Non-invasive detection of Oncostatin M and TNF-α in a microphysiological chip with embedded pH tuning capabilities

Abstract

The detection of disease biomarkers is crucial in biomedical research, diagnostics and personalized medicine. However, challenges still exist for the multiple and simultaneous analysis of different biomarkers due to their low concentration, the complexity of the biofluids and the presence of multiple interfering components. The Surface Enhanced Raman Scattering (SERS) technique combines high sensitivity, specificity, non-interference by water and compatibility with transparent packaging materials, thus making it suitable for Organ-on-Chip (OoC) applications. One of the main limitations of SERS is the spectral overlap resulting from the binding of different analytes to the substrate, which makes it difficult to identify specific biomarkers. The proposed approach addresses this limitation by integrating a SERS platform, functionalized with 3-mercaptopropionic acid (3-MPA), within a microfluidic platform. The carboxylic groups on the SERS surface can selectively interact with biomolecules by modulating the pH of the medium versus the isoelectric points of the proteins, thus improving the analyte–surface interaction. The proposed SERS-on-chip detection system has been optimised for detection in Ham's F-12K cell culture medium, without serum supplementation, of two colorectal cancer biomarkers, also known to be involved in epithelial–mesenchymal transition (EMT): Oncostatin M (OSM) and Tumor Necrosis Factor alpha (TNF-α). Furthermore, a Machine Learning (ML) classification was exploited to improve data resolution and to limit the spectral overlap. pH modulation was performed at the chip level with an embedded microfluidic mixer, without affecting the cell culture chamber, and Raman spectra were acquired through the chip cover under controlled temperature conditions. Trained on single-biomarker spectra, the models were tested in complex, realistic scenarios with non-target or both biomarkers. This represents a challenging, previously unseen scenario during the training phase and is highly relevant for practical applications where multiple analytes coexist in complex biological matrices, highlighting current limitations in mixture scenarios.