Single-Cell Omics in CVDs

CHAPTER 7

Single-Cell Omics in CVDs Hajra Qayyum, Anum Munir, Syeda Maham Fayyaz, Ayesha Aftab, Hina Aslam Butt, Naveed Iqbal Soomro, Sobia Khurshid, Muhammad Qasim Khan, Saeed Iqbal Soomro, Yumna Saghir, Iqra Bashir, Mohammad Nadeem, Syeda Marriam Bakhtiar Genetics and Molecular Epidemiology Research Group, Department of Biosciences, Capital University of Science and Technology, Islamabad, Pakistan

ABSTRACT This chapter highlights the role of single-cell omics in cardiovascular diseases (CVDs). It starts by highlighting CVDs as a major issue worldwide, covering their genetics and the inheritance pattern that differs from disease to disease. It then explains the two basic techniques, association studies and linkage analysis, used to determine candidate genes, reported to play an important role in the development of CVDs. It then discusses the complex genotype–phenotype correlation that is the outcome of different environmental factors playing a crucial role in disease development. It also explains techniques for single-cell analysis (SCA), which has great prospects to work miracles in the domain of CVDs. The chapter also highlights cardiometabolic phenotype analysis. By the end of the chapter, the utility of single-cell omics in special reference to CVDs has been discussed along with the challenges posed to this emerging method.

7.1

INTRODUCTION

Cardiovascular disease (CVD) is a class of diseases that affect the heart or blood vessels. CVD covers coronary artery diseases (CAD) such as myocardial infarction and angina (WHO et al., 2011). Further CVDs include stroke, heart failure, rheumatic heart illness, hypertensive heart illness cardiomyopathy, congenital heart syndrome, carditis, heart arrhythmia, valvular heart illness, aortic aneurysms, thromboembolic disease, peripheral artery disease, and venous thrombosis (WHO et al., 2011; GBD 2013 Mortality and Causes of Death Collaborators, 2015). CVD is among major medical concerns as it affects more Single-Cell Omics. https://doi.org/10.1016/B978-0-12-817532-3.00007-4 © 2019 Elsevier Inc. All rights reserved.

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than 80,000,000 people in the United States alone. In all the CVD-associated illnesses, inheritance is assumed to play a significant role in the onset and progress of the disease (Lloyd-Jones et al., 2004). The exact role of the inheritance varies from disorder to disorder and is affected by various factors, for example, the time of first ailment and subtype of the illness. Basically, CVD is a multifactorial disease with intricate pathophysiology (Kathiresan and Srivastava, 2012). Both the environmental factors and the heredity factors are responsible for the development of disease and influence its severity and prevalence. Biomedical research is continuously seeking to map genotype with phenotype, i.e., to recognize the particular genes and DNA sequence variations responsible for variations among people. Common polymorphisms have great potential to uncover causal biologic systems in a population (Pearson, 1999).

7.1.1

Genetics of Human Cardiovascular Diseases

For determination of the hereditary factors of CVD, we have to consider that cardiovascular attributes are divided among families (Marenberg et al., 1994) and that candidate genes for CVD are hereditarily polymorphic in nature. Hereditary variations inclining to CVD are thought to be common in a “typical” populace. The simultaneous presence of a few risk alleles, incidentally joined by exceedingly harmful ones, may prompt particular interactions of alleles regarding expansion or duplication of malicious allelic impacts on the phenotype (Risch and Merikangas, 1996). This makes up an individual “risk allele assembly” that converts into a highly individual cardiovascular hazard. It is obvious that our individual genome yields countless risk alleles that are often neglected when examining just a couple of genotypes of intrigue (Lander and Schork, 1994).

7.1.2

Methods to Ascertain Genes

Various methodologies presently exist for distinguishing the candidate genes or polymorphisms related to disease phenotypes. The two major approaches are linkage analysis and genetic association. The selection of method is dependent on the segregation pattern, described by Mendel. A few types of CVD display a straightforward pattern of inheritance suggestive of a single causal gene that prompts an extensive impact on phenotype. For huge numbers of these Mendelian types of CVD, coordinate DNA sequencing, as well as linkage analysis, has effectively yielded the causal gene and change. The low-density lipoprotein receptor (LDLR) gene was sequenced in a patient with homozygous familial hypercholesterolemia and revealed a 5 kilobase deletion that dispensed with a few exons, demonstrating the principal exhibition of a transformation for Mendelian CVD (Lehrman et al., 1985). Other cases in the CVD field incorporate valve defects, long QT disorder, Marfan’s disorder, Mendelian types of hypertension, serious hypercholesterolemia, and a

7.1

few types of inherent coronary disease including septal imperfections (Kathiresan and Srivastava, 2012). The tools and approaches to decipher human hereditary variations on a huge populace scale have started and are able to uncover the hereditary nature of infection at an exponential pace. It is necessary to note that linkage and association approaches are complementary in nature. Once a locus of a gene has been identified as a conceivable contender for given phenotypes by linkage examination, association studies are then carried out to affirm the relationship of distinguished gene polymorphisms with these phenotypes (Lander and Schork, 1994).

7.1.3

Association Studies

Association studies are an effective tool for searching the relationship between a hereditary variation and a phenotype with a significantly small population. Multiple steps are considered using the candidate gene approach in association studies. The steps are shown in Fig. 7.1.

7.1.4

Linkage Studies

Linkage studies are performed on families comprising multiple generations of affected individuals or carriers. It utilizes DNA markers spread over the genome, keeping in mind the end goal, which is to recognize genes related to CVD. The

FIG. 7.1 Steps involved in the identification of candidate genes for CVD.

Introduction

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linkage study tests whether specific alleles are cotransmitted with the disease, and if yes, then what is the frequency (Tegnander et al., 2006; Helgadottir et al., 2004; Lettre et al., 2011).

7.1.5

Genotypic and Phenotypic Variations

Until now, most of our knowledge about CVD-causing genes originates from the investigation of innate heart defects and several familial CVDs. Congenital heart defects in a person can be nonsyndromic or syndromic, and the genes responsible for the early developmental phases of the heart are usually affected by it. These genes are responsible for encoding signaling pathway components, transcription factors, and or structural proteins (Abbate et al., 2008). Cardiac developmental defects are the most widely recognized human birth defects and their investigation is still of great significance (Srivastava, 2006). Therefore, all instances of CVD have a complex multifactorial etiology. Neither hereditary nor environmental factors cause illness. CVD occurs as an outcome of interactions between the underlying conditions which are coded in the genotype, and exposures to several environmental factors (Lara-Pezzi et al., 2012). In response to the influence of environmental factors, a particular genotype responsible for a particular phenotypes can variate and produce a different phenotypic condition. These variations are continuous throughout the lifespan of an individual (Tegnander et al., 2006). Genetic predisposition, therefore, in one way or another controls the onset of the disease after exposure to a particular risk factor (Gottesman and Gould, 2003). The genome creates an autonomous, separated, and fixed flow of information from genotype to phenotype in a single direction. The phenotypic measures are always being formed, changed, and transposed as a result of epigenetic systems of the cell and organismal measurements that develop over the lifetime of the person. These systems influence the phenotype of a person associated with the particular genotype; several genetic studies of CVDs have identified the vigorous relationships between the genotype of an individual, their history of exposures to environmental factors, such as smoking, a high-fat diet, or drugs, and the contemporary phenotype in predicting the outcomes of a phenotype for specific environmental conditions and future points (Sing, 2003) (Fig. 7.2).

7.2

TECHNIQUES FOR SINGLE-CELL ANALYSIS

Cellular heterogeneity is one of the eminent properties of cells and is evident through a number of studies; most of the cardiac research is still based on bulk tissue sampling techniques because of the absence of well-defined markers to isolate the homogeneous stem cell population. Because of the averaging effect of cell population analysis, a realistic analysis of cardiac cells was limited. This limitation has now been removed with single-cell analysis techniques. These

7.2

Techniques for Single-Cell Analysis

FIG. 7.2 Relation of genotype with phenotype in CVD.

single-cell sequencing techniques, when applied to transcriptome and epigenome analysis, enable researchers to inspect the dynamics and regulation of gene expression as well as to model the cardiac mechanisms, such as those underlying cardiac cell progenitor and reprogramming (Linda et al., 2017). This sequencing technique also enables researchers to identify subpopulation-specific and novel cell type-specific markers in the heart that could aid CVD analysis (Gladka et al., 2018).

7.2.1

Single-Cell Isolation

Within a population, cellular heterogeneity is critical to its distinctive function and fate. Mixed studies of mutants and wild types may not be so revealing as to which particular cell responds to which clinical procedure. To better understand the cellular variations in detail, researchers can seek help from single-cell analysis where individual cellular phenotypes are taken into account. In the domain of cardiology, cardiomyocytes and cardiac stem cells (CSCs) are isolated and offer promising results in understanding the mechanisms of cardiac development, regeneration (Hu et al., 2016), and differentiation of healthy and diseased heart (Gladka et al., 2018). Cardiomyocytes are important from the ischemic heart’s point of view, as this condition results in myocardial infarction, amplifying the loss of cardiomyocytes. Chronic hypertension also causes progressive loss of cardiomyocytes. Studies suggest that cardiac function compromised by diseases can be upgraded by inhibiting apoptosis of cardiomyocytes and transplanting autologous CSCs, such as in the case of ischemic

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cardiomyopathy. Different techniques have been adopted to isolate CSCs, but none has been fully optimized and this persists as a hot area of CSC research. The clinical applications are currently limited because of the demand for an enormous number of CSCs. While CSCs have now emerged in regeneration research, the number of isolated CSCs is still very few, making a sufficient supply of functional elements an important concern in cardiovascular research (Choi et al., 2013).

7.2.1.1

Isolation of Cardiomyocyte

Various protocols are available for isolating cardiomyocytes from the heart. The general methodology includes preparation of digestion buffers (collection buffer, neutralization wash buffer) to perfuse the heart along with an isolation system. After setting up the isolation system, the heart is carefully taken out with the help of a pipette (animal model). It is then cut out of the thoracic cavity and placed in a cold digestion buffer for rinsing. The aorta and its branches are then carefully isolated and, through these arteries, the heart is perfused so that it is free of any blood. Once the heart is rinsed, it is placed in another digestion buffer, which is allowed to flow through the heart for 2–3 min. When the heart starts to deshape and begins to look round, the myocardium becomes lighter, indicating that digestion has taken place. At this moment, the ventricles are cut off and are placed into a collection buffer. These ventricles are then cut into small pieces while tissues are collected in a collection buffer. The tissues are gently mixed to dissolve cell–cell adhesion, yielding single-cell cardiomyocytes. These cells are mixed and suspended into a neutralization wash buffer and are ready to be used for single-cell analysis. Cardiomyocytes selected for single-cell analysis must be healthy, should not be damaged due to the isolation process, and should have distinctive normal morphology. These cells are then stored on dry ice and are ready to be used for further analyses (Flynn et al., 2011) (Fig. 7.3).

7.2.1.2

Isolation of Cardiac Stem Cells

Different protocols are available to isolate cardiac stem cells (CSCs); however, a general methodology includes isolation of CSCs from cardiac biopsies that are attained from cardiac patients. Cardiac tissues are placed on ice in cardioplegic solution till they are processed. Fatty tissues are excised out and cardiac tissues are suspended in basic buffer and are then minced into 1 mm3 pieces. These minced tissues and basic buffer are then collected and are dissolved in a digestive solution containing collagenase type II and are incubated for 1.5–2 h at 37°C with continuous shaking. The digestion solution is then refreshed and the suspension is centrifuged and then resuspended in CPC media. The final suspension is filtered twice and then centrifuged to collect cardiomyocytes. The supernatant is collected and resuspended in CPC media and is incubated in CO2. The next day, these are collected from suspension and any attached cell

7.2

Techniques for Single-Cell Analysis

FIG. 7.3 General methodology for isolating cardiomyocytes, where numerals indicate various procedural steps performed.

is dissociated. The resulting suspensions are then refiltered and centrifuged and are resuspended in wash buffer. C-kit + cells are isolated from the resultant suspension by incubating with c-kit-labeled beads and are sorted as per the manufacturer’s protocol. The c-kit + fractions can be divided further into different cell types that are treated using respective protocols once the cells reach the required respective confluency (Monsanto et al., 2017) (Fig. 7.4).

7.2.1.3

Sorting Single-Cells

Once the cells are isolated from cardiac tissues by enzymatic dispersion, they are separated into single-cells with the help of flow cytometry technology. Care is taken while sorting so that the entire range of cardiac cells is isolated without any damage caused to them. These sorted cells are tested using cell imaging techniques to ensure that the cells have remained intact throughout and are suitable to be used for single-cell sequencing applications. (Gladka et al., 2018).

7.2.2 7.2.2.1

Single-Cell Sequencing Single-Cell Sequencing and CVDs

Single-cell sequencing techniques have been applied to the human heart to determine the gene expression signatures of healthy and diseased hearts. This

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FIG. 7.4 General methodology for isolating cardiac stem cells.

application of single-cell sequencing has paved the way to effective therapeutic strategies against CVDs. This technique has revealed the presence of multiple cell types and the differential gene expression in the same type of cardiac cells. Such studies of cell populations and related gene expression have led to the identification of novel molecular mechanisms related to cellular changes that form the basis of CVDs. These genes also include several biomarkers, suggesting new diagnostic techniques for CVDs. For instance, one of the applications of single-cell sequencing has identified five types of subpopulations within cardiomyocytes. These subpopulations include endothelial cells, fibroblasts, macrophages, smooth muscle cells, and a cluster of erythrocytes. These subpopulations when comparatively studied in detail expressed a marker gene cardiac troponin T, Tnnt2 and Myozenin2 (Myoz2) that shows a high level of variations between different cellular subpopulations. Myoz2, also known as calsarcin-1, is an emerging biomarker for cardiac diseases. Calsarcin-1 is an inhibitor of the pathological, prohypertrophic phosphatase calcineurin. Likewise, Ckap4 has been observed to be activated in fibroblasts. Ckap4 is expressed as the result of stress and is a marker for fibroblasts in ischemic heart disease, which is one of the common CVDs (Gladka et al., 2018).

7.2

7.2.2.2

Techniques for Single-Cell Analysis

RNA Sequencing and CVDs

The power of single-cell RNA sequencing (RNA-Seq) has been utilized to generate transcriptional profiles of cardiac progenitors in the early stages of heart development. Such uses of this technique have led to the identification of progenitor subpopulations that are associated with particular regions of the heart. This implication has provided scientists with insight into the molecular mechanisms responsible for cardiac regulation and vascular development. Unraveling the mechanisms of cardiac regulations has led to better understanding of the cardiac malformations leading to CVDs. One such application of RNASeq has revealed that mesoderm posterior protein 1 (Mesp1) is a transcription factor that gives rise to all cardiac cell lineages. Deficiency of Mesp1 leads to a cardiac developmental block, giving birth to further complications, ultimately leading to CVDs (Lescroart et al., 2018) (Fig. 7.5).

7.2.2.3

Epigenetic Sequencing and CVDs

Epigenetic modifications refer to changes in the gene expression that are not caused by changes in the DNA sequences but are due to events like DNA methylations, histone modifications, miRNA expression modulation, etc. The concept of epigenetics in CVD has recently gained attention due to its significant role in inflammation and vascular involvements. It is believed to play a major part in the biological principles governing cardiovascular research.

FIG. 7.5 General methodology for single-cell sequencing.

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Various methods are available for monitoring the epigenetic modifications that can reveal the mechanisms of CVDs (Friedrich et al., 2016). Understanding such mechanisms and their interactions and alterations is a promising avenue to uncover more secrets of CVDs (Abi Khalil, 2014).

7.2.2.4

Epigenetics and Heart Diseases

Cardiac hypertrophy (CH) is an abnormal enlargement or thickening of the heart muscles in which epigenetic mechanisms like histone acetylation and histone methylation play critical roles (Abi Khalil, 2014) (Fig. 7.6). Cardiac failure (CF) is a state of heart failure caused by multiple genetic and environmental factors. Methylation of AMOTL2, PECAM1, and ARHGAP24

FIG. 7.6 Epigenetic mechanisms and induction of cardiac hypertrophy.

7.2

Techniques for Single-Cell Analysis

FIG. 7.7 Epigenetic mechanisms and induction of heart failure.

has been observed in end-stage CF, which indicates common epigenetic pathways in cardiac remodeling and vasculature. Tri-methylated histone H3H4 and H3K9 have also been observed to be altered in CF. Genome-wide histone methylation of heart tissues also indicates epigenetically modified histone H3H4 and H3K9 in the case of CF. Micro-RNAs (miRNAs) have distinct significance in CF epigenetics, which spotlights their role in controlling gene expression during the progression of the disease (Abi Khalil, 2014) (Fig. 7.7). Arrhythmias are disorders associated with heart rhythms and are reported to be caused by epigenetic changes. Atrial fibrillation is mostly found in the aging population. HDAC inhibitor diminishes atrial fibrillation vulnerability by reversing atrial fibrosis following an electrical stimulation. Deletion of both HDAC-1 and HDAC-2 from the myocardium has been proved fatal. MiRNAs, being significant regulators of normal cardiac electrophysiology, could potentially be involved in arrhythmias. Among miRNAs, miR-1 is involved in the conduction of normal electrophysiology and if deleted could cause sudden

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death. Heart deaths and arrhythmias also involve miR-208a and are increased in the case of miR-133a, leading to extended QT intervals. Furthermore, various miRNAs, including miR-212, miR-17-92, miR-155, miR-181, and miR-181a are also found to be associated with cardiac arrhythmias by regulating membrane and cellular proteins (Abi Khalil, 2014) (Fig. 7.8).

7.2.2.5

Epigenetics and Vascular Diseases

Atherosclerosis and artery disease: Atherosclerosis refers to plaque deposition in the arteries. Epigenetic mechanisms are well reported to be involved in this, especially DNA methylation. Hypomethylation events have been observed at the initial stages of atherosclerosis, before the clinical onset of the disease. In human atherosclerosis, hypermethylation of atheroprotective estrogen receptor α (ESR1) and estrogen receptor β (ESR2) in vascular smooth muscle cells has been reported, whereas methylation of the

FIG. 7.8 Epigenetic mechanism and induction of arrhythmias.

7.2

Techniques for Single-Cell Analysis

monocarboxylate transporter (MCT3) gene in the aorta and coronary arterial tissues also promotes the progression of atherosclerosis. Hypomethylation of LINE elements and promoter of coagulation factor VII are also involved in coronary heart diseases; miRNAs also play a key regulatory role in gene expression of the cells involved in atherosclerosis. They also bring about inflammation, cholesterol influx, cellular differentiation, and lipid uptake (Abi Khalil, 2014) (Fig. 7.9). Diabetic vascular disease: Certain epigenetic events mediating signaling pathways associated with diabetic CVDs have been indicated by prolonged exposures to hyperglycemia, inducing specific changes in the chromatin and

FIG. 7.9 Epigenetic mechanism and induction of vascular diseases.

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transcriptional responses. Genome-wide association studies (GWASs) provide evidence that hyperglycemic conditions, histone modifications, and DNA methylation take place concurrently. Exposure to hyperglycemia results in epigenetic changes modifying the inflammatory pathways (Abi Khalil, 2014).

7.3

TRANSCRIPTOME AND FUNCTIONAL ANALYSIS

7.3.1 7.3.1.1

Transcriptomics and CVDs Synthesis and Amplification of cDNA From Single-Cells

Single-cell cardiomyocytes and CSCs are subjected to cDNA synthesis and amplification once they are isolated. Methodology followed includes synthesis of cDNA using single-cell lysate and a strand of DNA/RNA chimeric mix along with reverse transcriptase. A portion of DNA present on the primer hybridizes to the 50 end of the poly A tail or across the transcript in a random fashion. Thus the first strand of cDNA is synthesized by extending the 30 end of DNA via reverse transcriptase. The derived cDNA/mRNA hybrid molecule carries a distinctive sequence at the 50 end of the cDNA strand and, as a result, a double-stranded cDNA with DNA/RNA heteroduplex is produced. One such amplification system is RiboSPIA, which can amplify cDNA enough to be used for labeling and microarray hybridization (Chen et al., 2016).

7.3.1.2

Single-Cell Whole-Transcriptomic Profiling by Microarray

A fixed concentration of amplified cDNA is subjected to fragmentation for every single-cell and is labeled using different labels, for example, biotin labeling. The labeled cDNA is allowed to hybridize with the specie-specific gene chip. Following hybridization, chips are washed out using standard protocols and microarray images are obtained. The resultant microarray images are then processed using specialized platforms like GeneChip Scanner 3000 7G (Affymetrix) and the expression values for genes are subjected to further analysis (Chen et al., 2016).

7.3.1.3

Differential Gene Expression Level Analysis

One such application of single-cell transcriptome study is the employment of the technique to compare and validate the intercellular relative abundance of genes specific to cardiomyocytes and CSCs. The tested genes included Myh6 from cardiomyocytes and c-kit and Sox2 genes from CSCs, along with two endogenous controls Actb and Gapdh. This validation was performed using TaqMan RT-qPCR employed at the single-cell level (Chen et al., 2016).

7.4

7.3.2

Applications of Single-Cell Technology in CVDs

Cardiometabolic Phenotypes Analysis

The connection between mitochondrial DNA copy number (mtDNA CN) and cardiometabolic traits is not well studied but an inverse relation between mtDNA CN and cardiometabolic risk components is often reported (Kim et al., 2012; Huang et al., 2011). A vast extent of the SNPs distinguished as being related to cardiometabolic disorders are situated in noncoding regions of the genome responsible for the expression of genes (Willer et al., 2013). Studies have reported 11,067 variations related to cardiometabolic phenotypes. Of these, 230 variations are present within miRNA-restricting regions in the 30 -untranslated area of 155 cardiometabolic phenotypes. The piece of the practical variations associated with deregulating the cardiometabolic phenotype is situated in miRNA-restricting destinations. A few epidemiological surveys have reported a strong relationship between adiposity and cardiometabolic traits. Adiposity results in a specific cardiometabolic phenotype, as obese individuals may share various phenotypic characters that are common between obesity and CVDs, such as hereditary variation (rs9939609) in the fat-mass-and obesity-related gene (FTO), linked with both expanded BMI and cardiometabolic phenotypes (Fall et al., 2013). Ethnicity is a major risk for chronic kidney disease (CKD) development ( Johnson et al., 2009). A hereditary investigation, including applicant genes and GWASs, has been well-exploited to study genetic variations involved in CKD and cardiometabolic risk factors. The strong relationship of PTPLAD2, SLC2A9, PVRL2, and BUD13 with CKD and cardiometabolic phenotypes is reported. An investigation of Caucasian patients with coronary artery disease found a connection between homozygosity of the allele in a polymorphism of the PVRL2/PRR2 gene and the cardiovascular infection (Freitas et al., 2002). Polymorphism in the PVRL2/PRR2 gene is reported in a Caucasian population with a strong evidence of linkage disequilibrium between the PRR2 and APOE genes (Willer et al., 2013). The APOE gene is well studied as a culprit of low-density lipoprotein (LDL) resulting in CVDs (Talmud et al., 2009) (Fig. 7.10).

7.4 APPLICATIONS OF SINGLE-CELL TECHNOLOGY IN CVDs 7.4.1

Differentiation of Cardiac Cell Types

In the process of cardiogenesis, cardiac progenitor cells (CPCs) give rise to different cardiac cells, such as cardiomyocytes, conductive cells, vascular smooth muscle cells, and endothelial cells. In order to differentiate these various cell types and understand the process of cardiac development in the context of

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FIG. 7.10 Ethnicity influences the risk of CKD relation with the other phenotypes.

lineage hierarchies and regulatory networks, high-resolution technologies like single-cell sequencing are required. Single-cell technologies help in cardiac biomarker identification and enable the detection of cellular heterogeneity even in seemingly similar cell subpopulations (Linda et al., 2017). Identification and isolation of different cardiac cell types could help in the study of simultaneous interactions of multiple cells, could serve regenerative therapy by inspecting cell lines derived from the population of heart failure patients, and could open up new avenues for CVD therapeutics by expanding the range of feasibilities for manipulation and modification of CSCs (Monsanto et al., 2017).

7.4.2

Cardiac Cell Lineages

Single-cell RNA sequencing (scRNA-Seq) is currently being used to study the mechanism of cardiac development and to outline the molecular events taking place at the transcriptome level. Cardiac cell populations can also be designated as different lineages based on their differing cellular locations at the different developmental stages and known lineage-specific biomarkers using SC transcriptome and RNA-Seq. These lineages include embryonic stem cell (ESCs), CPCs, cardiomyocytes (CMs), smooth muscle cells (SMCs), endothelial cells (EDCs), and fibroblasts. Genetic analysis of each cell populace could give birth to novel lineage-specific biomarkers that could be potentially useful for differentiating various cell populations (Linda et al., 2017).

7.4

7.4.3

Applications of Single-Cell Technology in CVDs

Study of Cardiac Renewal Mechanism

Single-cell sequencing technologies also have the potential to open up innovative methods for CVD therapeutic interventions by cellular activation to promote regenerative processes (Monsanto et al., 2017). Cardiac renewal mechanisms could be inspected by subjecting cardiac cells to scRNA-Seq at various instants of time during the process, and scRNA-Seq could help in the revelation of gene expression involved in cell cycle regulation in each cell along with cell cycle heterogeneity within the same cell type. This dynamic change of differentially expressed genes, especially the transcription factors between distinct cell types, aids in the reconstructing of regulatory networks involved in generating cardiomyocytes (Linda et al., 2017). Understanding the mechanism of cardiac renewal could facilitate myocardial regeneration by replacing CMs, SMCs, and EDCs (Monsanto et al., 2017).

7.4.4

Cardiac Repair

A mammalian cardiac heart has a limited capability of regeneration once it is damaged; therefore scar formation is a major compensative repair after a cardiac injury, such as in the case of myocardial infarction. To repair the heart requires an understanding of the cardiac repair mechanisms, including the molecular mechanisms steering the process. Such mechanisms could be revealed using high-throughput techniques such as SC-transcriptome analysis for the identification of key genes and pathways regulating the cardiac regeneration process (Linda et al., 2017).

7.4.5

Cardiac Stem Cell Research

The single-cell qPCR array has proved to be effective when it comes to determining the identity and lineage relationships for each individual cardiovascular cell as well as identifying the potential cell surface or intracellular markers. Analysis using single-cell sequencing techniques guarantees an unbiased genome-wide detection of differentially expressed transcripts, leading to novel discoveries. Currently, SC- transcriptomic and epigenomic applications in cardiovascular research have successfully gained the attention of research groups. SC-gene expression profiling of cardiomyocytes has been used to establish the protocols for SC-microarray for the whole transcriptome or single-cell qPCR for specific genes; qPCR-based analysis of SC-transcriptome and DNA-methylome of cardiomyocytes have demonstrated dedifferentiation and cell-cycle reprogramming, such as events planned by genome-wide epigenome reprogramming (Linda et al., 2017).

7.4.6

Discovery of Disease Specific Biomarkers

Focusing on the end goal, which is to determine cellular heterogeneity, singlecell transcriptomics has the potential to study RNA components, specifically

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RNA polymerase II, systematically. It can also help to analyze expression networks and to find disease-specific biomarkers. These advances can have great impacts on cardiovascular research: for example, Ckap4 has emerged as the biomarker for ischemic heart (Gladka et al., 2018).

7.4.7

Genetic Testing for Preimplantation Embryo

Preimplantation genetic diagnosis (PGD) is a preimplantation analysis performed on early developing embryos created through in vitro fertilization (IVF). In addition to the modification in fetal stages, the technique has promising applications in carrier counseling for late-onset diseases, for example CVDs (He et al., 2004). The principal PGD related to coronary disease was performed for Holt-Oram syndrome (HOS) (He et al., 2004), distinguished by atrial septal deformity and cardiovascular conduction issues, although the clinical indications might vary to a large extent, but it often remain unnoticed at the time of birth. So it is an ordinary condition for which PGD is an extremely appealing alternative, permitting parents to reproduce without the threat of producing offspring with HOS, with a chance for early or sudden death. The significance of PGD in this case is due to the fact that there is no prospect for treatment, as the condition may manifest in spite of presymptomatic analysis and development. So PGD appears to give conceivable relief from the danger of producing offspring with a risk of premature or sudden death (Kuliev et al., 2012).

7.4.8

Lab-on-a-Chip CVD Analysis

Lab-on-a-chip (LoC) tools can be utilized for the investigation of molecular and cell-based molecular markers of CVDs. This technique not only provides effective chemical control and response, sample preparation, high throughput, and movability, but also provides other attractive features, for example labelfree location and enhanced affectability because of the incorporation of different novel identification methods. These features adequately enhance the indicative test speed and improve the identification method. Moreover, microfluidic cell assays and organ-on-chip models provide new potential methodologies for CVDs (Yager et al., 2006).

7.4.9

Myocardium Regeneration Studies

Myocardium regeneration is one of the most promising therapeutic interventions and it could save patients suffering from myocardial infarctions. Although various cell populations from different tissue sources, such as bone marrow mononuclear cells, skeletal myoblasts, hematopoietic and endothelial

7.5

Conclusions and Future Prospects

progenitors, and induced pluripotent or embryonic stem cells, have been tested for their lost-myocardium regeneration ability, identification and selection of optimal cell types continue to be hot research areas for successful therapeutic interventions against CVDs (Monsanto et al., 2017). This issue has been resolved to some extent by single-cell technologies that offer high-resolution cardiac cell-type analyses.

7.4.10

Atherosclerosis

Aortic samples taken from different mice may contain significantly different amounts of disease. Thus, gene expression changes between samples may result more from differential disease burden than the experimental perturbation. This can be partially corrected by increasing the number of biological replicates in each experimental condition, but this still remains a significant confounding factor in bulk RNA-Seq experiments (Wirka et al., 2018).

7.4.11

Pre-natal Diagnosis

To exclude the presence of CVDs in developing embryos, techniques such as ultrasound, three- and four-dimensional echocardiography, fetal electrocardiography, magnetic resonance imaging, and magnetocardiography are available (Tegnander et al., 2006) (Fig. 7.11).

7.5

CONCLUSIONS AND FUTURE PROSPECTS

Single-cell omics has the potential to do wonders in the domain of CVDs. With the advances in both array and sequence-based technologies, cardiovascular research has gained momentum. For example, in the case of obstructive coronary artery disease, gene expression profiling of peripheral-blood specimens could potentially avoid an invasive endomyocardial biopsy in the inspection for rejection after cardiac transplantation (Kashima et al., 2018). Another example of single-cell functional genomics is cardiomyocytes during development and disease remodeling, which have provided promising targets to promote cardiac myogenesis (Wigler, 2012). Likewise, single-cell transcriptomic profiles of distinct cardiac lineages help to understand lineage-specific markers and subpopulations that confer normal and aberrant cardiac development, providing a robust benchmark to assess the maturity of stem cell-derived CMs. Single-cell analysis has also enabled researchers to study various cardiometabolic phenotypes, including adipose inflammation and insulin resistance (Friedrich et al., 2016). Integrating the genomic, epigenomic, transcriptomic, and proteomic data from every single-cell in a given tissue can open new avenues of research. Similarly, gene regulation could also be analyzed by quantification of allele-specific expression within a single-cell. In the case of

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FIG. 7.11 Utility of cell signal analysis in CVDs.

unavailability of tissue from a genotype of interest, genome editing of pluripotent stem cells is likely to be used as a viable surrogate. All these single-cell platforms can serve as valuable reagents in examining the effects of pharmacologic, molecular, or environmental perturbations on the transcriptome (Khera and Mehta, 2014). However, apart from the successful applications of single-cell omics in various fields of biology, this emerging method needs improvements both in terms of technicality and biology. Technically, it needs improvements in miniaturization, integration, and detection sensitivity techniques. It also requires automation, higher throughput, and the support of bioinformatics software in order to handle the large amount of data generated from multiple single-cells to achieve statistical significance. All these challenges make single-cell analysis a costintensive method. In addition, the biological challenges include the requirement for genes, proteins, and metabolites to be analyzed at a particular time outside their cellular environments, as this method requires cell lysis. Furthermore, the study and analysis of individual cells require experiments to be designed that keep in view the cellular interactions and the extracellular

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