ReViMS: Software tool for estimating the volumes of 3-D multicellular spheroids imaged using a light sheet fluorescence microscope

Replacement, Reduction, and Refinement of Animal Experiments in Anticancer Drug Development: The Contribution of 3D In Vitro Cancer Models in the Drug Efficacy Assessment

In the last decades three-dimensional (3D) in vitro cancer models have been proposed as a bridge between bidimensional (2D) cell cultures and in vivo animal models, the gold standards in the preclinical assessment of anticancer drug efficacy. 3D in vitro cancer models can be generated through a multitude of techniques, from both immortalized cancer cell lines and primary patient-derived tumor tissue. Among them, spheroids and organoids represent the most versatile and promising models, as they faithfully recapitulate the complexity and heterogeneity of human cancers. Although their recent applications include drug screening programs and personalized medicine, 3D in vitro cancer models have not yet been established as preclinical tools for studying anticancer drug efficacy and supporting preclinical-to-clinical translation, which remains mainly based on animal experimentation. In this review, we describe the state-of-the-art of 3D in vitro cancer models for the efficacy evaluation of anticancer agents, focusing on their potential contribution to replace, reduce and refine animal experimentations, highlighting their strength and weakness, and discussing possible perspectives to overcome current challenges.

Precision Medicine in Head and Neck Cancers: Genomic and Preclinical Approaches

Head and neck cancers (HNCs) represent the sixth most widespread malignancy worldwide. Surgery, radiotherapy, chemotherapeutic and immunotherapeutic drugs represent the main clinical approaches for HNC patients. Moreover, HNCs are characterised by an elevated mutational load; however, specific genetic mutations or biomarkers have not yet been found. In this scenario, personalised medicine is showing its efficacy. To study the reliability and the effects of personalised treatments, preclinical research can take advantage of next-generation sequencing and innovative technologies that have been developed to obtain genomic and multi-omic profiles to drive personalised treatments. The crosstalk between malignant and healthy components, as well as interactions with extracellular matrices, are important features which are responsible for treatment failure. Preclinical research has constantly implemented in vitro and in vivo models to mimic the natural tumour microenvironment. Among them, 3D systems have been developed to reproduce the tumour mass architecture, such as biomimetic scaffolds and organoids. In addition, in vivo models have been changed over the last decades to overcome problems such as animal management complexity and time-consuming experiments. In this review, we will explore the new approaches aimed to improve preclinical tools to study and apply precision medicine as a therapeutic option for patients affected by HNCs.

A machine learning pipeline revealing heterogeneous responses to drug perturbations on vascular smooth muscle cell spheroid morphology and formation

Machine learning approaches have shown great promise in biology and medicine discovering hidden information to further understand complex biological and pathological processes. In this study, we developed a deep learning-based machine learning algorithm to meaningfully process image data and facilitate studies in vascular biology and pathology. Vascular injury and atherosclerosis are characterized by neointima formation caused by the aberrant accumulation and proliferation of vascular smooth muscle cells (VSMCs) within the vessel wall. Understanding how to control VSMC behaviors would promote the development of therapeutic targets to treat vascular diseases. However, the response to drug treatments among VSMCs with the same diseased vascular condition is often heterogeneous. Here, to identify the heterogeneous responses of drug treatments, we created an in vitro experimental model system using VSMC spheroids and developed a machine learning-based computational method called HETEROID (heterogeneous spheroid). First, we established a VSMC spheroid model that mimics neointima-like formation and the structure of arteries. Then, to identify the morphological subpopulations of drug-treated VSMC spheroids, we used a machine learning framework that combines deep learning-based spheroid segmentation and morphological clustering analysis. Our machine learning approach successfully showed that FAK, Rac, Rho, and Cdc42 inhibitors differentially affect spheroid morphology, suggesting that multiple drug responses of VSMC spheroid formation exist. Overall, our HETEROID pipeline enables detailed quantitative drug characterization of morphological changes in neointima formation, that occurs in vivo, by single-spheroid analysis.

SpheroidJ: An Open-Source Set of Tools for Spheroid Segmentation

BACKGROUND AND OBJECTIVES

Spheroids are the most widely used 3D models for studying the effects of different micro-environmental characteristics on tumour behaviour, and for testing different preclinical and clinical treatments. In order to speed up the study of spheroids, imaging methods that automatically segment and measure spheroids are instrumental; and, several approaches for automatic segmentation of spheroid images exist in the literature. However, those methods fail to generalise to a diversity of experimental conditions. The aim of this work is the development of a set of tools for spheroid segmentation that works in a diversity of settings.

METHODS

In this work, we have tackled the spheroid segmentation task by first developing a generic segmentation algorithm that can be easily adapted to different scenarios. This generic algorithm has been employed to reduce the burden of annotating a dataset of images that, in turn, has been employed to train several deep learning architectures for semantic segmentation. Both our generic algorithm and the constructed deep learning models have been tested with several datasets of spheroid images where the spheroids were grown under several experimental conditions, and the images acquired using different equipment.

RESULTS

The developed generic algorithm can be particularised to different scenarios; however, those particular algorithms fail to generalise to different conditions. By contrast, the best deep learning model, constructed using the HRNet-Seg architecture, generalises properly to a diversity of scenarios. In order to facilitate the dissemination and use of our algorithms and models, we present SpheroidJ, a set of open-source tools for spheroid segmentation.

CONCLUSIONS

In this work, we have developed an algorithm and trained several models for spheroid segmentation that can be employed with images acquired under different conditions. Thanks to this work, the analysis of spheroids acquired under different conditions will be more reliable and comparable; and, the developed tools will help to advance our understanding of tumour behaviour.

Digital Spindle: A New Way to Explore Mitotic Functions by Whole Cell Data Collection and a Computational Approach

From cells to organisms, every living system is three-dimensional (3D), but the performance of fluorescence microscopy has been largely limited when attempting to obtain an overview of systems' dynamic processes in three dimensions. Recently, advanced light-sheet illumination technologies, allowing drastic improvement in spatial discrimination, volumetric imaging times, and phototoxicity/photobleaching, have been making live imaging to collect precise and reliable 3D information increasingly feasible. In particular, lattice light-sheet microscopy (LLSM), using an ultrathin light-sheet, enables whole-cell 3D live imaging of cellular processes, including mitosis, at unprecedented spatiotemporal resolution for extended periods of time. This technology produces immense and complex data, including a significant amount of information, raising new challenges for big image data analysis and new possibilities for data utilization. Once the data are digitally archived in a computer, the data can be reused for various purposes by anyone at any time. Such an information science approach has the potential to revolutionize the use of bioimage data, and provides an alternative method for cell biology research in a data-driven manner. In this article, we introduce examples of analyzing digital mitotic spindles and discuss future perspectives in cell biology.

A three dimensional model of multicellular aggregate compression

Multicellular aggregates are an excellent model system to explore the role of tissue biomechanics, which has been demonstrated to play a crucial role in many physiological and pathological processes. In this paper, we propose a three-dimensional mechanical model and apply it to the uniaxial compression of a multicellular aggregate in a realistic biological setting. In particular, we consider an aggregate of initially spherical shape and describe both its elastic deformations and the reorganisation of the cells forming the spheroid. The latter phenomenon, understood as remodelling, is accounted for by assuming that the aggregate undergoes plastic-like distortions. The study of the compression of the spheroid, achieved by means of two parallel, compressive plates, needs the formulation of a contact problem between the living spheroid itself and the plates, and is solved with the aid of the augmented Lagrangian method. The results of the performed numerical simulations are in qualitative agreement with the biological observations reported in the literature and can also be used to estimate quantitatively some fundamental aggregate mechanical parameters.

Anti-tumor Efficacy Assessment of the Sigma Receptor Pan Modulator RC-106. A Promising Therapeutic Tool for Pancreatic Cancer

Introduction: Pancreatic cancer (PC) is one of the most lethal tumor worldwide, with no prognosis improvement over the past 20-years. The silent progressive nature of this neoplasia hampers the early diagnosis, and the surgical resection of the tumor, thus chemotherapy remains the only available therapeutic option. Sigma receptors (SRs) are a class of receptors proposed as new cancer therapeutic targets due to their over-expression in tumor cells and their involvement in cancer biology. The main localization of these receptors strongly suggests their potential role in ER unfolded protein response (ER-UPR), a condition frequently occurring in several pathological settings, including cancer. Our group has recently identified RC-106, a novel pan-SR modulator with good in vitro antiproliferative activities toward a panel of different cancer cell lines. In the present study, we investigated the in vitro properties and pharmacological profile of RC-106 in PC cell lines with the aim to identify a potential lead candidate for the treatment of this tumor.

Methods: Pancreatic cancer cell lines Panc-1, Capan-1, and Capan-2 have been used in all experiments. S1R and TMEM97/S2R expression in PC cell lines was quantified by Real-Time qRT-PCR and Western Blot experiments. MTS assay was used to assess the antiproliferative effect of RC-106. The apoptotic properties of RC-106 was evaluated by TUNEL and caspase activation assays. GRP78/BiP, ATF4, and CHOP was quantified to evaluate ER-UPR. Proteasome activity was investigated by a specific fluorescent-based assay. Scratch wound healing assay was used to asses RC-106 effect on cell migration. In addition, we delineated the in vivo pharmacokinetic profile and pancreas distribution of RC-106 in male CD-1 mice.

Results: Panc-1, Capan-1, and Capan-2 express both SRs. RC-106 exerts an antiproliferative and pro-apoptotic effect in all examined cell lines. Cells exposure to RC-106 induces the increase of the expression of ER-UPR related proteins, and the inhibition of proteasome activity. Moreover, RC-106 is able to decrease PC cell lines motility. The in vivo results show that RC-106 is more concentrated in pancreas than plasma.

Conclusion: Overall, our data evidenced that the pan-SR modulator RC-106 is an optimal candidate for in vivo studies in animal models of PC.