524-P: Diabetic Foot Ulcer Wound Dressings May Serve as an Untapped Source of Predictive and Monitoring Biomarkers of Healing Outcome

Introduction and Objective: The purpose of our study was to utilize discarded wound dressings from diabetic foot ulcers (DFUs) in order to identify potential predictive and diagnostic biomarkers by the proteomic analysis of cells and exosomes captured from wound exudate.

Methods: DFU patients were followed weekly for 4 weeks and change in wound size measured by EKARE inSight imaging equipment at each visit, at which point the wound dressing was collected. Cells from each dressing were then isolated, number of viable cells quantified and immunophenotyped by flow cytometry. Differential centrifugation was then used to isolate exosomes from each specimen, with exosomes purified using magnetic beads and validated by western blotting and FACS flow cytometry. Next, all samples were subject to trapped ion mobility spectrometry analysis of cellular and exosomal proteome. Lastly, we utilized m/z to identify and quantify relative peptide species, followed by analysis of hierarchical clustering of samples and functional annotation of identified clusters.

Results: From cellular immunophenotyping, we were able to determine those wounds which exhibited higher levels of gamma/delta T-cells (CD45⁺CD3⁺TCRg/d⁺) also exhibited trends towards reductions in wound size. Next, we were able to validate previous proteomic profiling reports of wound exudate that associated with poor healing outcomes (CTSG, LCN2, DEF1, MMP9, HBA, HBB, CAMP, VTN, among others). Lastly, our data suggests that healing wounds exhibit higher levels of ALB, SERPING1, CERU, CO3, POSTN, FN1, FGA (among others) in their week 1 dressings, and may act to serve as potential predictive biomarkers of healing wounds.

Conclusions: Together, our innovative approach to proteomic profiling of DFU wound dressings has demonstrated reproducible and encouraging results and we anticipate that higher recruitment will shed light on variability in healing outcomes and identify other potential biomarkers.