A novel image analysis algorithm reveals that media conditioned with chitosan and platelet-rich plasma biomaterial dose dependently increases fibroblast migration in a scratch assay.
Laura AhunonFiona MilanoAnik ChevrierMarc LavertuPublished in: Biomedical physics & engineering express (2020)
Chitosan (CS) and Platelet-Rich Plasma (PRP) both display interesting properties for wound healing applications. A hybrid CS-PRP biomaterial was previously developped, consisting of a freeze dried CS formulation solubilized in PRP that promotes tissue repair and regeneration. The purpose of the current study was to investigate the ability of the CS-PRP biomaterial to stimulate cell migrationin vitro. Scratch assays revealed that CS-PRP significantly stimulates the migration rate of cells compared to cells in culture medium but not differently than PRP alone. The increase in the migration rate is dose-dependent at low dose and reaches a plateau corresponding with maximum cell motility. Cell migration rate as a function of the number of platelets that have degranulated in culture medium (to which total concentration of growth factors contributing to cell response is proportionnal), follows a modified Hill model. To analyze photographs taken during the assay and follow cell migration, an open source image analysis algorithm was developed: SAMScratch (Systematic Area Measurement of Scratch - available here:https://github.com/Biomaterials-and-Cartilage-Laboratory/SAM-Scratch). Compared with other existing analysis tools, the algorithm is precise in the determination of the scratch area and performs equally well with usual and challenging images. This study resulted in the creation of a freely available application for scratch assay analysis and provided evidence that CS-PRP implants hold promise for treatment of wounds.
Keyphrases
- platelet rich plasma
- cell migration
- wound healing
- single cell
- high throughput
- deep learning
- induced apoptosis
- low dose
- drug delivery
- cell therapy
- machine learning
- stem cells
- cell cycle arrest
- bone marrow
- staphylococcus aureus
- escherichia coli
- neural network
- mesenchymal stem cells
- extracellular matrix
- pi k akt
- high resolution