Cardiovascular diseases (CVDs) are one of the deadliest diseases in the world and seriously threaten people’s health, with a particularly alarming impact nations with moderate economic status. The mortality rate of CVDs is even higher than that of cancer. However, in high-income countries, improvements in the prevention and treatment of CVDs have led to a lower mortality rate for CVDs than for cancer (Timmis et al., 2022). The Institute for Health Metrics and Evaluation (IHME) classifies cardiovascular disease, as including mortality and morbidity, into 11 distinct cardiovascular conditions. These primarily include ischemic heart disease, cerebrovascular accidents, hypertensive cardiac disorders, and congestive heart failure (TIfHMa, 2021). Approximately 330 million individuals in China are believed to be afflicted with cardiovascular diseases, with the nation grappling with the compounding effects of an aging demographic and a consistent increase in the prevalence of metabolic risk factors (Sheng-Shou, 2022). Researchers are still finding new targets for the prevention and treatment of CVDs. Proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors block the binding of the PCSK9 protein to LDL receptors on the cell surface and prevent their degradation. This action allows more LDL receptors to be recycled back to the cell surface. As a result, there is increased uptake of LDL cholesterol, which reduces its levels in the bloodstream. Because they are effective at lowering LDL cholesterol over the long term, PCSK9 inhibitors offer a good alternative to statin therapy (McGettigan and Ferner, 2017; Burnett and Hooper, 2018). Calcium channel antagonists (CCAs) relieve arterial constriction by blocking calcium influx and binding to specific sites on voltage-sensitive calcium channels, making them the preferred treatment for hypertension (Godfraind, 2014; Carey et al., 2022). These examples show that treatment targets are very important for cardiovascular treatment. Therefore, the discovery of new targets is highly important for the study of cardiovascular diseases. In this study, we focused on the discovery of Hi-C-based therapeutic targets. Paul et al. captured Hi-C data from B cells and T cells to identify high-confidence candidate causal genes that could be used to treat these diseases (Martin et al., 2019).

Modern genetics shows that the use of genetic information to guide personalized medicine has great potential. There are many sequencing technologies, such as ChIP-seq (chromatin immunoprecipitation sequencing), ATAC-seq (assay for transposase-accessible chromatin using sequencing), methyl-seq (DNA methylation sequencing), and RNA-seq (transcriptome sequencing) (Goodwin et al., 2016). However, when detecting epigenetic modifications, the focus is on identifying changes in gene expression caused by changes in the spatial conformation of chromatin, such as long-range interactions. Few existing techniques are able to perform detection at high resolution. The interaction of chromosome spatial structure can regulate gene expression, leading to changes in the epigenetic state (Dekker, 2008). The 3D structure of chromatin is highly complex. Recently, an increasing number of studies have shown that gene regulation, including DNA replication, transcription, and translation, can be affected by the 3D structure of chromatin to some extent. Therefore, understanding the 3D structural epigenetics of chromatin in cardiovascular disease models is important, and clarifying the 3D structure of chromatin can improve disease detection.

Currently, 3D genomic chromatin conformation capture technologies and their derivatives, known as C technologies, can be broadly divided into 3C (one-to-one), 4C (one-to-many), 5C (many-to-many), and Hi-C (all-to-all) methods. Hi-C technology is a 3C technology. Compared with traditional 3C approaches, the Hi-C method has improved the resolution and accuracy, offering a more comprehensive view of interactions among different regulatory sequences. Hi-C is good at detecting extensive chromatin contacts in an unbiased, genome-wide manner (van Berkum et al., 2010) and can be used to study the spatial relationships of chromatin DNA, thereby obtaining high-resolution chromatin regulatory element interaction maps. Hi-C and other detection technologies open the door to explore the 3D world of chromatin, and these technologies are highly important for this research. Research indicates that the three-dimensional architecture of chromatin constitutes an epigenetic pathway influencing gene expression patterns in the development of tumors and the emergence of cancer stem cells (Feng and Pauklin, 2020). Hi-C technology has been used to detect the 3D chromatin topology of humans and mitochondria and its impact on mitochondrial diseases (Feng et al., 2021a). Hi-C technology can be used to guide the research and detection of diseases, such as cancer (Feng et al., 2021b). In other words, Hi-C technology can be used to analyze genomic changes in the occurrence of cardiovascular diseases from the perspective of 3D spatial structure, not only from the perspective of linear structure, to clarify the pathogenesis of cardiovascular diseases and predict their occurrence.

In recent years, many studies have investigated how 3D chromatin structure affects the pathogenesis of cardiovascular diseases. This review aims to summarize the integration of Hi-C data and that cardiovascular diseases (heart failure, congenital heart disease, and hypertension) are highly likely to cause cardiovascular diseases. (Figure 1), explain the importance of Hi-C technology for discovering new targets, and discuss whether Hi-C has deeper applications in exploring the pathogenesis of cardiovascular diseases and detecting diseases.

The three-dimensional architecture of chromatin plays a crucial role in gene expression. Its structure is made up of nucleosomes, where DNA is damaged and connected by DNA bridges. DNA in these coiled regions is less actively transcribed than the DNA segments that serve as bridges. This intricate network creates a complex topology that influences gene activity. To address the challenges posed by this complex topology and its impact on gene expression, the 3C technique was introduced. Initially, developed by Job Dekker in 2002, 3C was used to study interactions between nearby genomic regions across bacterial and human chromosomes, including those on the same or different chromosomes (Dekker et al., 2002). The principle of 3C technology is that it uses cross-linking of proteins in the nucleus to analyze whole-genome interactions, allowing inference about the physical interactions of chromosomes, their nature, and their three-dimensional conformation. However, 3C is limited in the size of interacting fragments and cannot be used to screen fragments directly across the whole genome, so it is necessary to predict interacting fragments in advance. Consequently, the 3C methodology is characterized by its limited output, time-intensive nature, and extensive processing requirements. Building on 3C, Hi-C technology offers a high-throughput and comprehensive approach for mapping the spatial organization of genomic DNA across the entire genome, enabling the unbiased differentiation of interactions between genes and their regulatory elements (van Berkum et al., 2010). Compared with 3C, capture Hi-C technology has improved accuracy and precision and can completely show the interaction between each regulatory element (Figure 2). However, Hi-C capture requires a large number of cells, which are not easy to obtain for relatively precious cells. Therefore, single-cell capture Hi-C technology was developed. However, extracting single cells from human tissues, especially living bodies, is difficult. Thus, Dip-C technology was developed to improve the throughput of parallel processing of single cells. After that, ChIA-Drop technology (a strategy for multi-chromatin interaction analysis) was also developed to address the shortcomings of Hi-C technology in cell library construction (Zheng et al., 2019). Currently, common Hi-C-derived techniques include capture Hi-C, single-cell Hi-C, in situ Hi-C, DLO Hi-C, ChIA-PET, and HiChIP, etc. (Figure 3).

Moreover, Hi-C detects not only the three-dimensional structure but also the methylation, carboxylation, and phosphorylation of DNA “bridges” and coiled segments. This is because these chemical changes can alter the three-dimensional structure of chromatin and cause differences in gene expression. If we understand the changes in the three-dimensional structure of a chromosome (and its related genes) that can cause diseases, we can find ways to overcome these diseases. Studies have shown that parts of genes linked to heart disease often interact with distant DNA regions. These regions have many specific protein binding sites and special marks on the DNA package. These interactions help confirm a detailed 3D map of the genome (Hi-C profile), showing how genes are organized and how their activity changes (Montefiori et al., 2018). In other words, Hi-C technology can be used to determine the promoters of CVD-related genes and accurately draw promoter maps. Although the promoter is only a small fragment of DNA, it is possible to infer the changed genes. In combination with experimental verification, several potential targets can be identified.

Hi-C technology not only resolves the 3D chromatin structure with high resolution but also helps us understand various substructures within it. A wealth of experimental data from modern medicine indicates that the occurrence of diseases is closely linked to changes in three-dimensional chromatin structure. Exploring the three-dimensional structure of chromatin is highly important for disease analysis, thereby advancing clinical medical diagnostic techniques and the research and development of drugs related to genomics. Many scientists have discovered new therapeutic targets for diseases via this technology (Table 1). Via Hi-C technology, drug developers can identify genes and regulatory elements related to cardiovascular diseases. Specifically, as mentioned earlier, Hi-C technology can detect interactions between genomic regions associated with diseases, helping to identify potential target genes. In addition, developers can combine Hi-C with other technologies (such as ChIP-seq) to verify the function of genomic regions identified by GWAS, thus determining the target genes that may be regulated. Furthermore, abnormalities in chromatin structure can serve as potential drug targets. Hi-C technology can provide clues to reveal such drug targets.

Hi-C technology provides more information than traditional high-throughput sequencing technology does. While high-throughput sequencing (NGS) can only assemble genomes at the contig/scaffold level, it fails to provide genome information at the chromosome level. In contrast, Hi-C assembly technology can place contigs/scaffolds onto different chromosomes, thereby enhancing genome quality.

Many emerging computational tools or machine learning approaches are enhancing the analysis of Hi-C data. For example, Qiao Liu et al. proposed scDEC-Hi-C. This is a new framework for single-cell Hi-C analysis with deep generative neural networks. It can comprehensively analyze sparse and heterogeneous single-cell Hi-C data (Diagnosis and classification of diabetes mellitus, 2013). In the report by Mattia Forcato et al., thirteen computational methods for Hi-C data analysis were detailed and compared, including algorithms for identifying chromatin interactions and algorithms for identifying topologically associating domains (TADs) (Forcato et al., 2017).

Since the prerequisite for using Hi-C technology is to obtain users’ genomic data, ensuring the privacy and security of users’ genetic data is essential. Moreover, researchers should ensure that the process of obtaining genomic information is compliant and that the data will not be leaked or misused. Accordingly, researchers should fulfill their duty to provide information, ensuring that users are informed and consenting throughout the process. Users should be aware of the purpose, methods, and potential risks of genomic research. When sharing research data, consent from users must be obtained first, ensuring that the entire process is conducted under strict legal and ethical frameworks. In gene therapy, the principle of treatment should be prioritized rather than economic benefits or scientific interests.

Hi-C technology is still in the development stage. Although it has a wide range of applications, it is not widely used at present. The main reasons are as follows: long experimental period, low correlation with high-resolution microscope results, high experimental threshold, difficulty of data analysis, low signal-to-noise ratio and low resolution. When Hi-C is performed with additional cross-linkers and/or with restriction enzymes that produce longer fragments, quantitative differences in cell type-specific chromosome organization can be missed or underestimated, depending on the 3C-based protocol used (Akgol et al., 2021). These factors also make it difficult for the Hi-C technique to detect weak chromatin interactions.

A kit can detect only one set of chromatin, and a single kit can cost as much as 50,000–80,000 RMB. To verify this conclusion, many repeated tests, which can be very expensive and involve hundreds of kits, are often needed. The experimental cost is very high, so few laboratories are involved in this field. The high threshold of Hi-C technology is also reflected in the high requirements for biological and statistical knowledge during operation. Understanding and skillfully operating Hi-C technology requires a certain understanding of the advantages and disadvantages of various statistical algorithms and knowing how to analyze the output results.

In many cases, Hi-C technology can be used to assess the rationality of new potential target genes, especially those far from disease-related SNPs. However, it is difficult to prove a gene’s role in a disease via only Hi-C technology. For example, consider laminopathies. For years, disruption of chromatin organization has been suspected to be a key part of disease pathogenesis. This is a very clear case. However, identifying and clearly identifying the pathogenic mechanism involving chromatin organization is still difficult. Moreover, other methods have been developed. They are used to identify related target genes and regulatory mechanisms that may involve 3D chromatin structure, such as epigenomic quantitative trait loci (eQTLs), mQTLs, etc.,, etc. and CRISPR-related technologies (CRISPR activation, inhibition, etc.). The use of Hi-C technology alone cannot provide strong evidence to prove the existence of regulatory mechanisms. It needs to be combined with CRISPR-related technologies and other methods.

To make Hi-C technology more widely used in cardiovascular research, the most important step is to reduce the cost of Hi-C technology. Researchers can lower the cost of using Hi-C by optimizing the technical workflow, updating relevant equipment, or using more economical reagents, making this technology more accessible to more laboratories. Moreover, simpler data algorithms and more efficient automated tools should be developed to reduce the complexity and time cost of experiments, improving their reproducibility and efficiency. The development of visualization software and artificial intelligence analysis tools can also help researchers process and analyze Hi-C data more efficiently.

Therefore, future research on this technology could focus on improving Hi-C technology, reducing production costs, increasing the utilization rate and reuse rate of reagents, reducing development and use costs, simplifying the experimental process, and improving the machine algorithm. If these issues are addressed, Hi-C technology could be expanded to the clinical market, not only limited to the laboratory but also becoming another powerful tool in the fight against disease.