Cardiovascular Computational Biology

(Dr. Liang Chen; MD, PhD)

Multi-omics approaches offer transformative insights into the complex biology underpinning cardiovascular diseases (CVDs). By integrating data from various molecular layers, such as proteomics, metabolomics, and single-cell transcriptomics, our research seeks to uncover critical biomarkers and mechanistic pathways that drive disease phenotypes. This integrative analysis spans several cardiovascular conditions, with a focus on heart failure and cardiomyopathies (e.g. arrhythmogenic cardiomyopathy, ACM), aiming to decode the molecular landscape associated with these diseases.

Our goal is to advance precise diagnostic tools and therapeutic strategies for CVDs by leveraging both clinical and computational approaches. Our team functions as a collaborative group, working jointly with the research team at Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences in Beijing. This setup promotes a close, multidisciplinary partnership, fostering regular exchange and shared expertise in advancing cardiovascular research.

Advanced computational biology and digital pathology have become invaluable tools in unveiling the complexity of cardiovascular diseases. Our team specializes in cardiovascular computational biology, using advanced bioinformatics approaches to address the complexities of disease mechanisms.

By integrating digital pathology with machine learning, we explore clinico-pathological subtypes of ACM through state-of-the-art imaging quantification and clustering algorithms. Leveraging high-resolution digital pathology and sophisticated computational methods, our research systematically classifies disease phenotypes and uncovers mechanisms driving disease progression. These approaches not only deepen our understanding of the underlying mechanisms of ACMs but have also led to the successful identification of distinct clinico-pathological subtypes, culminating in the establishment of the Fuwai classification.

Figure 1. (A) Hierarchical cluster heatmap showing the clinical characteristics, genetic background, and percentage of myocardium, fibrosis and adipose in six representative sections of each cluster.

Building on our expertise in advanced computational biology, we further applied single-nucleus RNA sequencing to construct a comprehensive single-cell atlas of the adult human heart. By mapping the spatial and functional zonation of cardiac cells across multiple anatomical regions, this study revealed distinct molecular profiles and heterogeneity in cardiomyocytes and vascular cells within both atrial and ventricular compartments. This approach enabled us to uncover critical gene expression gradients along the ventricular wall, providing a high-resolution reference for understanding the normal heart structure and laying the groundwork for investigating cardiac pathologies.

Figure 2. (A) Schematic diagram of different cardiac tissue regions used for sequencing. Left atrium (LA), right atrium (RA), interatrial septum (IAS), left ventricle (LV), right ventricle (RV), and interventricular septum (IVS). The ventricular specimens were vertically fine cut into multiple strata including 4 layers in LV (trabecular [LV-tra], subendocardial [LV-endo], middle [LV-mid] and subepicardial [LV-epi] layers), 2 layers in RV (trabecular [RV-tra] and compact [RV-com] layers), and 3 layers in IVS from left to right side (IVS-L, IVS-M, and IVS-R). (B) t-SNE visualization of 12 major heart cell types from the human heart. (C) Spatial Heterogeneity Index (SHI) of different cell types at the 3 levels.

With significant expertise in single-cell sequencing research, we have applied this technology to study myocardial fibrosis, where diverse cell populations with distinct molecular profiles complicate efforts to identify specific regulatory factors. Cardiac fibrosis arises through complex molecular networks that drive pathological tissue remodeling, often involving diverse regulatory factors across cell types. Single-nucleus RNA sequencing (snRNA-seq) has proven to be a powerful approach in this context, allowing for high-resolution mapping of gene expression at the single-cell level. Using transcription factor analysis and trajectory inference, this approach uncovers key regulatory molecules active in the fibrotic progression. Notably, alongside established factors like MEOX1, we identified PKNOX2 as a novel suppressor of fibrosis, with functional assays validating its role as a potential therapeutic target in cardiac fibrosis. This integrative analysis provides valuable insights into the transcriptional landscape governing fibrosis, highlighting novel avenues for targeted interventions. These findings underscore the pivotal role of single-cell sequencing in unveiling PKNOX2’s antifibrotic potential, providing a targeted pathway for therapeutic interventions in cardiac fibrosis.

Figure 3. (A) a snRNA-seq workflow using the 10X Genomic platform with five human donor hearts. (B) UMAP plot showing the six subclusters of fibroblasts. (C) Dot plot showing the representative marker genes of each subtype of fibroblasts. (C–D) Pseudo time analysis of fibroblasts and analysis colored by fibroblast subtypes.

Ongoing research lines

- Genetic and clinical investigations of cardiomyopathies, utilizing the extensive resources of the UK Biobank to elucidate genetic markers and enhance precision medicine in cardiovascular care.

-  Comprehensive multi-omics profiling of cardiomyopathies, including arrhythmogenic cardiomyopathy and dilated cardiomyopathy, integrating genomics, transcriptomics, proteomics, and metabolomics to uncover novel biomarkers and mechanistic pathways.

-  Discovery of proteomic signatures and autoimmune biomarkers for accurate diagnosis and prognosis of heart failure and cardiomyopathies, advancing personalized approaches in cardiovascular disease management.

Collaborations

National

Department of Cardiology, USZ, UZH, Zurich (Firat Duru, Ardan Saguner)

 

International

Maastricht University (Stephane Heymans)

Padua University (Cristina Basso, Kalliopi Pilichou)

Fuwai Hospital, Chinese Academy of Medical Sciences (Xiangjie Li)

Research Team

Zihao Zhang (PhD Student)

Yuxiao Hu (PhD Student)

Funding

Iten-Kohaut Foundation Research Grant

Selected publications

1.      Chen L, Song J, Chen X, Chen K, Ren J, Zhang N, Rao M, Hu Z, Zhang Y, Gu M, Zhao H, Tang H, Yang Z, Hu S. A novel genotype-based clinicopathology classification of arrhythmogenic cardiomyopathy provides novel insights into disease progression. European Heart Journal. 2019;40(21):1690-703. doi:10.1093/eurheartj/ehz172. (IF=24.9)

 

2.      Song J-P, Chen L, Chen X, Ren J, Zhang N-N, Tirasawasdichai T, Hu Z-L, Hua W, Hu Y-R, Tang H-R, Chen H-SV, Hu S-S. Elevated plasma β-hydroxybutyrate predicts adverse outcomes and disease progression in patients with arrhythmogenic cardiomyopathy. Science Translational Medicine. 2020;12(530). doi:10.1126/scitranslmed.aay8329. (IF=17.1)

 

3.      Chen L, Hua K, Zhang N, Wang J, Meng J, Hu Z, Gao H, Li F, Chen Y, Ren J, Liu L, Zhou Q, Gu J, Song J, Zhang X, Hu S. Multifaceted Spatial and Functional Zonation of Cardiac Cells in Adult Human Heart. Circulation. 2022;145(4):315-8. doi:10.1161/CIRCULATIONAHA.121.055690. (IF=39.9)

 

4.      Liu X, Yin K, Chen L, Chen W, Li W, Zhang T, Sun Y, Yuan M, Wang H, Song Y, Wang S, Hu S, Zhou Z. Lineage-specific regulatory changes in hypertrophic cardiomyopathy unraveled by single-nucleus RNA-seq and spatial transcriptomics. Cell Discov. 2023;9(1):6. doi:10.1038/s41421-022-00490-3. (IF=33.5)

 

5.      Chen L, Li H, Liu X, Zhang N, Wang K, Shi A, Gao H, Akdis D, Saguner AM, Xu X, Osto E, Van de Veen W, Li G, Bayés-Genís A, Duru F, Song J, Li X, Hu S. PBX/Knotted 1 homeobox-2 (PKNOX2) is a novel regulator of myocardial fibrosis. Signal Transduct Target Ther. 2024;9(1):94. doi:10.1038/s41392-024-01804-5. (IF=40.8)