We present a new experimental and computational workflow to analyze single-cell proteomics data applied to single-cardiomyocytes. By using an optimized isolation procedure and by taking advantage of the integrative capabilities of iSanXoT application, we eliminate batch effects, minimize cell size-related biases, obtain quantitative subcellular compartment information and detect protein alterations within subcellular compartments. This approach enhances data quantification accuracy and facilitates biological interpretation. We show that the overexpression of the Myc transcription factor switches the metabolism of adult cardiomyocytes and establishes a subpopulation of pro-regenerative cardiomyocytes. The reported advances pave the way for systematic analysis of single-cell proteomes, including those of sensitive cells, like cardiomyocytes.