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The field of cell biology has seen major advances in both cellular imaging modalities and the development of automated image analysis platforms that increase rigor, reproducibility, and throughput for large imaging data sets. However, there remains a need for tools that provide accurate morphometric analysis of single cells with complex, dynamic cytoarchitecture in a high-throughput and unbiased manner. We developed a fully automated image-analysis algorithm to rapidly detect and quantify changes in cellular morphology using microglia cells, an innate immune cell within the central nervous system, as representative of cells that exhibit dynamic and complex cytoarchitectural changes. We used two preclinical animal models that exhibit robust changes in microglia morphology: (1) a rat model of acute organophosphate intoxication, which was used to generate fluorescently labeled images for algorithm development; and (2) a rat model of traumatic brain injury, which was used to validate the algorithm using cells labeled using chromogenic detection methods. All ex vivo brain sections were immunolabeled for IBA-1 using fluorescence or diaminobenzidine (DAB) labeling, images were acquired using a high content imaging system and analyzed using a custom-built algorithm. The exploratory data set revealed eight statistically significant and quantitative morphometric parameters that distinguished between phenotypically distinct groups of microglia. Manual validation of single-cell morphology was strongly correlated with the automated analysis and was further supported by a comparison with traditional stereology methods. Existing image analysis pipelines rely on high-resolution images of individual cells, which limits sample size and is subject to selection bias. However, our fully automated method integrates quantification of morphology and fluorescent/chromogenic signals in images from multiple brain regions acquired using high-content imaging. In summary, our free, customizable image analysis tool provides a high-throughput, unbiased method for accurately detecting and quantifying morphological changes in cells with complex morphologies.

Neurotoxicity is mediated by a variety of modes-of-actions leading to disturbance of neuronal function. In order to screen larger numbers of compounds for their neurotoxic potential, in vitro functional neuronal networks (NN) might be helpful tools. We established and characterized human NN (hNN) from hiPSC-derived neural progenitor cells by comparing hNN formation with two different differentiation media: in presence (CINDA) and absence (neural differentiation medium (NDM)) of maturation-supporting factors. As a NN control we included differentiating rat NN (rNN) in the study. Gene/protein expression and electrical activity from in vitro developing NN were assessed at multiple time points. Transcriptomes of 5, 14 and 28 days in vitro CINDA-grown hNN were compared to gene expression profiles of in vivo human developing brains. Molecular expression analyses as well as measures of electrical activity indicate that NN mature into neurons of different subtypes and astrocytes over time. In contrast to rNN, hNN are less electrically active within the same period of differentiation time, yet hNN grown in CINDA medium develop higher firing rates than hNN without supplements. Challenge of NN with neuronal receptor stimulators and inhibitors demonstrate presence of inhibitory, GABAergic neurons, whereas glutamatergic responses are limited. hiPSC-derived GABAergic hNN grown in CINDA medium might be a useful tool as part of an in vitro battery for assessing neurotoxicity.

Developmental neurotoxicity (DNT) testing performed in rats is resource-intensive (costs, time, animals) and bears the issue of species extrapolation. Thus, reliable alternative human-based approaches are needed for predicting neurodevelopmental toxicity. Human induced pluripotent stem cells (hiPSCs) represent a basis for an alternative method possibly being part of an alternative DNT testing strategy. Here, we compared two hiPSC neural induction protocols resulting in 3D neurospheres: one using noggin and one cultivating cells in neural induction medium (NIM protocol). Performance of Nestin+/SOX2+ hiPSC-derived neural progenitor cells (NPCs) was compared to primary human NPCs. Generally, primary hNPCs first differentiate into Nestin+ and/or GFAP+ radial glia-like cells, while the hiPSC-derived NPCs (hiPSC-NPC) first differentiate into βIII-Tubulin+ neurons suggesting an earlier developmental stage of hiPSC-NPC. In the ‘Neurosphere Assay’, NIM generated hiPSC-NPC produced neurons with higher performance than with the noggin protocol. After long-term differentiation, hiPSC-NPC form neuronal networks, which become electrically active on microelectrode arrays after 85 days. Finally, methylmercury chloride inhibits hiPSC-NPC and hNPC migration with similar potencies. hiPSC-NPCs-derived neurospheres seem to be useful for DNT evaluation representing early neural development in vitro. More system characterization by compound testing is needed to gain higher confidence in this method.

Composite Extrusion Modelling (CEM) is an advanced additive manufacturing technique that enables rapid, cost-effective production of complex, customisable designs. In this study, CEM 3D printing of porous ceria structures is reported for the first time. First, the ceramic injection molding (CIM) feedstock with thermoplastic properties was prepared and optimised in terms of its rheological properties to ensure good workability for the printing process at 140–150 °C. Subsequently, the thermoplastic feedstock was used to print porous ceria structures for thermochemical splitting. The feasibility of printing various porous structures with different dimensions, geometries, and macropore sizes was investigated, and the printing parameters: extrusion multiplier (EM), extrusion temperature (ET), nozzle velocity (NV), and layer thickness (LT), were optimised to prevent clogging of the printing nozzle and to achieve homogeneous overlap of the printed layers. The optimised printing parameters for ceria structures are EM 1.3, ET 150 °C, LT ∼ 0.13 mm, and NV 50 mm/s, which were determined by multiple response optimisation process. The sintered 3D-printed bars yielded relative densities of ≥ 98 % and a total microporosity of 0.29 %, measured with a Hg porosimeter. Thermogravimetric analysis (TGA) and cyclic stability experiments were performed on the printed porous ceria structures and showed stability over 100-redox cycles.