The recent development of single cell gene expression technologies, and especially single
cell transcriptomics, have revolutionized the way biologists and clinicians investigate
organs and organisms, allowing an unprecedented level of resolution to the description
of cell demographics in both healthy and diseased states. Single cell transcriptomics
provide information on prevalence, heterogeneity, and gene co-expression at the
individual cell level. This enables a cell-centric outlook to define intracellular gene
regulatory networks and to bridge toward the definition of intercellular pathways
otherwise masked in bulk analysis. The technologies have developed at a fast pace
producing a multitude of different approaches, with several alternatives to choose from
at any step, including single cell isolation and capturing, lysis, RNA reverse transcription
and cDNA amplification, library preparation, sequencing, and computational analyses.
Here, we provide guidelines for the experimental design of single cell RNA sequencing
experiments, exploring the current options for the crucial steps. Furthermore, we provide
a complete overview of the typical data analysis workflow, from handling the raw
sequencing data to making biological inferences. Significantly, advancements in single
cell transcriptomics have already contributed to outstanding exploratory and functional
studies of cardiac development and disease models, as summarized in this review. In
conclusion, we discuss achievable outcomes of single cell transcriptomics’ applications
in addressing unanswered questions and influencing future cardiac clinical applications.