(E-192) From Organelles to organ: Multi-modal Mapping of Metabolic Dynamics in Aging and Diseases

Introduction:: Biomolecule synthesis, transformation, and degradation carry out the genetic blueprint. Understanding the metabolic dynamics of multicellular organisms is a crucial step towards unraveling the underlying mechanisms of various biological processes. However, conventional imaging techniques, such as MRI, PET, Fluorescence, and Mass Spectrometry, have inherent limitations including spatial resolution. To overcome these challenges, we have developed a multimodal imaging platform that combines stimulated Raman Scattering (SRS), multiphoton fluorescence (MPF), and second harmonic generation (SHG) to map the correlations between different metabolic activities in cells and tissues with subcellular resolution. We have further developed it into a super-resolution imaging platform with Adam-based Pointillism Deconvolution (A-PoD) algorithm. This platform enables direct visualization of metabolic heterogeneity for studying aging and diseases from cellular organelles to organs.


Materials and Methods:: Imaging platform setup: Our custom designed multimodal microscopy has integrated stimulated Raman scattering (SRS), second harmonic generation (SHG), and multiphoton fluorescence (MPF) together. We further integrated a new method that combines deuterium isotope probing and SRS microscopy to visualize metabolic dynamics in live animals. The enzymatic incorporation of deuterium (D) into biomolecules will generate carbon-deuterium (C-D) bonds in macromolecules. Within the broad vibrational spectra of C-D bonds, we discover lipid-, protein-, and DNA-specific Raman shifts and develop spectral unmixing methods to obtain C-D signals with macromolecular selectivity.

Analysis: After acquiring multimodal imaging data set, we processed them into super resolution images using our custom designed A-PoD deconvolution platform. Then, we generated quantitative ratio-metric images displaying the spatial interactions and correlations between different metabolic activities including redox ratio dynamics, lipid/protein turnovers, extracellular matrix changes, and lipid saturation heterogeneity. The correlations between each ratio-metric image were mapped out using Correlation Coefficient Mapping (CoCoMap).

Sample: From Drosophila in two ages of 15 days and 35 days, ovaries were collected. Xenograft cancer tissues were collected from mice to study two different cancer subtypes of BT474 and HCC1806. The samples were measured using multimodal imaging setup, and the images were analyzed following the analysis workflow of A-PoD and CoCoMap.


Results, Conclusions, and Discussions:: Results

The measured multimodal label free image set includes spatial distribution signals from old proteins, old lipids, NADH, Flavin, collagen fibers, newly synthesized proteins, newly synthesized lipids, unsaturated lipid, and saturated lipid channels.

Aging study: Ovaries from young and old Drosophila was imaged and analyzed. The metabolic correlations between different signal channels were mapped and quantified for studying the metabolic changes during the aging processes.

Cancer metabolism study: Metabolic activities in different types of breast cancer were imaged in situ and analyzed with CoCoMap approach. The spatial distribution of metabolic interactions between lipid/protein turnovers, redox ratios, lipid saturations, and collagen fiber network dynamics were quantitatively analyzed and evaluated.

Conclusions

Our new multimodal imaging approach and analysis methods enable the quantitative mapping of in situ metabolic activities and the correlations between different molecular events with our new deconvolution algorithms including A-PoD and CoCoMap. The integrated SRS, SHG, and MPF microscopy, allows us to study the underlying mechanisms of the metabolic activity changes from subcellular organelle to whole organ level during aging processes and cancer subtypes. This will potentially improve the lives of countless patients affected by aging or related diseases.

Discussions

The visualization of spatial distribution of multiple molecular events is essential to understanding various biological processes, and this is novel and broadly applicable. Quantitative analysis of spatial correlations occur between different metabolic activities enhances the multimodal imaging capability in a co-registered field of view (FOV) at different scales with various contrasts. The potential application range of this combined technique will be broadly applied in a wide range of biomedical research topics. The newly developed A-PoD can be applied to improve spatial resolutions of other imaging modalities such as photoacoustic or AFM, and CoCoMAP algorithm can also be applied for the quantification of multi-channel imaging results.


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