Cell Marker Accordion: interpretable single-cell and spatial omics annotation in health and disease

Emma Busarello (), Giulia Biancon, Ilaria Cimignolo, Fabio Lauria, Zuhairia Ibnat, Christian Ramirez, Gabriele Tomè, Marianna Ciuffreda, Giorgia Bucciarelli, Alessandro Pilli, Stefano Maria Marino, Vittorio Bontempi, Federica Ress, Kristin R. Aass, Jennifer VanOudenhove, Luca Tiberi, Maria Caterina Mione, Therese Standal, Paolo Macchi, Gabriella Viero, Stephanie Halene () and Toma Tebaldi ()
Additional contact information
Emma Busarello: University of Trento
Giulia Biancon: Yale University School of Medicine
Ilaria Cimignolo: University of Trento
Fabio Lauria: CNR Unit at Trento
Zuhairia Ibnat: University of Trento
Christian Ramirez: University of Trento
Gabriele Tomè: University of Trento
Marianna Ciuffreda: University of Trento
Giorgia Bucciarelli: University of Trento
Alessandro Pilli: University of Trento
Stefano Maria Marino: University of Trento
Vittorio Bontempi: University of Trento
Federica Ress: University of Trento
Kristin R. Aass: Norwegian University of Science and Technology (NTNU)
Jennifer VanOudenhove: Yale University School of Medicine
Luca Tiberi: University of Trento
Maria Caterina Mione: University of Trento
Therese Standal: Norwegian University of Science and Technology (NTNU)
Paolo Macchi: University of Trento
Gabriella Viero: CNR Unit at Trento
Stephanie Halene: Yale University School of Medicine
Toma Tebaldi: University of Trento

Nature Communications, 2025, vol. 16, issue 1, 1-18

Abstract: Abstract Single-cell technologies offer a unique opportunity to explore cellular heterogeneity in health and disease. However, reliable identification of cell types and states represents a bottleneck. Available databases and analysis tools employ dissimilar markers, leading to inconsistent annotations and poor interpretability. Furthermore, current tools focus mostly on physiological cell types, limiting their applicability to disease. We present the Cell Marker Accordion, a user-friendly platform providing automatic annotation and unmatched biological interpretation of single-cell populations, based on consistency weighted markers. We validate our approach on multiple single-cell and spatial datasets from different human and murine tissues, improving annotation accuracy in all cases. Moreover, we show that the Cell Marker Accordion can identify disease-critical cells and pathological processes, extracting potential biomarkers in a wide variety of disease contexts. The breadth of these applications elevates the Cell Marker Accordion as a fast, flexible, faithful and standardized tool to annotate and interpret single-cell and spatial populations in studying physiology and disease.

Date: 2025
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DOI: 10.1038/s41467-025-60900-4

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