We demonstrate optical redox ratio and fluorescence lifetime imaging microscopy of intrinsic metabolic co-factors NAD(P)H and FAD to quantify metabolic changes in human immune cells from peripheral blood. This approach is attractive because it does not require cell surface labels or transfection, enabling rapid assessment of single cell metabolism. Multiphoton microscopy provides near infrared excitation of these autofluorescent molecules, thereby maximizing cell viability. Newly trained neural networks automatically segment single cells for analysis of heterogeneity within and between patients. Overall, this approach is attractive for both basic research and patient management in cancer and immunology.
Single cell analysis of multi-dimensional microscopy images is repetitive, time consuming, and arduous. Numerous analysis steps are required to quantify and visualize cell heterogeneity and trends between experimental groups. The open-source community has created tools to facilitate this process. To further simplify analysis, we created a library of functions called cell-analysis-tools. This library includes functions that can streamline single-cell analysis for faster quality checking and automation. This library also includes example code with randomly generated data for dimensionality reduction [t-distributed stochastic neighbor embedding (t-SNE), principal component analysis (PCA), Uniform Manifold Approximation and Projection (UMAP)] and machine learning models [random forest, support vector machine (SVM), linear regression] that scientists can swap with their own data to visualize trends. Lastly, this library includes template scripts for feature extraction that can help identify differences between experimental groups and cell heterogeneity within a group. This library can significantly decrease user time while increasing robustness and reproducibility of results.
Intravital multiphoton microscopy of the metabolic co-enzymes NAD(P)H and FAD (optical metabolic imaging, or OMI) provides label-free imaging of metabolic changes in vivo. Since the metabolism of tumor and immune cells is associated with cancer progression, we aim to study metabolic changes during a triple-combination immunotherapy regimen that cures murine melanoma tumors. Our results demonstrate that intravital OMI can capture tumor and T cell autofluorescence intensity and lifetime changes during immunotherapy. Overall, this technology enables analysis of single cell metabolic changes in vivo to provide insight for immunotherapy development.
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