Authors
Pouya Nazari, Alexandre Arnould, Madhavi Dipak Andhari, Maite Fontecha, Jorge Martín, Bart De Moor, Jon Pey, Frederik De Smet, Francesca Maria Bosisio, Asier Antoranz
Published in
Cell reports methods. Pages 101663. Nov 12, 2026. Epub Nov 12, 2026.
Abstract
High-dimensional cyclic multiplexed immunofluorescence (cMIF) enables single-cell phenotyping within intact tissues. Cell annotations rely on a multi-step pipeline involving normalization, sampling, dimensionality reduction, and clustering, but the absence of standardized benchmarks for method selection, especially at the clustering stage, leads to inconsistent and less reproducible phenotyping. To address this, we developed CONCLAVE, a consensus-clustering-based workflow that optimizes upstream steps and integrates results from multiple clustering algorithms retaining only those cell labels supported by at least two independent methods. Through in silico simulations and real-world cMIF datasets, CONCLAVE consistently outperformed single-clustering-method approaches in agreement with expert-curated references, reproducibility, and robustness, with improvements becoming more evident when mapped within spatial tissue contexts. Additionally, CONCLAVE includes a scoring module that flags regions likely to contain unreliable or inconsistent data, facilitating targeted quality control. In summary, CONCLAVE offers a robust framework for cell annotation in cMIF datasets, enhancing the reliability of downstream spatial proteomics analyses.
PMID:
43020066
Bibliographic data and abstract were imported from PubMed on 13 Nov 2026.
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