A Comprehensive, FAIR File Format for Neuroanatomical Structure Modeling.

With advances in microscopy and computer science, the technique of digitally reconstructing, modeling, and quantifying microscopic anatomies has become central to many fields of biological research. MBF Bioscience has chosen to openly document their digital reconstruction file format, the Neuromorphological File Specification, available at www.mbfbioscience.com/filespecification (Angstman et al., 2020). The format, created and maintained by MBF Bioscience, is broadly utilized by the neuroscience community. The data format's structure and capabilities have evolved since its inception, with modifications made to keep pace with advancements in microscopy and the scientific questions raised by worldwide experts in the field. More recent modifications to the neuromorphological file format ensure it abides by the Findable, Accessible, Interoperable, and Reusable (FAIR) data principles promoted by the International Neuroinformatics Coordinating Facility (INCF; Wilkinson et al., Scientific Data, 3, 160018,, 2016). The incorporated metadata make it easy to identify and repurpose these data types for downstream applications and investigation. This publication describes key elements of the file format and details their relevant structural advantages in an effort to encourage the reuse of these rich data files for alternative analysis or reproduction of derived conclusions.

Topographically modified surfaces affect orientation and growth of hippocampal neurons.

Extracellular matrix molecules provide biochemical and topographical cues that influence cell growth in vivo and in vitro. Effects of topographical cues on hippocampal neuron growth were examined after 14 days in vitro. Neurons from hippocampi of rat embryos were grown on poly-L-lysine-coated silicon surfaces containing fields of pillars with varying geometries. Photolithography was used to fabricate 1 microm high pillar arrays with different widths and spacings. Beta(III)-tubulin and MAP-2 immunocytochemistry and scanning electron microscopy were used to describe neuronal processes. Automated two-dimensional tracing software quantified process orientation and length. Process growth on smooth surfaces was random, while growth on pillared surfaces exhibited the most faithful alignment to pillar geometries with smallest gap sizes. Neurite lengths were significantly longer on pillars with the smallest inter-pillar spacings (gaps) and 2 microm pillar widths. These data indicate that physical cues affect neuron growth, suggesting that extracellular matrix topography may contribute to cell growth and differentiation. These results demonstrate new strategies for directing and promoting neuronal growth that will facilitate studies of synapse formation and function and provide methods to establish defined neural networks.