TY  - EJOUR
AU  - Khan, Sarmad Ahmad
AU  - Faerber, Dominik
AU  - Kirkey, Danielle
AU  - Raffel, Simon
AU  - Hadland, Brandon
AU  - Deininger, Michael
AU  - Buettner, Florian
AU  - Zhao, Helong Gary
TI  - Cross-Species Morphology Learning Enables Nucleic Acid-Independent Detection of Live Mutant Blood Cells.
JO  - bioRxiv beta
CY  - Cold Spring Harbor
PB  - Cold Spring Harbor Laboratory, NY
M1  - DKFZ-2025-02616
PY  - 2025
N1  - Missing Journal: bioRxiv = 2692-8205 (import from CrossRef, PubMed, , Journals: inrepo02.dkfz.de)
AB  - 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.
LB  - PUB:(DE-HGF)25
C6  - pmid:41279355
C2  - pmc:PMC12633555
DO  - DOI:DOI:10.1101/2025.10.20.682949
UR  - https://inrepo02.dkfz.de/record/306575
ER  -