Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease.

Biomedical single-cell atlases describe disease at the cellular level. However, analysis of this data commonly focuses on cell-type-centric pairwise cross-condition comparisons, disregarding the multicellular nature of disease processes. Here, we propose multicellular factor analysis for the unsupervised analysis of samples from cross-condition single-cell atlases and the identification of multicellular programs associated with disease. Our strategy, which repurposes group factor analysis as implemented in multi-omics factor analysis, incorporates the variation of patient samples across cell-types or other tissue-centric features, such as cell compositions or spatial relationships, and enables the joint analysis of multiple patient cohorts, facilitating the integration of atlases. We applied our framework to a collection of acute and chronic human heart failure atlases and described multicellular processes of cardiac remodeling, independent to cellular compositions and their local organization, that were conserved in independent spatial and bulk transcriptomics datasets. In sum, our framework serves as an exploratory tool for unsupervised analysis of cross-condition single-cell atlases and allows for the integration of the measurements of patient cohorts across distinct data modalities.

A network medicine approach to study comorbidities in heart failure with preserved ejection fraction.

BACKGROUND: Comorbidities are expected to impact the pathophysiology of heart failure (HF) with preserved ejection fraction (HFpEF). However, comorbidity profiles are usually reduced to a few comorbid disorders. Systems medicine approaches can model phenome-wide comorbidity profiles to improve our understanding of HFpEF and infer associated genetic profiles. METHODS: We retrospectively explored 569 comorbidities in 29,047 HF patients, including 8062 HFpEF and 6585 HF with reduced ejection fraction (HFrEF) patients from a German university hospital. We assessed differences in comorbidity profiles between HF subtypes via multiple correspondence analysis. Then, we used machine learning classifiers to identify distinctive comorbidity profiles of HFpEF and HFrEF patients. Moreover, we built a comorbidity network (HFnet) to identify the main disease clusters that summarized the phenome-wide comorbidity. Lastly, we predicted novel gene candidates for HFpEF by linking the HFnet to a multilayer gene network, integrating multiple databases. To corroborate HFpEF candidate genes, we collected transcriptomic data in a murine HFpEF model. We compared predicted genes with the murine disease signature as well as with the literature. RESULTS: We found a high degree of variance between the comorbidity profiles of HFpEF and HFrEF, while each was more similar to HFmrEF. The comorbidities present in HFpEF patients were more diverse than those in HFrEF and included neoplastic, osteologic and rheumatoid disorders. Disease communities in the HFnet captured important comorbidity concepts of HF patients which could be assigned to HF subtypes, age groups, and sex. Based on the HFpEF comorbidity profile, we predicted and recovered gene candidates, including genes involved in fibrosis (COL3A1, LOX, SMAD9, PTHL), hypertrophy (GATA5, MYH7), oxidative stress (NOS1, GSST1, XDH), and endoplasmic reticulum stress (ATF6). Finally, predicted genes were significantly overrepresented in the murine transcriptomic disease signature providing additional plausibility for their relevance. CONCLUSIONS: We applied systems medicine concepts to analyze comorbidity profiles in a HF patient cohort. We were able to identify disease clusters that helped to characterize HF patients. We derived a distinct comorbidity profile for HFpEF, which was leveraged to suggest novel candidate genes via network propagation. The identification of distinctive comorbidity profiles and candidate genes from routine clinical data provides insights that may be leveraged to improve diagnosis and identify treatment targets for HFpEF patients.

Mast cell activation and degranulation in acute artery injury: A target for post-operative therapy.

The increasing incidence of cardiovascular disease (CVD) has led to a significant ongoing need to address this surgically through coronary artery bypass grafting (CABG) and percutaneous coronary interventions (PCI). From this, there continues to be a substantial burden of mortality and morbidity due to complications arising from endothelial damage, resulting in restenosis. Whilst mast cells (MC) have been shown to have a causative role in atherosclerosis and other vascular diseases, including restenosis due to vein engraftment; here, we demonstrate their rapid response to arterial wire injury, recapitulating the endothelial damage seen in PCI procedures. Using wild-type mice, we demonstrate accumulation of MC in the femoral artery post-acute wire injury, with rapid activation and degranulation, resulting in neointimal hyperplasia, which was not observed in MC-deficient KitW-sh/W-sh mice. Furthermore, neutrophils, macrophages, and T cells were abundant in the wild-type mice area of injury but reduced in the KitW-sh/W-sh mice. Following bone-marrow-derived MC (BMMC) transplantation into KitW-sh/W-sh mice, not only was the neointimal hyperplasia induced, but the neutrophil, macrophage, and T-cell populations were also present in these transplanted mice. To demonstrate the utility of MC as a target for therapy, we administered the MC stabilizing drug, disodium cromoglycate (DSCG) immediately following arterial injury and were able to show a reduction in neointimal hyperplasia in wild-type mice. These studies suggest a critical role for MC in inducing the conditions and coordinating the detrimental inflammatory response seen post-endothelial injury in arteries undergoing revascularization procedures, and by targeting the rapid MC degranulation immediately post-surgery with DSCG, this restenosis may become a preventable clinical complication.

Medial Arterial Calcification: JACC State-of-the-Art Review.

Medial arterial calcification (MAC) is a chronic systemic vascular disorder distinct from atherosclerosis that is frequently but not always associated with diabetes mellitus, chronic kidney disease, and aging. MAC is also a part of more complex phenotypes in numerous less common diseases. The hallmarks of MAC include disseminated and progressive precipitation of calcium phosphate within the medial layer, a prolonged and clinically silent course, and compromise of hemodynamics associated with chronic limb-threatening ischemia. MAC increases the risk of complications during vascular interventions and mitigates their outcomes. With the exception of rare monogenetic defects affecting adenosine triphosphate metabolism, MAC pathogenesis remains unknown, and causal therapy is not available. Implementation of genetics and omics-based approaches in research recognizing the critical importance of calcium phosphate thermodynamics holds promise to unravel MAC molecular pathogenesis and to provide guidance for therapy. The current state of knowledge concerning MAC is reviewed, and future perspectives are outlined.

Consensus Transcriptional Landscape of Human End-Stage Heart Failure.

Background Transcriptomic studies have contributed to fundamental knowledge of myocardial remodeling in human heart failure (HF). However, the key HF genes reported are often inconsistent between studies, and systematic efforts to integrate evidence from multiple patient cohorts are lacking. Here, we aimed to provide a framework for comprehensive comparison and analysis of publicly available data sets resulting in an unbiased consensus transcriptional signature of human end-stage HF. Methods and Results We curated and uniformly processed 16 public transcriptomic studies of left ventricular samples from 263 healthy and 653 failing human hearts. First, we evaluated the degree of consistency between studies by using linear classifiers and overrepresentation analysis. Then, we meta-analyzed the deregulation of 14 041 genes to extract a consensus signature of HF. Finally, to functionally characterize this signature, we estimated the activities of 343 transcription factors, 14 signaling pathways, and 182 micro RNAs, as well as the enrichment of 5998 biological processes. Machine learning approaches revealed conserved disease patterns across all studies independent of technical differences. These consistent molecular changes were prioritized with a meta-analysis, functionally characterized and validated on external data. We provide all results in a free public resource (https://saezlab.shinyapps.io/reheat/) and exemplified usage by deciphering fetal gene reprogramming and tracing the potential myocardial origin of the plasma proteome markers in patients with HF. Conclusions Even though technical and sampling variability confound the identification of differentially expressed genes in individual studies, we demonstrated that coordinated molecular responses during end-stage HF are conserved. The presented resource is crucial to complement findings in independent studies and decipher fundamental changes in failing myocardium.

Big Data Approaches in Heart Failure Research.

PURPOSE OF REVIEW: The goal of this review is to summarize the state of big data analyses in the study of heart failure (HF). We discuss the use of big data in the HF space, focusing on "omics" and clinical data. We address some limitations of this data, as well as their future potential. RECENT FINDINGS: Omics are providing insight into plasmal and myocardial molecular profiles in HF patients. The introduction of single cell and spatial technologies is a major advance that will reshape our understanding of cell heterogeneity and function as well as tissue architecture. Clinical data analysis focuses on HF phenotyping and prognostic modeling. Big data approaches are increasingly common in HF research. The use of methods designed for big data, such as machine learning, may help elucidate the biology underlying HF. However, important challenges remain in the translation of this knowledge into improvements in clinical care.

TGF-ß signaling mediates endothelial-to-mesenchymal transition (EndMT) during vein graft remodeling.

Veins grafted into an arterial environment undergo a complex vascular remodeling process. Pathologic vascular remodeling often results in stenosed or occluded conduit grafts. Understanding this complex process is important for improving the outcome of patients with coronary and peripheral artery disease undergoing surgical revascularization. Using in vivo murine cell lineage-tracing models, we show that endothelial-derived cells contribute to neointimal formation through endothelial-to-mesenchymal transition (EndMT), which is dependent on early activation of the Smad2/3-Slug signaling pathway. Antagonism of transforming growth factor-ß (TGF-ß) signaling by TGF-ß neutralizing antibody, short hairpin RNA-mediated Smad3 or Smad2 knockdown, Smad3 haploinsufficiency, or endothelial cell-specific Smad2 deletion resulted in decreased EndMT and less neointimal formation compared to controls. Histological examination of postmortem human vein graft tissue corroborated the changes observed in our mouse vein graft model, suggesting that EndMT is operative during human vein graft remodeling. These data establish that EndMT is an important mechanism underlying neointimal formation in interpositional vein grafts, and identifies the TGF-ß-Smad2/3-Slug signaling pathway as a potential therapeutic target to prevent clinical vein graft stenosis.