Abstract Lipids play crucial roles in many biological processes under physiological and pathological conditions. Mapping spatial distribution and examining metabolic dynamics of different lipids in cells and tissues in situ are critical for understanding aging and diseases. Commonly used imaging methods, including mass spectrometry-based technologies or labeled imaging techniques, tend to disrupt the native environment of cells/tissues and have limited spatial or spectral resolution, while traditional optical imaging techniques still lack the capacity to distinguish chemical differences between lipid subtypes. To overcome these limitations, we developed a new hyperspectral imaging platform that integrates a Penalized Reference Matching algorithm with Stimulated Raman Scattering (PRM-SRS) microscopy. With this new approach, we directly visualized and identified multiple lipid species in cells and tissues in situ with high chemical specificity and subcellular resolution. High density lipoprotein (HDL) particles containing non-esterified cholesterol was observed in the kidney, indicating that these pools of cholesterol are ectopic deposits, or have yet to be enriched. We detected a higher Cholesterol to phosphatidylethanolamine (PE) ratio inside the granule cells of hippocampal samples in old mice, suggesting altered membrane lipid synthesis and metabolism in aging brains. PRM-SRS imaging also revealed subcellular distributions of sphingosine and cardiolipin in the human brain sample. Compared with other techniques, PRM-SRS demonstrates unique advantages, including faster data processing and direct user-defined visualization with enhanced chemical specificity for distinguishing clinically relevant lipid subtypes in different organs and species. Our method has broad applications in multiplexed cell and tissue imaging.
Optical-resolution photoacoustic microscopy (OR-PAM) has been increasingly utilized for in vivo imaging of biological tissues, offering structural, functional, and molecular information. In OR-PAM, it is often necessary to make a trade-off between imaging depth, lateral resolution, field of view, and imaging speed. To improve the lateral resolution without sacrificing other performance metrics, we developed a virtual-point-based deconvolution algorithm for OR-PAM (VP-PAM). VP-PAM has achieved a resolution improvement ranging from 43% to 62.5% on a single-line target. In addition, it has outperformed Richardson-Lucy deconvolution with 15 iterations in both structural similarity index and peak signal-to-noise ratio on an OR-PAM image of mouse brain vasculature. When applied to an in vivo glass frog image obtained by a deep-penetrating OR-PAM system with compromised lateral resolution, VP-PAM yielded enhanced resolution and contrast with better-resolved microvessels.
We have developed a novel methodology to capture images of various biomolecules at a resolution surpassing the traditional diffraction limit of optical microscopy. By harnessing a multimodal imaging platform that combines stimulated Raman scattering (SRS), multiphoton fluorescence (MPF), and second harmonic generation (SHG), together with sophisticated image deconvolution algorithms, we have successfully generated super-resolution images that reveal the details of biomolecular metabolism. These images enable us to explore the intricate associations between metabolic activities and the spatial distribution of metabolites within breast cancer tissues. To enhance the accuracy of this measurement technique, in this study, we designed a pre-processing workflow that incorporates both denoising and drift correction processes. Our cutting-edge, nonlinear multimodal imaging approach, when applied in a super-resolution context with new workflow, holds significant promise for advancing early detection of breast cancer, prognostication, evaluation of therapeutic outcomes, and deepening our mechanistic understanding of diseases.
Our high-speed multimodal imaging platform combines stimulated Raman Scattering (SRS), multiphoton fluorescence (MPF), and second harmonic generation (SHG) to explore metabolic activities of biomolecules in cells and tissues. Using the A-PoD algorithm, we achieve super-resolution imaging, while deuterium oxide probing enhances SRS imaging. The Correlation Coefficient Mapping (CoCoMap) technique helps understand coordination and regulation of metabolic activities. This tool enables early disease detection, prognosis, and therapeutic assessment, providing valuable insights into aging and metabolic diseases. Our approach revolutionizes metabolism visualization, advancing knowledge of biological processes and their implications in health and disease.
The advances in microscopy techniques have led to new findings in biology. The recent advances in super-resolution microscopy technologies revealed precise molecular distribution. The techniques to visualize the distributions of multiple molecules in biological samples from experimental techniques to computational approaches are reviewed. By summarizing the techniques, the future direction of collaborative research of the techniques is highlighted to show the nanoscopic chemical details of biological samples.
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