Hyperspectral imaging provides rich spectral and spatial data but often collects unnecessary information, leading to inefficiencies. To address this, we developed the Explorative Spectral Acquisition Guide (ESAG), which optimizes the selection of essential wavenumbers, with or without priors, avoiding redundant spectral bands irrelevant to downstream tasks. In hyperspectral stimulated Raman scattering imaging, ESAG significantly reduced the number of spectral frames needed. Notably, ESAG achieved a lower error rate on unmixing tasks using only 60% of the spectral frames compared to the full spectrum. In segmentation tasks involving polymer mixtures, ESAG attained 93.96% average accuracy with only 50% of the spectral window. Interestingly, for pure polymer segmentation, the accuracy of ESAG using 70% of the spectral window surpassed that of the full spectrum. Overall, ESAG enhances both the efficiency and accuracy of hyperspectral imaging, with broad potential applications in chemistry and biomedicine.
Vitamin A, a fat-soluble vitamin essential for various biological processes, exists in multiple metabolic forms that are primarily converted in the liver. This complex and organ-specific metabolism depends on subcellular localization for proper conversion and function. Even within a single cell, the distribution of vitamin A is highly heterogeneous. Leveraging sub-micron spatial resolution and chemical selectivity, hyperspectral stimulated Raman scattering microscopy (hSRS) has been widely used to visualize metabolites in complex biological samples. Here, we employ (hSRS) imaging in fingerprint regions to visualize vitamin A distribution in mammalian cells.
The endoplasmic reticulum (ER) is an organelle consisting of a network of membranous structures essential for various cellular activities, making its homeostasis critical for proper cell function. The composition of its membrane can be easily affected by various cellular stressors, triggering ER stress response. Therefore, conducting a detailed structural and compositional analysis of ER is crucial. However, due to resolution limits, analyzing the ER composition in situ remains difficult. Here, we propose a dual-modality imaging and analysis method integrating stimulated Raman scattering (SRS) and structured illumination microscopy (SIM) for imaging the lipid and protein contents of ER structures. With super-resolved structural guidance provided by SIM, the protein/lipid ratio was quantified for ER using multispectral SRS imaging. The spatial mapping of ER compositions in a single cell revealed subcellular diversity in the protein and lipid ratios in the ER structures, which significantly altered under ER stress.
Understanding metabolite dynamics in living systems is crucial for biomedical research. Fluorescence-based imaging has contributed tremendously to revealing mechanisms of organelle trafficking and interactions, however, not all biomolecules or organelles are labeled efficiently simultaneously without substantial perturbation from the labeling molecules. Vibrational spectroscopic imaging techniques are increasingly recognized as unique tools for investigating biomolecules in their natural microenvironment. Here, we developed a fluorescence-guided multimodal imaging approach to study organelle distribution and compositional changes in single cells. These advancements enable quantitative mapping of chemical dynamics, providing insights into subcellular biomolecule dynamics and mechanisms regulating cell physiology.
Non-obstructive azoospermia (NOA) is a severe form of male infertility characterized by impaired or absent sperm production in the testes. Microsurgical testicular sperm extraction (Micro TESE) is the primary treatment for NOA, but it faces challenges in differentiating between normal and abnormal seminiferous tubules based solely on morphology. To address this, our study employed stimulated Raman scattering (SRS) and second harmonic generation (SHG) microscopy to identify diagnostic features in human testicular tissues. Additionally, a deep learning-assisted diagnostic algorithm using multimodal imaging datasets demonstrated excellent performance in azoospermia diagnosis. Utilizing a weakly supervised Multiple Instance Learning-Convolutional Neuron Network (MIL-CNN) model framework, we achieved a 96% classification accuracy, surpassing the supervised CNN model. Gradient-weighted class activation mapping (Grad CAM) visualization confirmed the model’s focus on the spermatogenic region, demonstrating the potential of SRS/SHG microscopy coupled with deep learning to accurately classify normal and abnormal spermatogenic tubules, enhancing the efficiency and accuracy of pathological diagnosis.
Lipid peroxidation, linked to neurodegenerative diseases and mitochondrial dysfunction, targets polyunsaturated fatty acids (PUFAs) in cellular membranes. RT001, a novel drug with deuterium-substituted linoleic acid, inhibits lipid peroxidation by slowing down the chain reaction initiated by reactive oxygen species. Studies suggest that deuterated PUFAs presented in a fraction of the total PUFA pool, provide cellular protection. To gain insights into the underlying mechanisms, we utilized stimulated Raman scattering (SRS) imaging to analyze the subcellular distribution of RT001 using yeast as a model system. The isotopic substitution of deuterium enhances chemical contrast, enabling the observation of drug delivery dynamics. Concentration- and time-dependent aggregation of RT001 in lipid droplets and cell membranes were observed. SRS imaging holds promise for investigating metabolic processes of deuterated drugs such as RT001, thereby facilitating drug screening and development.
Non-obstructive azoospermia (NOA) is a severe male infertility condition characterized by impaired or absent sperm production in the testes. The primary treatment for NOA is microsurgical testicular sperm extraction (micro-TESE), which relies on accurately identifying healthy seminiferous tubules. In addressing this clinical need, we propose the utilization of mid-infrared photothermal (MIP) microscopy to identify spectroscopic signatures associated with NOA. Our preliminary results revealed that NOA tissues exhibited distinctive lipid distribution and reduced lipid peak intensity compared to tissues with normal sperm production. Leveraging principal components analysis (PCA), we successfully extracted key infrared spectroscopic features. When combined with logistic regression (LR), this approach achieved an impressive prediction accuracy of 95.0% in classifying testicular tissues. These findings highlight the potential of MIP microscopy in facilitating sperm retrieval by distinguishing seminiferous tubules based on their molecular composition.
Cholesterol is connected to multiple health problems. Although the biochemical pathway is well-understood, many disease-related mechanisms, such as subcellular regulation of cholesterol synthesis, are still unclear. It presents a grand challenge for conventional characterization methods, which have difficulties providing non-invasive, high-speed, and high-resolution measurements of metabolites in living cells. Stimulated Raman scattering (SRS) can selectively visualize metabolic biomolecules based on vibrational spectroscopic features. Here, by SRS imaging of subcellular sterol, we found an unconventional regulatory mechanism based on the distribution of sterols on the plasma membrane and lipid droplets. The combination of vibrational imaging and genetic modulation promises great potential in finding new regulatory mechanisms, which would open opportunities for diagnosing and prognosis of cholesterol-related diseases.
Cell classification is a fundamental task in biological research and medical practices. In this study, we proposed a singlecell classification pipeline through machine learning and hyperspectral stimulated Raman scattering imaging. The pipeline proposed is validated by using hyperspectral SRS images of two types of pancreatic cancer cells before and after the treatment of drugs that affects cellular cholesterol level. The result demonstrates that the proposed machine learning pipeline is capable of classifying cells with different metabolite dynamics, which provides possibilities for wide applications in cell analysis.
Label-free phenotypic classification at a single-cell level is a challenging yet important task in cell biology. Stimulated Raman scattering (SRS) microscopy provides high chemical selectivity and sensitivity for label-free imaging of biological samples. With the capability to record hyperspectral SRS images with high-speed, mapping of biomolecules inside living cells enables label-free phenotyping. However, like all high-dimensional data, it remains challenging to fully exploit the excessive amount of information contained in hyperspectral data for single-cell analysis. Here, we developed and compared two machine-learning-based methods - the convolutional neural network and support vector machine - to automatically extract important features from high dimensional data and achieve a high-accuracy label-free single-cell classification. These methods serve as a robust approach to classify cells based on their molecular features, allowing unbiased, high-throughput data analysis.
Voltage imaging has become an emerging technique to record membrane potential change in living cells. Yet, compared to electrophysiology, microscopy approaches are still limited to relative membrane voltage changes, lacking important information conveyed by absolute membrane voltage. This talk will cover a spectroscopy approach to tackle this challenge. A spectroscopic signature of membrane potential was identified through stimulated Raman scattering (SRS) imaging, which enabled label-free, sub-cellular voltage imaging of mammalian neurons. We also employed pre-resonance electronic absorption to enhance SRS imaging sensitivity and specificity. microbial rhodopsin voltage sensors, providing a quantitative approach to measure membrane potential. Quantitative voltage imaging by SRS has enabled mapping of absolute voltage and has great potential in neurology and brain sciences.
Voltage imaging has become an emerging technique to record membrane potential change in living cells. Yet, compared to electrophysiology, microscopy approaches are still limited to relative membrane voltage changes, lacking important information conveyed by absolute membrane voltage. This talk will cover a spectroscopy approach to tackle this challenge. A spectroscopic signature of membrane potential was identified through stimulated Raman scattering (SRS) imaging, which enabled label-free, sub-cellular voltage imaging of mammalian neurons. We employed pre-resonance electronic absorption to enhance SRS imaging sensitivity and specificity. microbial rhodopsin voltage sensors, providing a quantitative approach to measure membrane potential. Quantitative voltage imaging by SRS has enabled mapping of absolute voltage in a neural network and has great potential in neurology and brain sciences.
Experimental design: We used Raman spectroscopic analysis of human prostate cancer patient tissues to characterize composition of lipid droplets in the metastatic lesions. The therapeutic efficacy was tested in prostate cancer orthotopic and intra-cardiac injection mouse models. Gene expression profiling was used to identify genes that are affected by cholesteryl ester depletion. Additionally, immunoblotting, immunofluorescent staining and transwell assay were used to further verify inactivation of Wnt/β-catenin pathway by cholesteryl ester depletion. Stimulated Raman scattering microscopy and mass spectrometry were used to assess lipogenic potential of cholesteryl ester-depleted prostate cancer cells.
Results: We observed accumulation of cholesteryl ester in metastatic lesions of human prostate cancer patient tissues. Inhibiting cholesterol esterification significantly reduced the number of metastatic clusters by 50% in the prostate cancer orthotopic mouse model. We showed suppression of metastatic tumor growth in the intra-cardiac injection model without observable toxicity to the mice. The mechanism study further supports that cholesteryl ester depletion suppresses metastasis by upregulation of regulators that negatively impact prostate cancer metastasis. Notably, Wnt/β-catenin is downregulated upon cholesteryl ester depletion, and we found evidence suggesting that cholesteryl ester depletion significantly blocks secretion of Wnt3a through reduction of monounsaturated fatty acid levels, which limits Wnt3a acylation. In conclusion, we demonstrate that targeting cholesterol esterification can treat metastatic prostate cancer effectively with minimum toxicity.
Voltage imaging has become an emerging technique to record membrane potential change in living cells. Yet, compared to the conventional electrophysiology, imaging approaches are still limited to relative membrane potential changes, losing important information conveyed by absolute value of membrane voltage. This challenge comes from several factors affecting the signal intensity, such as concentration, illumination intensity, and photobleaching. Spectroscopy is a quantitative method that shows potential to report the state of molecules in situ. Here, we apply electronic pre-resonance stimulated Raman scattering (SRS) imaging to detect near-infrared absorbing microbial rhodopsin voltage sensors in E. coli. The use of newly developed near-infrared microbial rhodopsins (Ganapathy et. al. 2017. JACS, 2017, 139(6):2338- 44) enables electronic pre-resonance SRS imaging with single cell sensitivity. By spectral profile analysis, we identified voltage-sensitive SRS peaks. The spectral signature can be used as part of a quantitative approach to measure membrane potential and enable mapping of absolute voltage in a neural network.
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