Label-free Microscope for Pancreatic Cancer Detection

POSTER

Abstract

Pancreatic cancer remains a major challenge due to the limitations of conventional histopathology. We developed a label-free ultraviolet (UV) microscopic imaging technique that quantifies the microscopic nucleic acid-to-protein ratio (NPr) based on intrinsic molecular absorption at 260 nm and 280 nm. This method enables high-resolution, quantitative distinction between malignant and non-malignant cells without stains or exogenous markers. Using ImageJ and CellProfiler, image subtraction and ratio mapping were implemented to produce spatially resolved NPr maps and perform cell population analysis. The system was validated on cultured pancreatic cell lines and human pancreatic tissue microarrays containing 40 pancreatic ductal adenocarcinoma (PDAC) and 8 healthy samples. Cancerous cells and tissues consistently exhibited elevated NPr values relative to non-cancerous controls, reflecting higher nucleic acid content and structural heterogeneity characteristic of malignancy. Statistical analysis confirmed significant group differences (p < 0.001). Receiver operating characteristic (ROC) analysis produced an area under the curve (AUC) of 0.89, corresponding to 82% sensitivity and 89% specificity for PDAC detection. These findings demonstrate that NPr imaging can reliably distinguish malignant from healthy pancreatic samples with performance comparable to carbohydrate antigen (CA 19-9), but without staining or labeling. This label-free method provides a promising alternative for early pancreatic cancer detection and integration into existing diagnostic pipelines.

*This work was financially supported by grants from the National Cancer Institute (R21CA270748, R03CA252783) and the National Institute of General Medical Sciences (U54GM128729) of National Institutes of Health to DS, National Science Foundation (NSF) CAREER Award (#2236885) to DS.

Publication: Microscopic nucleic acid-to-protein ratio: A label-free approach for
pancreatic cancer detection

Presenters

  • Emmanuel C Ogberefor

    • University of Denver

Authors

  • Emmanuel C Ogberefor

    • University of Denver
  • Sky Gao

    • University of Denver
  • Keerthi Jangili

    • North Dakota Sate University
  • Alfred Akinlalu

    • University Of Denver
    • University of Denver
  • Tommy Gao

    • University of Denver
  • Kalpana Devaraj

    • University Of Colorado School of Medicine
  • Dali Sun

    • University of Denver