Self-assembling 3D vessel-on-chip model with hiPSC-derived astrocytes.

Functionality of the blood-brain barrier (BBB) relies on the interaction between endothelial cells (ECs), pericytes, and astrocytes to regulate molecule transport within the central nervous system. Most experimental models for the BBB rely on freshly isolated primary brain cells. Here, we explored human induced pluripotent stem cells (hiPSCs) as a cellular source for astrocytes in a 3D vessel-on-chip (VoC) model. Self-organized microvascular networks were formed by combining hiPSC-derived ECs, human brain vascular pericytes, and hiPSC-derived astrocytes within a fibrin hydrogel. The hiPSC-ECs and pericytes showed close interactions, but, somewhat unexpectedly, addition of astrocytes disrupted microvascular network formation. However, continuous fluid perfusion or activation of cyclic AMP (cAMP) signaling rescued the vascular organization and decreased vascular permeability. Nevertheless, astrocytes did not affect the expression of proteins related to junction formation, transport, or extracellular matrix, indicating that, despite other claims, hiPSC-derived ECs do not entirely acquire a BBB-like identity in the 3D VoC model.

Spinocerebellar Ataxia Type 1 Characteristics in Patient-Derived Fibroblast and iPSC-Derived Neuronal Cultures.

BACKGROUND: Spinocerebellar ataxia type 1 (SCA1) is a neurodegenerative disease caused by a polyglutamine expansion in the ataxin-1 protein resulting in neuropathology including mutant ataxin-1 protein aggregation, aberrant neurodevelopment, and mitochondrial dysfunction. OBJECTIVES: Identify SCA1-relevant phenotypes in patient-specific fibroblasts and SCA1 induced pluripotent stem cells (iPSCs) neuronal cultures. METHODS: SCA1 iPSCs were generated and differentiated into neuronal cultures. Protein aggregation and neuronal morphology were evaluated using fluorescent microscopy. Mitochondrial respiration was measured using the Seahorse Analyzer. The multi-electrode array (MEA) was used to identify network activity. Finally, gene expression changes were studied using RNA-seq to identify disease-specific mechanisms. RESULTS: Bioenergetics deficits in patient-derived fibroblasts and SCA1 neuronal cultures showed altered oxygen consumption rate, suggesting involvement of mitochondrial dysfunction in SCA1. In SCA1 hiPSC-derived neuronal cells, nuclear and cytoplasmic aggregates were identified similar in localization as aggregates in SCA1 postmortem brain tissue. SCA1 hiPSC-derived neuronal cells showed reduced dendrite length and number of branching points while MEA recordings identified delayed development in network activity in SCA1 hiPSC-derived neuronal cells. Transcriptome analysis identified 1050 differentially expressed genes in SCA1 hiPSC-derived neuronal cells associated with synapse organization and neuron projection guidance, where a subgroup of 151 genes was highly associated with SCA1 phenotypes and linked to SCA1 relevant signaling pathways. CONCLUSIONS: Patient-derived cells recapitulate key pathological features of SCA1 pathogenesis providing a valuable tool for the identification of novel disease-specific processes. This model can be used for high throughput screenings to identify compounds, which may prevent or rescue neurodegeneration in this devastating disease. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

An organ-on-chip device with integrated charge sensors and recording microelectrodes.

Continuous monitoring of tissue microphysiology is a key enabling feature of the organ-on-chip (OoC) approach for in vitro drug screening and disease modeling. Integrated sensing units are particularly convenient for microenvironmental monitoring. However, sensitive in vitro and real-time measurements are challenging due to the inherently small size of OoC devices, the characteristics of commonly used materials, and external hardware setups required to support the sensing units. Here we propose a silicon-polymer hybrid OoC device that encompasses transparency and biocompatibility of polymers at the sensing area, and has the inherently superior electrical characteristics and ability to house active electronics of silicon. This multi-modal device includes two sensing units. The first unit consists of a floating-gate field-effect transistor (FG-FET), which is used to monitor changes in pH in the sensing area. The threshold voltage of the FG-FET is regulated by a capacitively-coupled gate and by the changes in charge concentration in close proximity to the extension of the floating gate, which functions as the sensing electrode. The second unit uses the extension of the FG as microelectrode, in order to monitor the action potential of electrically active cells. The layout of the chip and its packaging are compatible with multi-electrode array measurement setups, which are commonly used in electrophysiology labs. The multi-functional sensing is demonstrated by monitoring the growth of induced pluripotent stem cell-derived cortical neurons. Our multi-modal sensor is a milestone in combined monitoring of different, physiologically-relevant parameters on the same device for future OoC platforms.

MEA-ToolBox: an Open Source Toolbox for Standardized Analysis of Multi-Electrode Array Data.

Functional assessment of in vitro neuronal networks-of relevance for disease modelling and drug testing-can be performed using multi-electrode array (MEA) technology. However, the handling and processing of the large amount of data typically generated in MEA experiments remains a huge hurdle for researchers. Various software packages have been developed to tackle this issue, but to date, most are either not accessible through the links provided by the authors or only tackle parts of the analysis. Here, we present ''MEA-ToolBox'', a free open-source general MEA analytical toolbox that uses a variety of literature-based algorithms to process the data, detect spikes from raw recordings, and extract information at both the single-channel and array-wide network level. MEA-ToolBox extracts information about spike trains, burst-related analysis and connectivity metrics without the need of manual intervention. MEA-ToolBox is tailored for comparing different sets of measurements and will analyze data from multiple recorded files placed in the same folder sequentially, thus considerably streamlining the analysis pipeline. MEA-ToolBox is available with a graphic user interface (GUI) thus eliminating the need for any coding expertise while offering functionality to inspect, explore and post-process the data. As proof-of-concept, MEA-ToolBox was tested on earlier-published MEA recordings from neuronal networks derived from human induced pluripotent stem cells (hiPSCs) obtained from healthy subjects and patients with neurodevelopmental disorders. Neuronal networks derived from patient's hiPSCs showed a clear phenotype compared to those from healthy subjects, demonstrating that the toolbox could extract useful parameters and assess differences between normal and diseased profiles.