Background: Genome-wide transcriptome analysis has greatly advanced our understanding of the regulatory networks underlying basic cardiac biology and mechanisms driving disease. However, so far, the resolution of studying gene expression patterns in the adult heart has been limited to the level of extracts from whole tissues. The use of tissue homogenates inherently causes the loss of any information on cellular origin or cell type-specific changes in gene expression. Recent developments in RNA amplification strategies provide a unique opportunity to use small amounts of input RNA for genome-wide sequencing of single cells. Methods: Here, we present a method to obtain high-quality RNA from digested cardiac tissue from adult mice for automated single-cell sequencing of both the healthy and diseased heart. Results: After optimization, we were able to perform single-cell sequencing on adult cardiac tissue under both homeostatic conditions and after ischemic injury. Clustering analysis based on differential gene expression unveiled known and novel markers of all main cardiac cell types. Based on differential gene expression, we could identify multiple subpopulations within a certain cell type. Furthermore, applying single-cell sequencing on both the healthy and injured heart indicated the presence of disease-specific cell subpopulations. As such, we identified cytoskeleton-associated protein 4 as a novel marker for activated fibroblasts that positively correlates with known myofibroblast markers in both mouse and human cardiac tissue. Cytoskeleton-associated protein 4 inhibition in activated fibroblasts treated with transforming growth factor β triggered a greater increase in the expression of genes related to activated fibroblasts compared with control, suggesting a role of cytoskeleton-associated protein 4 in modulating fibroblast activation in the injured heart. Conclusions: Single-cell sequencing on both the healthy and diseased adult heart allows us to study transcriptomic differences between cardiac cells, as well as cell type-specific changes in gene expression during cardiac disease. This new approach provides a wealth of novel insights into molecular changes that underlie the cellular processes relevant for cardiac biology and pathophysiology. Applying this technology could lead to the discovery of new therapeutic targets relevant for heart disease.