Single-cell RNA sequencing (scRNA-seq) is by far the most widely used single-cell technology in cardiovascular biology. It is a powerful tool for analysis of all transcripts in a single cell. The typical workflow of a scRNA-seq experiment includes single-cell capture, reverse transcription, amplification, library preparation, sequencing and data analysis (Potter, 2018). Since the publication of the first single-cell transcriptomics study in 2009 (Tang et al., 2009), a variety of scRNA-seq techniques, have been developed (Svensson et al., 2018). Each variation of scRNA-seq has its own advantages and drawbacks, thus, choosing the appropriate method is highly dependent on tissue/cell type and experimental design (Table 1). For example, single cell selection approaches such as micropipetting and laser capture microdissection allow the visualization of cells prior to selection, but are laborious and low-throughput, and are prone to bias. By contrast, droplet-based cell selection techniques are high-throughput, suffer from limitations including cell size restraint [not suitable for adult cardiomyocytes (CMs)] and “blind” selection of cells. The latter attribute therefore requires high input cell quantity (10⁵–10⁶ cells per sample) and quality (>80% viability) (Gao et al., 2020). One way to circumventing cell size restraints is the use of nuclei instead of intact cells. Single-nucleus RNA sequencing (snRNA-seq) thus reduces the technical challenge of isolating adult CMs, and minimizes stress-induced aberrant gene expression (Hu P. et al., 2018; Cui et al., 2020). It is worthwhile to note, however, while most studies found comparable clustering of cell populations with scRNA-seq and snRNA-seq (Bakken et al., 2018; Slyper et al., 2020), adult CMs are different from most other cell types in that they can contain more than one nuclei, which may confound data interpretation (Ding et al., 2020). Microwell-based selection approaches (e.g., ICELL8) come with the advantage of visualizing cells and staining them for viability before capture. In addition, the large nozzle diameter does not limit target cell size (i.e., compatible with adult CMs). However, these methods are considered medium-throughput, capturing approximately 2,000 cells per chip.

Different sequencing protocols utilize different strategies to capture, amplify and sequence mRNA molecules. Some protocols generate a strong bias toward either the 5′-end [e.g., Single-cell tagged reverse transcription (STRT-seq)] (Natarajan, 2019) or the 3′-end (e.g., Drop-seq), cell expression by linear amplification and sequencing 2 (CEL-seq2) (Hashimshony et al., 2012; Bageritz and Raddi, 2019; Natarajan, 2019). These are commonly used for their high-throughput and relatively low costs. However, if the goal of the experiment goes beyond quantification of gene expression, then one may use full-length transcript sequencing [e.g., Switching mechanism at 5′ end of the RNA transcript sequencing (Smart-seq2) (Picelli et al., 2013), Smart-seq3 (Hagemann-Jensen et al., 2020)] to obtain a greater coverage of transcripts. Moreover, full-length sequencing allows characterization of alternative splicing, single-nucleotide variants, transcription start sites, and the detection of monoallelic and imprinted genes (Ramsköld et al., 2012; Picelli et al., 2014; Volden et al., 2018; Picelli, 2019).

In addition to the transcriptome, other ensembles of molecules (e.g., genome, proteome, and metabolome) and cellular or molecular states (e.g., chromatin accessibility and methylome) of can also be profiled at single-cell level. Techniques such as single-cell chromatin accessibility sequencing (scATAC-seq), proteomics and DNA methylomics are now widely used either alone, or to complement transcriptomic data (Table 1) (Tanay and Regev, 2017; Efremova and Teichmann, 2020).

The aim of genomics is to study an organism’s complete set of DNA (genome), including its structure, function, evolution, and the impact of its changes on organisms. Single-cell whole-genome sequencing is generally accomplished via the two major whole genome amplification (WGA) methods, including multiple displacement amplification (MDA) (Dean et al., 2002), multiple annealing and looping based amplification cycles (MALBAC) (Zong et al., 2012). As one of the pioneering WGA techniques, PCR-based methods [e.g., degenerate oligonucleotide-primed polymerase chain reaction (DOP-PCR)] are less susceptible to DNA sample quality, but suffer from several limitations, which include inadequate average product size, non-specific amplification biases and incomplete genome coverage. MDA is an isothermal technique that utilizes the ϕ29 DNA polymerase for its exceptional fidelity (due to its 3′→5′ proofreading exonuclease activity), strand-displacement activity and processivity (up to 70–100 kb without dissociation from template). Thus, MDA reduces the amplification bias from 40-fold to less than 3-fold and achieves much gearter genome coverage (∼70%) (Dean et al., 2002). The more advanced MALBAC relies on quasilinear DNA preamplification, which is used to reduce bias associated with non-linear amplification, followed by exponential amplification (regular PCR) to acquire sufficient material for sequencing. This design yields a more even representation of the genome (i.e., uniformity) and better reproducibility. However, both of these two techniques have their own limitations. For MDA, the irreproducible sequence-dependent bias and production of chimeric products are the protential limitations; for MALBAC, it is the high false positive rate for single-nucleotide variations (SNVs) detection (Huang et al., 2015).

ATAC-seq uses hyperactive Tn5 transposase to tag and fragment DNA sequences in open chromatin regions simultaneously, thereby identifying chromatin accessibility (Buenrostro et al., 2015a). Initially, three major strategies for scATAC-seq were developed: combinatorial cellular indexing (e.g., sci-ATAC-seq), microfluidics-based approach using an integrated fluidics circuit (IFC), and droplet-based approach (Buenrostro et al., 2015b; Cusanovich et al., 2015; Satpathy et al., 2019). In sci-ATAC-seq, the nuclei of lysed cells are labeled by unique combinations of two barcodes during transposition and PCR amplification, respectively. This approach allows sequencing of approximately 1,500 cells with median reads of 2,500 and at a collision rate of ∼11%. IFC scATAC-seq enable get more than 70,000 reads per cell (Buenrostro et al., 2015b). In droplet-based ATAC-seq, single nuclei are prepared using the 10X Genomics protocol, transposed, and are loaded onto a microfluidic chip for generating barcoded GEMs (gel bead in emulsion). The throughput of each sample well on a microfluidic chip is up to 10,000 single nuclei. The capture ratio of this method is more than 65%. Building upon these three strategies, other scATAC-seq methods have emerged, such as Perturb-ATAC (Rubin et al., 2019) and plate-based scATAC-seq (Chen et al., 2018). Since this method provides a genome-wide landscape of chromatin accessibility at single-cell resolution, scientists are able to identify cell types, define genomic features, such as cis-regulatory elements (e.g., promoters and enhancers) and trans-regulatory elements (e.g., transcription factors) and build gene regulatory networks with it. Gene activity and accessibility to genetic variants can also be derived using scATAC-seq data (Satpathy et al., 2019). Furthermore, integration of single-cell transcriptome and chromatin accessibility data may improve cell identity annotation (Jia et al., 2018). Specifically, joint analysis of these two types of omics data facilitates the detection of correlations between trans- and cis-regulatory elements and the cellular state of interest (Stuart et al., 2019). However, the noisy and sparse nature of scATAC-seq signals remains a big challenge for computational analysis.

DNA methylation is an important epigenetic modification that establishes patterns of gene repression. Currently, there are two approaches for single-cell DNA-methylome profiling. The first method is single-cell reduced-representation bisulfite sequencing (scRRBS) (Guo et al., 2015). This protocol integrates the steps from MspI digestion to bisulfite conversion into one tube of cell lysate, thus minimizing DNA loss and gathering methylation information on approximately 1 million CpG sites within an individual cell. The other method, single-cell bisulfite sequencing (scBS-seq), employs a modified post-bisulfite adapter tagging (PBAT) approach (Miura et al., 2012; Smallwood et al., 2014). This approach allows for a lower starting amount of DNA (∼100 ng) (Miura et al., 2012). Compared to scRRBS, scBS-seq is capable of measuring DNA methylation at up to 48.4% of the CpG sites and achieves higher recovery rates simultaneously (Smallwood et al., 2014; Schwartzman and Tanay, 2015).

Proteomics has been used for decades to study the characteristics of all proteins in a sample in large-scale, including protein expression level, posttranslational modification and protein–protein interaction (Aslam et al., 2017). However, it is not until recent years that single-cell protein and proteomic techniques were developed, offering opportunities to analyze the functional states of individual cells. Mass cytometry, also known as CyTOF (cytometry by time-of flight) permits cytometric measurement of up to 42 proteins per cell by using time-of-flight mass spectrometry and heavy-metal tagged antibodies, and theoretically can detect up to 100 isotopes (Cheung and Utz, 2011). It is a potentially powerful tool for immune-monitoring and clinical diagnosis. Many studies have demonstrated its applicability in clinical prognosis and diagnosis (Amir et al., 2013; Behbehani et al., 2015; Ferrell et al., 2016; Fisher et al., 2017). Imaging mass cytometry (IMC) and multiplexed ion beam imaging (MIBI) are two techniques related to CyTOF. IMC is in essence a combination of immunohisto(cyto)chemistry and mass cytometry, made possible through the integration of a laser ablation device (Giesen et al., 2014). Stained tissue sections and cells are almost ablated by a pulsed laser pixel by pixel, where associated metal isotopes are measured and indexed. Currently, IMC offers 1 μm-image resolution and requires almost 50 copies of an epitope per pixel for minimal detection. Different from laser-based IMC, MIBI is based on secondary ionization mass spectrometry, only a few hundred nanometers of tissue are lost during a MIBI scan, which means that the stained tissue can be re-analyzed multiple times with this technique (Keren et al., 2018). When combined with TOF detection system, MIBI-TOF can measure a large scale of atomic masses (Keren et al., 2018, 2019). This instrument has an image resolution of 250 nm and a rate reaching 10,000 pixels per second. The major advantage of IMC and MIBI is that they can monitor protein modifications, such as histone acetylation and kinase phosphorylation, with spatial information. Single-cell barcode chip (SCBC) is an ELISA-based approach, it allows detection of more than 40 proteins per cell and provides data with deep depths (Shi et al., 2012). Only a few hundred cells are required for each assay. The advantage of this technique is that single cells can be cultured in the microchambers on the chip, and is therefore suitable for the study of secreted protein and paracrine (Xue et al., 2015).

The term metabolomics was first proposed in 2001, and is defined as the comprehensive and quantitative analysis of all metabolites (small molecules, typically less than 1 kDa, including nucleosides, lipids, amino acids and carbohydrates) of the biological system (Fiehn, 2001). The metabolite heterogeneity of individual cells from tissue or organ reflects stochastic biochemical processes, cell cycle stages, environmental stress, and diseased states. However, analyzing the metabolomes of single cells is technically extremely challenging owing to the chemical diversity and instability of metabolites, as well as the low amount input material because metabolites cannot be amplified. Thus, both the resolution and the sensitivity of the analytical method are important points to consider. At present, mass spectrometry (MS)-based techniques are by far the most popular methods to analyze single-cell metabolomes. Typically, the contents of isolated single cells are processed via a separation- or non-separation-based approach, before subjecting them to MS analysis. Currently, a variety of ionization techniques have been applied to single-cell metabolomics, including time-of-flight secondary ion mass spectrometry (TOF-SIMS) (Kleinfeld et al., 2004), matrix-assisted laser desorption-ionization (MALDI) (Dueñas et al., 2017), nanostructure-initiator MS imaging (NIMS) (O’Brien et al., 2013), live single-cell mass spectrometry (LSCMS) (Shimizu et al., 2015), and laser-ablation electrospray ionization mass spectrometry (LAESI-MS) (Taylor et al., 2021). Among them, MALDI and LSCMS are the most frequently used methods. In MALDI, cells are co-crystallized with matrix (a solution that supplies protons) on a metal plate and are irradiated and ionized by a UV laser beam. The ions are then analyzed depending on mass-to-charge rations (m/z). The sensitivity of this method is good enough to be used in single-cell analytical studies. However, the crystalized condition of cells and the vacuum environment on the metal plate is not sufficiently physiologically relevant, hence, doubts were raised whether MALDI causes metabolite distortion or exhaustion of molecules. In addition, lipids dominate MALDI ionization, which consequently impacts the detection of other metabolites. Compared with MALDI, LSCMS enables direct and real-time analysis of molecules at single-cell resolution. Living cells are monitored on a video microscope, allowing behavior- and morphology-based cell selection. Cells are kept in a suitable medium until ionization, a protocol that maximally preserves the natural cellular environment.

Despite the rapid development of single-cell omics, the studies depend on a certain single omics technique are unable to draw a complete picture of the regulatory networks in a cell. Therefore, single-cell multi-omics techniques, which are combinations of simultaneous measurements of the genome, transcriptome, proteome, or epigenome from individual cells, has gained momentum for its capability to directly study the correlations between genetic and phenotypic changes. At present, single-cell multi-omics technologies can be generally divided into four classes: (1) transcriptome and genome, for investigating the relationship between gene expression and genomic alterations, includes DR-seq (Huo et al., 2016), G&T-seq (Macaulay et al., 2015), SIDR (Han et al., 2018), TARGET-seq (Chaligne et al., 2019), and scTrio-seq (Hou et al., 2016); (2) transcriptome and DNA methylation, for exploring the relationship between gene expression and DNA methylation, includes scM&T-seq (Angermueller et al., 2016), scMT-seq (Hu et al., 2016), scTrio-seq (Hou et al., 2016), and scNMT-seq (Clark et al., 2018); (3) transcriptome and chromatin accessibility, for studying relationship between gene expression and chromatin accessibility, includes sci-CAR (Cao et al., 2018), SNARE-seq (Chen et al., 2019), scNMT-seq (Clark et al., 2018); (4) transcriptome and proteome, for discovering the relationship between gene expression and protein expression, includes PEA/STA (Genshaft et al., 2016), PLAYR (Frei et al., 2016), CITE-seq (Stoeckius et al., 2017), REAP-seq (Peterson et al., 2017), RAID (Gerlach et al., 2019), and ECCITE-seq (Mimitou et al., 2019).

On a temporal axis, the heart undergoes significant changes in cellular composition and function during development and disease progression (Harris and Black, 2010; Espinoza-Lewis and Wang, 2012; Buijtendijk et al., 2020). During development, the cardiac cellular composition undergoes drastic changes as multipotent cells make multiple step-wise decisions to differentiate into various states (DeLaughter et al., 2016; Li et al., 2016; Scialdone et al., 2016). Homeobox genes Nkx2.5 and Isl1, expressed by cardiac progenitor cells (CPCs), play critical roles in heart formation and development during early embryonic stages (Moretti et al., 2006; Wu et al., 2006). By integrating scRNA-seq and scATAC-seq to map the developmental trajectories of Nkx2.5+ and Isl1 + CPCs in early (E7.5, E8.5, and E9.5) mouse embryonic hearts (Jia et al., 2018), Jia et al. (2018) revealed that Isl1 + CPCs pass through an attractor state before separating into different developmental branches, while extended expression of Nkx2.5 commits CPCs into a unidirectional cardiomyocyte (CM) fate. Interestingly, cells within the same cluster defined by transcriptomic sequencing possessed different chromatin accessibility. CPC fate transitions were associated with distinct chromatin states, which is critically dependent on Isl1 and Nkx2.5 (Jia et al., 2018). Notably, scATAC-seq exhibited higher sensitivity toward delineating cellular heterogeneity than scRNA-seq, because it detected five subpopulations of Isl1 + CPCs at E8.5 and E9.5, while the latter only detected three (Jia et al., 2018). Therefore, integration of different types of data may improve the resolution with which we define cellular states and events.

Recently, Cui et al. (2019) systematically revealed the transcriptional landscape of human fetal heart during development at single-cell resolution. They identified four main cell types in human embryonic heart, including CMs, fibroblast-like cells, endothelial cells and valvar cell, as well as some other cell types, such as smooth muscle cells and immune cells (e.g., macrophages, T cell, B cells). During development, the proportion of CMs in the atrium and ventricle dramatically declined, whereas the proportion of NCMs, such as fibroblast-like cells and macrophages, increased gradually, accompanied by a set of up-regulated ECM related genes, including DCN, COL1A1, and LUM, indicating a critical role of NCMs in heart development. In addition, in the developing heart, CMs experiences a transition from loose trabecular CMs to a more mature and compact CMs. This study identified the gene expression changes between these two states. The authors compared the transcriptional changes in different cardiac chambers, and discovered that the difference arose as early as 5 weeks. New knowledge provided by this work deepens our understanding of human heart development and may provide us some inspiration on the differentiation of mature functional cardiac cells in vitro from stem cells.

An even more precise spatiotemporal map of the developing human heart was accomplished by the integration of single-cell transcriptomics, spatial transcriptomics (ST) and in situ sequencing (ISS) of four human developmental hearts across three time points (Asp et al., 2019). At first, with ST, the authors investigated unique gene expression patterns in each anatomical region, classified cells of human developing heart into 10 clusters and depicted global spatiotemporal information at the three time points. Then, through scRNA-seq, the authors characterized cellular heterogeneity of human fetal heart and classified cells into 15 clusters, which was incorporated the result from ISS. The result of ISS was not only consistent with ST and scRNA-seq data, but also complemented other methods. For example, analysis of the spatial position and functional heterogeneity of epicardium and epicardium-derived cells (EPDCs), four fibroblasts clusters and two endothelial cells clusters, could not be accomplished by scRNA-seq. Finally, the sections of ST and the sections of ISS were aligned to construct two types of 3D models of human fetal hearts. Compared with cellular spatial distribution predicted via ST, ISS provided spatial information at a finer structural resolution. This work exemplifies how incorporation of various RNA sequencing approaches can facilitate our comprehension of heart development in different dimensions, a strategy that could be extended to other organs to decipher the global process of human development.

Hu P. et al. (2018) investigated the transcriptional landscape of postnatal maturing mouse hearts in both healthy states and in a pediatric mitochondrial cardiomyopathy model by snRNA-seq. Based on gene expression signatures, they classified CMs into developing and mature CMs, each of which encompass several subpopulations. Gata4 and Myocd are critical transcription factors for CM development (Huang et al., 2012; Borok et al., 2016). The authors found that Gata4 and Myocd were highly expressed in developing CMs, but not in mature CMs or non-myocyte cells, indicating that the function of these two transcription factors were confined to a specific CM population. In addition, they discovered a small population of presumably proliferating CMs, which express cell cycle genes, including Mki67, Cenp, and Kif15. Compared with P6 control mice, the percentage of this population in P10 declined from 5.5 to 1.8%, whereas the percentages of other cell types remained relatively stable. Mitochondrial cardiomyopathy is defined as cardiomyopathy caused by mitochondrial DNA mutations (Ozawa, 1994). In this study, dramatic cell type-specific and subtype-specific transcriptional remodeling occurred in mice with mitochondrial cardiomyopathy. This study provided some of the first insights into the postnatal developing heart at single-cell resolution, and shed light on cell type alterations in mitochondrial cardiomyopathy. Wang L. et al. (2020) investigated, for the first time, the heterogeneity of adult human heart and compared the distinct cellular compositions among healthy hearts, hearts with heart failure (HF), as well as hearts partially recovered from HF, at single-cell resolution (Wang L. et al., 2020). This is the first and only high-throughput sequencing study of intact adult human CMs. The authors revealed distinct cellular compositions and functions in different anatomic regions of the heart. For example, atrial CMs possess secretary capability, whereas the CMs in left ventricle (LV) were mainly responsible for lipid metabolism. Furthermore, different LV CM clusters express distinct sets of genes for contraction and metabolism, indicating the functional heterogeneity of CMs in the LV.

Recently, Litviňuková et al. (2020) characterized the molecular signatures of six anatomical regions of the heart (left and right ventricular free walls, left and right atrium, the left ventricular apex, and interventricular septum) from 14 adult hearts, using snRNA-seq for CMs and scRNA-seq for the rest of the cell types. Cells were classified into 11 major populations comprising atrial cardiomyocytes, ventricular cardiomyocytes, fibroblasts and smooth muscle cells. When studying cardiomyocytes specifically, the authors found that the percentage of ventricular cardiomyocytes was higher in female versus male hearts, and that the percentage of cardiomyocytes in ventricles was higher than that in the atria. Furthermore, ventricular cardiomyocytes expressed more sarcomeric proteins, including MYH7 and MYL2, than their atrial counterparts. The latter was enriched in the expression genes such as HAMP, ADLH1A2, and ROR2, indicating significant roles of iron homeostasis (Huang et al., 2020), retinoic acid synthesis and Wnt signaling pathway (Mazzotta et al., 2016) in atrial function. These findings shed light on chamber-specific cardiomyocyte molecular signatures related to sex differences and cardiac function.

Atherosclerosis (AS) is considered an inflammatory disease involving complex crosstalk between immune and vascular cells (Libby, 2012; Tabas and Lichtman, 2017; Tay et al., 2019). Prior to the application of single-cell techniques, many studies have tried to demonstrate the complex composition and function of leukocytes in atherosclerosis. Macrophages were considered the most abundant leukocytes in any type of lesion and the most significant factor for the size of lesion (Hansson and Libby, 2006). Among the many types of macrophages, pro-inflammatory M1 macrophages are mainly non-foamy cells and express inflammatory markers, such as TNF, NLRP3, ZPF36, IL1β, CXCL2, and CCL2, and are therefore considered as the main mediator of inflammation in lesions (Willemsen and de Winther, 2020). Anti-inflammatory M2 macrophages can be further divided into M2a, M2b, and M2c subtypes (Colin et al., 2014). TREM2^hi macrophages, characterized by excessive uptake of lipids, seems to exhibit the M2 phenotype (Willemsen and de Winther, 2020). Various types of T cells were detected in atherosclerotic plaques. For example, T helper 1 (T_H1) cells play pro-atherogenic roles, whereas regulatory T (T_reg) cells play anti-atherogenic roles in plaques. However, T_reg cells can alter their phenotype and turn pro-atherogenic (Sawant and Vignali, 2014). Yet the roles of other T cells, such as T_H2, T_H9, T_H17, T_H22, follicular helper T cells, and CD8⁺ T cells in atherosclerosis are still unclear. Hence, accumulating studies are unraveling the cellular diversity in atherosclerosis, single-cell omics are still necessary to obtain unbiased profile cell types in AS and predict their functional implications. With CyTOF, CITE-seq, and scRNA-seq, Fernandez et al. (2019) characterized immune cells in carotid artery plaques and blood samples from symptomatic (SYM) or asymptomatic (ASYM) AS patients. They discovered that: (1) in plaques, the quantity of CD8⁺ T cells were much higher than CD4⁺ T cells; (2) compared with blood, some T cells clusters (MC11, MC12, and MC20) in plaques expressed more PD-1 (programmed cell death protein 1, the marker of T cell exhaustion), indicating that T cell exhaustion may be caused by the inflammatory microenvironment; (3) genes expressed in plaque T cells were associated with inflammation, differentiation, and cell proliferation, whereas the gene expressed by T cells in the blood were related to inhibition of T cell function; (4) dysfunction of T cells and macrophages in plaques was a crucial driver for cardiovascular (CV) events. This work shed light on critical role of immune cells in clinical CV events therapy and hinted toward new targets for AS treatment.

In addition, combined with transgenic mice, scRNA-seq enables to further illustrate the role of certain molecular in AS progression. SETDB2 is a histone lysine methyltransferase and catalyzes trimethylation of H3K9 (Torrano et al., 2019). Due to its function on inflammatory factors Ccl2 and Cxcl1, SETDB2 is considered to play a regulatory role in monocyte and neutrophil recruitment (Kroetz et al., 2015; Schliehe et al., 2015). Recently, Zhang et al. (2021) utilized scRNA-seq to profile CD45⁺ cells from atherosclerotic plaques of bone marrow-transplanted mice, and found that the proportions of a monocytes cluster and a neutrophil cluster in Setdb2-deficient leukocytes were increased compared with WT leukocytes. Meanwhile, in Setdb2-deficient leukocytes, the expression of proinflammatory factors, such as Cebpb, S100a8, S100a9, Ccr1, and Trem1, and genes related to the unfolded protein response, including Clec4e, Clec4d, and Clec4n, were elevated in the monocyte/mocrophage clusters. Genes upregulated in Setdb2-deficient samples were enriched in cell apoptosis and atherosclerosis signaling, whereas downregulated ones were associated with impaired regulation of anti-inflammatory response. These findings strongly supported the postulation of Zhang et al. (2021) that SETDB2 deficiency in hematopoietic cells enable exacerbate inflammation and aggravate atherosclerosis.

Recently, Örd et al. (2021) studied AS-relevant non-coding genetic variation by combining single-nucleus ATAC-seq with genome-wide association study (GWAS). They provided the first chromatin accessibility map of human AS lesions at single-cell resolution and classified cells into different types, including ECs, SMCs, and monocyte/macrophages. They discovered that ECs and SMCs possessed most CAD-associated genetic variants, and optimized the identification of potential causal single-nucleotide polymorphisms (SNPs) and the identification of the target genes for over 30 CAD loci. This work complements the view previously presented by scRNA-seq study (Wirka et al., 2019), and provides a new approach to discover disease-leading or disease-relevant cell types and gene variants.

While atherosclerosis remains the major vascular disease that has been studied with single-cell technologies (Cochain et al., 2018; Winkels et al., 2018; Lin et al., 2019; Pan et al., 2020), multiple other vascular diseases have also received attention (Pedroza et al., 2020; Zhang et al., 2020). Chen et al. (2020) recently investigated the pathogenesis of aortic aneurysm using mass cytometry, imaging mass spectroscopy (IMC) and scRNA-seq. Aortic aneurysm is characterized by loss of elastin fibers, medial degeneration, and low-grade aortic wall inflammation (Isselbacher, 2005; Guo et al., 2006). In aortic aneurysms caused by genetic anomalies, such as Marfan and Loeys-Dietz syndromes, activation of TGF-β signaling in SMCs is the well-known molecular mechanism (Lindsay and Dietz, 2014). However, for older patients suffering from chronic vascular diseases, such as atherosclerosis and hypertension, the pathogenesis of aortic aneurysm may be more complicated. With the combined use of multiple single-cell techniques, Chen et al. (2020) demonstrated the existence of a distinct SMC-derived mesenchymal stem cell (MSC)-like cell population caused by ablation of TGF-β signaling, which then differentiated into several mesenchymal lineage cell types, including adipocytes, chondrocytes, osteoblasts, and macrophages. These transformations led to the degradation of ECM, cartilage and bone formation, extensive lipid storage, and serious inflammation that ultimately gave rise to aortic aneurysm. This work demonstrated the advantage of combinational use of single-cell methods in mapping cell fate conversions responsible for disease onset.

Hypertension is a top risk factor for many cardiovascular diseases (Doyle, 1991). Vascular remodeling results in increased vascular resistance, which is a central event in hypertension, but its molecular underpinnings are still unclear. Previous studies primarily focused on EC dysfunction and phenotypic switching of SMCs (Brown et al., 2018; Touyz et al., 2018; Barman et al., 2019; Helmstädter et al., 2020). Using scRNA-seq, Cheng et al. systematically depicted artery type-specific gene expression changes in all major cell types (e.g., SMC, EC, MSC) and compared cell-cell communication changes in spontaneously hypertensive rats, which was not detected with previous conventional approaches (Cheng et al., 2021). This included discovering interaction of Eng with various growth factors of TGF signaling pathway, such as Tgfb1, Tgfb3, and Bmp2, in hypertension rats. Eng is a component of the TGF-β superfamily of receptors. The Eng-mediated crosstalk of TGF-β pathway regulates the function of vascular endothelial cells and angiogenesis in vivo (Tian et al., 2012). Variations of the Eng was found in pulmonary arterial hypertension patients, indicating the important role of Eng in hypertension (Uznañska-Loch et al., 2018).