Reading the heart at single-cell resolution

https://doi.org/10.1016/j.yjmcc.2020.08.010Get rights and content

Abstract

The burgeoning field of single-cell transcriptomics augments our ability to scrutinize organ systems at unprecedented resolutions. Single-cell RNA sequencing (scRNA-seq) and analytical techniques have shed light on the cellular heterogeneity, developmental trajectories, intercellular communications of the cardiac system, and thus contributed much to the understanding of cardiac development, homeostasis and disorders. Although generalized protocols are well established for scRNA-seq pipelines, customized sample preparation, quality control, and data interpretation are still needed in cardiac research. In this article, we highlight major steps that impact data quality in scRNA-seq experiments, with particular focus on sample and data processing of cardiomyocytes. We also summarize popular applications of scRNA-seq, outlining general tools, caveats and examples in cardiac research.

Introduction

Cells are the most basic units of life. The ability to obtain transcriptomic landscapes of single cells has transformed our view of the human body, which spurred the Human Cell Atlas Project [1,2]. Started in 2017, this project is an international effort of hundreds of scientists aiming to classify and map all of the estimated 37 trillion cells in the human body. Single-cell RNA sequencing (scRNA-seq) and complementary techniques, such as spatial transcriptomics, are capable of reconstructing organs at ultra-high resolution in both temporal and spatial dimensions. A spatiotemporal cell atlas of the developing human heart has recently been reported [3,4], showcasing part of the fruitful achievements of the continuous efforts to comprehend the biology of the heart at single-cell resolution.

Studies involving scRNA-seq are inherently interdisciplinary as biology and bioinformatics are both integral components. The latter has rapidly evolved over the past decade, with many software packages developed each year to facilitate the detection of real and meaningful biological information and minimize the impacts of technical artifacts. Numerous factors are known to distort data output, including, but not restricted to, minute amounts of starting material leading to biases and dropouts, cellular integrity, batch effects, curse of dimensionality, etc. [[5], [6], [7], [8]]. Therefore, high quality biological samples and sequencing data are necessary for reliable single-cell analysis. In the following text, we summarize key points with respect to ensuring the quality of both sample and data in cardiac studies (Fig. 1). Once high-quality sequencing data is produced, aim-directed bioinformatic analyses coupled with rigorous wet-lab validation and careful interpretation comprise the final steps in reaching informative conclusions. Thus, in addition to outlining popular applications of scRNA-seq, we provide examples of context-dependent analyses (Table 1) for gaining biological insights.

Section snippets

Sample preparation

Single-cell preparation is the critical first step of all single-cell studies. The heart consists of multiple cell types, including cardiomyocytes, the muscle cells of the heart, and other supporting cell types such as endothelial cells, fibroblasts, and immune cells, which are collectively called non-cardiomyocytes or non-myocytes. Since methods for isolating non-myocytes and embryonic/fetal, as well as neonatal, cardiomyocytes are already established [[9], [10], [11]], and even commercially

Technical variability

To interrogate biological phenomena, it is vital to accurately estimate and then account for technical variability (Fig. 2). The sources of biologically irrelevant variation are collectively termed “batch effects”, and have been under intense scrutiny since the advent of high-throughput sequencing [43,44]. Technical variations contribute significantly to the overall variability in single-cell transcriptome analysis, and if not accounted for, can significantly confound data interpretation and

Bioinformatic analyses and data interpretation

The ultimate goal of scRNA-seq is to look through the mist of sequencing reads, and discern real biological phenomena. In concert with complex computations, scRNA-seq enables the assessment of cellular heterogeneity, identification of rare cell types, elucidation of cellular dynamics, and unraveling of intercellular crosstalk. These and other applications have radically changed our perception of the heart. Here, we elaborate on three fundamental types of bioinformatic analysis that are

Future perspectives

In the era of molecular cell typing, single-cell RNA sequencing has penetrated nearly all areas of biological and biomedical research. Fortified with increasingly versatile sequencing platforms, as well as newer and faster algorithms, scRNA-seq has virtually become indispensible in portraying the vast cellular heterogeneity and complex dynamics of organ systems. Unscrambling data can nevertheless be hard, and researchers are advised to strictly adhere to protocols for maximizing sample quality

Disclosures

The authors report no commercial or proprietary interest in any product or concept discussed in this article.

Acknowledgments

This work is supported by the National Key R&D Program of China (2017YFA0103700), CAMS Innovation Fund for Medical Sciences (CIFMS, 2018-I2M-3-002, 2017-I2M-1-003), Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (2019PT320026), and grants (31671542, 81722006 to L.W., 81700337 to B.Z.) from the National Natural Science Foundation of China.

References (127)

  • G.K. Smyth et al.

    Normalization of cDNA microarray data

    Methods

    (2003)
  • D.A. Skelly et al.

    Single-Cell transcriptional profiling reveals cellular diversity and intercommunication in the mouse heart

    Cell Rep.

    (2018)
  • S.C. Bendall et al.

    Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development

    Cell

    (2014)
  • D. Grun et al.

    De novo prediction of stem Cell identity using single-Cell Transcriptome data

    Cell Stem Cell

    (2016)
  • J. Chen et al.

    Constructing cell lineages from single-cell transcriptomes

    Mol. Asp. Med.

    (2018)
  • J.Z. Tsien et al.

    Subregion- and cell type-restricted gene knockout in mouse brain

    Cell

    (1996)
  • A. Regev et al.

    The Human Cell Atlas

    Elife

    (2017)
  • O. Rozenblatt-Rosen et al.

    The Human Cell Atlas: from vision to reality

    Nature

    (2017)
  • M. Asp, S. Giacomello, L. Larsson, C. Wu, D. Furth, X. Qian, E. Wardell, J. Custodio, J. Reimegard, F. Salmen, C....
  • R. Phansalkar et al.

    Techniques converge to map the developing human heart at single-cell level

    Nature

    (2020)
  • P.V. Kharchenko et al.

    Bayesian approach to single-cell differential expression analysis

    Nat. Methods

    (2014)
  • C.A. Vallejos et al.

    Normalizing single-cell RNA sequencing data: challenges and opportunities

    Nat. Methods

    (2017)
  • N. Altman et al.

    The curse(s) of dimensionality

    Nat. Methods

    (2018)
  • A. Dal Molin et al.

    How to design a single-cell RNA-sequencing experiment: pitfalls, challenges and perspectives

    Brief. Bioinform.

    (2019)
  • G. Li et al.

    Identification of cardiovascular lineage descendants at single-cell resolution

    Development

    (2015)
  • J. Plackic et al.

    Isolation of atrial and ventricular Cardiomyocytes for in vitro studies

    Methods Mol. Biol.

    (2018)
  • N. Voigt et al.

    Isolation of human atrial myocytes for simultaneous measurements of Ca2+ transients and membrane currents

    J. Vis. Exp.

    (2013)
  • R. Coppini et al.

    Isolation and functional characterization of human ventricular cardiomyocytes from fresh surgical samples

    J. Vis. Exp.

    (2014)
  • G.R. Guo et al.

    A modified method for isolation of human cardiomyocytes to model cardiac diseases

    J. Transl. Med.

    (2018)
  • L. Wang et al.

    Single-cell reconstruction of the adult human heart during heart failure and recovery reveals the cellular landscape underlying cardiac function

    Nat. Cell Biol.

    (2020)
  • S. Nomura et al.

    Cardiomyocyte gene programs encoding morphological and functional signatures in cardiac hypertrophy and failure

    Nat. Commun.

    (2018)
  • M.M. Gladka et al.

    Single-Cell sequencing of the healthy and diseased heart reveals cytoskeleton-associated protein 4 as a new modulator of fibroblasts activation

    Circulation

    (2018)
  • K. Kretzschmar et al.

    M. van den Born, Q.D. Gunst, D. Versteeg, L. Kooijman, S. van der Elst, J.H. van Es, E. van Rooij, M.J.B. van den Hoff, H. Clevers, Profiling proliferative cells and their progeny in damaged murine hearts

    Proc. Natl. Acad. Sci. U. S. A.

    (2018)
  • M. Ackers-Johnson et al.

    Following hearts, one cell at a time: recent applications of single-cell RNA sequencing to the understanding of heart disease

    Nat. Commun.

    (2018)
  • S. Kannan et al.

    Large particle fluorescence-activated Cell sorting enables high-quality single-Cell RNA sequencing and functional analysis of adult Cardiomyocytes

    Circ. Res.

    (2019)
  • Z. Ren et al.

    Single-Cell reconstruction of progression trajectory reveals intervention principles in pathological cardiac hypertrophy

    Circulation

    (2020)
  • M. Yekelchyk et al.

    Mono- and multi-nucleated ventricular cardiomyocytes constitute a transcriptionally homogenous cell population

    Basic Res. Cardiol.

    (2019)
  • P. Hu et al.

    Single-nucleus transcriptomic survey of cell diversity and functional maturation in postnatal mammalian hearts

    Genes Dev.

    (2018)
  • K. See et al.

    Single cardiomyocyte nuclear transcriptomes reveal a lincRNA-regulated de-differentiation and cell cycle stress-response in vivo

    Nat. Commun.

    (2017)
  • M. Wolfien et al.

    Single-nucleus sequencing of an entire mammalian heart: Cell type composition and velocity

    Cells

    (2020)
  • N. Linscheid et al.

    Quantitative proteomics and single-nucleus transcriptomics of the sinus node elucidates the foundation of cardiac pacemaking

    Nat. Commun.

    (2019)
  • A. Selewa, R. Dohn, H. Eckart, S. Lozano, B. Xie, E. Gauchat, R. Elorbany, K. Rhodes, J. Burnett, Y. Gilad, S. Pott, A....
  • T.E. Bakken et al.

    Single-nucleus and single-cell transcriptomes compared in matched cortical cell types

    PLoS One

    (2018)
  • B.B. Lake et al.

    A comparative strategy for single-nucleus and single-cell transcriptomes confirms accuracy in predicted cell-type expression from nuclear RNA

    Sci. Rep.

    (2017)
  • R.A. Barthelson et al.

    Comparison of the contributions of the nuclear and cytoplasmic compartments to global gene expression in human cells

    BMC Genomics

    (2007)
  • R. Bacher et al.

    Design and computational analysis of single-cell RNA-sequencing experiments

    Genome Biol.

    (2016)
  • O. Stegle et al.

    Computational and analytical challenges in single-cell transcriptomics

    Nat Rev Genet

    (2015)
  • T. Ilicic et al.

    Classification of low quality cells from single-cell RNA-seq data

    Genome Biol.

    (2016)
  • P. Zhou et al.

    Recounting cardiac cellular composition

    Circ. Res.

    (2016)
  • J.T. Leek et al.

    Tackling the widespread and critical impact of batch effects in high-throughput data

    Nat Rev Genet

    (2010)
  • Cited by (0)

    View full text