SUMMARYThe tumor suppressor maspin (mammary serpin) was originally identified as a component of human mammary epithelial cells that is downregulated as mammary tumor cells progress from the benign to the invasive and metastatic states. Maspin inhibits cellular invasion, motility, and proliferation, but its mechanism of action is currently unknown. Because the cellular machinery responsible for these processes is cytoplasmic, we have reexamined the tissue distribution and subcellular localization of maspin. We find that maspin, or a maspin-like protein, is present in many human organs, in which it localizes to epithelia. In cultured human mammary myoepithelial cells, maspin is predominantly a soluble cytoplasmic protein that associates with secretory vesicles and is present at the cell surface. In vitro assays show that the vesicle association is due to the existence of an uncleaved facultative secretion signal that allows small amounts of maspin to partition into the endoplasmic reticulum. These results demonstrate that maspin is more widespread than previously believed. The subcellular localization studies indicate that soluble intracellular and vesicleassociated maspin probably play an important role in controlling the invasion, motility, and proliferation of cells expressing it, whereas extracellular maspin may also regulate these processes in adjacent cells.

A novel technique for the quantitative observation of cell migration along linear gradient substrates functionalized with adhesive proteins is presented. Gradients of the cell adhesion molecule fibronectin are generated by the cross diffusion of functionalizable alkanethiols on gold and characterized by X-ray photoelectron spectroscopy and surface plasmon resonance. Two distinct migration assays are described that characterize the movement of either sparsely populated noncontacting cells or a confluent monolayer of cells into free space. The drift speed of bovine aortic endothelial cells is measured and shown to increase along a fibronectin gradient when compared to a uniform control substrate using both assays. The results of these experiments establish reproducible conditions for studies of cell migration on gradients of surface-bound ligands.

Fluorescence lifetime imaging (FLI) provides unique quantitative information in biomedical and molecular biology studies but relies on complex data-fitting techniques to derive the quantities of interest. Herein, we propose a fit-free approach in FLI image formation that is based on deep learning (DL) to quantify fluorescence decays simultaneously over a whole image and at fast speeds. We report on a deep neural network (DNN) architecture, named fluorescence lifetime imaging network (FLI-Net) that is designed and trained for different classes of experiments, including visible FLI and near-infrared (NIR) FLI microscopy (FLIM) and NIR gated macroscopy FLI (MFLI). FLI-Net outputs quantitatively the spatially resolved lifetime-based parameters that are typically employed in the field. We validate the utility of the FLI-Net framework by performing quantitative microscopic and preclinical lifetime-based studies across the visible and NIR spectra, as well as across the 2 main data acquisition technologies. These results demonstrate that FLI-Net is well suited to accurately quantify complex fluorescence lifetimes in cells and, in real time, in intact animals without any parameter settings. Hence, FLI-Net paves the way to reproducible and quantitative lifetime studies at unprecedented speeds, for improved dissemination and impact of FLI in many important biomedical applications ranging from fundamental discoveries in molecular and cellular biology to clinical translation.

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