Differential proteomics profile of microcapillary networks in response to sound pattern-driven local cell density enhancement.

Spatial cell organization and biofabrication of microcapillary networks in vitro has a great potential in tissue engineering and regenerative medicine. This study explores the impact of local cell density enhancement achieved through an innovative sound-based patterning on microcapillary networks formation and their proteomic profile. Human umbilical vein endothelial cells (HUVEC) and human pericytes from placenta (hPC-PL) were mixed in a fibrin suspension. The mild effect of sound-induced hydrodynamic forces condensed cells into architected geometries showing good fidelity to the numerical simulation of the physical process. Local cell density increased significantly within the patterned areas and the capillary-like structures formed following the cell density gradient. Over five days, these patterns were well-maintained, resulting in concentric circles and honeycomb-like structures. Proteomic analysis of the pre-condensed cells cultured for 5 days, revealed over 900 differentially expressed proteins when cells were preassembled through mild-hydrodynamic forces. Gene ontology (GO) enrichment analysis identified cellular components, molecular functions, and biological processes that were up- and down-regulated, providing insights regarding molecular processes influenced by the local density enhancement. Furthermore, we employed Ingenuity Pathway Analysis (IPA) to identify altered pathways and predict upstream regulators. Notably, VEGF-A emerged as one of the most prominent upstream regulators. Accordingly, this study initiates the unraveling of the changes in microcapillary networks at both molecular and proteins level induced by cell condensation obtained through sound patterning. The findings provide valuable insights for further investigation into sound patterning as a biofabrication technique for creating more complex microcapillary networks and advancing in vitro models.

Modelling red blood cell optical trapping by machine learning improved geometrical optics calculations.

Optically trapping red blood cells allows for the exploration of their biophysical properties, which are affected in many diseases. However, because of their nonspherical shape, the numerical calculation of the optical forces is slow, limiting the range of situations that can be explored. Here we train a neural network that improves both the accuracy and the speed of the calculation and we employ it to simulate the motion of a red blood cell under different beam configurations. We found that by fixing two beams and controlling the position of a third, it is possible to control the tilting of the cell. We anticipate this work to be a promising approach to study the trapping of complex shaped and inhomogeneous biological materials, where the possible photodamage imposes restrictions in the beam power.