Cells function through a complex interplay of intracellular, membrane, and extracellular dynamics. Among these, extracellular dynamics involve interactions with the environment via biomolecule secretion, crucial for communication. Key factors influencing secretion-based communication include: (1) spatial and temporal regulation to ensure messages are delivered at the right time and place; (2) heterogeneous secretory behaviors for scenario-specific responses; and (3) varying culture conditions, such as 2D and 3D models. Understanding secretion-based signaling requires technologies that monitor secretions with temporal and spatial resolution, analyze single-cell variability, and operate across culture models.

Conventional methods, though informative, have limitations: they often assess populations, provide only end-point data, or compromise cell viability. Novel biosensors aim to overcome these drawbacks. Fluorescence-based methods allow high-throughput, multi-target analysis at the single-cell level but are limited to end-point measurements due to labeling and washing steps. To capture real-time kinetics non-invasively, label-free approaches have emerged. Various electrochemical and optical biosensors aim to retain conventional advantages while addressing their flaws, though many are still early in development.

In the first part of this thesis, we introduce a microwell array for spatiotemporal observation of extracellular secretions from hundreds of single cells. It integrates gold nanohole array biosensors leveraging extraordinary optical transmission, coupled with machine learning-enhanced image processing. This platform is demonstrated in three applications: monitoring secretion and motility of cell lines over time; tracking release kinetics during distinct cell death modalities; and visualizing antibody-secreting behaviors of human donor-derived cells.

The second part presents a multimodal imaging platform combining nanoplasmonic sensing with multichannel fluorescence imaging to analyze intra- and extracellular processes at single-cell resolution. The plasmonic module tracks secretion distribution in real time, while the fluorescence microscopy visualizes interconnected intracellular and membrane dynamics. This multiparametric approach is applied to studying secretion alongside organelles and metabolism, simultaneous protein expression and secretion, and correlating cell cycle phases with secretory profiles.

The third part extends the platform (introduced in previous chapter) to enable concurrent monitoring of dynamics within and around arrays of single spheroids at high spatiotemporal resolution. To support long-term, multiparametric analysis of multichannel data, we apply deep learning-enhanced image processing. Applied to tumor spheroids, the platform captures growth factor secretion patterns along with 2D/3D morphometric changes and viability, distinguishing between untreated and drug-treated groups.

In summary, the thesis demonstrates the potential of multimodal imaging for exploring interconnected cellular behaviors. Simultaneous analysis of processes like protein expression, metabolism, secretion, morphology, and viability yields holistic insights into cellular mechanisms, supporting advances in basic science, disease modeling, and therapeutic development.