By: Kang Lai, Class of 2026
Current digital medicine relies heavily on complex laboratory equipment and fluorescence-based molecular tests. EnCODE, an enzymatic colorimetric encoding-based system, harnesses color and leverages it to detect biomarkers of disease. Beyond predicting simple positive or negative results, EnCODE has the potential to reveal diagnostic information about biomarker expression and disease states.
Much of digital medicine depends on the detection of biomarkers to distinguish diseases, but color can expand how we can visualize disease states. A team led by Fenyong Sun and Xiaoli Zhu at Shanghai Tenth People’s Hospital researched whether Enzymatic Colorimetric Encoding-based Digital Medicine Platform (EnCODE) can accurately predict disease states.
EnCODE is an encoding-based digital medicine platform that uses DNA technology to convert multidimensional biomarker signals into two-dimensional colorimetric outputs, which are then analyzed through dimensionality reduction and visualization techniques. Current fluorescence based digital medicine technologies including those used for lung cancer detection and differentiating viral bacterial infections are not without limitations. These techniques require a controlled and specific environment and produce one-dimensional readouts, which prevent the visualizations of graded biomarker expression levels. In contrast, EnCODE integrates signal amplification, color mixing, and two-dimensional colorimetric encoding to allow the visualization of various expression states.
In this study, EnCODE was applied to multidimensional miRNA computation in a cohort of 163 pancreatic cancer clinical samples to test its diagnostic accuracy. Through rolling circle amplification and colorimetric enzymes that have unique DNA barcodes, color signals detected by EnCODE can be linked to each miRNA type and concentration data. In pancreatic cancer blood samples, EnCODE successfully identified 96% of cancer-positive samples and 86% of cancer-negative samples, an overall accuracy of 90%. Reverse signal decoding was used as a validation step to test the mathematical rigor of the system. Additionally, continuous weighting improved alignment with current digital medicine principles. The integration of three or more colors allows high-dimensional encoding and increases the scope of diseases measured. Hyperspectral imaging allows rapid visual screening and enables real-time in situ detection important for future development towards point of care testing.
While this study demonstrates EnCODE’s application using miRNA, the system’s design can later be adapted across diverse molecular targets such as DNA, mRNA, and the protein biomarkers cytokines and antigens. This versatility furthers colorimetric encoding as a potential diagnostic tool toward cancer, infectious disease, disease stage, and treatment response monitoring. Last but not least, the low cost and accessibility of light color sensors, EnCODE represents a promising step in medicine towards rapid, accurate, non-invasive diagnosis, especially in point-of-care and limited resource settings.
Work’s Cited:
[1] Photo by http://www.kaboompics.com from Pexels: https://www.pexels.com/photo/a-scientist-drop-pipetting-liquid-samples-in-test-tubes-8540031/
[2] Mao, D., Liu, C., Zhang, R. et al. Enzymatic colorimetric encoding-based digital medicine for pancreatic cancer diagnosis. Nat Commun (2026). https://doi.org/10.1038/s41467-026-70343-0
