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

Phenotypic analysis of complex bioengineered 3D models

With advances in underlying technologies such as complex multicellular systems, synthetic materials, and bioengineering techniques, we can now generate in vitro miniaturized human tissues that recapitulate the organotypic features of normal or diseased tissues. Importantly, these 3D culture models have increasingly provided experimental access to diverse and complex tissues architectures and their morphogenic assembly in vitro. This review presents an analytical toolbox for biological researchers using 3D modeling technologies through which they can find a collation of currently available methods to phenotypically assess their 3D models in their normal state as well as their response to therapeutic or pathological agents.

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 their relatively simple morphology and high organoid-to-organoid structural variability. To address these limitations we developed a soft, composite yield-stress extracellular matrix that supports optimal organoid morphogenesis following freeform 3D bioprinting of cell slurries at tissue-like densities. The material is designed with two temperature regimes: at 4 °C it 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 first characterize the rheological properties of MAGIC matrices that optimize organoid morphogenesis, including low stiffness and high stress relaxation. Next, we combine this material with a custom piezoelectric printhead that allows more reproducible and robust self-organization from uniform and spatially organized tissue "seeds." We apply MAGIC matrix bioprinting for high-throughput generation of intestinal, mammary, vascular, salivary gland, and brain organoid arrays that are structurally similar to those grown in pure Matrigel, but exhibit dramatically improved homogeneity in organoid size, shape, maturation time, and efficiency of morphogenesis. The flexibility of this method and material enabled fabrication of fully 3D microphysiological systems, including perfusable organoid tubes that experience cyclic 3D strain in response to pressurization. Furthermore, the reproducibility of organoid structure increased the statistical power of a drug response assay by up to 8 orders-of-magnitude for a given number of comparisons. Combined, these advances lay the foundation for the efficient fabrication of complex tissue morphologies by canalizing their self-organization in both space and time.

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.

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.