Annotation of Cell Types in Single-Cell Sequencing for Cardiovascular Disease: Concepts, Workflows, Challenges, and Best Practices.

Abstract

Single-cell and single-nucleus RNA sequencing (scRNA-seq and snRNA-seq) have transformed cardiovascular biology by resolving cellular heterogeneity and disease-specific cell states. The interpretive power of these technologies, however, hinges critically on accurate cell-type annotation, the assignment of biologically meaningful labels to clusters or individual cells. Mis-annotation risks systematic bias in mechanistic inference, obscures rare populations, and undermines cross-study reproducibility. This Review systematically integrates current conceptual frameworks, computational methodologies, and domain-specific best practices for cell-type annotation in cardiovascular single-cell research. We first examine the biological principles that underpin annotation, including the definition of cell types, the distinction between cell states and transitional phenotypes, considerations of granularity, and ontology mapping. We then delineate comprehensive analytical workflows spanning preprocessing and quality control (empty droplet filtering, doublet detection, normalization, and batch correction), dimensionality reduction (PCA, UMAP, t-SNE), and graph-based clustering. Turning to annotation strategies, we survey manual marker-based curation with canonical cardiovascular markers, reference-correlation methods (SingleR), supervised machine-learning classifiers (CellTypist, SingleCellNet, Garnett, scBERT), reference-mapping and label-transfer platforms (Azimuth, scArches, Symphony), and hybrid probabilistic frameworks (scANVI). We also summarize the software ecosystems of R/Seurat and Python/Scanpy/scvi-tools. Benchmarking evidence indicates that annotation accuracy depends more on reference data quality than on algorithmic sophistication. SingleR, CellTypist, and Azimuth have emerged as leading performers, whereas ensemble consensus approaches that integrate two or more independent methods enhance robustness. We further highlight cardiovascular-specific challenges, including modality-dependent differences in cardiomyocyte representation between scRNA-seq and snRNA-seq, vascular and stromal heterogeneity, immune cell tissue adaptation, disease-induced transitional cell states, and barriers to cross-species translation. Finally, we present a ten-step best-practice workflow, a comprehensive reporting and reproducibility checklist, and illustrative case studies drawn from the Adult Human Heart Atlas and CardioAtlas. We offer practical recommendations and discuss future directions encompassing multimodal integration, spatial transcriptomics, and AI-assisted annotation, all aimed at ensuring reproducible and interpretable annotations across laboratories [1-5].