphoproteomics and metabolomics data are integrated in a set of coherent mechanistic hypotheses using CARNIVAL, a tool contextualizing causal networks.
This set of coherent mechanistic hypotheses can be mined to identify disease mechanisms and therapeutic targets.
A network built for a cohort of kidney cancer patients shows coherence with other studies and known therapeutic targets.
A new approach integrates multi‐omics datasets with a prior knowledge network spanning signaling, metabolism and allosteric regulations. Application to a kidney cancer patient cohort captures relevant cross‐talks among deregulated processes.
Fibrosis represents the common end stage of chronic organ injury independent of the initial insult, destroying tissue architecture and driving organ failure. Here we discover a population of ...profibrotic macrophages marked by expression of Spp1, Fn1, and Arg1 (termed Spp1 macrophages), which expands after organ injury. Using an unbiased approach, we identify the chemokine (C-X-C motif) ligand 4 (CXCL4) to be among the top upregulated genes during profibrotic Spp1 macrophage differentiation. In vitro and in vivo studies show that loss of Cxcl4 abrogates profibrotic Spp1 macrophage differentiation and ameliorates fibrosis after both heart and kidney injury. Moreover, we find that platelets, the most abundant source of CXCL4 in vivo, drive profibrotic Spp1 macrophage differentiation. Single nuclear RNA sequencing with ligand-receptor interaction analysis reveals that macrophages orchestrate fibroblast activation via Spp1, Fn1, and Sema3 crosstalk. Finally, we confirm that Spp1 macrophages expand in both human chronic kidney disease and heart failure.
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•ECM regulator scoring identifies a SPP1+ macrophage subset in myocardial infarction•Chemokine CXCL4 drives SPP1+ macrophage activation and macrophage-fibroblast crosstalk•Platelet-derived CXCL4 mediates SPP1+ macrophage activation and organ fibrosis•SPP1+ macrophages expand in human heart failure and chronic kidney disease
By ranking immune cells after myocardial infarction based on ECM regulator expression, Hoeft et al. find an SPP1+ macrophage subset. They identify the chemokine CXCL4 as critical for SPP1+ macrophage activation, macrophage-fibroblast crosstalk, and organ fibrosis. They confirm expansion of SPP1+ macrophages in human heart failure and chronic kidney disease.
The pathogenesis of the development of sclerotic lesions in focal segmental glomerulosclerosis (FSGS) remains unknown. Here, we selectively tagged podocytes or parietal epithelial cells (PECs) to ...determine whether PECs contribute to sclerosis. In three distinct models of FSGS (5/6-nephrectomy + DOCA-salt; the murine transgenic chronic Thy1.1 model; or the MWF rat) and in human biopsies, the primary injury to induce FSGS associated with focal activation of PECs and the formation of cellular adhesions to the capillary tuft. From this entry site, activated PECs invaded the affected segment of the glomerular tuft and deposited extracellular matrix. Within the affected segment, podocytes were lost and mesangial sclerosis developed within the endocapillary compartment. In conclusion, these results demonstrate that PECs contribute to the development and progression of the sclerotic lesions that define FSGS, but this pathogenesis may be relevant to all etiologies of glomerulosclerosis.
Investigations of Glucocorticoid Action in GN Kuppe, Christoph; van Roeyen, Claudia; Leuchtle, Katja ...
Journal of the American Society of Nephrology,
05/2017, Letnik:
28, Številka:
5
Journal Article
Recenzirano
Odprti dostop
For several decades, glucocorticoids have been used empirically to treat rapid progressive GN. It is commonly assumed that glucocorticoids act primarily by dampening the immune response, but the ...mechanisms remain incompletely understood. In this study, we inactivated the glucocorticoid receptor (GR) specifically in kidney epithelial cells using Pax8-Cre/GR
mice. Pax8-Cre/GR
mice did not exhibit an overt spontaneous phenotype. In mice treated with nephrotoxic serum to induce crescentic nephritis (rapidly progressive GN), this genetic inactivation of the GR in kidney epithelial cells exerted renal benefits, including inhibition of albuminuria and cellular crescent formation, similar to the renal benefits observed with high-dose prednisolone in control mice. However, genetic inactivation of the GR in kidney epithelial cells did not induce the immunosuppressive effects observed with prednisolone.
, prednisolone and the pharmacologic GR antagonist mifepristone each acted directly on primary cultures of parietal epithelial cells, inhibiting cellular outgrowth and proliferation. In wild-type mice, pharmacologic treatment with the GR antagonist mifepristone also attenuated disease as effectively as high-dose prednisolone without the systemic immunosuppressive effects. Collectively, these data show that glucocorticoids act directly on activated glomerular parietal epithelial cells in crescentic nephritis. Furthermore, we identified a novel therapeutic approach in crescentic nephritis, that of glucocorticoid antagonism, which was at least as effective as high-dose prednisolone with potentially fewer adverse effects.
Parietal epithelial cells (PECs) are involved in the development of sclerotic lesions in primary focal and segmental glomerulosclerosis (FSGS). Here, the role of PECs was explored in the more common ...secondary FSGS lesions in 68 patient biopsies, diagnosed with 11 different frequently or rarely encountered glomerular pathologies and additional secondary FSGS lesions. For each biopsy, one section was quadruple stained for PECs (ANXA3), podocytes (synaptopodin), PEC matrix (LKIV69), and Hoechst (nuclei), and a second was quadruple stained for activated PECs (CD44 and cytokeratin-19), PEC matrix, and nuclei. In all lesions, cellular adhesions (synechiae) between Bowman's capsule and the tuft were formed by cells expressing podocyte and/ or PEC markers. Cells expressing PEC markers were detected in all FSGS lesions independent of the underlying glomerular disease and often stained positive for markers of activation. Small FSGS lesions, which were hardly identified on PAS sections previously, were detectable by immunofluorescent staining using PEC markers, potentially improving the diagnostic sensitivity to identify these lesions. Thus, similar patterns of cells expressing podocyte and/or PEC markers were found in the formation of secondary FSGS lesions independent of the underlying glomerular disease. Hence, our findings support the hypothesis that FSGS lesions follow a final cellular pathway to nephron loss that includes involvement of cells expressing PEC markers.
Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell or pathomics data to find ...sample-level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures, and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics or pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell or pathomics data. Moreover, PILOT provides a statistical approach to find changes in cell populations, gene expression, and tissue structures related to the trajectories or clusters supporting interpretation of predictions.
Synopsis
PILOT is a computational framework of analysis of multi-scale single cell or pathomics data measured over distinct patients.
It allows the estimation of sample-level clustering and trajectories
Statistical methods allow the interpretation of results, i.e., association of clusters/trajectories with cell clusters, genes and tissue structures.
PILOT is showcased in scRNA-seq of myocardial infarction and pathomics data of kidney IgA nephropathy.
PILOT is a computational framework of analysis of multi-scale single cell or pathomics data measured over distinct patients.
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