1,720,973 research outputs found
Non-destructive monitoring of 3D cell cultures: new technologies and applications
Full text link3D 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 Overall, an extensive technology shift is required, in order for monolayer cultures to be superseded, but the potential benefit derived from an increased accuracy of in vitro studies, justifies the effort and the investment
A computational approach to the automatic classification of Mesenchymal vs Epithelial Cancer cell phenotype
No full textReducing phenotypic variability in Synthetic Biology applications via post-transcriptional control of noise in gene expression
No full textin vitro/ in silico analysis of phenotypic noise under transcriptional and post-transcriptional control in elementary synthetic gene circuits
No full textAIM: A Computational Tool for the Automatic Quantification of Scratch Wound Healing Assays
Full text linkCell invasiveness quantification is of paramount importance in cancer research and is often evaluated in vitro through scratch wound healing assays that determine the rate at which a population of cells fills a gap created in a confluent 2D culture. The quantification of the results of this experiment, however, lacks standardization and is often highly time consuming and user dependent. To overcome these limitations, we have developed AIM (Automatic Invasiveness Measure), a freely-available software tool for the automatic quantification of the cell-free region in scratch wound healing assays. This study will completely describe AIM and will show its equivalence to three analysis methods commonly used for the quantification of the scratch area and the measure of true wound extension. Furthermore, the analysis time and the dependency of the results of these techniques on the structure of the time course (total duration and number of points) will be studied. To the best of our knowledge, AIM is the first entirely-automated analysis method for scratch wound healing assays and represents a significant improvement of this technique both in terms of results’ quality and reliability
Computational models to explore the complexity of the epithelial to mesenchymal transition in cancer
No full textEpithelial 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
Accurate Identification of Cancer Cells in Complex Pre‐Clinical Models Using a Deep‐Learning Neural Network: A Transfection‐Free Approach
Full text link3D 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
A comparative analysis of 2D and 3D experimental data for the identification of the parameters of computational models
Full text link: 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
Fiber Thickness and Porosity Control in a Biopolymer Scaffold 3D Printed through a Converted Commercial FDM Device
Full text link3D 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
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