Theoretical analysis of insulin-dependent glucose uptake heterogeneity in 3D bioreactor cell culture

Electroporation enhances cell death in 3D scaffold-based MDA-MB-231 cells treated with metformin

Triple-negative breast cancer (TNBC), the most aggressive subtype of breast cancer lacks estrogen, progesterone, and HER2 receptors and hence, is therapeutically challenging. Towards this, we studied an alternate therapy by repurposing metformin (FDA-approved type-2 diabetic drug with anticancer properties) in a 3D-scaffold culture, with electrical pulses. 3D cell culture was used to simulate the tumor microenvironment more closely and MDA-MB-231, human TNBC cells, treated with both 5 mM metformin (Met) and 8 electrical pulses at 2500 V/cm, 10 µs (EP1) and 800 V/cm, 100 µs (EP2) at 1 Hz were studied in 3D and 2D. They were characterized using cell viability, reactive oxygen species (ROS), glucose uptake, and lactate production assays at 24 h. Cell viability, as low as 20 % was obtained with EP1 + 5 mM Met. They exhibited 1.65-fold lower cell viability than 2D with EP1 + 5 mM Met. ROS levels indicated a 2-fold increase in oxidative stress for EP1 + 5 mM Met, while the glucose uptake was limited to only 9 %. No significant change in the lactate production indicated glycolytic arrest and a non-conducive environment for MDA-MB-231 growth. Our results indicate that 3D cell culture, with a more realistic tumor environment that enhances cell death using metformin and electrical pulses could be a promising approach for TNBC therapeutic intervention studies.

Generation and evaluation of input values for computational analysis of transport processes within tissue cultures

Techniques for tissue culture have seen significant advances during the last decades and novel 3D cell culture systems have become available. To control their high complexity, experimental techniques and their Digital Twins (modelling and computational tools) are combined to link different variables to process conditions and critical process parameters. This allows a rapid evaluation of the expected product quality. However, the use of mathematical simulation and Digital Twins is critically dependent on the precise description of the problem and correct input parameters. Errors here can lead to dramatically wrong conclusions. The intention of this review is to provide an overview of the state‐of‐the‐art and remaining challenges with respect to generating input values for computational analysis of mass and momentum transport processes within tissue cultures. It gives an overview on relevant aspects of transport processes in tissue cultures as well as modelling and computational tools to tackle these problems. Further focus is on techniques used for the determination of cell‐specific parameters and characterization of culture systems, including sensors for on‐line determination of relevant parameters. In conclusion, tissue culture techniques are well‐established, and modelling tools are technically mature. New sensor technologies are on the way, especially for organ chips. The greatest remaining challenge seems to be the proper addressing and handling of input parameters required for mathematical models. Following Good Modelling Practice approaches when setting up and validating computational models is, therefore, essential to get to better estimations of the interesting complex processes inside organotypic tissue cultures in the future.

Digital Twins for Tissue Culture Techniques—Concepts, Expectations, and State of the Art

Techniques to provide in vitro tissue culture have undergone significant changes during the last decades, and current applications involve interactions of cells and organoids, three-dimensional cell co-cultures, and organ/body-on-chip tools. Efficient computer-aided and mathematical model-based methods are required for efficient and knowledge-driven characterization, optimization, and routine manufacturing of tissue culture systems. As an alternative to purely experimental-driven research, the usage of comprehensive mathematical models as a virtual in silico representation of the tissue culture, namely a digital twin, can be advantageous. Digital twins include the mechanistic of the biological system in the form of diverse mathematical models, which describe the interaction between tissue culture techniques and cell growth, metabolism, and the quality of the tissue. In this review, current concepts, expectations, and the state of the art of digital twins for tissue culture concepts will be highlighted. In general, DT’s can be applied along the full process chain and along the product life cycle. Due to the complexity, the focus of this review will be especially on the design, characterization, and operation of the tissue culture techniques.

Advanced 3D Cell Culture Techniques in Micro-Bioreactors, Part II: Systems and Applications

In this second part of our systematic review on the research area of 3D cell culture in micro-bioreactors we give a detailed description of the published work with regard to the existing micro-bioreactor types and their applications, and highlight important results gathered with the respective systems. As an interesting detail, we found that micro-bioreactors have already been used in SARS-CoV research prior to the SARS-CoV2 pandemic. As our literature research revealed a variety of 3D cell culture configurations in the examined bioreactor systems, we defined in review part one “complexity levels” by means of the corresponding 3D cell culture techniques applied in the systems. The definition of the complexity is thereby based on the knowledge that the spatial distribution of cell-extracellular matrix interactions and the spatial distribution of homologous and heterologous cell–cell contacts play an important role in modulating cell functions. Because at least one of these parameters can be assigned to the 3D cell culture techniques discussed in the present review, we structured the studies according to the complexity levels applied in the MBR systems.

High Temporal Resolution Detection of Patient-Specific Glucose Uptake from Human ex Vivo Adipose Tissue On-Chip

Human tissue in vitro models on-chip are highly desirable to dissect the complexity of a physio-pathological in vivo response because of their advantages compared to traditional static culture systems in terms of high control of microenvironmental conditions, including accurate perturbations and high temporal resolution analyses of medium outflow. Human adipose tissue (hAT) is a key player in metabolic disorders, such as Type 2 Diabetes Mellitus (T2DM). It is involved in the overall energy homeostasis not only as passive energy storage but also as an important metabolic regulator. Here, we aim at developing a large scale microfluidic platform for generating high temporal resolution of glucose uptake profiles, and consequently insulin sensitivity, under physio-pathological stimulations in ex vivo adipose tissues from nondiabetic and T2DM individuals. A multiscale mathematical model that integrates fluid dynamics and an intracellular insulin signaling pathway description was used for assisting microfluidic design in order to maximize measurement accuracy of tissue metabolic activity in response to perturbations. An automated microfluidic injection system was included on-chip for performing precise dynamic biochemical stimulations. The temporal evolution of culture conditions could be monitored for days, before and after perturbation, measuring glucose concentration in the outflow with high temporal resolution. As a proof of concept for detection of insulin resistance, we measured insulin-dependent glucose uptake by hAT from nondiabetic and T2DM subjects, mimicking the postprandial response. The system presented thus represents an important tool in dissecting the role of single tissues, such as hAT, in the complex interwoven picture of metabolic diseases.

A receptor state space model of the insulin signalling system in glucose transport

Insulin is a potent peptide hormone that regulates glucose levels in the blood. Insulin-sensitive cells respond to insulin stimulation with the translocation of glucose transporter 4 (GLUT4) to the plasma membrane (PM), enabling the clearance of glucose from the blood. Defects in this process can give rise to insulin resistance and ultimately diabetes. One widely cited model of insulin signalling leading to glucose transport is that of Sedaghat et al. (2002) Am. J. Physiol. Endocrinol. Metab. 283, E1084-E1101. Consisting of 20 deterministic ordinary differential equations (ODEs), it is the most comprehensive model of insulin signalling to date. However, the model possesses some major limitations, including the non-conservation of key components. In the current work, we detail mathematical and sensitivity analyses of the Sedaghat model. Based on the results of these analyses, we propose a reduced state space model of the insulin receptor subsystem. This reduced model maintains the input-output relation of the original model but is computationally more efficient, analytically tractable and resolves some of the limitations of the Sedaghat model.