HeartMAP: A multi-chamber spatial framework for cardiac cell-cell communication.

Tumo Kgabeng, Lulu Wang, Harry Ngwangwa, Thanyani Pandelani

Abstract read

In one paragraph

Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Tumo KgabengUnisa Biomedical Engineering Research Group, Department of Mechanical, Bioresources, and Biomedical Engineering, School of Engineering, College of Science, Engineering, and Technology, University of South Africa, Florida Science Campus, South Africa.

Lulu WangUnisa Biomedical Engineering Research Group, Department of Mechanical, Bioresources, and Biomedical Engineering, School of Engineering, College of Science, Engineering, and Technology, University of South Africa, Florida Science Campus, South Africa.

Harry NgwangwaUnisa Biomedical Engineering Research Group, Department of Mechanical, Bioresources, and Biomedical Engineering, School of Engineering, College of Science, Engineering, and Technology, University of South Africa, Florida Science Campus, South Africa.

Thanyani PandelaniUnisa Biomedical Engineering Research Group, Department of Mechanical, Bioresources, and Biomedical Engineering, School of Engineering, College of Science, Engineering, and Technology, University of South Africa, Florida Science Campus, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding cell-cell communication within and between the four distinct cardiac chambers is fundamental to elucidating cardiac function and disease mechanisms. Each chamber exhibits unique cellular and molecular characteristics that reflect specialised physiological roles, yet existing frameworks for mapping chamber-specific intercellular networks have remained limited. 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 0.037-0.047, 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.

Indexed as

Cardiac chambersCell-cell communicationSingle-cell RNA-seqSpatial transcriptomicsTherapeutic targets

Identifiers

PMID41322007

DOI10.1016/j.csbj.2025.11.015

PMCPMC12661431

What OpenQuestion holds

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.