Metabolic features of mitosis remain poorly understood because this phase of the cell cycle is rapid and heterogeneous between cells within a dish. Label-free optical metabolic imaging (OMI) can monitor rapid changes in cell metabolism with single cell resolution using two-photon microscopy of the optical redox ratio (NAD(P)H/FAD) and NAD(P)H fluorescence lifetimes. Here, we brought together image analysis tools to quantify OMI time-courses of single cells undergoing mitosis across multiple cell lines. Alignment of OMI and morphological features over time provided unique insight into metabolic changes during mitosis within unperturbed systems.
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.
Fetal membranes have important mechanical and antimicrobial roles in maintaining pregnancy. However, compared to other pregnancy tissues (e.g., uterus, cervix, placenta), they are understudied. Their low thickness (<800 µm) places them outside the resolution limits of most ultrasound and magnetic resonance scanners. As such, optical imaging methods like OCT have the potential to fill this technical gap. Here, an application of OCT imaging and machine learning for studying (ex vivo) the mechanical properties of the multilayered fetal membranes and correlating them with gestation and birth condition (i.e., labored vs. unlabored), and anatomy (i.e., near vs. far from cervix) is presented.
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