The biopharmaceutical industry relies on selecting high-performing cell lines to meet quality and manufacturability criteria. However, this process is time- and labor-intensive. To address this, label-free multimodal multiphoton microscopy techniques were employed to characterize biopharmaceutical cell lines in early passages. Using a machine learning-assisted single-cell analysis pipeline, over 95% accuracy for monoclonal cell line classification was achieved in all passages. Additionally, Open Set Recognition allowed the differentiation of desired cell lines in polyclonal pools. The study offers a promising solution to expedite the cell line selection process, reducing time and resources while ensuring the identification of high-performance biopharmaceutical cell lines.
Simultaneous Label-free Autofluorescence Multiharmonic (SLAM) microscopy is a nonlinear multimodal optical imaging technique with sub-micron spatial resolution, enabling 3-D visualization and analysis of live cells, complex in vitro models, and tissues. SLAM microscopy detects NAD(P)H and FAD autofluorescence as well as second and third harmonic generation signals simultaneously from biological samples. It can be used for a wide range of applications in cell-to-clinic pharmaceutical research. To run proof-of-concept, longitudinal, and clinical studies of interest to GSK project teams, the GSK Center for Optical Molecular Imaging (COMI) was established in 2015. Based on promising results from these studies, GSK contracted with spin-out start-up, LiveBx, to design and develop the first portable SLAM microscope, and is currently being used for studies on-site at GSK. In this presentation, major milestones and challenges in translating the SLAM technology from academia to industry and key learnings from this process will be shared from multiple perspectives.
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.
Efficient cell line development is crucial for optimizing biopharmaceutical production. We demonstrate the potential of SLAM and FLIM microscopy to optimize this process by correlating metabolism-related features with measured productivity in early CHO cell passages. Eight CHO cell lines were imaged using SLAM and FLIM microscopy, and a pipeline was developed to classify the cells. A linear SVM achieved 95% accuracy in predicting productivity. Important features and their channel affiliations were identified, revealing optical metabolic characteristics from NAD(P)H and FAD associated with productivity. SLAM features correlated with growth and viability, while FLIM features correlated with protein production, highlighting the importance of multimodal label-free imaging.
We investigated the potential of Simultaneous Label-free Autofluorescence Multiharmonic (SLAM) microscopy for quantitative evaluation of cisplatin-induced nephrotoxicity in rats. To determine the structural and functional changes associated with cisplatin treatment occurring over a period of time, rats were euthanized at different time points post-treatment (days 2, 6 and 29), and SLAM images were collected from 4% PFA-fixed sagittal kidney sections. Signs of renal tubular injury including hyaline cast formations were detected in SLAM images obtained from day 6 and 29 time points. This study demonstrated the capability of SLAM for visualization and evaluation of cisplatin-induced nephrotoxicity in a label-free manner.
Fast-acquisition Raman methodologies such as stimulated Raman scattering (SRS) and coherent anti-Stokes Raman scattering (CARS) microscopy, which generate image contrast using the Raman active vibrational frequency of a given chemical, provide information on the biochemical composition of tissues. This has enabled detailed imaging of the interaction between tumor cells and the surrounding tumor microenvironment (e.g. tumor vasculature, stromal cells) without the need for additional labelling. In addition, we have developed novel, highly Raman active spectroscopically bioorthogonal labels, for the sensitive and specific intracellular visualisation of small-molecules and cells, allowing monitoring of drug uptake and tracking of individual cell populations.
In the production of biotherapeutics, Chinese hamster ovary (CHO) cells are known as the gold standard. One challenge in the development of these cell lines is the identification of high expressing, yet stable CHO cells. Here we apply simultaneous label-free autofluorescence multi-harmonic (SLAM) microscopy to four CHO cell lines of varying levels of productivity and stability. With the assistance of machine learning, we were able to classify the CHO cell lines into their respective categories with an accuracy of 85%. Application of this CHO cell characterization technology to upstream bioprocessing can potentially improve workflows such as high-throughput screening and monitoring.
Label-free multimodal optical bioimaging allows non-perturbative profiling of biological samples based on their intrinsic optical molecular properties. In this study, we utilized SLAM and FLIM microscopy to identify CHO cell lines with favorable process performance for the production of therapeutic monoclonal antibodies and proteins. Here, a single-cell analysis pipeline was developed to quantitatively characterize CHO cell lines based on their phenotypes. To perceive the rich information in the multi-modal bioimages, a custom-built multi-task deep neural network was built, which can extract features from different aspects of the optical and molecular properties of the sample. This work demonstrated the potential of ML-assisted multi-modal optical imaging in the identification of cell lines with desirable characteristics for biopharmaceutical production at earlier time points.
The ability to study drug distribution in real time, without disrupting the biological milieu and structure, has become a cornerstone of modern pharmaceutical development. GSK has developed a portfolio of these methods and this work is often performed in collaboration with academic partners within our Bioimaging Expertise Network (BEN).
This presentation will review the successes achieved through this approach, highlighting recent advances; outline project specific case studies and demonstrate the integration of the data into drug development decisions. Finally, the current major challenges will be discussed as new opportunities for imaging collaboration.
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.
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.
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.
Characterizing the performance of fluorescence microscopy and nonlinear imaging systems is an essential step required for imaging system optimization and quality control during longitudinal experiments. Emerging multimodal nonlinear imaging techniques require a new generation of microscopy calibration targets that are not susceptible to bleaching, and can provide a contrast across the multiple modalities. Here, we present a nanodiamond-based calibration target for microscopy, designed for facilitating reproducible measurements at the object plane. Since fluorescent nanodiamonds are not prone to bleaching shelf-stable sample can provide a rapid reference measurements for ensuring consistent performance of microscopy systems in microscopy laboratories and imaging facilities.
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.
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.
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.
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), 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.
Extracellular vesicles (EVs) are biologically derived nanovectors important for intercellular communication and trafficking, yet understanding of their underlying biological mechanisms remains poor. Advances have been hampered by both the complex biological origins of EVs and a lack of suitable imaging techniques. Here, we present a strategy for simultaneous in vitro imaging and molecular characterisation of EVs in 2D and 3D based on Raman spectroscopy and minimally-obstructive metabolic deuterium labelling. Metabolically-incorporated deuterium acts as a bio-orthogonal Raman-active tag for direct Raman identification of EVs and provides insights into their biocomposition and trafficking, with implications for their development as therapeutic delivery vectors.
Coherent anti-Stokes Raman scattering (CARS) microscopy utilises intrinsic vibrational resonances of molecules to drive inelastic scattering of light, and thus eradicates the need for exogenous fluorescent labelling, whilst providing high-resolution three-dimensional images with chemical specificity. Replacement of hydrogen atoms with deuterium presents a labelling strategy that introduces minimal change to compound structure yet is compatible with CARS due to an induced down-shift of the CH2 peak into a region of the Raman spectrum which does not contain contributions from other chemical species, thus giving contrast against other cellular components.
We present our work using deuterated oleic acid to optimise setup of an in-house-developed multimodal, multiphoton, laser-scanning microscope for precise identification of carbon-deuterium-associated peaks within the silent region of the Raman spectrum. Application of the data analysis procedure, factorisation into susceptibilities and concentrations of chemical components (FSC3), enables the identification and quantitative spatial resolution of specific deuterated chemical components within a hyperspectral CARS image. Full hyperspectral CARS datasets were acquired from HeLa cells incubated with either deuterated or non-deuterated oleic acid, and subsequent FSC3 analysis enabled identification of the intracellular location of the exogenously applied deuterated lipid against the chemical background of the cell. Through application of FSC3 analysis, deuterium-labelling may provide a powerful technique for imaging small molecules which are poorly suited to conventional fluorescence techniques.
Multiphoton microscopy uses ultrafast nonlinear light-matter interactions to generate signal contrast from biological samples. The imaging of tissue from various organs plays an important role for a better understanding of cellular processes within their microenvironment and helps to reveal mechanisms of cellular changes in tissues during disease processes. Most tissue imaging studies by the pharmaceutical industry or by pathologists have typically been performed using harvested and sectioned tissue from organs to investigate drug toxicity or disease-related changes. However, immediately following biopsy, tissues begin to degrade due to cell necrosis and apoptosis, and substantial information is lost during the process. We demonstrate tissue degradation monitoring at different time points after tissue excision by using our label-free multimodal multiphoton imaging system which integrates SHG, TPEF, FLIM, and CARS in one platform. We examined whole organs and tissues harvested from mice, including kidney, liver, pancreas, and brain, and immersed each in several different media including saline, Euro-Collins solution, UW solution, HTK, and formalin. We collected time-lapse images from each sample and compared rates of cell degradation, tissue structure changes, and variations in optical properties including the intensities of NADH and FAD, the metabolic redox ratio, and FLIM of free/bound NADH. As a result, we quantified rates of degradation and metabolic changes associated with the preservation methods based on these label-free optical properties. Therefore, these results can be used as reference values for most ex vivo tissue research that relies on tissue and cell viability.
Pre-clinical toxicology is a statutory requirement of drug development and plays a significant role in reducing attrition in drug discovery. Histopathology and indirect methods such as measurement of toxicity-associated systemic markers in blood or urine samples are the state-of-the-art techniques for toxicity evaluation. Further improvements over these conventional techniques are needed to detect signs of drug-induced toxicity at earlier stages with higher sensitivity and specificity. Multiphoton nonlinear imaging techniques such as two-/three-photon microscopy (2PF/3PF), fluorescence lifetime imaging microscopy (FLIM), second/third harmonic generation (SHG/THG) and coherent anti-Stokes Raman scattering (CARS) microscopy can extract complimentary structural and metabolic information of the target tissue in a label-free manner. In this study, we investigated the capability of a multimodal multiphoton microscopy technique (2PF/3PF/SHG/THG/FLIM/CARS) for detecting both functional and structural changes associated with drug-induced toxicity. Cisplatin, a platinum-based chemotherapy drug, is a cytotoxic agent used to treat many types of cancers. Common side effects of Cisplatin include nephrotoxicity and gonadal dysfunction. We obtained multimodal optical images of organs such as kidney, liver, and testis harvested from mice treated with a single dose of Cisplatin (3mg/kg) by intraperitoneal injection. A control group was treated with 0.9% saline. Structural and metabolic biomarkers related to Cisplatin-induced toxicity were identified and characterized from these multimodal optical images obtained ex vivo. The preliminary results suggest that it may be possible to develop a novel platform for drug toxicity identification and assessment based on multimodal nonlinear optical imaging techniques.
The ability of a drug molecule to reach the right protein in the correct intracellular compartment of the target cell type, in the desired tissue following a systemic dose, is governed by a complex network of active and passive transport processes. Furthermore, the same biological mechanisms that ensure on-target exposure are also responsible for the delivery of drug to off-target sites, potentially causing negative outcomes. Understanding the concentration of drug at target (D@T) is therefore one of the “3 pillars of successful drug discovery” [Morgan et al 2012].
The understanding of D@T can be further characterised by three components; the precise location (geography), the accurate concentration (Maths) and the chemical identity of the molecule (Chemistry) at scales ranging from whole body in the clinic to ex vivo tissue samples to single cells in culture. To that end GSK have harnessed a wide spectrum of analytical techniques utilising radioactive tracers, mass spectroscopy detection and optical methodologies, often in partnership with external academic and governmental collaborators..
This presentation will review the challenges of determining D@T in the diverse GSK portfolio and highlight the opportunities that Biophotonic methods, such as Raman and CARS, bring to our work. Finally we will review the key contribution of the external academic network to Bioimaging at GSK and share a few of our future challenges.
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