In-silico and in-vitro morphometric analysis of intestinal organoids

Understanding patient-derived tumor organoid growth through an integrated imaging and mathematical modeling framework

Patient-derived tumor organoids (PDTOs) are novel cellular models that maintain the genetic, phenotypic and structural features of patient tumor tissue and are useful for studying tumorigenesis and drug response. When integrated with advanced 3D imaging and analysis techniques, PDTOs can be used to establish physiologically relevant high-throughput and high-content drug screening platforms that support the development of patient-specific treatment strategies. However, in order to effectively leverage high-throughput PDTO observations for clinical predictions, it is critical to establish a quantitative understanding of the basic properties and variability of organoid growth dynamics. In this work, we introduced an innovative workflow for analyzing and understanding PDTO growth dynamics, by integrating a high-throughput imaging deep learning platform with mathematical modeling, incorporating flexible growth laws and variable dormancy times. We applied the workflow to colon cancer organoids and demonstrated that organoid growth is well-described by the Gompertz model of growth. Our analysis showed significant intrapatient heterogeneity in PDTO growth dynamics, with the initial exponential growth rate of an organoid following a lognormal distribution within each dataset. The level of intrapatient heterogeneity varied between patients, as did organoid growth rates and dormancy times of single seeded cells. Our work contributes to an emerging understanding of the basic growth characteristics of PDTOs, and it highlights the heterogeneity in organoid growth both within and between patients. These results pave the way for further modeling efforts aimed at predicting treatment response dynamics and drug resistance timing.

MAGIC matrices: freeform bioprinting materials to support complex and reproducible organoid morphogenesis

Organoids are powerful models of tissue physiology, yet their applications remain limited due to a lack of complex tissue morphology and high organoid-to-organoid structural variability. To address these limitations we developed a soft, composite yield-stress extracellular matrix that supports freeform 3D bioprinting of cell slurries at tissue-like densities. Combined with a custom piezoelectric printhead, this platform allows more reproducible and complex morphogenesis from uniform and spatially organized organoid "seeds." At 4 °C the material exhibits reversible yield-stress behavior to support long printing times without compromising cell viability. When transferred to cell culture at 37 °C, the material cross-links and exhibits similar viscoelasticity and plasticity to basement membrane extracts such as Matrigel. We use this setup for high-throughput generation of intestinal and salivary gland organoid arrays that are morphologically indistinguishable from those grown in pure Matrigel, but exhibit dramatically improved homogeneity in organoid size, shape, maturation time, and budding efficiency. The reproducibility of organoid structure afforded by this approach increases the sensitivity of assays by orders of magnitude, requiring less input material and reducing analysis times. The flexibility of this approach additionally enabled the fabrication of perfusable intestinal organoid tubes. Combined, these advances lay the foundation for the efficient design of complex tissue morphologies in both space and time.

Mechanistic Model-Driven Biodesign in Mammalian Synthetic Biology

Mathematical modeling plays a vital role in mammalian synthetic biology by providing a framework to design and optimize design circuits and engineered bioprocesses, predict their behavior, and guide experimental design. Here, we review recent models used in the literature, considering mathematical frameworks at the molecular, cellular, and system levels. We report key challenges in the field and discuss opportunities for genome-scale models, machine learning, and cybergenetics to expand the capabilities of model-driven mammalian cell biodesign.