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.
Chinese hamster ovary (CHO) cells are the most widely used cell line for the recombinant expression of human therapeutics. To investigate a select cell line monoclonal antibody production, we monitor NAD(P)H, a crucial enzymatic cofactor, and an auto-fluorescent bio-marker, with two-photon fluorescence lifetime imaging microscopy (2P-FLIM). This represents a high-resolution, label-free technique for longitudinally characterizing a changing environment (if any) during metabolic transitions. 2P-FLIM analysis of NAD(P)H in four different CHO cell lines helps us predict productive cell types from others. A detailed single cell analysis is also presented that can separate cell types based on optical and morphological classification.
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.
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.
Recent advances in tissue engineering and microfabrication have led to development of novel Complex In Vitro models (CIVMs) that more closely mimic pathophysiological functions of human tissues and organs. CIVMs can provide deeper insights into the mechanisms of human disease and pharmacological properties of new drug candidates during early stages of development. In this study, a multimodal optical imaging platform was used for characterizing the structural and functional features of a liver-on-a-chip model (CN Bio Innovations, UK).
Antisense oligonucleotides (ASOs) are single stranded negatively charged molecules which downregulate the translation of specific target messenger RNA (mRNA). Chemically modified ASOs with phosphorothioate (PS) linkages have been extensively studied as research tools and as clinical therapeutics and nine oligonucleotide-based drugs have been approved by regulatory agencies. While several cell surface proteins that bind PS-ASOs and mediate their cellular uptake have been identified, the mechanisms leading to productive internalization of PS-ASOs are not well understood. We demonstrate the potential of hyperspectral CARS imaging to detect the intracellular presence of ASOs in a label-free manner.
Antisense oligonucleotides (ASOs), a novel paradigm in modern therapeutics, modulate cellular gene expression by binding to complementary RNA sequences. Triantennary N-acetyl galactosamine (GN)-conjugated ASOs show greatly improved potency via Asialoglycoprotein receptor (ASGR)-mediated uptake in hepatocytes. Here, we compare the uptake kinetics and subsequent distribution of untargeted ASOs to that of GN-ASOs in mouse macrophages and hepatocytes using simultaneous coherent anti-Stokes Raman scattering (CARS) and two-photon excited fluorescence imaging. While the CARS modality captured the changing lipid distributions and overall morphology of the cell, two-photon fluorescence imaging measured the uptake and the subsequent distribution of the fluorescently labeled (Alexa-488) ASOs inside the cells.
An efficient and automated image analysis pipeline is essential for extracting quantitative information from multimodal image datasets. In this study, a multimodal optical imaging platform was used to capture CARS, 2PF, and FLIM images from control and drug-treated cells. Images were collected using both fluorescent label-based and label-free approaches. Here we present a single-cell analysis pipeline for the multimodal cellular image analysis. The results demonstrate the capability of our single-cell analysis pipeline for quantitatively measuring the intracellular drug distribution and its longitudinal uptake using a multimodal optical imaging platform, which can provide novel insights into the uptake pathways and target-sites.
Fluorescence Lifetime Imaging Microscopy (FLIM), providing unique quantitative functional information, has gained popularity in various biomedical and molecular biology studies. Here we present an open-source Python package, FlimTK, a toolkit that enables state-of-the-art functions for FLIM image analysis and visualization. It contains comprehensive functionalities for reading FLIM raw files, fluorescence lifetime estimation, heterogeneity analysis, and spatial distribution analysis. FlimTK package is optimized for high performance and ease of use for integration into custom Python-based analysis workflows. FlimTK source code, demo analysis workflows, and tutorial documentation are available for download from GitHub.
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