Anatomic Images-based 3d Bioprinting And Computational Simulation Of Human Lung Tissue

ABSTRACT

Purpose/Objectives: Bioprinted models of lung tissue are in high demand but in short supply, particularly for addressing the research needs in response to COVID-19 pandemic. The lung is arguably one of the most complex organs in the body, with a multiscale cytoarchitectural organization serving its multiple functions. In particular, the cellular structure of the alveolar sacs poses a big challenge to extrusion bioprinting, which is more adept at capturing the external shape of biological objects than their cell-level details.Methodology: Recently, we proposed a constructive compromise, attainable by bioprinting of equivalent 3D constructs derived from individual (such as 'precision cut lung slices') or stackable (serial histological sections) anatomic images. The advantage of this approach is that in these images, which can be obtained either from regular histology, or confocal fluorescence or electron microscopy (EM), is already incorporated a wealth of structural information. This can be first transferred to '2.5 dimensional' models (by giving them a finite thickness), and then these can be printed layer-by-layer and stacked as tissue-equivalent 3D volumes. Here we illustrate this proposed workflow with 3D printed human lung sections, and with a lung fragment reconstituted from serial sections, while also simulating the infection with SARS-CoV-2 virus in the same constructs by an agent-based modeling approach.Results: As proof of concept, we processed a human lung histological section in CAD, converted it as .stl file and then 3D printed it using as materials both polycaprolactone (by fused filament fabrication), and by the FRESH method using alginate as hydrogel bioink. Similarly, we extracted from a serial EM stack an image selection which was imported in CAD as well and printed as a self-standing object by photolithography. Here we also report the re-purposing of a simulation program of SARS-CoV-2 infection created on the CompuCell 3D (CC3D) platform, to analyze the propagation of infection in cellular patterns derived from the same histological and ultrastructural sections of human lungs. Using it, we explored the spatial distribution and kinetics of several cell classes (infected, virus shedding, apoptotic), the associated viral and cytokine fields, as well as the impact of the presence of generic inflammatory cells, in comparison with the comparable situations when the cell distribution was a uniform epithelial monolayer. We noted a good reproducibility of these simulations, in spite of the section-characteristic cell distribution patterns, and of the initial locations ('seeding') of the viral infection. In addition, we reconstructed thicker virtual tissue slices from multiple single-cell layers for the study of their viral infection as well.Conclusion/Significance: In conclusion, while more sophisticated methods to capture the tissue structure in 3D constructs certainly exist, the extrusion bioprinting is shown here to be capable to offer a simpler, more practical, and more affordable alternative. We also demonstrated how computational simulations on the same images as used in bioprinting, can be used as a useful heuristic instrument to anticipate the results of the interaction of viruses with bioprinted structures that are more complex than cellular monolayers.