We present a new approach for imaging intracellular neurotransmitter molecules with stimulated Raman scattering microscopy. We leverage the isolated vibrational peaks of carbon-deuterium bonds to observe these neurotransmitters directly and quantitatively. By using deuterated versions of neurotransmitters, we minimize perturbation to neurochemical activity with respect to previously demonstrated fluorescence-based methods. We show direct imaging of deuterated dopamine and GABA uptake and release dynamics in PC12 chromafin cells, and in primary hippocampal neurons, respectively. Specifically, we show that stimulation of neurotransmitter release results in a 20-50% intracellular neurotransmitter concentration reduction, with the ability to observe inter- and intracellular variation in vesicular neurotransmitter release. Taken together, our data suggest that neurotransmitter isotopologues can serve as a generic, commercially-available, non-perturbative, and biocompatible method to image neurotransmitters that are chemically homologous to their native counterparts.
Aging related biological mechanisms are often difficult to probe in situ without exogenous fluorophores. Here, we leverage label-free stimulated Raman scattering (SRS) microscopy to provide new insights into aging in C. elegans. We demonstrate multispectral SRS imaging of whole worms in vivo with quantitative chemical insights across different ages. We show that both lipid and protein synthesis and compartmentalization are associated with aging in worms. We additionally use SRS in combination with simultaneous two-photon fluorescence imaging to characterize the putatively aberrant protein accumulation. Moreover, we observe notable SRS image differences when worms are subjected to calorie restriction, suggesting a promising avenue towards understanding calorie-restriction’s enhancing effects on longevity when coupled to proteomic and metabolomic analysis.
Aging related biological mechanisms are often difficult to probe in situ without exogenous fluorophores. Here, we leverage label-free stimulated Raman scattering (SRS) microscopy to provide new insights into aging in C. elegans. We demonstrate multispectral SRS imaging of whole worms in vivo with quantitative chemical insights across different ages. We show that both lipid and protein synthesis and compartmentalization are associated with aging in worms. Moreover, we observe notable SRS image differences when worms are subjected to calorie restriction, suggesting a promising avenue towards understanding calorie restriction’s enhancing effects on longevity when coupled to proteomic and metabolomic analysis.
T cell differentiation has warranted intense study to understand the mechanisms behind the adaptive immune system. While much of the research so far has relied on antibody staining and flow cytometry separation to isolate and study T cells, we present hyperspectral stimulated Raman scattering (SRS) microscopy as a potential label-free imaging method to directly observe and characterize T cells. We show that a deep learning model can be trained to identify and classify T cell differentiations from hyperspectral SRS images with 99% accuracy. We also show that fluorescent T cells in lymph node tissue can be predicted from SRS images, demonstrating potential towards an entirely label-free in-situ imaging strategy. SRS microscopy augmented with deep learning shows strong promise towards label-free in situ observation of T Cells.
In recent studies, stimulated Raman scattering (SRS) and transient absorption microscopy (TAM) have been employed for label-free mapping of biomolecules (e.g., proteins and lipids) in brain tissues and hemoglobin in red blood cells, respectively. In this study, we combined SRS and TAM to simultaneously image cell densities and capillary structure in vivo at the highest reported imaging depth, ~300 µm, for either technique. This multimodal approach resulted in label-free identification of endothelial cells and pericytes in vivo with 90% accuracy using a machine learning classifier. Simultaneous two-photon excited fluorescence microscopy serving as the ground truth.
Hyperspectral stimulated Raman scattering (hsSRS) microscopy provides rich chemical and spatial information not regularly available to traditional microscopy methods. However, analysis of hsSRS images is often confounded by convolved and overlapping spectral features requiring use of machine learning methods to extract information. Here, we demonstrate the use of our recently published deep learning architecture (the U-within-U-Net) designed for hyperspectral images on hsSRS images. We demonstrate segmentation, classification, and prediction of orthogonal imaging modalities. We also show the architecture is applicable to other hyperspectral imaging modalities with implications for remote sensing and mass spectrometry imaging.
Stimulated Raman scattering (SRS) images often suffer from low signal to noise ratio (SNR) due to absorption and scattering of light as well as limited optical power. We use deep learning to significantly improve the SNR of SRS images. Our algorithm, based on a U-Net convolutional neural network, significantly outperforms existing denoising algorithms. The trained denoising algorithm is applicable to images acquired at different imaging powers, depths, and experimental geometries not explicitly included in the training. Our results identify potential towards in vivo applications, where ground-truth images are not always available to create a paired training set for supervised learning.
Hyperspectral Stimulated Raman scattering (hsSRS) microscopy has recently emerged as a powerful non-destructive technique for label-free chemical imaging of biological samples. In most hsSRS imaging experiments, the SRS spectral range is limited by the total bandwidth of the excitation laser to ~300 cm-1 and spectral resolution of ~25 cm-1. Here we present a novel approach for broadband hsSRS microscopy based on parabolic fiber amplification to provide linearly chirped broadened Stokes pulses. This novel hsSRS instrument provides >600 cm-1 spectral coverage and ~10 cm-1 spectral resolution. We further demonstrated broadband hsSRS imaging of the entire Raman fingerprint region for resolving distribution of major biomolecules in fixed cells. Moreover, we applied broadband hsSRS in imaging amyloid plaques in human brain tissue with Alzheimer’s disease.
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