Understanding the structure of single neurons is critical for understanding how they function within neural circuits. BigNeuron is a new community effort that combines modern bioimaging informatics, recent leaps in labeling and microscopy, and the widely recognized need for openness and standardization to provide a community resource for automated reconstruction of dendritic and axonal morphology of single neurons.

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BackgroundSPARC (secreted protein acidic and rich in cysteine) is a nonstructural, cell-matrix modulating protein involved in angiogenesis and endothelial barrier function, yet its potential role in cerebrovascular development, inflammation, and repair in the central nervous system (CNS) remains undetermined.MethodsThis study examines SPARC expression in cultured human cerebral microvascular endothelial cells (hCMEC/D3)—an in vitro model of the blood-brain barrier (BBB)—as they transition between proliferative and barrier phenotypes and encounter pro-inflammatory stimuli. SPARC protein levels were quantified by Western blotting and immunocytochemistry and messenger RNA (mRNA) by RT-PCR.ResultsConstitutive SPARC expression by proliferating hCMEC/D3s is reduced as cells mature and establish a confluent monolayer. SPARC expression positively correlated with the proliferation marker Ki-67 suggesting a role for SPARC in cerebrovascular development. The pro-inflammatory molecules tumor necrosis factor-α (TNF-α) and endotoxin lipopolysaccharide (LPS) increased SPARC expression in cerebral endothelia. Interferon gamma (IFN-γ) abrogated SPARC induction observed with TNF-α alone. Barrier function assays show recombinant human (rh)-SPARC increased paracellular permeability and decreased transendothelial electrical resistance (TEER). This was paralleled by reduced zonula occludens-1 (ZO-1) and occludin expression in hCMEC/D3s exposed to rh-SPARC (1–10 μg/ml) compared with cells in media containing a physiological dose of SPARC.ConclusionsTogether, these findings define a role for SPARC in influencing cerebral microvascular properties and function during development and inflammation at the BBB such that it may mediate processes of CNS inflammation and repair.Electronic supplementary materialThe online version of this article (doi:10.1186/s12974-016-0657-9) contains supplementary material, which is available to authorized users.

Pathogenic CAG (cytosine-adenine-guanine) expansions beyond certain thresholds in the ataxin-2 (ATXN2) gene cause spinocerebellar ataxia type 2 (SCA2) and were shown to contribute to Parkinson disease, amyotrophic lateral sclerosis and frontotemporal lobar degeneration. Regulation of ATXN2 gene expression and the function of the protein product are not known. SCA2 exhibits an inverse correlation between the size of the CAG repeat and the age at disease onset. However, a wide range of age at onset are typically observed, with CAG repeat number alone explaining only partly this variability. In this study, we explored the hypothesis that ATXN2 levels could be controlled by DNA methylation and that the derangement of this control may lead to escalation of disease severity and influencing the age at onset. We found that CpG methylation in human ATXN2 gene promoter is associated with pathogenic CAG expansions in SCA2 patients. Different levels of methylation in a SCA2 pedigree without an intergenerational CAG repeat instability caused the disease anticipation in a SCA2 family. DNA methylation also influenced the disease onset in SCA2 homozygotes and SCA3 patients. In conclusion, our study points to a novel regulatory mechanism of ATXN2 expression involving an epigenetic event resulting in differential disease course in SCA2 patients.

Global neuroscience projects are producing big data at an unprecedented rate that informatic and artificial intelligence (AI) analytics simply cannot handle. Online games, like Foldit, Eterna, and Eyewire-and now a new neuroscience game, Mozak-are fueling a people-powered research science (PPRS) revolution, creating a global community of "new experts" that over time synergize with computational efforts to accelerate scientific progress, empowering us to use our collective cerebral talents to drive our understanding of our brain.

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BigNeuron is an open community bench-testing platform combining the expertise of neuroscientists and computer scientists toward the goal of setting open standards for accurate and fast automatic neuron reconstruction. The project gathered a diverse set of image volumes across several species representative of the data obtained in most neuroscience laboratories interested in neuron reconstruction. Here we report generated gold standard manual annotations for a selected subset of the available imaging datasets and quantified reconstruction quality for 35 automatic reconstruction algorithms. Together with image quality features, the data were pooled in an interactive web application that allows users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and reconstruction data, and benchmarking of automatic reconstruction algorithms in user-defined data subsets. Our results show that image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. By benchmarking automatic reconstruction algorithms, we observed that diverse algorithms can provide complementary information toward obtaining accurate results and developed a novel algorithm to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms. Finally, to aid users in predicting the most accurate automatic reconstruction results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic reconstructions.

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