Within the field of cardiovascular biology, there have been many questions about understanding how the heart develops and how developmental cues go awry in the disease progres-sion of congenital disorders. The mammalian heart contains many specialized cell types with differences in morphology, location, gene expression patterns, and functions. These hete-rogenous cell types that comprise of the heart have been inves-tigated at the cellular and molecular levels. However, a major drawback of these techniques is in measuring the average ex-pression or patterns across the samples examined, resulting in the loss individual of cellular and molecular resolutions. To better understand cardiovascular development and disease, re-solving the subtle differences between heterogeneous cardiac cells may have profound therapeutic implications.
scRNA-seq technologies have achieved significant progress in the last several years and have become one of the most po-pular tools for understanding cell heterogeneity and lineage progression (1-3). Tang
The scRNA-seq method involves dissociation of a tissue, which requires breaking the connection between single cells by breaking down extracellular connections, leading to a loss of individual cell localization information and can cause incom-plete interpretation. Presently, there are two main scRNA-seq approaches: SMARTseq2 and 10x genomics. SMART-seq2 is used to profile the full length of transcripts but provides a relatively low throughput; 10x genomics can capture more cells but has limited ability to capture transcripts (5-8). Besides these scRNA-seq techniques, spatially resolved transcriptomics and
By means of scRNA-seq, researchers can map expression profiles at single-cell resolution. Since the introduction of scRNA-seq and ST, the number of single-cell experiments has greatly increased. The scRNA-seq technique has become a powerful tool for identifying and classifying cell subpopulations, charac-terizing rare or small subpopulations, and tracing cells along dynamic cellular stages, such as during differentiation, which has combined to understanding cardiovascular development and pathogenesis (3, 11, 14, 15). In this review, we briefly describe the history and progression of scRNA-seq and ST technologies, and summarize the recent scRNA-seq studies in cardiovascular research and their potential implications for understanding the cardiovascular system and associated diseases (16).
Recent advances in biotechnology, specifically single-cell tech-nologies, have revolutionized basic science research. In 1992, Eberwine
More recently, the Chan Zuckerberg Biohub released
Detecting gene expression in single cells is challenging, as the concentration of total RNA present in a cell is on the order of picograms, and of those, polyadenylated mRNA constitutes less than 5% of total cellular RNA (19, 20). In the last decades, different high-throughput methods for scRNA-seq have been developed to isolate and capture these finite biomolecules efficiently, with different degrees of coverage, sensitivity, and multiplexing capabilities for single-cell transcriptome analysis. The key technological developments and protocol improve-ments that enabled large-scale scRNA-seq experiments have been previously reviewed by Svensson
Current scRNA-seq methods can be classified based on the single-cell isolation techniques and capture methods for library preparation (Table 1). Three platforms are available for sample preparation, plate-based, microfluidics, and droplets. Of those, the two most-popular approaches are plate-based SMART-seq in the Fluidigm C1 system and microdroplet-based scRNA-seq from 10X Chromium. SMART-seq2 is an improved version of SMART-seq, which relies on template switching and pream-plification, with an improvement in both the cDNA library yield and average length from individual cells (22). This method has several tradeoffs, but makes possible the analysis of all exons of each transcript and detection of the different splice variants. SMART-seq2 also enables comprehensive SNP and mutation analysis, widening its field of application (23). Limitations of this approach include refining the selectivity for polyadenylated RNA, precluding analysis of polyA-RNA that does not reflect the strand-specific nature of mRNAs. In addi-tion, this method relies on the labor of individual researchers rather than automated methods of multiplexing (19, 23).
STRT was one of the early multiplexing methods developed, although it precludes splice isoforms. This method reflects the strand-specific nature of mRNAs and identifies the transcrip-tion start site (TSS) (19). CEL-seq is another early method that implemented barcoding and pooling samples, but was the first to use linear
Recent development of droplet-based, ultra-high-throughput scRNA-seq platforms, including 10X Chromium, inDrop, and Drop-seq, enable large-scale scRNA-seq studies, such as
An automated massively parallel scRNA-seq (MARS-seq) approach was introduced in 2014. This approach is based on FACS of individual cells into 384-well plates and uses three levels of barcoding (molecular, cellular, and plate-level tags) thus facilitates molecule counting with a high degree of multiplexing (34). The second generation of MARS-seq 2.0 was published in 2019. In this generation they used longer cell barcodes (from 6 bp to 7 bp) and UMIs (from 4 bp to 8 bp), which have improved throughput, robustness, and reduced noise and costs (35).
An ideal platform should combine high throughput capa-bilities with flexibility in sample size to allow for profiling of increasing numbers of cells and multiple samples in parallel and identify new or rare subpopulations while maintaining sensitivity and robustness. Additional desirable features include imaging software to identify contaminating doublets and measure fluorescent reporters. Although the aforementioned methods meet most of these requirements, they are often ex-pensive and require customized equipment, microfluidics, microwells, or cell sorters. Droplet microfluidics allow for high throughput methods at a low cost per cell, however this comes at the expense of flexibility and imaging. A more scalable sin-gle-cell profiling method with no need of customized equip-ment, split-pool ligation-based transcriptome sequencing (SPLiT-seq) was reported in 2018 (36). This method is compatible with both fixed cells and nuclei, allowing efficient sample multiplexing and library preparation using only basic laboratory equipment (36).
Although recent advances in scRNA-seq techniques have led to increased use of the technology, it is important to recognize the limitations of single-cell isolation including the loss of spatial information. This limitation was overcome by researchers in 2016, when ST was developed (10). The ideas behind this technique were simple; they introduced positional molecular barcodes and synthesized cDNA on a surface of the tissue sections and were captured for downstream next-generation sequencing. This technique can capture mRNA from tissues with minimal diffusion and maintain positional representation of these mRNAs (10). The barcoded reads can then be over-layed on the tissue section images. Although this method gives a single, spatially resolved whole-transcriptome read out, the resolution of barcoded regions is limited 100 µm in diameter, which is not at the level of single cell. ST was further devel-oped, and both resolution (from 100 µm to 50 µm) and pro-cessing time were improved by 10X Genomics and were available under the name “10X Visium” (12, 37).
A higher spatial-resolution technique called Slide-seq was introduced in 2019 (12), and an improved second generation was described earlier this year (Slide-seq2) (38). Stickels
The scRNA-seq studies of the mammalian heart enable researchers to profile a wide range of tissues and cell types in the cardiovascular system. With advances in the resolution of scRNA-seq technologies, novel cell types of the heart have been identified and reported, along with the spatial and temporal expression patterns of various cell types along with the heterogeneity within these subpopulations of cells.
The scRNA-seq method has been used to identify mecha-nisms of how early cardiac cells emerge and segregate from the heart. As cells develop, their expression patterns change to fulfill specific functions, and the expression of these specific genes mark a mature, differentiated phenotype. One can use scRNA-seq to help understand these transitional states and elucidate the activation and deactivation of specific genes that must occur in order for cells to change their profile and identity (39). In the heart, this occurs in the premature venous cells that develop an immature network of vessels, or plexus, during the development of the heart. As these cells continue to mature, some change states by downregulating specific genes, such as
For example, in the postnatal murine heart, valve interstitial cells cluster into two distinct subpopulations at P7 and con-tinue to differentiate into four distinct clusters by P30, under-going changes in the expression pattern of collagen and elastin and completing morphogenesis of the heart valves (41). In addition to capturing valve interstitial cells, Hulin
Using scRNA-seq to analyze the cells that constitute different regions of the heart has also allowed for insight into cell signaling, gene expression patterns and signaling gradients that contribute to the development and physiology of the heart. Employing scRNA-seq in murine embryonic hearts, Li
The scRNA-seq technique has been used to reveal patho-genic mechanisms of congenital heart disease by defining local changes of subpopulations. Soysa
Similar to the use of scRNA-seq during development, the technique is also used for characterizing cell subpopulations, including rare cell type that constitute adult tissues. This en-ables researchers to elucidate pathological conditions or cell-type, and determine the cell trajectory in injury or disease by characterizing changes in gene expression, signaling pathways, and interaction networks in the adult cardiac cells.
The identification of different subpopulations and rare cell types has major clinical implication for cardiovascular patho-genesis. For example, Skelly
The scRNA-seq technique has found greater heterogeneity across these cell types that was not determined from bulk RNA-seq. See
A major hurdle of transcriptome-wide scRNA-seq studies is that they don’t retain spatial information of the original cells, because scRNA-seq analysis processing involves dissociation of a tissue into hundreds or thousands of individual cells, each of which is analyzed to define its gene-expression profile (48). Without local information on individual cells, accurate inter-pretation and application of the data can be difficult (37, 38).
To analyze cardiac gene-expression patterns and cell hetero-geneity simultaneously, Asp
Although scRNA-seq technology has become a powerful and common place tool for researchers to understand physiological and pathological mechanisms, technical challenges and limita-tions still remain (21). As shown in Fig. 1, tissue dissociation is the initial step for the scRNA-seq pipeline; hence an optimal tissue dissociation protocol is essential depending on the tis-sue of interest. The isolation and handling of individual cells have become much simpler in recent years. However, a limi-tation of automated scRNA-seq platforms is the requirement for large volumes of cells compared to those needed to per-form manual scRNA-seq methods. In principle, this can be overcome by expanding the pool of barcoded beads and reducing the sample capture speed, with cost timeliness as a tradeoff. Most importantly, only 5-20% transcripts are typically captured per cell; any events or conditions that may induce stress and influence the transcriptomes need to be addressed and opti-mized. This issue can be partially addressed by adjusting the depth of sequencing but again, increase cost.
As innovation and advancement in scRNA-seq technologies continues, deciding which scRNA-seq technique is most con-ducive towards addressing the goals of the study should include analysis of the benefits and drawbacks of different sequencing platforms. Currently, scRNA-seq techniques and the fundamental differences in their approach to obtain single-cell transcripts are summarized in Table 1. There is no one-size fits all; each technique has method-specific advantages and limita-tions. Hence researchers should choose a technique that aligns with their desired goals and sample availability. Regardless of these technical limitations, single-cell analysis has revolutionized our understanding of cardiovascular development and disease. The applications and insights that
This work was supported by the Ministry of Health & Welfare (HI18C2383) and the Ministry of Trade, Industry and Energy (N0002310) to J.K., Republic of Korea
The authors have no conflicting interests.