Much information is accumulating on the effect of cerium oxide nanoparticles (CNPs) as cell-protective agents, reducing oxidative stress through their unique ability of scavenging noxious reactive oxygen species via an energy-free, auto-regenerative redox cycle, where superoxides and peroxides are sequentially reduced exploiting the double valence (Ce3+/Ce4+) on nanoparticle surface. In vitro and in vivo studies consistently report that CNPs are responsible for attenuating and preventing almost any oxidative damage and pathology. Particularly, CNPs were found to exert strong anticancer activities, helping correcting the aberrant homeostasis of cancer microenvironment, normalizing stroma-epithelial communication, contrasting angiogenesis, and strengthening the immune response, leading to reduction of tumor mass in vivo. Since these homeostatic alterations are of an oxidative nature, their relief is generally attributed to CNPs redox activity. Other studies however reported that CNPs exert selective cytotoxic activity against cancer cells and sensitize cancer cells to chemotherapy- and radiotherapy-induced apoptosis: such effects are hardly the result of antioxidant activity, suggesting that CNPs exert such important anticancer effects through additional, non-redox mechanisms. Indeed, using Sm-doped CNPs devoid of redox activity, we could recently demonstrate that the radio-sensitizing effect of CNPs on human keratinocytes is independent from the redox switch. Mechanisms involving particle dissolution with release of toxic Ce4+ atoms, or differential inhibition of the catalase vs. SOD-mimetic activity with accumulation of H2O2 have been proposed, explaining such intriguing findings only partially. Much effort is urgently required to address the unconventional mechanisms of the non-redox bioactivity of CNPs, which may provide unexpected medicinal tools against cancer.

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We describe a novel method to achieve a universal, massive, and fully automated analysis of cell motility behaviours, starting from time-lapse microscopy images. the approach was inspired by the recent successes in application of machine learning for style recognition in paintings and artistic style transfer. the originality of the method relies i) on the generation of atlas from the collection of single-cell trajectories in order to visually encode the multiple descriptors of cell motility, and ii) on the application of pre-trained Deep Learning convolutional neural network architecture in order to extract relevant features to be used for classification tasks from this visual atlas. Validation tests were conducted on two different cell motility scenarios: 1) a 3D biomimetic gels of immune cells, co-cultured with breast cancer cells in organ-on-chip devices, upon treatment with an immunotherapy drug; 2) Petri dishes of clustered prostate cancer cells, upon treatment with a chemotherapy drug. for each scenario, single-cell trajectories are very accurately classified according to the presence or not of the drugs. This original approach demonstrates the existence of universal features in cell motility (a so called "motility style") which are identified by the DL approach in the rationale of discovering the unknown message in cell trajectories. Cell motility is fundamental for life, along the entire evolutionary tree, being involved in bacteria collective motion 1 , in the morphogenesis of pluricellular organisms 2 , in adult physiological process (such as tissue repair and immune cell trafficking) 3 and in some pathologies (such as cancer metastasis) 4-7. Nature evolved a variety of cell motility modes, single-cell or collective, mesenchymal or amoeboid, random or directed, etc. Yet, since the driving force of cell motility is always the active reorganization of the cellular cytoskeleton, it is reasonable to assume that some universal principles of cell motility behaviours have been conserved. We applied machine learning approach to explore this hypothesis exploiting Deep Learning (DL) architecture, by presenting a novel tool called Deep Tracking. DL is a recent machine learning framework 8 developed on the basis of the human brain machine. DL technique learns how to extract the "style" of an atlas of digital images (like the style from an atlas of an artist's paintings 9,10) in order to represent a given set of pictures in terms of most relevant quantitative descriptors (i.e., features) 8. We addressed the question of whether DL could be proficient in extracting the motility styles, i.e. the paintings drawn by cells while moving. Typically, cell motility experiments use time-lapse microscopy imaging (Fig. 1). Starting from the image stacks (Fig. 1A), video processing methods are used to track cell trajectories (see the description of the Cell Hunter tool 11,12 in Steps 2 and 3, Methods section) (Fig. 1B). The first step of our Deep Tracking method relies on the assembly of the individual cell tracks collected for e...

Apoptotic cells stimulate compensatory proliferation through the caspase-3-cPLA-2-COX-2-PGE-2-STAT3 Phoenix Rising pathway as a healing process in normal tissues. Phoenix Rising is however usurped in cancer, potentially nullifying pro-apoptotic therapies. Cytotoxic therapies also promote cancer cell plasticity through epigenetic reprogramming, leading to epithelial-to-mesenchymal-transition (EMT), chemo-resistance and tumor progression. We explored the relationship between such scenarios, setting-up an innovative, straightforward one-pot in vitro model of therapy-induced prostate cancer repopulation. Cancer (castration-resistant PC3 and androgen-sensitive LNCaP), or normal (RWPE-1) prostate cells, are treated with etoposide and left recovering for 18 days. After a robust apoptotic phase, PC3 setup a coordinate tissue-like response, repopulating and acquiring EMT and chemo-resistance; repopulation occurs via Phoenix Rising, being dependent on high PGE-2 levels achieved through caspase-3-promoted signaling; epigenetic inhibitors interrupt Phoenix Rising after PGE-2, preventing repopulation. Instead, RWPE-1 repopulate via Phoenix Rising without reprogramming, EMT or chemo-resistance, indicating that only cancer cells require reprogramming to complete Phoenix Rising. Intriguingly, LNCaP stop Phoenix-Rising after PGE-2, failing repopulating, suggesting that the propensity to engage/complete Phoenix Rising may influence the outcome of pro-apoptotic therapies. Concluding, we established a reliable system where to study prostate cancer repopulation, showing that epigenetic reprogramming assists Phoenix Rising to promote post-therapy cancer repopulation and acquired cell-resistance (CRAC).

Cerium oxide nanoparticles (CNPs) are potent radical scavengers protecting cells from oxidative insults, including ionizing radiation. Here we show that CNPs prevent X-ray-induced oxidative imbalance reducing DNA breaks on HaCat keratinocytes, nearly abating mutagenesis. At the same time, and in spite of the reduced damage, CNPs strengthen radiation-induced cell cycle arrest and apoptosis outcome, dropping colony formation; notably, CNPs do not possess any intrinsic toxicity toward non-irradiated HaCat, indicating that they act on damaged cells. Thus CNPs, while exerting their antioxidant action, also reinforce the stringency of damage-induced cell integrity checkpoints, promoting elimination of the “tolerant” cells, being in fact radio-sensitizers. These two contrasting pathways are mediated by different activities of CNPs: indeed Sm-doped CNPs, which lack the Ce3+/Ce4+ redox switch and the correlated antioxidant action, fail to decrease radiation-induced superoxide formation, as expected, but surprisingly maintain the radio-sensitizing ability and the dramatic decrease of mutagenesis. The latter is thus attributable to elimination of damaged cells rather than decreased oxidative damage. This highlights a novel redox-independent activity of CNPs, allowing selectively eliminating heavily damaged cells through non-toxic mechanisms, rather reactivating endogenous anticancer pathways in transformed cells.

Cell motility is the brilliant result of cell status and its interaction with close environments. Its detection is now possible, thanks to the synergy of high-resolution camera sensors, time-lapse microscopy devices, and dedicated software tools for video and data analysis. In this scenario, we formulated a novel paradigm in which we considered the individual cells as a sort of sensitive element of a sensor, which exploits the camera as a transducer returning the movement of the cell as an output signal. In this way, cell movement allows us to retrieve information about the chemical composition of the close environment. To optimally exploit this information, in this work, we introduce a new setting, in which a cell trajectory is divided into sub-tracks, each one characterized by a specific motion kind. Hence, we considered all the sub-tracks of the single-cell trajectory as the signals of a virtual array of cell motility-based sensors. The kinematics of each sub-track is quantified and used for a classification task. To investigate the potential of the proposed approach, we have compared the achieved performances with those obtained by using a single-trajectory paradigm with the scope to evaluate the chemotherapy treatment effects on prostate cancer cells. Novel pattern recognition algorithms have been applied to the descriptors extracted at a sub-track level by implementing features, as well as samples selection (a good teacher learning approach) for model construction. The experimental results have put in evidence that the performances are higher when a further cluster majority role has been considered, by emulating a sort of sensor fusion procedure. All of these results highlighted the high strength of the proposed approach, and straightforwardly prefigure its use in lab-on-chip or organ-on-chip applications, where the cell motility analysis can be massively applied using time-lapse microscopy images.

Modulation of macrophage plasticity is emerging as a successful strategy in tissue engineering (TE) to control the immune response elicited by the implanted material. Indeed, one major determinant of success in regenerating tissues and organs is to achieve the correct balance between immune pro-inflammatory and pro-resolution players. In recent years, nanoparticle-mediated macrophage polarization towards the pro- or anti-inflammatory subtypes is gaining increasing interest in the biomedical field. In TE, despite significant progress in the use of nanomaterials, the full potential of nanoparticles as effective immunomodulators has not yet been completely realized. This work discusses the contribution that nanotechnology gives to TE applications, helping native or synthetic scaffolds to direct macrophage polarization; here, three bioactive metallic and ceramic nanoparticles (gold, titanium oxide, and cerium oxide nanoparticles) are proposed as potential valuable tools to trigger skeletal muscle regeneration.

The incremented uptake provided by time-lapse microscopy in Organ-on-a-Chip (OoC) devices allowed increased attention to the dynamics of the co-cultured systems. However, the amount of information stored in long-time experiments may constitute a serious bottleneck of the experimental pipeline. Forward long-term prediction of cell trajectories may reduce the spatial–temporal burden of video sequences storage. Cell trajectory prediction becomes crucial especially to increase the trustworthiness in software tools designed to conduct a massive analysis of cell behavior under chemical stimuli. To address this task, we transpose here the exploitation of the presence of “social forces” from the human to the cellular level for motion prediction at microscale by adapting the potential of Social Generative Adversarial Network predictors to cell motility. To demonstrate the effectiveness of the approach, we consider here two case studies: one related to PC-3 prostate cancer cells cultured in 2D Petri dishes under control and treated conditions and one related to an OoC experiment of tumor-immune interaction in fibrosarcoma cells. The goodness of the proposed strategy has been verified by successfully comparing the distributions of common descriptors (kinematic descriptors and mean interaction time for the two scenarios respectively) from the trajectories obtained by video analysis and the predicted counterparts.