High-speed multiplex Stimulated Raman Scattering (SRS) microscopy has emerged as a powerful imaging technique in the field of biomedical research. This cutting-edge technology combines the benefits of traditional Raman spectroscopy with the high speed and resolution of microscopy, enabling real-time, label-free, and non-invasive visualization of biological samples at the molecular level. In this article, we delve into the principles, advantages, and applications of high-speed multiplex SRS microscopy in the context of biomedical studies.
We developed a new super resolution multi-molecular optical metabolic imaging platform with Adam optimization-based Pointillism Deconvolution (A-PoD) and penalized reference matching (PRM) algorithms for DO-SRS hyperspectral imaging detection of metabolic changes in cells and animals during aging processes and in diseases conditions.
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've established a nonlinear multimodal imaging system that incorporates stimulated Raman Scattering (SRS), multiphoton fluorescence (MPF), and second harmonic generation (SHG) to explore the connections between metabolic activities and the distribution of metabolites in cells and tissues. Furthermore, we've devised the Adam-based Pointillism Deconvolution (A-PoD) and Correlation Coefficient Mapping (CoCoMap) algorithms, enabling a deeper insight into the simultaneous recording and regulation of various metabolic processes within super-resolved images of nanoscale Regions of Interest (ROIs). In our pursuit of specifically identifying signals originating from distinct subcellular organelles, we've introduced a pioneering clustering algorithm known as Multi-SRS reference matching (Multi-SRM). This approach has the potential to improve early disease detection, prognosis, the evaluation of therapeutic effects, and our comprehension of the mechanisms underpinning aging and biomedicine.
Investigating metabolic reactions within cells is crucial for unraveling the numerous biological functions. Established imaging modalities, including MRI, PET, Fluorescence, and Mass Spectrometry, present various drawbacks. To address these issues, we have developed a nonlinear multimodal imaging system. This system combines stimulated Raman scattering, multiphoton fluorescence, and second harmonic generation. It was designed to probe the spatial distribution of metabolic activities within cells and tissues by measuring multiple molecular signals. To support the analysis scheme, we have also pioneered cutting-edge algorithms, notably the Adam-based Pointillism Deconvolution (A-PoD) and Correlation Coefficient Mapping (CoCoMap). They allow us to analyze correlations between super-resolution images of nanoscale Regions of Interest. Additionally, our research has introduced a novel clustering algorithm known as Multi-SRS reference matching (Multi-SRM). This algorithm is particularly tailored to isolate signals exclusively from specific subcellular organelles. The application of this innovation offers significant potential to study aging and disease related metabolic changes.
Osteoporosis (OP) is a metabolic bone disease that affects more than 10 million people in the USA and leads to over two million fractures every year. The disease results in serious long-term disability and death in a large number of patients. Bone mineral density (BMD) measurement is the current standard in assessing fracture risk; however, the majority of fractures cannot be explained by BMD alone. Bone is a composite material of mineral, organic matrix, and water. While bone mineral provides stiffness and strength, collagen provides ductility and the ability to absorb energy before fracturing, and water provides viscoelasticity and poroelasticity. These bone components are arranged in a complex hierarchical structure. Both material composition and physical structure contribute to the unique strength of bone. The contribution of mineral to bone's mechanical properties has dominated scientific thinking for decades, partly because collagen and water are inaccessible using X-ray based techniques. Accurate evaluation of bone requires information about its components (mineral, collagen, water) and structure (cortical porosity, trabecular microstructure), which are all important in maintaining the mechanical integrity of bone. Magnetic resonance imaging (MRI) is routinely used to diagnose soft tissue diseases, but bone is "invisible" with clinical MRI due to its short transverse relaxation time. This review article discusses using ultrashort echo time (UTE) sequences to evaluate bone composition and structure. Both morphological and quantitative UTE MRI techniques are introduced. Their applications in osteoporosis are also briefly discussed. These UTE-MRI advancements hold great potential for improving the diagnosis and management of osteoporosis and other metabolic bone diseases by providing a more comprehensive assessment of bone quantity and quality.
We developed a multimodal non-linear optical super resolution imaging platform for studying metabolic changes during aging and diseases. This platform integrates deuterium probed Stimulated Raman scattering (DO-SRS), multiphoton fluorescence (MPF), and second harmonic generation (SHG) into a super resolution multimolecular microscopy.
Emerging studies have shown that oxidative imbalance is critical in disease progression such as cancer and Alzheimer's [1, 2]. This variation can lead to the upregulation of certain metabolic pathways inducing diseases and disorders. Aromatic amino acids (AAA) are involved with the production of Reactive Oxygen Species (ROS), resulting in the increase of oxidative stress [3]. AAA studies typically rely on gas chromatography (GC) or mass spectroscopy (MS)-based imaging techniques to study lipids; however, these methods lack the ability to show the cell's lipid spatial distribution or require fluorescent dyes that can interfere with the cell's molecular activities [4, 5]. Here, we established an optical imaging approach that combines D2O (heavy water) probed Stimulated Raman scattering (DO-SRS) and Multiphoton Fluorescence (MPF) microscopy to directly visualize metabolic activities in situ in cancer cells under the regulation of excess AAA, specifically Phenylalanine and Tryptophan. The cellular spatial distribution of de novo lipogenesis, unsaturated and saturated lipids, NADH, Flavin, and new protein synthesis were quantitatively imaged and examined. We discovered an increase in de novo lipogenesis, Flavin/(Flavin + NADH), and unsaturated to saturated lipids in the cancer cells treated with excess AAAs. Decrease of protein turnover rate occurred in the same treated cells with observations of higher lipid droplet content. These observed metabolic activities are signs of mitochondrial dysfunction and oxidative stress. Our study demonstrates that DO-SRS can be used as a high-resolution imaging platform to study AAA regulated metabolic activities in cells and elucidates the linkage between lipid metabolism and cancer.
