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The success of chemotherapy and radiotherapy anti-cancer treatments can result in tumor suppression or senescence induction. Senescence was previously considered a favorable therapeutic outcome, until recent advancements in oncology research evidenced senescence as one of the culprits of cancer recurrence. Its detection requires multiple assays, and nonlinear optical (NLO) microscopy provides a solution for fast, non-invasive, and label-free detection of therapy-induced senescent cells. Here, we develop several deep learning architectures to perform binary classification between senescent and proliferating human cancer cells using NLO microscopy images and we compare their performances. As a result of our work, we demonstrate that the most performing approach is the one based on an ensemble classifier, that uses seven different pre-trained classification networks, taken from literature, with the addition of fully connected layers on top of their architectures. This approach achieves a classification accuracy of over 90%, showing the possibility of building an automatic, unbiased senescent cells image classifier starting from multimodal NLO microscopy data. Our results open the way to a deeper investigation of senescence classification via deep learning techniques with a potential application in clinical diagnosis.

Wild-type p53 cancer therapy-induced senescent cells frequently engulf and degrade neighboring ones inside a massive vacuole in their cytoplasm. After clearance of the internalized cell, the vacuole persists, seemingly empty, for several hours. Despite large vacuoles being associated with cell death, this process is known to confer a survival advantage to cancer engulfing cells, leading to therapy resistance and tumor relapse. Previous attempts to resolve the vacuolar structure and visualize their content using dyes were unsatisfying for lack of known targets and ineffective dye penetration and/or retention. Here, we overcame this problem by applying optical diffraction tomography and Raman spectroscopy to MCF7 doxorubicin-induced engulfing cells. We demonstrated a real ability of cell tomography and Raman to phenotype complex microstructures, such as cell-in-cells and vacuoles, and detect chemical species in extremely low concentrations within live cells in a completely label-free fashion. We show that vacuoles had a density indistinguishable to the medium, but were not empty, instead contained diluted cell-derived macromolecules, and we could discern vacuoles from medium and cells using their Raman fingerprint. Our approach is useful for the noninvasive investigation of senescent engulfing (and other peculiar) cells in unperturbed conditions, crucial for a better understanding of complex biological processes.

Substantial epidemiological evidence indicates that a diet rich in polyphenols protects against developing type 2 diabetes. The phenylethanoid glycoside verbascoside/acteoside, a widespread polyphenolic plant compound, has several biological properties including strong antioxidant, anti-inflammatory and neuroprotective activities. The aim of this research was to test the possible effects of verbascoside on pancreatic β-cells, a target never tested before. Mouse and human β-cells were incubated with verbascoside (0.8–16 µM) for up to five days and a combination of biochemical and imaging techniques were used to assess the β-cell survival and function under normal or endoplasmic reticulum (ER)-stress inducing conditions. We found a dose-dependent protective effect of verbascoside against oxidative stress in clonal and human β-cells. Mechanistic studies revealed that the polyphenol protects β-cells against ER-stress mediated dysfunctions, modulating the activation of the protein kinase RNA-like endoplasmic reticulum kinase (PERK) branch of the unfolded protein response and promoting mitochondrial dynamics. As a result, increased viability, mitochondrial function and insulin content were detected in these cells. These studies provide the evidence that verbascoside boosts the ability of β-cells to cope with ER-stress, an important contributor of β-cell dysfunction and failure in diabetic conditions and support the therapeutic potential of verbascoside in diabetes.