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Rapid sepsis diagnosis with protease activity measurement

Caton, E. R.; Pan, Y.; Kiser, K. M.; Haddaway, C. R.; Bryden, W. A.; McLoughlin, M.; Mirski, M. A.; Christenson, R. H.; Sevilla, C. C.; Feng, S.; Chen, S.; Chen, D.

2025-07-15 intensive care and critical care medicine
10.1101/2025.07.14.25331514 medRxiv
Show abstract

Human proteases play major roles in various pathological conditions, including dysregulated immune responses in sepsis, making them strong candidates for developing diagnostic markers. Despite this potential, the progress of developing protease-based diagnostic tools has remained slow due to significant technical barriers associated with measuring protease activity, mainly stemming from the vast diversity and the lack of substrate specificity, which complicate the interpretation of protease activity profiles. In this work, we advanced the current state of assay development by designing substrate molecule sensors and implementing an analytical approach based on mass spectrometry. Specifically, we chemically modified protease substrates for human neutrophil elastase (HNE) and matrix metalloproteinases (MMPs) to enhance specificity in mass spectrometry. This approach yields distinct cleavage products with non-overlapping mass-to-charge signatures, allowing precise differentiation of each proteases activity. We then integrated the modified substrates into a mass spectrometry-based multiplexed assay platform, enabling quantification of multiple protease activities in a single run. We applied the assay to plasma samples and demonstrated that the assay detects distinct protease activity profiles. Our study demonstrated that the assay achieved a diagnostic sensitivity of 88% and specificity of 87% for sepsis detection. The combination of low cost, rapidness, and robust diagnostic performance makes this platform well-suited to a wide range of clinical settings. One Sentence SummaryNovel modifications to protease substrates enable a multiplexed activity assay for accurate sepsis diagnosis in a 3-hour timeframe.

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