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Untargeted metabolomics by mass spectrometry in the form of mass over charge and intensity of ions, provides insight into the metabolic activity in a sample and is therefore essential to understand regulation and expression at the protein and transcription level. Problematically, it is often challenging to analyze untargeted metabolomics data as many m/z values are detected per sample and it is difficult to identify what compound they represent. We aimed to facilitate the process of finding m/z biomarkers through statistical analysis, machine learning and searching for their putative identities. To address this challenge, we developed MetaboShiny, a novel R and RShiny based metabolomics data analysis package. MetaboShiny features bi/multivariate and temporal statistics, an extensive machine learning module, interactive plotting and result exploration, and compound identification through a variety of chemical databases. As a result, MetaboShiny enables rapid and rigorous analysis of untargeted metabolomics data as well as target identification at unprecedented scale. To demonstrate its efficacy and ease-of-use, we apply MetaboShiny to a publicly accessible metabolomics dataset generated from the urine of smokers and non-smokers. Replication of the main results of the original publication, which includes importing, normalization and several statistical analyses, is achieved within minutes. Moreover, MetaboShiny enables deeper exploration of the data thereby revealing novel putative biomarkers and hypotheses. For instance, by using MetaboShiny’s subsetting feature, iodine is found to be significantly increased in non-smoking lung cancer patients. Furthermore, by allowing for custom adducts, MetaboShiny reveals a putative identification for an m/z value which could not be identified by the original authors. This validates MetaboShiny as a flexible and customizable data analysis package that greatly enhances metabolomics biomarker discovery.

Summary Neuron morphology and function are highly dependent on proper organization of the cytoskeleton. In neurons, the centrosome is inactivated early in development, and acentrosomal microtubules are generated by mechanisms that are poorly understood. Here, we show that neuronal migration, development, and polarization depend on the multi-subunit protein HAUS/augmin complex, previously described to be required for mitotic spindle assembly in dividing cells. The HAUS complex is essential for neuronal microtubule organization by ensuring uniform microtubule polarity in axons and regulation of microtubule density in dendrites. Using live-cell imaging and high-resolution microscopy, we found that distinct HAUS clusters are distributed throughout neurons and colocalize with γ-TuRC, suggesting local microtubule nucleation events. We propose that the HAUS complex locally regulates microtubule nucleation events to control proper neuronal development., Graphical Abstract, Highlights • The HAUS/augmin complex regulates migration and polarization in vivo • Axonal and dendritic development are regulated by HAUS/augmin complex • HAUS/augmin regulates microtubule density in dendrites and polarity in axons • Discrete clusters of HAUS/augmin regulate local microtubule nucleation in neurons, Cunha-Ferreira et al. report that the HAUS/augmin complex regulates neuronal migration, polarization, and development. In neurons, the HAUS complex is distributed as discrete clusters regulating local microtubule nucleation. These findings shed light into how microtubules are generated in developing neurons after centrosome inactivation in early development.