O10 - Advanced Multimodal Imaging Technique for Visualizing Interrelated Metabolic Reactions of Subcellular Organelles in Aging and Disease

Introduction: : Understanding the functional alterations associated with aging necessitates a thorough and nuanced investigation of the intricate biochemical reactions occurring at the subcellular organelle level. This complexity arises because each organelle within a cell is equipped with specialized structures and unique metabolic profiles that contribute distinctly to the cell’s overall function and its responses to aging-related stressors. Analyzing these differences at nano scale is crucial, as changes in the biochemical environment within organelles can influence cellular health, signaling pathways, and ultimately the organism’s physiological state. Traditional labeling techniques, however, present significant limitations in this endeavor. These methods are often unable to fully represent or distinguish the immense molecular diversity present within organelles, particularly when trying to map out all the minor yet important biomolecules. Consequently, there are challenges in accurately quantifying a broad spectrum of biomolecules, which hampers our ability to gain a comprehensive understanding of how organelle-specific metabolic changes contribute to aging. While advances such as super-resolution fluorescence imaging have pushed the boundaries of cellular investigation, offering spatial resolutions down to a few nanometers, this technology alone is not sufficient. Despite its remarkable structural insight, super-resolution imaging struggles to effectively depict the full nanoscale distribution patterns and the complex heterogeneity of various biochemical components within organelles, leaving gaps in our understanding of aging at the subcellular level.

Materials and

Methods: : To address the limitations of conventional organelle imaging and molecular labeling, we have devised an innovative strategy that fuses advanced super-resolution image deconvolution with nonlinear multimodal microscopy such as Stimulated Raman Scattering (SRS), Second Harmonic Generation (SHG), and Multiphoton Fluorescence (MPF). This integrated technique enables the collection of highly detailed spatial data alongside functional biochemical information, thereby offering a holistic perspective on cellular processes at an unprecedented level of resolution. Notably, this approach allows for the simultaneous visualization of multiple molecular events occurring within distinct subcellular compartments, enhancing our understanding of the cellular microenvironment under various physiological and pathological conditions.
A further enhancement involves the application of heavy water probing. When cells metabolize heavy water, deuterium atoms get incorporated into newly synthesized biomolecules, forming carbon-deuterium (-CD) bonds. These bonds produce unique Raman scattering signals that stand out from endogenous molecular vibrations, as they appear in a cell-silent region of the Raman spectrum. This spectrally isolated signal allows for background-free and highly sensitive detection of nascent molecules, effectively enabling researchers to distinguish between newly formed and pre-existing molecules and thus obtain dynamic metabolic information over time.
Additionally, we developed an algorithm to compute correlation coefficients between various imaging channels. By mapping these correlations across the spatial and functional datasets, the algorithm uncovers relationships and coordinated metabolic activities among different organelles and molecules within the same cell.

Results, Conclusions, and Discussions:: To demonstrate the effectiveness and versatility of this integrated imaging approach, the method was applied to tissue sections obtained from diverse organs, providing a platform to reveal dynamic metabolic changes with unprecedented clarity and detail. Specifically, the use of MPF microscopy allowed researchers to capture the intrinsic fluorescence signals of essential metabolic cofactors—NADH and FAD—within the tissue microenvironment. These fluorescence signals were subsequently translated into spatial maps of optical redox ratios, which serve as sensitive indicators of cellular redox balance and metabolic status across different regions of the tissue. This spatial mapping enabled detailed assessment of metabolic heterogeneity, revealing hotspots of metabolic activity and underlying cellular differences that are often masked by bulk measurements.
Complementing MPF imaging, deuterium oxide–SRS (DO-SRS) and hyperspectral SRS microscopy provided quantitative measurements of protein and lipid anabolic activities, as well as high-resolution mapping of specific lipid subtype distributions. With DO-SRS, the incorporation of deuterium into newly synthesized biomolecules generated distinct carbon-deuterium bond signals in the cell-silent region, thereby allowing background-free detection and tracking of nascent molecules. Hyperspectral SRS imaging further distinguished various lipid species, enabling researchers to identify shifts in lipid metabolism associated with either physiological adaptation or pathological change.
By directly comparing these functional and spatial datasets from tissue slices, researchers were able to extract valuable biochemical information related to aging and disease. Changes in metabolic profiles, such as alterations in redox states or biosynthetic activity, were found to correspond to key features of degenerative or disease-associated processes. Moreover, by analyzing the spatial correlations between different biochemical reaction activities, the approach provided a framework to explain the complex metabolic reprogramming induced by aging or disease progression.
This ability to visualize and quantify the intricate relationships among multiple metabolic networks not only enriches our understanding of cellular metabolism but also offers significant potential for the early detection of disease, enhanced prognosis, and more refined evaluation of therapeutic interventions. In summary, this cutting-edge multimodal imaging platform overcomes major limitations of traditional imaging and labeling methods by enabling comprehensive, dynamic monitoring of metabolism at the subcellular level.

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