A study of macrophage mechanical properties and functional modulation based on the Young's modulus of PLGA-PEG fibers

Biomechanics of Macrophages on Disordered Surface Nanotopography

Macrophages are involved in every stage of the innate/inflammatory immune responses in the body tissues, including the resolution of the reaction, and they do so in close collaboration with the extracellular matrix (ECM). Simplified substrates with nanotopographical features attempt to mimic the structural properties of the ECM to clarify the functional features of the interaction of the ECM with macrophages. We still have a limited understanding of the macrophage behavior upon interaction with disordered nanotopography, especially with features smaller than 10 nm. Here, we combine atomic force microscopy (AFM), finite element modeling (FEM), and quantitative biochemical approaches in order to understand the mechanotransduction from the nanostructured surface into cellular responses. AFM experiments show a decrease of macrophage stiffness, measured with the Young's modulus, as a biomechanical response to a nanostructured (ns-) ZrOx surface. FEM experiments suggest that ZrOx surfaces with increasing roughness represent weaker mechanical boundary conditions. The mechanical cues from the substrate are transduced into the cell through the formation of integrin-regulated focal adhesions and cytoskeletal reorganization, which, in turn, modulate cell biomechanics by downregulating cell stiffness. Surface nanotopography and consequent biomechanical response impact the overall behavior of macrophages by increasing movement and phagocytic ability without significantly influencing their inflammatory behavior. Our study suggests a strong potential of surface nanotopography for the regulation of macrophage functions, which implies a prospective application relative to coating technology for biomedical devices.

Genipin crosslinking promotes biomechanical reinforcement and pro-regenerative macrophage polarization in bioartificial tubular substitutes

Traumatic nerve injuries are nowadays a significant clinical challenge and new substitutes with adequate biological and mechanical properties are in need. In this context, fibrin-agarose hydrogels (FA) have shown the possibility to generate tubular scaffolds with promising results for nerve repair. However, to be clinically viable, these scaffolds need to possess enhanced mechanical properties. In this line, genipin (GP) crosslinking has demonstrated to improve biomechanical properties with good biological properties compared to other crosslinkers. In this study, we evaluated the impact of different GP concentrations (0.05, 0.1 and 0.2% (m/v)) and reaction times (6, 12, 24, 72 h) on bioartificial nerve substitutes (BNS) consisting of nanostructured FA scaffolds. First, crosslinked BNS were studied histologically, ultrastructurally and biomechanically and then, its biocompatibility and immunomodulatory effects were ex vivo assessed with a macrophage cell line. Results showed that GP was able to improve the biomechanical resistance of BNS, which were dependent on both the GP treatment time and concentration without altering the structure. Moreover, biocompatibility analyses on macrophages confirmed high cell viability and a minimal reduction of their metabolic activity by WST-1. In addition, GP-crosslinked BNS effectively directed macrophage polarization from a pro-inflammatory (M1) towards a pro-regenerative (M2) phenotype, which was in line with the cytokines release profile. In conclusion, this study considers time and dose-dependent effects of GP in FA substitutes which exhibited increased biomechanical properties while reducing immunogenicity and promoting pro-regenerative macrophage shift. These tubular substitutes could be useful for nerve application or even other tissue engineering applications such as urethra.

Deep learning strategy for small dataset from atomic force microscopy mechano-imaging on macrophages phenotypes

The cytoskeleton is involved during movement, shaping, resilience, and functionality in immune system cells. Biomarkers such as elasticity and adhesion can be promising alternatives to detect the status of cells upon phenotype activation in correlation with functionality. For instance, professional immune cells such as macrophages undergo phenotype functional polarization, and their biomechanical behaviors can be used as indicators for early diagnostics. For this purpose, combining the biomechanical sensitivity of atomic force microscopy (AFM) with the automation and performance of a deep neural network (DNN) is a promising strategy to distinguish and classify different activation states. To resolve the issue of small datasets in AFM-typical experiments, nanomechanical maps were divided into pixels with additional localization data. On such an enlarged dataset, a DNN was trained by multimodal fusion, and the prediction was obtained by voting classification. Without using conventional biomarkers, our algorithm demonstrated high performance in predicting the phenotype of macrophages. Moreover, permutation feature importance was employed to interpret the results and unveil the importance of different biophysical properties and, in turn, correlated this with the local density of the cytoskeleton. While our results were demonstrated on the RAW264.7 model cell line, we expect that our methodology could be opportunely customized and applied to distinguish different cell systems and correlate feature importance with biophysical properties to unveil innovative markers for diagnostics.