HeartMAP: A Multi-Chamber Spatial Framework for Cardiac Cell-Cell Communication

Here, we present HeartMAP (Heart Multi-chamber Analysis Platform), a computational framework that infers cardiac cell-cell communication networks at chamber resolution through integration of single-cell RNA-seq co-expression patterns and ligand-receptor interaction databases. Using a dataset of 287,269 cells from seven healthy human heart donors (Single Cell Portal SCP498), we identified chamber-specific cell populations, communication networks, and therapeutic targets. HeartMAP employs a progressive three-tier analytical approach comprising basic pipeline analysis, advanced communication modelling and multi-chamber atlas construction to reveal both conserved and chamber-specific signalling pathways; cross-chamber correlation analysis demonstrated the highest similarity between ventricles (r = 0.985) and the lowest between left atrium and left ventricle (r = 0.870), reflecting functional specialisation. Communication hub analysis identified atrial cardiomyocytes and adipocytes as key signalling centres with hub scores of 0.037 to 0.047, while differential expression analysis revealed over 150 significantly different genes per chamber pair. These findings establish a molecular foundation for precision cardiology approaches, enabling chamber-specific therapeutic strategies that could improve treatment outcomes for cardiovascular diseases. HeartMAP is freely available as a Python package than can be installed using “pip install heartmap”, the package’s documentation can be found on https://pypi.org/project/heartmap/, it can also be accessed via a user-friendly web interface freely available at https://huggingface.co/spaces/Tumo505/heartmap-cell-analysis.