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.
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.
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