Neuroblastoma (NB) is a highly vascularized pediatric tumor arising from undifferentiated neural crest cells early in life, exhibiting both traditional endothelial-cell-driven vasculature and an intriguing alternative vasculature. The alternative vasculature can arise from cancer cells undergoing transdifferentiation into tumor-derived endothelial cells (TEC), a trait associated with drug resistance and tumor relapse. The lack of effective treatments targeting NB vasculature primarily arises from the challenge of establishing predictive in vitro models that faithfully replicate the alternative vasculature phenomenon. In this study, we aim to recreate the intricate vascular system of NB in an in vitro context, encompassing both types of vascularization, by developing a novel neuroblastoma-on-a-chip model. We designed a collagen I/fibrin-based hydrogel closely mirroring NB's physiological composition and tumor stiffness. This biomaterial created a supportive environment for the viability of NB and endothelial cells. Implementing a physiological shear stress value, aligned with the observed range in arteries and capillaries, within the microfluidic chip facilitated the successful development of vessel-like structures and triggered transdifferentiation of NB cells into TECs. The vascularized neuroblastoma-on-a-chip model introduced here presents a promising and complementary strategy to animal-based research with a significant capacity for delving into NB tumor biology and vascular targeting therapy.
Many neurodegenerative diseases are identified but their causes and cure are far from being well-known. The problem resides in the complexity of the neural tissue and its location which hinders its easy evaluation. Although necessary in the drug discovery process, in vivo animal models need to be reduced and show relevant differences with the human tissues that guide scientists to inquire about other possible options which lead to in vitro models being explored. From organoids to organ-on-a-chips, 3D models are considered the cutting-edge technology in cell culture. Cell choice is a big parameter to take into consideration when planning an in vitro model and cells capable of mimicking both healthy and diseased tissue, such as induced pluripotent stem cells (iPSC), are recognized as good candidates. Hence, we present a critical review of the latest models used to study neurodegenerative disease, how these models have evolved introducing microfluidics platforms, 3D cell cultures, and the use of induced pluripotent cells to better mimic the neural tissue environment in pathological conditions.
Introduction: Three-dimensional (3D) bioprinting is a promising technique for the development of neuronal in vitro models because it controls the deposition of materials and cells. Finding a biomaterial that supports neural differentiation in vitro while ensuring compatibility with the technique of 3D bioprinting of a self-standing construct is a challenge.Methods: In this study, gelatin methacryloyl (GelMA), methacrylated alginate (AlgMA), and hyaluronic acid (HA) were examined by exploiting their biocompatibility and tunable mechanical properties to resemble the extracellular matrix (ECM) and to create a suitable material for printing neural progenitor cells (NPCs), supporting their long-term differentiation. NPCs were printed and differentiated for up to 15 days, and cell viability and neuronal differentiation markers were assessed throughout the culture.Results and Discussion: This composite biomaterial presented the desired physical properties to mimic the ECM of the brain with high water intake, low stiffness, and slow degradation while allowing the printing of defined structures. The viability rates were maintained at approximately 80% at all time points. However, the levels of beta-III tubulin marker increased over time, demonstrating the compatibility of this biomaterial with neuronal cell culture and differentiation. Furthermore, these cells showed increased maturation with corresponding functional properties, which was also demonstrated by the formation of a neuronal network that was observed by recording spontaneous activity via Ca2+ imaging.
Microfluidics represents a very promising technological solution for conducting massive biological experiments. However, the difficulty of managing the amount of information available often precludes the wide potential offered. Using machine learning, we aim to accelerate microfluidics uptake and lead to quantitative and reliable findings. In this work, we propose complementing microfluidics with machine learning (MLM) approaches to enhance the diagnostic capability of lab-on-chip devices. The introduction of data analysis methodologies within the deep learning framework corroborates the possibility of encoding cell morphology beyond the standard cell appearance. The proposed MLM platform is used in a diagnostic test for blood diseases in murine RBC samples in a dedicated microfluidics device in flow. The lack of plasticity of RBCs in Pyruvate Kinase Disease (PKD) is measured massively by recognizing the shape deformation in RBCs walking in a forest of pillars within the chip. Very high accuracy results, far over 85 %, in recognizing PKD from control RBCs either in simulated and in real experiments demonstrate the effectiveness of the platform.
Over the most recent decades, the development of new biological platforms to study disease progression and drug efficacy has been of great interest due to the high increase in the rate of neurodegenerative diseases (NDDs). Therefore, blood-brain barrier (BBB) as an organ-on-a-chip (OoC) platform to mimic brain-barrier performance could offer a deeper understanding of NDDs as well as a very valuable tool for drug permeability testing for new treatments. A very attractive improvement of BBB-oC technology is the integration of detection systems to provide continuous monitoring of biomarkers in real time and a fully automated analysis of drug permeably, rendering more efficient platforms for commercialization. In this Perspective, an overview of the main BBB-oC configurations is introduced and a critical vision of the BBB-oC platforms integrating electronic read out systems is detailed, indicating the strengths and weaknesses of current devices, proposing the great potential for biosensors integration in BBB-oC. In this direction, we name potential biomarkers to monitor the evolution of NDDs related to the BBB and/or drug cytotoxicity using biosensor technology in BBB-oC.
Currently, three major circumstances threaten the management of bacterial infections: increasing antimicrobial resistance, expansion of chronic biofilm-associated infections, and lack of an appropriate approach to treat them. To date, the development of accelerated drug susceptibility testing of biofilms and of new antibiofouling systems has not been achieved despite the availability of different methodologies. There is a need for easy-to-use methods of testing the antibiotic susceptibility of bacteria that form biofilms and for screening new possible antibiofilm strategies. Herein, we present a microfluidic platform with an integrated interdigitated sensor (BiofilmChip). This new device allows an irreversible and homogeneous attachment of bacterial cells of clinical origin, even directly from clinical specimens, and the biofilms grown can be monitored by confocal microscopy or electrical impedance spectroscopy. The device proved to be suitable to study polymicrobial communities, as well as to measure the effect of antimicrobials on biofilms without introducing disturbances due to manipulation, thus better mimicking real-life clinical situations. Our results demonstrate that BiofilmChip is a straightforward tool for antimicrobial biofilm susceptibility testing that could be easily implemented in routine clinical laboratories.
Combining microfluidics technology with machine learning represents an innovative approach to conduct massive quantitative cell behavior study and implement smart decision-making systems in support of clinical diagnostics. The spleen plays a key-role in rare hereditary hemolytic anemia (RHHA), being the organ responsible for the premature removal of defective red blood cells (RBCs). The goal is to adapt the physiological spleen filtering strategy for in vitro study and monitoring of blood diseases through RBCs shape analysis. Then, a microfluidic device mimicking the slits of the spleen red pulp area and video data analysis are combined for the characterization of RBCs in RHHA. This microfluidic unit is designed to evaluate RBC deformability by maintaining them fixed in planar orientation, allowing the visual inspection of RBC's capacity to restore their original shape after crossing microconstrictions. Then, two cooperative learning approaches are used for the analysis: the majority voting scheme, in which the most voted label for all the cell images is the class assigned to the entire video; and the maximum sum of scores to decide the maximally scored class to assign. The proposed platform shows the capability to discriminate healthy controls and patients with an average efficiency of 91%, but also to distinguish between RHHA subtypes, with an efficiency of 82%.
Cookie Consent The IBEC website uses cookies and similar technologies to ensure the basic functionality of the site and for statistical and optimisation purposes. It also uses cookies to display content such as YouTube videos that use marketing cookies. This last category consists of tracking cookies: these make it possible for your online behaviour to be tracked. You consent to this by clicking on Accept. Also read our Privacy statement.
Read our cookie policy
Villasante, A, Lopez-Martinez, MJ, Quiñonero, G, Garcia-Lizarribar, A, Peng, XF, Samitier, J, (2024). Microfluidic model of the alternative vasculature in neuroblastoma In Vitro Models 3, 49-63 