We will demonstrate Raman spectroscopy (RS) as a rapid, noninvasive, and label-free omics technology enabling metabolic profiling and protein biomarker detection in wound exudates. Each type of a metabolite has a unique vibrational fingerprint that can be identified within the measured Raman spectra. Alterations in the metabolome and the associated metabolic pathways can also be identified from the changes of different Raman bands.

Artificial wound liquids will be prepared in order to establish correlations between concentration of individual components and Raman spectral features (intensity, position and width of peaks). Raman spectra of the individual components will be compared to the Raman database of biomolecules, if available, in order to establish references. Following this, Raman measuremnts will be made on wound liquids from patients.

We will combine RS with cutting-edge machine leering (ML) approaches which will allow multimodal data aggregation, identification of biomarkers for non-healing wounds and development of a database of clinical predictors that can facilitate rapid clinical decisions. Such progressive approaches will advance RS from a discovery tool to a patient oriented diagnostic method. This will enable timely identification of instances with a high risk for the chronic wound formation, which is critical for improving patient care.