Biology subjects

Doenmez, A.

Publications and source records attributed to Doenmez, A..

2 recordsLinked to original sources

bioRxiv2023

Molecular and functional characterization of different BrainSphere models for use in neurotoxicity testing on microelectrode arrays

The currently accepted methods for neurotoxicity (NT) testing rely on animal studies. However, high costs and low testing throughput hinder their application for large numbers of chemicals. To overcome these limitations, in vitro methods are currently developed which are based on human induced pluripotent stem cells (hiPSC) that allow higher testing throughput at lower costs. We applied six different protocols to generate 3D BrainSphere models for acute NT evaluation. These include three different media for 2D neural induction and two media for subsequent 3D differentiation resulting in self-organized, organotypic neuron/astrocyte microtissues. All induction protocols yielded nearly 100 % nestin-positive hiPSC-derived neural progenitor cells (hiNPCs) yet with different gene expression profiles concerning regional patterning. Moreover, gene expression and immunocytochemistry analyses revealed that the choice of media determines neural differentiation patterns. On the functional level, BrainSpheres exhibited different levels of electrical activity on microelectrode arrays (MEA). Spike sorting allowed BrainSphere functional characterization with the mixed cultures consisting of GABAergic, glutamatergic, dopaminergic, serotonergic, and cholinergic neurons. A test method for acute NT testing, the human multi-neurotransmitter receptor (hMNR) assay, was proposed applying such MEA-based spike sorting. These models are not only promising tools in toxicology but also for drug development and disease modeling.

neuroscience↗

bioRxiv2022

Biostatistics and its impact on hazard characterization using in vitro developmental neurotoxicity assays

In the field of hazard assessment, Benchmark concentrations (BMC) and their associated uncertainty are of particular interest for regulatory decision making. The BMC estimation consists of various statistical decisions to be made, which depend largely on factors such as experimental design and assay endpoint features. In current data practice, the experimenter is often responsible for the data analysis and therefore relies on statistical software without being aware about the software default settings and how they can impact the outputs of data analysis. To provide more insight into how statistical decision making can influence the outcomes of data analysis and interpretation, we have used case studies on a large dataset produced by a developmental neurotoxicity (DNT) in vitro battery (DNT IVB). Here we focused on the BMC and its confidence interval (CI) estimation, as well as on the final hazard classification. We identified five crucial statistical decisions experimenter have to face during data analysis: choice of replicate averaging, response data normalization, regression modelling, BMC and CI estimation, as well as choice of benchmark response levels. In addition, the strength of our data evaluation platform is the integration of endpoint-specific hazard classifications, including flagging systems for uncertain cases, which none of the so far existing statistical data analysis platforms provide. The insights gained in this study demonstrate how important fit-for-purpose, internationally harmonized and accepted data evaluation and analysis procedures are for an objective hazard classification.

bioinformatics↗