This session will explore the convergence of advanced imaging technologies, spatially resolved molecular profiling, and computational analysis approaches that are transforming biological discovery. Topics will include spatial omics and multiplex imaging methods, such as high-plex fluorescence imaging, in situ sequencing, and spatial proteomics, which enable comprehensive characterization of cells and tissues while preserving their native spatial organization. Speakers will discuss experimental design considerations, imaging platforms, and strategies for integrating multimodal datasets to uncover complex biological relationships within heterogeneous samples. The session will also highlight emerging and hybrid microscopy technologies, including smart and adaptive imaging systems, automation-driven workflows, and non-fluorescence-based approaches such as phase, label-free, and spectroscopic imaging. These innovations expand the range of observable biological phenomena, improve imaging efficiency, and provide complementary information to established fluorescence-based methods. Recognizing that modern imaging experiments generate increasingly large and complex datasets, presentations will address computational approaches for image segmentation, tracking, quantification, visualization, and data interpretation. Topics will include machine learning, artificial intelligence, reproducible analysis workflows, and best practices for managing and extracting knowledge from high-dimensional imaging data. Together, these advances demonstrate how the integration of novel imaging modalities, spatial molecular measurements, and powerful analytical tools is enabling deeper, more quantitative insights into biological systems across scales.