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@ARTICLE{Khan:306575,
      author       = {S. A. Khan$^*$ and D. Faerber and D. Kirkey and S. Raffel
                      and B. Hadland and M. Deininger and F. Buettner$^*$ and H.
                      G. Zhao},
      title        = {{C}ross-{S}pecies {M}orphology {L}earning {E}nables
                      {N}ucleic {A}cid-{I}ndependent {D}etection of {L}ive
                      {M}utant {B}lood {C}ells.},
      journal      = {bioRxiv beta},
      address      = {Cold Spring Harbor},
      publisher    = {Cold Spring Harbor Laboratory, NY},
      reportid     = {DKFZ-2025-02616},
      year         = {2025},
      note         = {Missing Journal: bioRxiv = 2692-8205 (import from CrossRef,
                      PubMed, , Journals: inrepo02.dkfz.de)},
      abstract     = {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.},
      cin          = {FM01},
      ddc          = {570},
      cid          = {I:(DE-He78)FM01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)25},
      pubmed       = {pmid:41279355},
      pmc          = {pmc:PMC12633555},
      doi          = {DOI:10.1101/2025.10.20.682949},
      url          = {https://inrepo02.dkfz.de/record/306575},
}