In Vitro Culture of White Adipose Tissue.

White adipose tissue (WAT) plays a crucial endocrine organ that regulates blood glucose and lipid levels, satiety, and inflammation. Before the described technique, primary white adipocytes could not be stably cultured in vitro. The lack of a reliable primary culture model impeded research in WAT metabolism and drug development. We have developed a novel technique for WAT primary culture called "sandwiched white adipose tissue" (SWAT). SWAT overcomes the natural buoyancy of adipocytes by sandwiching minced WAT between sheets of adipose-derived stromal cells. The resulting constructs are viable for at least 8 weeks in culture. SWAT maintains the intact extracellular matrix, cell-to-cell contacts, and physical pressures of in vivo WAT conditions; additionally, SWAT maintains a robust transcriptional profile, sensitivity to exogenous chemical signaling, and whole tissue function. SWAT represents a simple, reproducible, and effective method of primary adipose culture. Potentially, it is a broadly applicable platform for research in WAT physiology, pathophysiology, metabolism, and pharmaceutical development.

Engineering Tissue Fabrication With Machine Intelligence: Generating a Blueprint for Regeneration.

Regenerating lost or damaged tissue is the primary goal of Tissue Engineering. 3D bioprinting technologies have been widely applied in many research areas of tissue regeneration and disease modeling with unprecedented spatial resolution and tissue-like complexity. However, the extraction of tissue architecture and the generation of high-resolution blueprints are challenging tasks for tissue regeneration. Traditionally, such spatial information is obtained from a collection of microscopic images and then combined together to visualize regions of interest. To fabricate such engineered tissues, rendered microscopic images are transformed to code to inform a 3D bioprinting process. If this process is augmented with data-driven approaches and streamlined with machine intelligence, identification of an optimal blueprint can become an achievable task for functional tissue regeneration. In this review, our perspective is guided by an emerging paradigm to generate a blueprint for regeneration with machine intelligence. First, we reviewed recent articles with respect to our perspective for machine intelligence-driven information retrieval and fabrication. After briefly introducing recent trends in information retrieval methods from publicly available data, our discussion is focused on recent works that use machine intelligence to discover tissue architectures from imaging and spectral data. Then, our focus is on utilizing optimization approaches to increase print fidelity and enhance biomimicry with machine learning (ML) strategies to acquire a blueprint ready for 3D bioprinting.