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.
Weak magnetic fields affect a multitude of biological processes including cell metabolism and are hypothesized to be a result of magnetic field-sensitive spin-selective radical-pair reactions. To provide much needed visualization of this process, we demonstrate the use of a custom-built multimodal nonlinear optical imaging system capable of measuring the redox state of cells through multi-photon-excited autofluorescence and autofluorescence lifetime of metabolic cofactors. We demonstrate a custom multi-axis Helmholtz coil system to apply time-varying magnetic fields across the sample during imaging. This imaging platform allows for characterization and optimization of the effects of magnetic fields on live cells and tissues.
Optical Coherence Tomography (OCT) has shown its detection and diagnostic capabilities for otitis media (OM), enabling visualization through scattering tissues including the tympanic membrane and biofilms, and into the middle ear cavity. Preliminary results from an ongoing five-year 235-subject study at Children’s Wisconsin, Medical College of Wisconsin, are presented. A vision-language machine learning model was trained on OCT image features and clinical metadata to differentiate OM disease states and predict required interventions. This study demonstrates the prognostic value of OCT in assessing OM and offers the potential for improving the management of patients with OM.
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.
Fluorescence lifetime imaging microscopy (FLIM) provides valuable insights into molecular interactions and states in complex cellular environments. Conventional FLIM analysis methods struggle with accurate lifetime estimation with low photons-per-pixel (PPP). We propose DeepFLR, a self-supervised deep learning framework for robust FLIM signal restoration with limited photons. By exploiting the spatiotemporal dependencies of FLIM signals, DeepFLR reconstructs the fluorescence decay curves, leading to accurate lifetime estimations using existing lifetime estimation methods. The results demonstrate that DeepFLR enables reliable lifetime estimation with less than 10 PPP for a diverse set of biological samples. The proposed approach significantly reduces the photon budget of FLIM and opens up numerous low-light FLIM applications.
Earwax or cerumen is a substance secreted by the ceruminous and sebaceous glands of the ear canal. The main function of this biofluid is as a physical barrier, but its buildup can lead to earwax impaction and result in hearing loss. Optical coherence tomography (OCT) is one potential method for assessing earwax. A catheter-based OCT system with a handheld probe and custom-made 3D-printed specula was designed and used to non-invasively acquire cross-sectional and volumetric images of the canal of adult human subjects. Features relating to quantity, structure, texture, and optical attenuation were extracted and correlated back to subjects’ ear health.
Otitis media (OM) is a common disease of the middle ear, with 80% of children experiencing an infection before age three. Diagnostic methods rely on interpretation of symptoms from an otoscope, which help physicians visualize the eardrum. To provide precise structural and biochemical information, a prototype non-contact multimodal Raman spectroscopy (RS) and optical coherence tomography (OCT) system and handheld probe were created. Observation of in vitro physiologically-relevant ear models and comparison to in vivo scans from pediatric subjects presenting with OM detail application-specific development. Design challenges for clinical use, including maximum permissible exposure and physical size constraints, are presented.
Otitis media (OM) is a common middle ear disease that is treated with antibiotics. However, over-prescription of antibiotics heightens the risk of antibiotic resistance. Here, we report the development and testing of a new cold microplasma (CMP) device to treat OM, and demonstrate the translation for in vivo use in a chinchilla animal model. In vitro nontypeable Haemophilus influenzae bacterial and biofilm samples and ex vivo tissue specimens were evaluated for inactivation and injury. CMP-induced effects on any infectious symptoms (middle ear fluid, biofilms) were longitudinally observed with OCT. This represents the first application of CMP treatments for OM therapy.
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.
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