Biochemical and Morphological Imaging of the Tumor Microenvironment

The intricate interplay between cellular morphology and metabolism is crucial for understanding cell states and trajectories, particularly within the tumor microenvironment (TME). Our primary goal is to develop an innovative single-cell morpho-metabolic analytic system capable of predicting cell states and their trajectories. This system, while initially focused on the TME, has broad potential applications across various disease contexts, including the impact of aging on cellular dynamics. The TME comprises a diverse array of cell types whose metabolic and morphological characteristics significantly influence tumor progression and therapeutic responses. Current methodologies fall short in capturing the full spectrum of these dynamic interactions due to limitations in temporal resolution, molecular specificity, and the need for exogenous labels. Our proposed system aims to overcome these limitations by integrating high-resolution tomographic phase microscopy with advanced Raman spectroscopy techniques. Tomographic Phase Microscopy (TPM) provides detailed 3D structural information and quantifies refractive index distributions within cells and tissues, offering insights into cellular morphology and dry mass distribution. Complementing TPM, Raman spectroscopy leverages the unique vibrational signatures of biological molecules to quantify key metabolic parameters, such as lactate, amino acids, and lipids, in a non-invasive manner. Our recent development, Raman2RNA, has demonstrated the capability to deduce transcriptomic profiles from Raman spectra, aligning closely with single-cell RNA sequencing data (Kobayashi-Kirschvink et al., Nature Biotechnology, 2024). By combining these technologies, we will develop a comprehensive system to map the TME, providing a detailed morpho-metabolic landscape. This system will allow for the non-invasive, high-throughput analysis of cellular states and metabolic activities, offering a holistic view of the TME's complexity. The integration of Raman2RNA will enable us to correlate metabolic data with gene expression profiles, further enhancing our understanding of cellular dynamics. A critical sub-aim of our project includes studying the impact of aging on the TME, as aging profoundly affects cell morphology, metabolism, and immune function. Understanding these changes is essential for developing targeted therapies for elderly patients, who often exhibit different tumor behaviors and treatment responses compared to younger individuals. Our approach holds promise not only for advancing foundational knowledge in oncology but also for its applicability in other disease contexts where cell morphology and metabolism play pivotal roles such as autoimmune disorders or neurodegenerative diseases. The anticipated outcomes include identifying novel biomarkers, refining therapeutic strategies, and improving patient outcomes by providing a deeper understanding of disease mechanisms at the single-cell level. This proposed study represents a significant step towards bridging the gap in current cancer research and beyond, offering a powerful tool for the comprehensive analysis of cell states and trajectories in various pathological conditions.