️ Episode 98: Cell Marker Accordion: Interpretable Single-Cell and Spatial Omics Annotation in Health and Disease

In this episode of PaperCast Base by Base, we explore a user-friendly platform developed by Busarello et al. that integrates and standardizes over 23 human and murine gene-marker databases into a weighted resource for robust annotation and interpretation of single-cell and spatial omics data across both physiological and pathological contexts. This resource advances functional genomics, structural genomics, and proteomics research by harmonizing nomenclature and improving marker reliability.

Study Highlights:

The Cell Marker Accordion harmonizes cell-type and tissue nomenclature using the Cell Ontology and Uber-anatomy ontology while weighting markers by specificity and consistency to enhance annotation reliability. An R Shiny app and an R package enable automatic annotation of cell populations and pathway analyses without requiring programming expertise. Benchmarking across blood, bone marrow, pancreas, liver, lung, and spatial MERFISH datasets demonstrated a 23% increase in annotation accuracy and faster runtimes compared to five existing tools. Disease-focused modules reveal aberrant cell populations in leukemia, glioblastoma, lung adenocarcinoma, and myelodysplastic syndromes, facilitating identification of clinically relevant cell subsets. Application to murine models of METTL3 perturbation uncovered shifts in cell cycle dynamics and innate immune activation in hematopoietic stem and progenitor cells.

Conclusion:

By combining comprehensive marker integration with ontology-driven standardization and evidence-weighted interpretability, the Cell Marker Accordion empowers researchers in gene–disease association, variant interpretation, and Mendelian randomization to uncover cellular heterogeneity and disease-critical populations with unprecedented speed and clarity.

Reference:

Busarello, E., Biancon, G., Cimignolo, I., Lauria, F., Ibnat, Z., Ramirez, C., … Tebaldi, T. (2025). Cell Marker Accordion: interpretable single-cell and spatial omics annotation in health and disease. Nature Communications, 16, 5399. https://doi.org/10.1038/s41467-025-60900-4

License:

This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/

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