Decoding tissue complexity: multiscale mapping of chemistry-structure-function relationships through advanced...

Comprehensively acquiring biological tissue information is pivotal for advancing our understanding of biological systems, elucidating disease mechanisms, and developing innovative clinical strategies. Biological tissues, as nature's archetypal biomaterials, exhibit multiscale structural and functional complexity that provides critical principles for synthetic biomaterials. Tissues/organs integrate molecular, biomechanical, and hierarchical architectural features across scales, offering a blueprint for engineering functional materials capable of mimicking or interfacing with living systems. Biological visualization technologies have emerged as indispensable tools for decoding tissue complexity, leveraging their unique technical advantages and multidimensional analytical capabilities to bridge the gap between macroscopic observations and molecular insights. The integration of cutting-edge technologies such as artificial intelligence (AI), augmented reality, and deep learning is revolutionizing the field and enabling real-time, high-resolution, and predictive analyses that transcend the limitations of traditional imaging modalities. This review systematically explores the principles, applications, and limitations of state-of-the-art biological visualization technologies, with a particular emphasis on the transformative advancements in AI-driven image analysis, multidimensional imaging and reconstruction, and multimodal data integration. By analyzing these technological trends, we envision a future where biological visualization evolves towards greater intelligence, multidimensionality, and multiscale precision, offering unprecedented theoretical and methodological support for deciphering tissue complexity and further advancing biomaterials development. These advancements promise to accelerate breakthroughs in precision medicine, tissue engineering, and therapeutic development, ultimately reshaping the landscape of biomedical research and clinical practice.