Polydopamine Interfacial Coating for Stable Tumor-on-a-Chip Models: Application for Pancreatic Ductal Adenocarcinoma.

Addressing current challenges in solid tumor research requires advanced in vitro three-dimensional (3D) cellular models that replicate the inherently 3D architecture and microenvironment of tumor tissue, including the extracellular matrix (ECM). However, tumor cells exert mechanical forces that can disrupt the physical integrity of the matrix in long-term 3D culture. Therefore, it is necessary to find the optimal balance between cellular forces and the preservation of matrix integrity. This work proposes using polydopamine (PDA) coating for 3D microfluidic cultures of pancreatic cancer cells to overcome matrix adhesion challenges to sustain representative tumor 3D cultures. Using PDA's distinctive adhesion and biocompatibility, our model uses type I collagen hydrogels seeded with different pancreatic cancer cell lines, prompting distinct levels of matrix deformation and contraction. Optimizing the PDA coating enhances the adhesion and stability of collagen hydrogels within microfluidic devices, achieving a balance between the disruptive forces of tumor cells on matrix integrity and the maintenance of long-term 3D cultures. The findings reveal how this tension appears to be a critical determinant in spheroid morphology and growth dynamics. Stable and prolonged 3D culture platforms are crucial for understanding solid tumor cell behavior, dynamics, and responses within a controlled microenvironment. This advancement ultimately offers a powerful tool for drug screening, personalized medicine, and wider cancer therapeutics strategies.

An agent-based method to estimate 3D cell migration trajectories from 2D measurements: Quantifying and comparing T vs CAR-T 3D cell migration.

BACKGROUND AND OBJECTIVE: Immune cell migration is one of the key features that enable immune cells to find invading pathogens, control tissue damage, and eliminate primary developing tumors. Chimeric antigen receptor (CAR) T-cell therapy is a novel strategy in the battle against various cancers. It has been successful in treating hematological tumors, yet it still faces many challenges in the case of solid tumors. In this work, we evaluate the three-dimensional (3D) migration capacity of T and CAR-T cells within dense collagen-based hydrogels. Quantifying three-dimensional (3D) cell migration requires microscopy techniques that may not be readily accessible. Thus, we introduce a straightforward mathematical model designed to infer 3D trajectories of cells from two-dimensional (2D) cell trajectories. METHODS: We develop a 3D agent-based model (ABM) that simulates the temporal changes in the direction of migration with an inverse transform sampling method. Then, we propose an optimization procedure to accurately orient cell migration over time to reproduce cell migration from 2D experimental cell trajectories. With this model, we simulate cell migration assays of T and CAR-T cells in microfluidic devices conducted under hydrogels with different concentrations of type I collagen and validate our 3D cell migration predictions with light-sheet microscopy. RESULTS: Our findings indicate that CAR-T cell migration is more sensitive to collagen concentration increases than T cells, resulting in a more pronounced reduction in their invasiveness. Moreover, our computational model reveals significant differences in 3D movement patterns between T and CAR-T cells. T cells exhibit migratory behavior in 3D whereas that CAR-T cells predominantly move within the XY plane, with limited movement in the Z direction. However, upon the introduction of a CXCL12 chemical gradient, CAR-T cells present migration patterns that closely resemble those of T cells. CONCLUSIONS: This framework demonstrates that 2D projections of 3D trajectories may not accurately represent real migration patterns. Moreover, it offers a tool to estimate 3D migration patterns from 2D experimental data, which can be easily obtained with automatic quantification algorithms. This approach helps reduce the need for sophisticated and expensive microscopy equipment required in laboratories, as well as the computational burden involved in producing and analyzing 3D experimental data.

Physico-chemical characterization of the tumour microenvironment of pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive lethal malignancy that accounts for more than 90% of pancreatic cancer diagnoses. Our research is focused on the physico-chemical properties of the tumour microenvironment (TME), including its tumoural extracellular matrix (tECM), as they may have an important impact on the success of cancer therapies. PDAC xenografts and their decellularized tECM offer a great material source for research in terms of biomimicry with the original human tumour. Our aim was to evaluate and quantify the physico-chemical properties of the PDAC TME. Both cellularized (native TME) and decellularized (tECM) patient-derived PDAC xenografts were analyzed. A factorial design of experiments identified an optimal combination of factors for effective xenograft decellularization. Our results provide a complete advance in our understanding of the PDAC TME and its corresponding stroma, showing that it presents an interconnected porous architecture with very low permeability and small pores due to the contractility of the cellular components. This fact provides a potential therapeutic strategy based on the therapeutic agent size.

Fluid flow to mimic organ function in 3D in vitro models.

Many different strategies can be found in the literature to model organ physiology, tissue functionality, and disease in vitro; however, most of these models lack the physiological fluid dynamics present in vivo. Here, we highlight the importance of fluid flow for tissue homeostasis, specifically in vessels, other lumen structures, and interstitium, to point out the need of perfusion in current 3D in vitro models. Importantly, the advantages and limitations of the different current experimental fluid-flow setups are discussed. Finally, we shed light on current challenges and future focus of fluid flow models applied to the newest bioengineering state-of-the-art platforms, such as organoids and organ-on-a-chip, as the most sophisticated and physiological preclinical platforms.

Effects of tumour heterogeneous properties on modelling the transport of radiative particles.

Dose calculation plays a critical role in radiotherapy (RT) treatment planning, and there is a growing need to develop accurate dose deposition models that incorporate heterogeneous tumour properties. Deterministic models have demonstrated their capability in this regard, making them the focus of recent treatment planning studies as they serve as a basis for simplified models in RT treatment planning. In this study, we present a simplified deterministic model for photon transport based on the Boltzmann transport equation (BTE) as a proof-of-concept to illustrate the impact of heterogeneous tumour properties on RT treatment planning. We employ the finite element method (FEM) to simulate the photon flux and dose deposition in real cases of diffuse intrinsic pontine glioma (DIPG) and neuroblastoma (NB) tumours. Importantly, in light of the availability of pipelines capable of extracting tumour properties from magnetic resonance imaging (MRI) data, we highlight the significance of such data. Specifically, we utilise cellularity data extracted from DIPG and NB MRI images to demonstrate the importance of heterogeneity in dose calculation. Our model simplifies the process of simulating a RT treatment system and can serve as a useful starting point for further research. To simulate a full RT treatment system, one would need a comprehensive model that couples the transport of electrons and photons.

A hybrid physics-based and data-driven framework for cellular biological systems: Application to the morphogenesis of organoids.

How cells orchestrate their cellular functions remains a crucial question to unravel how they organize in different patterns. We present a framework based on artificial intelligence to advance the understanding of how cell functions are coordinated spatially and temporally in biological systems. It consists of a hybrid physics-based model that integrates both mechanical interactions and cell functions with a data-driven model that regulates the cellular decision-making process through a deep learning algorithm trained on image data metrics. To illustrate our approach, we used data from 3D cultures of murine pancreatic ductal adenocarcinoma cells (PDAC) grown in Matrigel as tumor organoids. Our approach allowed us to find the underlying principles through which cells activate different cell processes to self-organize in different patterns according to the specific microenvironmental conditions. The framework proposed here expands the tools for simulating biological systems at the cellular level, providing a novel perspective to unravel morphogenetic patterns.

Hydrocolloids of Egg White and Gelatin as a Platform for Hydrogel-Based Tissue Engineering.

Innovative materials are needed to produce scaffolds for various tissue engineering and regenerative medicine (TERM) applications, including tissue models. Materials derived from natural sources that offer low production costs, easy availability, and high bioactivity are highly preferred. Chicken egg white (EW) is an overlooked protein-based material. Whilst its combination with the biopolymer gelatin has been investigated in the food technology industry, mixed hydrocolloids of EW and gelatin have not been reported in TERM. This paper investigates these hydrocolloids as a suitable platform for hydrogel-based tissue engineering, including 2D coating films, miniaturized 3D hydrogels in microfluidic devices, and 3D hydrogel scaffolds. Rheological assessment of the hydrocolloid solutions suggested that temperature and EW concentration can be used to fine-tune the viscosity of the ensuing gels. Fabricated thin 2D hydrocolloid films presented globular nano-topography and in vitro cell work showed that the mixed hydrocolloids had increased cell growth compared with EW films. Results showed that hydrocolloids of EW and gelatin can be used for creating a 3D hydrogel environment for cell studies inside microfluidic devices. Finally, 3D hydrogel scaffolds were fabricated by sequential temperature-dependent gelation followed by chemical cross-linking of the polymeric network of the hydrogel for added mechanical strength and stability. These 3D hydrogel scaffolds displayed pores, lamellae, globular nano-topography, tunable mechanical properties, high affinity for water, and cell proliferation and penetration properties. In conclusion, the large range of properties and characteristics of these materials provide a strong potential for a large variety of TERM applications, including cancer models, organoid growth, compatibility with bioprinting, or implantable devices.

Collagen-Laponite Nanoclay Hydrogels for Tumor Spheroid Growth.

The extracellular matrix (ECM) plays an important regulatory role in the development and progression of tumoral tissue. Its functions and properties are crucial in determining tumor cell behavior such as invasion, migration, and malignancy development. Our study explores the role of collagen type I in cancer development and spread using engineered tumor models like multicellular spheroids grown in collagen-based hydrogels to simulate early tumor formation. We employ microfluidic techniques to test the hypothesis that (i) adding Laponite nanoclay to collagen hydrogels modifies mechanical and rheological properties and (ii) changing the stiffness of the collagen microenvironment affects tumor spheroid growth. Our findings support our theories and suggest the use of ECM components and engineered tumor models in cancer research, offering a biocompatible and biomimetic method to tailor the mechanical properties of conventional collagen hydrogels.

Tumour growth: An approach to calibrate parameters of a multiphase porous media model based on in vitro observations of Neuroblastoma spheroid growth in a hydrogel microenvironment.

To unravel processes that lead to the growth of solid tumours, it is necessary to link knowledge of cancer biology with the physical properties of the tumour and its interaction with the surrounding microenvironment. Our understanding of the underlying mechanisms is however still imprecise. We therefore developed computational physics-based models, which incorporate the interaction of the tumour with its surroundings based on the theory of porous media. However, the experimental validation of such models represents a challenge to its clinical use as a prognostic tool. This study combines a physics-based model with in vitro experiments based on microfluidic devices used to mimic a three-dimensional tumour microenvironment. By conducting a global sensitivity analysis, we identify the most influential input parameters and infer their posterior distribution based on Bayesian calibration. The resulting probability density is in agreement with the scattering of the experimental data and thus validates the proposed workflow. This study demonstrates the huge challenges associated with determining precise parameters with usually only limited data for such complex processes and models, but also demonstrates in general how to indirectly characterise the mechanical properties of neuroblastoma spheroids that cannot feasibly be measured experimentally.

PhysiCOOL: A generalized framework for model Calibration and Optimization Of modeLing projects.

In silico models of biological systems are usually very complex and rely on a large number of parameters describing physical and biological properties that require validation. As such, parameter space exploration is an essential component of computational model development to fully characterize and validate simulation results. Experimental data may also be used to constrain parameter space (or enable model calibration) to enhance the biological relevance of model parameters. One widely used computational platform in the mathematical biology community is PhysiCell, which provides a standardized approach to agent-based models of biological phenomena at different time and spatial scales. Nonetheless, one limitation of PhysiCell is the lack of a generalized approach for parameter space exploration and calibration that can be run without high-performance computing access. Here, we present PhysiCOOL, an open-source Python library tailored to create standardized calibration and optimization routines for PhysiCell models.

Hybrid computational models of multicellular tumour growth considering glucose metabolism.

Cancer cells metabolize glucose through metabolic pathways that differ from those used by healthy and differentiated cells. In particular, tumours have been shown to consume more glucose than their healthy counterparts and to use anaerobic metabolic pathways, even under aerobic conditions. Nevertheless, scientists have still not been able to explain why cancer cells evolved to present an altered metabolism and what evolutionary advantage this might provide them. Experimental and computational models have been increasingly used in recent years to understand some of these biological questions. Multicellular tumour spheroids are effective experimental models as they replicate the initial stages of avascular solid tumour growth. Furthermore, these experiments generate data which can be used to calibrate and validate computational studies that aim to simulate tumour growth. Hybrid models are of particular relevance in this field of research because they model cells as individual agents while also incorporating continuum representations of the substances present in the surrounding microenvironment that may participate in intracellular metabolic networks as concentration or density distributions. Henceforth, in this review, we explore the potential of computational modelling to reveal the role of metabolic reprogramming in tumour growth.

Computational modelling of the mechanical behaviour of protein-based hydrogels.

Protein-based hydrogels have been extensively studied in the field of biomaterials given their ability to mimic living tissues and their special resemblance to the extracellular matrix. Despite this, the methods used for the control of mechanical properties of hydrogels are very limited, focusing mainly on their elasticity, with an often unrealistic characterization of mechanical properties such as extensibility, stiffness and viscoelasticity. Being able to control these properties is essential for the development of new biomaterials, since it has been demonstrated that mechanical properties affect cell behaviour and biological processes. To better understand the mechanical behaviour of these biopolymers, a computational model is here developed to characterize the mechanical behaviour of two different protein-based hydrogels. Strain-stress tests and stress-relaxation tests are evaluated computationally and compared to the results obtained experimentally in a previous work. To achieve this goal the Finite Element Method is used, combining hyperelastic and viscoelastic models. Different hyperelastic constitutive models (Mooney-Rivlin, Neo-Hookean, first and third order Ogden, and Yeoh) are proposed to estimate the mechanical properties of the protein-based hydrogels by least-square fitting of the in-vitro uniaxial test results. Among these models, the first order Ogden model with a viscoelastic model defined in Prony parameters better reproduces the strain-stress response and the change of stiffness with strain observed in the in-vitro tests.

Feedback between mechanosensitive signaling and active forces governs endothelial junction integrity.

The formation and recovery of gaps in the vascular endothelium governs a wide range of physiological and pathological phenomena, from angiogenesis to tumor cell extravasation. However, the interplay between the mechanical and signaling processes that drive dynamic behavior in vascular endothelial cells is not well understood. In this study, we propose a chemo-mechanical model to investigate the regulation of endothelial junctions as dependent on the feedback between actomyosin contractility, VE-cadherin bond turnover, and actin polymerization, which mediate the forces exerted on the cell-cell interface. Simulations reveal that active cell tension can stabilize cadherin bonds, but excessive RhoA signaling can drive bond dissociation and junction failure. While actin polymerization aids gap closure, high levels of Rac1 can induce junction weakening. Combining the modeling framework with experiments, our model predicts the influence of pharmacological treatments on the junction state and identifies that a critical balance between RhoA and Rac1 expression is required to maintain junction stability. Our proposed framework can help guide the development of therapeutics that target the Rho family of GTPases and downstream active mechanical processes.

A 3D multi-agent-based model for lumen morphogenesis: the role of the biophysical properties of the extracellular matrix.

The correct function of many organs depends on proper lumen morphogenesis, which requires the orchestration of both biological and mechanical aspects. However, how these factors coordinate is not yet fully understood. Here, we focus on the development of a mechanistic model for computationally simulating lumen morphogenesis. In particular, we consider the hydrostatic pressure generated by the cells' fluid secretion as the driving force and the density of the extracellular matrix as regulators of the process. For this purpose, we develop a 3D agent-based-model for lumen morphogenesis that includes cells' fluid secretion and the density of the extracellular matrix. Moreover, this computer-based model considers the variation in the biological behavior of cells in response to the mechanical forces that they sense. Then, we study the formation of the lumen under different-mechanical scenarios and conclude that an increase in the matrix density reduces the lumen volume and hinders lumen morphogenesis. Finally, we show that the model successfully predicts normal lumen morphogenesis when the matrix density is physiological and aberrant multilumen formation when the matrix density is excessive. Supplementary Information: The online version contains supplementary material available at 10.1007/s00366-022-01654-1.

A microfluidic-based analysis of 3D macrophage migration after stimulation by Mycobacterium, Salmonella and Escherichia.

Macrophages play an essential role in the process of recognition and containment of microbial infections. These immune cells are recruited to infectious sites to reach and phagocytose pathogens. Specifically, in this article, bacteria from the genus Mycobacterium, Salmonella and Escherichia, were selected to study the directional macrophage movement towards different bacterial fractions. We recreated a three-dimensional environment in a microfluidic device, using a collagen-based hydrogel that simulates the mechanical microarchitecture associated to the Extra Cellular Matrix (ECM). First, we showed that macrophage migration is affected by the collagen concentration of their environment, migrating greater distances at higher velocities with decreasing collagen concentrations. To recreate the infectious microenvironment, macrophages were exposed to lateral gradients of bacterial fractions obtained from the intracellular pathogens M. tuberculosis and S. typhimurium. Our results showed that macrophages migrated directionally, and in a concentration-dependent manner, towards the sites where bacterial fractions are located, suggesting the presence of attractants molecules in all the samples. We confirmed that purified M. tuberculosis antigens, as ESAT-6 and CFP-10, stimulated macrophage recruitment in our device. Finally, we also observed that macrophages migrate towards fractions from non-pathogenic bacteria, such as M. smegmatis and Escherichia coli. In conclusion, our microfluidic device is a useful tool which opens new perspectives to study the recognition of specific antigens by innate immune cells.

A mechanistic protrusive-based model for 3D cell migration.

Cell migration is essential for a variety of biological processes, such as embryogenesis, wound healing, and the immune response. After more than a century of research-mainly on flat surfaces-, there are still many unknowns about cell motility. In particular, regarding how cells migrate within 3D matrices, which more accurately replicate in vivo conditions. We present a novel in silico model of 3D mesenchymal cell migration regulated by the chemical and mechanical profile of the surrounding environment. This in silico model considers cell's adhesive and nuclear phenotypes, the effects of the steric hindrance of the matrix, and cells ability to degradate the ECM. These factors are crucial when investigating the increasing difficulty that migrating cells find to squeeze their nuclei through dense matrices, which may act as physical barriers. Our results agree with previous in vitro observations where fibroblasts cultured in collagen-based hydrogels did not durotax toward regions with higher collagen concentrations. Instead, they exhibited an adurotactic behavior, following a more random trajectory. Overall, cell's migratory response in 3D domains depends on its phenotype, and the properties of the surrounding environment, that is, 3D cell motion is strongly dependent on the context.

Position of the AI for Health Imaging (AI4HI) network on metadata models for imaging biobanks.

A huge amount of imaging data is becoming available worldwide and an incredible range of possible improvements can be provided by artificial intelligence algorithms in clinical care for diagnosis and decision support. In this context, it has become essential to properly manage and handle these medical images and to define which metadata have to be considered, in order for the images to provide their full potential. Metadata are additional data associated with the images, which provide a complete description of the image acquisition, curation, analysis, and of the relevant clinical variables associated with the images. Currently, several data models are available to describe one or more subcategories of metadata, but a unique, common, and standard data model capable of fully representing the heterogeneity of medical metadata has not been yet developed. This paper reports the state of the art on metadata models for medical imaging, the current limitations and further developments, and describes the strategy adopted by the Horizon 2020 "AI for Health Imaging" projects, which are all dedicated to the creation of imaging biobanks.

A theoretical analysis of the scale separation in a model to predict solid tumour growth.

Solid tumour growth depends on a host of factors which affect the cell life cycle and extracellular matrix vascularization that leads to a favourable environment. The whole solid tumour can either grow or wither in response to the action of the immune system and therapeutics. A personalised mathematical model of such behaviour must consider both the intra- and inter-cellular dynamics and the mechanics of the solid tumour and its microenvironment. However, such wide range of spatial and temporal scales can hardly be modelled in a single model, and require the so-called multiscale models, defined as orchestrations of single-scale component models, connected by relation models that transform the data for one scale to another. While multiscale models are becoming common, there is a well-established engineering approach to the definition of the scale separation, e.g., how the spatiotemporal continuum is split in the various component models. In most studies scale separation is defined as natural, linked to anatomical concepts such as organ, tissue, or cell; but these do not provide reliable definition of scales: for examples skeletal organs can be as large as 500 mm (femur), or as small as 3 mm (stapes). Here we apply a recently proposed scale-separation approach based on the actual experimental and computational limitations to a patient-specific model of the growth of neuroblastoma. The resulting multiscale model can be properly informed with the available experimental data and solved in a reasonable timeframe with the available computational resources.

Unravelling cell migration: defining movement from the cell surface.

Cell motility is essential for life and development. Unfortunately, cell migration is also linked to several pathological processes, such as cancer metastasis. Cells' ability to migrate relies on many actors. Cells change their migratory strategy based on their phenotype and the properties of the surrounding microenvironment. Cell migration is, therefore, an extremely complex phenomenon. Researchers have investigated cell motility for more than a century. Recent discoveries have uncovered some of the mysteries associated with the mechanisms involved in cell migration, such as intracellular signaling and cell mechanics. These findings involve different players, including transmembrane receptors, adhesive complexes, cytoskeletal components , the nucleus, and the extracellular matrix. This review aims to give a global overview of our current understanding of cell migration.

Decellularization of tumours: A new frontier in tissue engineering.

Cancer is one of the leading causes of death worldwide. The tumour extracellular matrix (ECM) has unique features in terms of composition and mechanical properties, resulting in a structurally and chemically different ECM to that of native, healthy tissues. This paper reviews to date the efforts into decellularization of tumours, which in the authors' view represents a new frontier in the ever evolving field of tumour tissue engineering. An overview of the ECM and its importance in cancer is given, ending with examples of research using decellularized tumours, which has already indicated potential therapeutic targets, unravelled malignancy mechanisms or response to chemotherapy agents. The review highlights that more research is needed in this area, which can answer important questions related to tumour formation and progression to ultimately identify new and effective therapeutic targets. Within the near-future of personalized medicine, this research can create patient-specific tumour models and therapeutic regimes.