2026 Theses Doctoral

Scalable tools for rapid 3D imaging of biological samples and in vitro modeling for complex brain disorders

Gong, Cheng

Modern life science is increasingly shifting toward data-driven approaches that require scalable, high-resolution experimental systems capable of generating and interrogating complex biological data. However, current methodologies remain limited by a lack of experimental platforms that can simultaneously provide high-throughput data acquisition and capture system-level biological dynamics across multiple spatial and temporal scales.

To address this challenge, this dissertation develops two complementary experimental frameworks for scalable biological observation and functional interrogation. First, a cost-effective optical imaging platform is established through the development of Hybrid Solid–Liquid Optics (HySIL), a refractive design framework that integrates a solid optical element with a refractive index-matched liquid to form a continuous optical system. This framework is implemented as SCOPE and Super-SCOPE, enabling aberration-corrected, submicron-resolution three-dimensional imaging using long-working-distance air objectives. The system is integrated with light-sheet microscopy to achieve scalable volumetric imaging across diverse biological samples, including cleared and expanded brain tissues, organoids, and large intact human specimens for three-dimensional histopathology.

Second, a scalable in vitro neural system is developed to reconstruct and interrogate emergent network-level dynamics. Long-Range Modular Neuronal Networks (LR-MoNNets) are generated from dissociated embryonic hippocampal neurons, forming macroscale, self-organized modular architectures. Combined with the IncStim platform, a compact all-optical system for simultaneous calcium imaging and patterned optogenetic stimulation, this framework enables high-throughput interrogation of large-scale neuronal activity. These systems exhibit intrinsic network assemblies that recapitulate key properties of in vivo hippocampal cell assemblies, including sequential activation, hierarchical organization, stability over time, resilience to perturbation, and attractor-like pattern completion. Pharmacological experiments further demonstrate that ketamine induces selective reconfiguration of these network dynamics. The framework is extended to human iPSC-derived neurons, which similarly self-organize into functional modular networks.

Together, these results demonstrate that scalable experimental platforms can enable both high-resolution data acquisition and functional modeling of complex biological systems. By bridging optical imaging and in vitro system reconstruction, this work provides a foundation for generating high-dimensional, structured datasets required for data-driven and AI-assisted biological discovery. More broadly, this dissertation highlights the importance of scalable experimental design in advancing life science research and accelerating the development of predictive models for complex diseases.