Predicting inflammatory response of biomimetic nanofibre scaffolds for tissue regeneration using machine learning and graph theory.

Abstract

Tissue regeneration after a wound occurs through three main overlapping and interrelated stages namely inflammatory, proliferative, and remodelling phases, respectively. The inflammatory phase is key for successful tissue reconstruction and triggers the proliferative phase. The macrophages in the non-healing wounds remain in the inflammatory loop, but their phenotypes can be changed via interactions with nanofibre-based scaffolds mimicking the organisation of the native structural support of healthy tissues. However, the organisation of extracellular matrix (ECM) is highly complex, combining order and disorder, which makes it difficult to replicate. The possibility of predicting the desirable biomimetic geometry and chemistry of these nanofibre scaffolds would streamline the scaffold design process. Fifteen families of nanofibre scaffolds, electrospun from combinations of polyesters (polylactide, polyhydroxybutyrate), polysaccharides (polysucrose, carrageenan, cellulose), and polyester ether (polydioxanone) were investigated and analysed using machine learning (ML). The Random Forest model had the best performance (92.8%) in predicting inflammatory responses of macrophages on the nanoscaffolds using tumour necrosis factor-alpha as the output. CellProfiler proved to be an effective tool to process scanning electron microscopy (SEM) images of the macrophages on the scaffolds, successfully extracting various features and measurements related to cell phenotypes M0, M1, and M2. Deep learning modelling indicated that convolutional neural network models have the potential to be applied to SEM images to classify macrophage cells according to their phenotypes. The complex organisation of the nanofibre scaffolds can be analysed using graph theory (GT), revealing the underlying connectivity patterns of the nanofibres. Analysis of GT descriptors showed that the electrospun membranes closely mimic the connectivity patterns of the ECM. We conclude that ML-facilitated, GT-quantified engineering of cellular scaffolds has the potential to predict cell interactions, streamlining the pipeline for tissue engineering.