Organoidogenesis: modulating morphology and perturbation

Organoids, three-dimensional (3D) structures derived from stem cells, hold immense promise for disease modeling and regenerative medicine by recapitulating native tissue architecture and function, yet their full potential is often limited by morphological heterogeneity and a lack of precise developmental control. This review provides a comprehensive overview of cutting-edge strategies to modulate and perturb organoid morphology, addressing these critical challenges. The ‘top-down’ bioengineering approaches, including the engineering of the extracellular matrix (ECM) and culture environment, alongside advanced fabrication techniques such as bioprinting that impose external geometric constraints were explored. Further, ‘bottom-up’ strategies that leverage intrinsic biological processes through biochemical and genetic perturbations, such as targeted modulation of key signaling pathways were detailed. Critically, the transformative impact of artificial intelligence (AI)-driven image analysis for robust, high-throughput quantification of morphological outcomes were highlighted. By synthesizing these complementary approaches, this review provides a roadmap for engineering next-generation organoids with enhanced control over form and function, paving the way for more predictive and reliable in vitro systems. An organoid’s physical architecture directly reflects its functional maturity and physiological fidelity. Complex, organized structures often signify a closer recapitulation of the native organ’s in vivo state. Dynamic culture systems, like microfluidics, overcome the nutrient and oxygen limitations of static methods, preventing necrotic cores and promoting the development of larger, more mature organoids. Systematically perturbing organoid development with biochemical, genetic, or biophysical tools is a powerful strategy to dissect the fundamental mechanisms of tissue morphogenesis, organization, and disease pathogenesis. Bioengineering techniques like scaffolding, bioprinting, and ECM engineering guide the stochastic self-assembly of organoids, enabling the formation of predictable and reproducible morphologies with desired functions. Precisely modulating key signaling pathways (e.g., Wnt, BMP) with small molecules is a fundamental bottom-up strategy to control cell fate decisions, differentiation, and emergent architectural organization. AI-driven deep learning models automate image analysis, enabling rapid, objective, and high-throughput quantification of morphology while minimizing human bias and allowing for longitudinal tracking of organoid development.