Analysis of Intracellular Magnesium and Mineral Depositions during Osteogenic Commitment of 3D Cultured Saos2 Cells.

In this study, we explore the behaviour of intracellular magnesium during bone phenotype modulation in a 3D cell model built to mimic osteogenesis. In addition, we measured the amount of magnesium in the mineral depositions generated during osteogenic induction. A two-fold increase of intracellular magnesium content was found, both at three and seven days from the induction of differentiation. By X-ray microscopy, we characterized the morphology and chemical composition of the mineral depositions secreted by 3D cultured differentiated cells finding a marked co-localization of Mg with P at seven days of differentiation. This is the first experimental evidence on the presence of Mg in the mineral depositions generated during biomineralization, suggesting that Mg incorporation occurs during the bone forming process. In conclusion, this study on the one hand attests to an evident involvement of Mg in the process of cell differentiation, and, on the other hand, indicates that its multifaceted role needs further investigation.

Driving cell response through deep learning, a study in simulated 3D cell cultures.

Computational simulations are becoming increasingly relevant in biomedical research, providing strategies to reproduce experimental results, improve the resolution of in-vitro experiments, and predict the system's behavior in untested conditions. Their use to determine the features associated with an extensive response to treatment and optimize treatment schedules has, however received little attention. To bridge this gap, we propose a deep learning framework capable of reliably classifying simulated time series data and identifying class-defining features. This information will be shown to be useful for the determination of which changes in treatment schedule elicit a more extensive cellular response. This analysis pipeline will be initially tested on a synthetic dataset created ad-hoc to identify its accuracy in identifying the most relevant portion of the signals. Successively this method will be applied to simulations describing the behaviors of populations of cancer cells treated with either one or two drugs in different concentrations. The proposed method will be shown to be effective in identifying which changes in the treatment protocol lead to a more extensive response to treatment. While lacking direct experimental validation, this result holds great potential for the integration of in-silico and in-vitro analyses and the effective optimization of experimental conditions in complex experimental setups.

Beyond 2D cell cultures: how 3D models are changing the in vitro study of ovarian cancer and how to make the most of them.

3D cell cultures are a fundamental tool in ovarian cancer research that can enable more effective study of the main features of this lethal disease, including the high rates of recurrence and chemoresistance. A clearer, more comprehensive understanding of the biological underpinnings of these phenomena could aid the development of more effective treatments thus improving patient outcomes. Selecting the most appropriate model to investigate the different aspects of cell biology that are relevant to cancer is challenging, especially since the assays available for the study of 3D cultures are not fully established yet. To maximise the usefulness of 3D cell cultures of ovarian cancer, we undertook an in-depth review of the currently available models, taking into consideration the strengths and limitations of each approach and of the assay techniques used to evaluate the results. This integrated analysis provides insight into which model-assay pair is best suited to study different parameters of ovarian cancer biology such as cell proliferation, gene expression or treatment response. We also describe how the combined use of multiple models is likely to be the most effective strategy for the in vitro characterisation of complex behaviours.

Accurate Identification of Cancer Cells in Complex Pre-Clinical Models Using a Deep-Learning Neural Network: A Transfection-Free Approach.

3D co-cultures are key tools for in vitro biomedical research as they recapitulate more closely the in vivo environment while allowing a tighter control on the culture's composition and experimental conditions. The limited technologies available for the analysis of these models, however, hamper their widespread application. The separation of the contribution of the different cell types, in particular, is a fundamental challenge. In this work, ORACLE (OvaRiAn Cancer ceLl rEcognition) is presented, a deep neural network trained to distinguish between ovarian cancer and healthy cells based on the shape of their nucleus. The extensive validation that are conducted includes multiple cell lines and patient-derived cultures to characterize the effect of all the major potential confounding factors. High accuracy and reliability are maintained throughout the analysis (F1score> 0.9 and Area under the ROC curve -ROC-AUC- score = 0.99) demonstrating ORACLE's effectiveness with this detection and classification task. ORACLE is freely available (https://github.com/MarilisaCortesi/ORACLE/tree/main) and can be used to recognize both ovarian cancer cell lines and primary patient-derived cells. This feature is unique to ORACLE and thus enables for the first time the analysis of in vitro co-cultures comprised solely of patient-derived cells.

Cell-free DNA from ascites identifies clinically relevant variants and tumour evolution in patients with advanced ovarian cancer.

The emergence of targeted therapies has transformed ovarian cancer treatment. However, biomarker profiling for precision medicine is limited by access to quality, tumour-enriched tissue samples. The use of cell-free DNA (cfDNA) in ascites presents a potential solution to this challenge. In this study, next-generation sequencing was performed on ascites-derived cfDNA samples (26 samples from 15 human participants with ovarian cancer), with matched DNA from ascites-derived tumour cells (n = 5) and archived formalin-fixed paraffin-embedded (FFPE) tissue (n = 5). Similar tumour purity and variant detection were achieved with cfDNA compared to FFPE and ascites cell DNA. Analysis of large-scale genomic alterations, loss of heterozygosity and tumour mutation burden identified six cases of high genomic instability (including four with pathogenic BRCA1 and BRCA2 mutations). Copy number profiles and subclone prevalence changed between sequential ascites samples, particularly in a case where deletions and chromothripsis in Chr17p13.1 and Chr8q resulted in changes in clinically relevant TP53 and MYC variants over time. Ascites cfDNA identified clinically actionable information, concordant to tissue biopsies, enabling opportunistic molecular profiling. This advocates for analysis of ascites cfDNA in lieu of accessing tumour tissue via biopsy.

A comparative analysis of 2D and 3D experimental data for the identification of the parameters of computational models.

Computational models are becoming an increasingly valuable tool in biomedical research. Their accuracy and effectiveness, however, rely on the identification of suitable parameters and on appropriate validation of the in-silico framework. Both these steps are highly dependent on the experimental model used as a reference to acquire the data. Selecting the most appropriate experimental framework thus becomes key, together with the analysis of the effect of combining results from different experimental models, a common practice often necessary due to limited data availability. In this work, the same in-silico model of ovarian cancer cell growth and metastasis, was calibrated with datasets acquired from traditional 2D monolayers, 3D cell culture models or a combination of the two. The comparison between the parameters sets obtained in the different conditions, together with the corresponding simulated behaviours, is presented. It provides a framework for the study of the effect of the different experimental models on the development of computational systems. This work also provides a set of general guidelines for the comparative testing and selection of experimental models and protocols to be used for parameter optimization in computational models.

Non-destructive monitoring of 3D cell cultures: new technologies and applications.

3D cell cultures are becoming the new standard for cell-based in vitro research, due to their higher transferrability toward in vivo biology. The lack of established techniques for the non-destructive quantification of relevant variables, however, constitutes a major barrier to the adoption of these technologies, as it increases the resources needed for the experimentation and reduces its accuracy. In this review, we aim at addressing this limitation by providing an overview of different non-destructive approaches for the evaluation of biological features commonly quantified in a number of studies and applications. In this regard, we will cover cell viability, gene expression, population distribution, cell morphology and interactions between the cells and the environment. This analysis is expected to promote the use of the showcased technologies, together with the further development of these and other monitoring methods for 3D cell cultures. Overall, an extensive technology shift is required, in order for monolayer cultures to be superseded, but the potential benefit derived from an increased accuracy of in vitro studies, justifies the effort and the investment.

Fiber Thickness and Porosity Control in a Biopolymer Scaffold 3D Printed through a Converted Commercial FDM Device.

3D printing has opened exciting new opportunities for the in vitro fabrication of biocompatible hybrid pseudo-tissues. Technologies based on additive manufacturing herald a near future when patients will receive therapies delivering functional tissue substitutes for the repair of their musculoskeletal tissue defects. In particular, bone tissue engineering (BTE) might extensively benefit from such an approach. However, designing an optimal 3D scaffold with adequate stiffness and biodegradability properties also guaranteeing the correct cell adhesion, proliferation, and differentiation, is still a challenge. The aim of this work was the rewiring of a commercial fuse deposition modeling (FDM) 3D printer into a 3D bioplotter, aiming at obtaining scaffold fiber thickness and porosity control during its manufacturing. Although it is well-established that FDM is a fast and low-price technology, the high temperatures required for printing lead to limitations in the biomaterials that can be used. In our hands, modifying the printing head of the FDM device with a custom-made holder has allowed to print hydrogels commonly used for embedding living cells. The results highlight a good resolution, reproducibility and repeatability of alginate/gelatin scaffolds obtained via our custom 3D bioplotter prototype, showing a viable strategy to equip a small-medium laboratory with an instrument for manufacturing good-quality 3D scaffolds for cell culture and tissue engineering applications.

Development of an electrical impedance tomography set-up for the quantification of mineralization in biopolymer scaffolds.

Objective. 3D cell cultures are becoming a fundamental resource forin-vitrostudies, as they mimic more closelyin-vivobehavior. The analysis of these constructs, however, generally rely on destructive techniques, that prevent the monitoring over time of the same construct, thus increasing the results variability and the resources needed for each experiment.Approach. In this work, we focus on mineralization, a crucial process during maturation of artificial bone models, and propose electrical impedance tomography (EIT) as an alternative non-destructive approach. In particular, we discuss the development of an integrated hardware/software system capable of acquiring experimental data from 3D scaffolds and reconstructing the corresponding conductivity maps. We also show how the same software can test how the measurement is affected by biological features such as scaffold shrinking during the culture.Main results. An initial validation, comprising the acquisition of both a non-conductive phantom and alginate/gelatin scaffolds with known calcium content will be presented, together with thein-silicostudy of a cell-induced mineralization process. This analysis will allow for an initial verification of the systems functionality while limiting the effects of biological variability due to cell number and activity.Significance. Our results show the potential of EIT for the non-destructive quantification of matrix mineralization in 3D scaffolds, and open to the possible long term monitoring of this fundamental hallmark of osteogenic differentiation in hybrid tissue engineered constructs.

A brief very-low oxygen tension regimen is sufficient for the early chondrogenic commitment of human adipose-derived mesenchymal stem cells.

PURPOSE: The aim of this study was to evaluate the effects exerted over chondrogenic commitment of human adipose-derived mesenchymal stem cells (ADSCs) by a very low oxygen tension (<1% pO2). MATERIALS/METHODS: Cell morphology, mRNA levels of chondrocyte-specific marker genes and the involvement of p38 MAPK signalling were monitored in human ADSCs under a very low oxygen tension. RESULTS: Cell morphology was significantly changed after two days of hypoxic preconditioning when they featured as elongated spindle-shaped cells. SRY-box containing gene 9, aggrecan and collagen type II mRNA levels were enhanced under severe hypoxic culture conditions. Moreover, the inhibition of p38 MAPK resulted in a substantial reduction in transcription of the above-mentioned specific genes, proving the pivotal role of this pathway in the transcriptional regulation of chondrogenesis. CONCLUSIONS: Here, we propose a protocol showing the early commitment of stem cells towards the chondrogenic phenotype in only 2 days of culture via a very low hypoxic environment, in the absence of growth factors added in the culture medium.

Perfusion Flow Enhances Viability and Migratory Phenotype in 3D-Cultured Breast Cancer Cells.

Conventional 2D cell culture, a traditional tool in pre-clinical studies, can hardly be regarded as a representation of a natural cell microenvironment. In this respect, it might result in altered cellular behaviors. To overcome such a limitation, different approaches have been tested to conduct more representative in vitro studies. In particular, the use of 3D cell culture introduces variables, such as cell-cell and cell-extracellular matrix interactions; cell features such as survival, proliferation and migration are consequently influenced. For an example, an enhanced drug resistance and increased invasiveness are shown by cancer cells when cultured in 3D versus 2D conventional culture models. In this setting however, non-uniform cell distribution and biological behaviors appear throughout the scaffold, due to reduced diffusion of oxygen and nutrients. Perfusion in bioreactor systems can be used to improve medium transport. In this line of reasoning, this study proposes a breast cancer cell culture model sustained by an integrated approach that couples a 3D environment and a fluid perfusion. This model improves viability and uniformness of cell distribution, while inducing morphological, functional and molecular cancer cell remodeling.

An in-silico study of cancer cell survival and spatial distribution within a 3D microenvironment.

3D cell cultures are in-vitro models representing a significant improvement with respect to traditional monolayers. Their diffusion and applicability, however, are hampered by the complexity of 3D systems, that add new physical variables for experimental analyses. In order to account for these additional features and improve the study of 3D cultures, we here present SALSA (ScAffoLd SimulAtor), a general purpose computational tool that can simulate the behavior of a population of cells cultured in a 3D scaffold. This software allows for the complete customization of both the polymeric template structure and the cell population behavior and characteristics. In the following the technical description of SALSA will be presented, together with its validation and an example of how it could be used to optimize the experimental analysis of two breast cancer cell lines cultured in collagen scaffolds. This work contributes to the growing field of integrated in-silico/in-vitro analysis of biological systems, which have great potential for the study of complex cell population behaviours and could lead to improve and facilitate the effectiveness and diffusion of 3D cell culture models.

Computational models to explore the complexity of the epithelial to mesenchymal transition in cancer.

Epithelial to mesenchymal transition (EMT) is a complex biological process that plays a key role in cancer progression and metastasis formation. Its activation results in epithelial cells losing adhesion and polarity and becoming capable of migrating from their site of origin. At this step the disease is generally considered incurable. As EMT execution involves several individual molecular components, connected by nontrivial relations, in vitro techniques are often inadequate to capture its complexity. Computational models can be used to complement experiments and provide additional knowledge difficult to build up in a wetlab. Indeed in silico analysis gives the user total control on the system, allowing to identify the contribution of each independent element. In the following, two kinds of approaches to the computational study of EMT will be presented. The first relies on signal transduction networks description and details how changes in gene expression could influence this process, both focusing on specific aspects of the EMT and providing a general frame for this phenomenon easily comparable with experimental data. The second integrates single cell and population level descriptions in a multiscale model that can be considered a more accurate representation of the EMT. The advantages and disadvantages of each approach will be highlighted, together with the importance of coupling computational and experimental results. Finally, the main challenges that need to be addressed to improve our knowledge of the role of EMT in the neoplastic disease and the scientific and translational value of computational models in this respect will be presented. This article is categorized under: Analytical and Computational Methods > Computational Methods.

Identification via Numerical Computation of Transcriptional Determinants of a Cell Phenotype Decision Making.

Complex cellular processes, such as phenotype decision making, are exceedingly difficult to analyze experimentally, due to the multiple-layer regulation of gene expression and the intercellular variability referred to as biological noise. Moreover, the heterogeneous experimental approaches used to investigate distinct macromolecular species, and their intrinsic differential time-scale dynamics, add further intricacy to the general picture of the physiological phenomenon. In this respect, a computational representation of the cellular functions of interest can be used to extract relevant information, being able to highlight meaningful active markers within the plethora of actors forming an active molecular network. The multiscale power of such an approach can also provide meaningful descriptions for both population and single-cell level events. To validate this paradigm a Boolean and a Markov model were combined to identify, in an objective and user-independent manner, a signature of genes recapitulating epithelial to mesenchymal transition in-vitro. The predictions of the model are in agreement with experimental data and revealed how the expression of specific molecular markers is related to distinct cell behaviors. The presented method strengthens the evidence of a role for computational representation of active molecular networks to gain insight into cellular physiology and as a general approach for integrating in-silico/in-vitro study of complex cell population dynamics to identify their most relevant drivers.

I-AbACUS: a Reliable Software Tool for the Semi-Automatic Analysis of Invasion and Migration Transwell Assays.

The quantification of invasion and migration is an important aspect of cancer research, used both in the study of the molecular processes involved in this collection of diseases and the evaluation of the efficacy of new potential treatments. The transwell assay, while being one of the most widely used techniques for the evaluation of these characteristics, shows a high dependence on the operator's ability to correctly identify the cells and a low protocol standardization. Here we present I-AbACUS, a software tool specifically designed to aid the analysis of transwell assays that automatically and specifically recognizes cells in images of stained membranes and provides the user with a suggested cell count. A complete description of this instrument, together with its validation against the standard analysis technique for this assay is presented. Furthermore, we show that I-AbACUS is versatile and able to elaborate images containing cells with different morphologies and that the obtained results are less dependent on the operator and their experience. We anticipate that this instrument, freely available (Gnu Public Licence GPL v2) at www.marilisacortesi.com as a standalone application, could significantly improve the quantification of invasion and migration of cancer cells.

Noninvasive quantification of blood potassium concentration from ECG in hemodialysis patients.

Blood potassium concentration ([K+]) influences the electrocardiogram (ECG), particularly T-wave morphology. We developed a new method to quantify [K+] from T-wave analysis and tested its clinical applicability on data from dialysis patients, in whom [K+] varies significantly during the therapy. To elucidate the mechanism linking [K+] and T-wave, we also analysed data from long QT syndrome type 2 (LQT2) patients, testing the hypothesis that our method would have underestimated [K+] in these patients. Moreover, a computational model was used to explore the physiological processes underlying our estimator at the cellular level. We analysed 12-lead ECGs from 45 haemodialysis and 12 LQT2 patients. T-wave amplitude and downslope were calculated from the first two eigenleads. The T-wave slope-to-amplitude ratio (TS/A) was used as starting point for an ECG-based [K+] estimate (KECG). Leave-one-out cross-validation was performed. Agreement between KECG and reference [K+] from blood samples was promising (error: -0.09 ± 0.59 mM, absolute error: 0.46 ± 0.39 mM). The analysis on LQT2 patients, also supported by the outcome of computational analysis, reinforces our interpretation that, at the cellular level, delayed-rectifier potassium current is a main contributor of KECG correlation to blood [K+]. Following a comprehensive validation, this method could be effectively applied to monitor patients at risk for hyper/hypokalemia.

Reliable measurement of E. coli single cell fluorescence distribution using a standard microscope set-up.

BACKGROUND: Quantifying gene expression at single cell level is fundamental for the complete characterization of synthetic gene circuits, due to the significant impact of noise and inter-cellular variability on the system's functionality. Commercial set-ups that allow the acquisition of fluorescent signal at single cell level (flow cytometers or quantitative microscopes) are expensive apparatuses that are hardly affordable by small laboratories. METHODS: A protocol that makes a standard optical microscope able to acquire quantitative, single cell, fluorescent data from a bacterial population transformed with synthetic gene circuitry is presented. Single cell fluorescence values, acquired with a microscope set-up and processed with custom-made software, are compared with results that were obtained with a flow cytometer in a bacterial population transformed with the same gene circuitry. RESULTS: The high correlation between data from the two experimental set-ups, with a correlation coefficient computed over the tested dynamic range > 0.99, proves that a standard optical microscope- when coupled with appropriate software for image processing- might be used for quantitative single-cell fluorescence measurements. The calibration of the set-up, together with its validation, is described. CONCLUSIONS: The experimental protocol described in this paper makes quantitative measurement of single cell fluorescence accessible to laboratories equipped with standard optical microscope set-ups. Our method allows for an affordable measurement/quantification of intercellular variability, whose better understanding of this phenomenon will improve our comprehension of cellular behaviors and the design of synthetic gene circuits. All the required software is freely available to the synthetic biology community (MUSIQ Microscope flUorescence SIngle cell Quantification).

Novel Polyamine-Naphthalene Diimide Conjugates Targeting Histone Deacetylases and DNA for Cancer Phenotype Reprogramming.

A series of hybrid compounds was designed to target histone deacetylases and ds-/G-quadruplex DNAs by merging structural features deriving from Scriptaid and compound 1. Compound 6 binds different DNA arrangements, inhibits HDACs both in vitro and in cells, and is able to induce a reduction of cell proliferation. Moreover, compound 6 displays cell phenotype-reprogramming properties since it prevents the epithelial to mesenchymal transition in cancer cells, inducing a less aggressive and migratory phenotype, which is one of the goals of present innovative strategies in cancer therapies.