%0 Electronic Article
%A Khan, Sarmad Ahmad
%A Faerber, Dominik
%A Kirkey, Danielle
%A Raffel, Simon
%A Hadland, Brandon
%A Deininger, Michael
%A Buettner, Florian
%A Zhao, Helong Gary
%T Cross-Species Morphology Learning Enables Nucleic Acid-Independent Detection of Live Mutant Blood Cells.
%J bioRxiv beta
%C Cold Spring Harbor
%I Cold Spring Harbor Laboratory, NY
%M DKFZ-2025-02616
%D 2025
%Z Missing Journal: bioRxiv = 2692-8205 (import from CrossRef, PubMed, , Journals: inrepo02.dkfz.de)
%X In hematology/oncology clinics, molecular diagnostics based on nucleic acid sequencing or hybridization are routinely employed to detect malignancy-associated genetic mutations and are instrumental in therapeutic stratification and prognostication. However, their limited cost-efficiency constrains their use in pre-malignant screening-specifically, the detection of rare circulating mutant blood cells in asymptomatic individuals. In both neonates and adults, the presence of malignancy-associated mutations in peripheral blood correlates with an elevated risk of future neoplastic transformation, with certain mutations, such as KMT2A rearrangements, exhibiting near-complete penetrance. If feasible, pre-malignant screening could enable early intervention and even disease prevention. Here, we introduce a high-throughput, single-cell computer vision platform capable of identifying mutant peripheral blood cells by recognizing mutation-specific morphological features. The morphology recognition module was developed through cross-species learning from murine to human datasets, enabling a generalizable and cost-effective approach for detecting mutations in live blood cells. The platform holds promise for translation into pre-malignant screening applications in asymptomatic neonates and adults as well as measurable residual disease monitoring in malignancies. Furthermore, it provides a novel single-cell morphological data modality that complements existing molecular layers, including genomics, epigenomics, transcriptomics, and proteomics.
%F PUB:(DE-HGF)25
%9 Preprint
%$ pmid:41279355
%2 pmc:PMC12633555
%R DOI:10.1101/2025.10.20.682949
%U https://inrepo02.dkfz.de/record/306575