Single-cell RNA sequencing in cardiovascular development, disease and medicine

Abstract

Advances in single-cell RNA sequencing (scRNA-seq) technologies in the past 10 years have had a transformative effect on biomedical research, enabling the profiling and analysis of the transcriptomes of single cells at unprecedented resolution and throughput. Specifically, scRNA-seq has facilitated the identification of novel or rare cell types, the analysis of single-cell trajectory construction and stem or progenitor cell differentiation, and the comparison of healthy and disease-related tissues at single-cell resolution. These applications have been critical in advances in cardiovascular research in the past decade as evidenced by the generation of cell atlases of mammalian heart and blood vessels and the elucidation of mechanisms involved in cardiovascular development and stem or progenitor cell differentiation. In this Review, we summarize the currently available scRNA-seq technologies and analytical tools and discuss the latest findings using scRNA-seq that have substantially improved our knowledge on the development of the cardiovascular system and the mechanisms underlying cardiovascular diseases. Furthermore, we examine emerging strategies that integrate multimodal single-cell platforms, focusing on future applications in cardiovascular precision medicine that use single-cell omics approaches to characterize cell-specific responses to drugs or environmental stimuli and to develop effective patient-specific therapeutics.

Fig. 1 |. Workflow of single-cell RNA sequencing.

The general experimental workflow of single-cell RNA-sequencing begins with dissociation of the organ or tissue of interest to live single cells, which requires a fine-tuned digestion protocol that maximizes cell number and cell quality while minimizing the duration of digestion and cell death. Cultured cells are likewise detached and prepared as single cells. Prepared cells are then captured by various methods of single-cell capture. Reverse transcription of single-cell RNA is performed, followed by PCR amplification and library preparation of the resulting cDNA. Next-generation sequencing is subsequently performed to generate the readouts, which are aligned to a reference genome, processed for quality control and analysed by the user. References for Fig. 1B CEL-seq with UMI (Grün et al., 2014) SCRB-seq (Soumillon et al., 2014) MARS-seq (Jaitin et al., 2014) STRT-C1 (Islam et al., 2014) Drop-seq (Macosko et al., 2015) CEL-seq2 (Hashimshony et al., 2016) SORT-seq (Muraro et al., 2016) DroNc-seq (Habib et al., 2017) Seq-Well (Gierahn et al., 2017) SPLiT-seq (Rosenberg et al., 2018) sci-RNA-seq (Cao et al., 2017) STRT-2i (Hochgerner et al., 2018) Quartz-seq2 (Sasagawa et al., 2017) 10× Genomics Chromium (Zheng et al., 2017) Wafergen ICELL8 (Gao et al., 2017) Illumina ddSEQ SureCell inDrops (Zilionis et al., 2017; Klein et al. 2015) mcSCRB-seq (Bagnoli et al., 2018) CEL-seq (Hashimshony et al., 2012) Smart-seq (Ramskold et al., 2012) Smart-seq2 (Picelli et al., 2013)

Fig. 2 |. Applications of scRNA-seq in cardiovascular research.

Single-cell RNA sequencing (scRNA-seq) technologies have a wide-range of advantages over conventional bulk gene analysis techniques. In cardiovascular research, scRNA-seq is especially useful for detecting rare cell populations, reconstructing cardiovascular cell trajectory, identifying cell-to-cell interactions, understanding organ-specific or tissue-specific characteristics of vascular cells, spatial transcriptomic mapping of cardiovascular organs and for developing more effective precision medicine tools for better prediction of patient-specific drug responses. All the aforementioned applications are critical to improving our understanding of cardiovascular development, organ homeostasis and disease mechanisms by deciphering cellular heterogeneity at an unprecedented resolution. GRN, gene regulatory network; t-SNE, t-distributed stochastic neighbour embedding.

Fig. 3 |. Comparison of cell population clustering methods.

a | Simulated 2D single-cell RNA-sequencing data representing circular (top left), linear (top right), curved (bottom right) and noisy (bottom left) distribution of cells. Colours indicate clusters identified by different clustering methods. Four original clusters are shown. Clusters of the same dataset can change depending on b | hierarchical clustering on Euclidean distance, c | hierarchical clustering on Canberra distance, d | k-means (k = 4), e | Louvain clustering after converting to a 20-nearest neighbour (NN) graph and f | Louvain clustering after converting to a 40-NN graph. In our simulation, hierarchical clustering based on Canberra distance incorrectly subdivided a noisy cluster and merged two distinct clusters. The k-means approach improperly unified the linear trajectory cluster and the ascending part of curve trajectory cluster. The Louvain algorithm incorrectly divided a large cluster into several subclusters when performed with an insufficient number of NNs, whereas the algorithm provided correct clustering when performed with the sufficient number of NNs. Consequently, a universally optimal clustering method for all datasets does not exist, as different types of datasets possess intrinsically unique patterns of cell populations, such as trajectory shape, complexity and noise. Although graph-based algorithms (such as Louvain) show the best performance in general, other clustering methods are still encouraged for comparison. Biological validation must subsequently be performed for verification of the obtained results.

Fig. 4 |. Single-cell characterization of the human adult heart.

a | Single-cell sequencing enables characterization of tissue maturation changes in the human adult heart during the course of natural ageing, as well as in response to cardiac disease, such as structural and ischaemic cardiomyopathies. b | All cell types in the human heart can be analysed by single-cell sequencing, including but not limited to chamber-specific cardiomyocytes, vascular and immune cells within the coronary vessels and microvessels, nodal cells that constitute the cardiac conduction system, stromal cells such as fibroblasts and valvular epithelial cells, and rare cell populations resident in the adult heart such as melanocytes, neurons, and cardiac stem or progenitor cells.

Fig. 5 |. Single-cell multiomics approaches for cardiovascular precision medicine.

To date, single-cell RNA sequencing (scRNA-seq) has been used most effectively to identify novel or rare cell populations, to confirm the cellular heterogeneity of the tissue or organ of interest, and to construct cell trajectory of developmental or differentiation processes. The increasing and expected technical advances in single-cell analyses of other macromolecules present a unique opportunity to combine multiple single-cell omics approaches to advance cardiovascular precision medicine. In addition to single-cell genomics and transcriptomics, single-cell chromatin accessibility, DNA methylome and proteomics will improve our ability to understand cellular heterogeneity unique to each individual, allowing us to better predict the individual-specific responses to cardiovascular drugs and therapies. CITE-seq, cellular indexing of transcriptomes and epitopes by sequencing; iPSC, induced pluripotent stem cell; LC-MS, liquid chromatography–mass spectrometry; MS, mass spectrometry; scATAC-seq, single-cell assay for transposase-accessible chromatin using sequencing; WES, whole-exome sequencing; WGS, whole-genome sequencing.