Understanding drug fingerprints in complex biological samples is essential for drug development. We demonstrate a deep learning-assisted hyperspectral coherent anti-Stokes Raman scattering (HS-CARS) imaging approach for identifying drug fingerprints at single-cell resolution. The attention-based deep neural network, Hyperspectral Attention Net (HAN), highlights informative spatial and spectral regions in a weakly supervised manner. Using this approach, drug fingerprints of a hepatitis B virus therapy in murine liver tissues was investigated. Higher classification accuracy was observed with increasing drug dosage, reaching an average AUC of 0.942. Results demonstrate the potential for label-free profiling and localization of drug fingerprints in complex biological samples.
Self-amplifying mRNA (SAM), a synthetic RNA vaccine which self-replicates upon delivery into the cytoplasm encapsulated with lipid nanoparticles (LNPs), leads to a strong and sustained immune response. In this study, we investigated SAM-LNP uptake and subsequent SAM release and distribution in baby hamster kidney (BHK-21) cells using coherent anti-Stokes Raman scattering (CARS) and multiphoton imaging techniques. This work demonstrates the significance of multimodal imaging techniques to capture the successful delivery of SAM and the subsequent production of proteins within cells. Our study can be further extended to label-free detection techniques to investigate targeted drug-delivery.
The primary goal of this study was to track PS-ASO and GalNAc-PS-ASO uptake in two cell cultures as the first step to understand the observations from the clinical studies. The multimodal imaging setup of CARS and 2PF modalities in conjunction with the image analysis pipeline made it uniquely possible to address these challenges. We report here the time-dependent uptake, internalization, and localization differences between GalNAc-PS-ASOs and PS-ASOs in liver cells. We believe our findings will help us form the basis for further investigations with more complex cellular co-cultures and with tissue and animal models.
Label-free nonlinear optical microscopy has become a powerful tool for biomedical research. However, the low imaging speed and the accompanying photodamage risk hinder further clinical applications. To reduce these adverse effects, in this study, we constructed a new generation of simultaneous label-free autofluorescence multiharmonic (SLAM) microscopy, featuring high-speed, multimodal imaging, monitorable-photodamage, and tunable ultrashort pulses. By using birefringent photonic crystal fiber and a pulse shaper, this system has the ability to allow users to independently adjust repetition rate, pulse width, and average power without overlapping interference, and can realize multiphoton generation in each channel from a single excitation pulse. These outstanding advantages represent a powerful and user-friendly imaging platform.
Liver-on-a-chip is a 3D in vitro hepatic microphysiological system aiming to recreate the conditions of liver tissue on a microscopic scale. CN Bio microphysiological system (CN Bio Innovations, UK) is one of the advanced liver-on-a-chip models. In this study, a multimodal optical imaging platform incorporating nonlinear optical imaging techniques such as multiphoton microscopy (MPM), fluorescence lifetime imaging microscopy (FLIM), coherent anti-Stokes Raman scattering (CARS) microscopy, and simultaneous label-free autofluorescence multiharmonic (SLAM) microscopy was used for characterizing the structural and functional changes associated with inflammation, lipid accumulation and drug uptake in the CNBio liver-on-a-chip model.
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