Effect of Allogeneic Oral Mucosa Mesenchymal Stromal Cells on Equine Wound Repair.

OBJECTIVE: To assess the clinical value and safety of the application of allogeneic equine oral mucosa mesenchymal stromal cells (OM-MSCs) to wounds. Animals. 8 healthy adult horses without front limb skin lesions or musculoskeletal disease. Procedures. Stem cells were isolated from the oral mucosa of a donor horse. Horses were subjected to the creation of eight full-thickness cutaneous wounds, two on each distal forelimb (FL) and two on both sides of the thorax (TH). Each wound was subjected to one out of four treatments: no medication (T1), hyaluronic acid- (HA-) gel containing OM-MSC (T2), HA-gel containing OM-MSC secretome (T3), and HA-gel alone (T4). Gross macroscopic evaluation and laser digital photographic documentation were regularly performed to allow wound assessment including wound surface area. Full-thickness skin punch biopsy was performed at each site before wound induction (D0, normal skin) and after complete wound healing (D62, repaired skin). RESULTS: All wounds healed without adverse effect at D62. Distal limb wounds are slower to heal than body wounds. OM-MSC and its secretome have a positive impact on TH wound contraction. OM-MSC has a positive impact on the contraction and epithelialization of FL wounds. No significant difference between wound sites before and after treatment was noted at histological examination. Conclusion and Clinical Relevance. Using horse cells harvested from oral mucosa is a feasible technique to produce OM-MSC or its secretome. The gel produced by the combination of these biologic components with HA shows a positive impact when applied during the early stage of wound healing.

Automating proteome analysis: improvements in throughput, quality and accuracy of protein identification by peptide mass fingerprinting.

The use of robots has major effects on maximizing the proteomic workflow required in an increasing number of high-throughput projects and on increasing the quality of the data. In peptide mass finger printing (PMF), automation of steps downstream of two-dimensional gel electrophoresis is essential. To achieve this goal, the workflow must be fluid. We have developed tools using macros written in Microsoft Excel and Word to complete the automation of our platform. Additionally, because sample preparation is crucial for identification of proteins by matrix-assisted laser desorption/ionization (MALDI) mass spectrometry, we optimized a sandwich method usable by any robot for spotting digests on a MALDI target. This procedure enables further efficient automated washing steps directly on the MALDI target. The success rate of PMF identification was evaluated for the automated sandwich method, and for the dried-droplet method implemented on the robot as recommended by the manufacturer. Of the two methods, the sandwich method achieved the highest identification success rate and sequence coverage of proteins.