Neuronal morphologies built for reliable physiology in a rhythmic motor circuit

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in eLife 8 (2019): e41728. doi: 10.7554/eLife.41728.It is often assumed that highly-branched neuronal structures perform compartmentalized computations. However, previously we showed that the Gastric Mill (GM) neuron in the crustacean stomatogastric ganglion (STG) operates like a single electrotonic compartment, despite having thousands of branch points and total cable length >10 mm (Otopalik et al., 2017a; 2017b). Here we show that compact electrotonic architecture is generalizable to other STG neuron types, and that these neurons present direction-insensitive, linear voltage integration, suggesting they pool synaptic inputs across their neuronal structures. We also show, using simulations of 720 cable models spanning a broad range of geometries and passive properties, that compact electrotonus, linear integration, and directional insensitivity in STG neurons arise from their neurite geometries (diameters tapering from 10-20 µm to < 2 µm at their terminal tips). A broad parameter search reveals multiple morphological and biophysical solutions for achieving different degrees of passive electrotonic decrement and computational strategies in the absence of active properties.We thank Jennifer Bestman for assistance in spinning disk and confocal microscopy; the Marine Resources Center at the Marine Biological Laboratories for acquiring and maintaining animals; Louie Kerr at the Central Microscopy Facility; Dana Mock-Munoz de Luna for administrative support; Kam-ran Kodhakhah, Heather Rhodes, and the 2017 Grass Fellows for their support and feedback; and lastly, Edward Dougherty at the Brandeis University Confocal Imaging Lab for support and microscope maintenance. This study was funded by the Grass Foundation and NINDS awards to F31NS092126 to AO and R35NS097343 to EM

Data from: Neuronal morphologies built for reliable physiology in a rhythmic motor circuit

It is often assumed that highly-branched neuronal structures perform compartmentalized computations. However, previously we showed that the Gastric Mill (GM) neuron in the crustacean stomatogastric ganglion (STG) operates like a single electrotonic compartment, despite having thousands of branch points and total cable length >10 mm (Otopalik et al., 2017a; 2017b). Here we show that compact electrotonic architecture is generalizable to other STG neuron types, and that these neurons present direction-insensitive, linear voltage integration, suggesting they pool synaptic inputs across their neuronal structures. We also show, using simulations of 720 cable models spanning a broad range of geometries and passive properties, that compact electrotonus, linear integration, and directional insensitivity in STG neurons arise from their neurite geometries (diameters tapering from 10-20 µm to < 2 µm at their terminal tips). A broad parameter search reveals multiple morphological and biophysical solutions for achieving different degrees of passive electrotonic decrement and computational strategies in the absence of active properties

When complex neuronal structures may not matter

Much work has explored animal-to-animal variability and compensation in ion channel expression. Yet, little is known regarding the physiological consequences of morphological variability. We quantify animal-to-animal variability in cable lengths (CV = 0.4) and branching patterns in the Gastric Mill (GM) neuron, an identified neuron type with highly-conserved physiological properties in the crustacean stomatogastric ganglion (STG) of Cancer borealis. We examined passive GM electrotonic structure by measuring the amplitudes and apparent reversal potentials (Erevs) of inhibitory responses evoked with focal glutamate photo-uncaging in the presence of TTX. Apparent Erevs were relatively invariant across sites (mean CV ± SD = 0.04 ± 0.01; 7–20 sites in each of 10 neurons), which ranged between 100–800 µm from the somatic recording site. Thus, GM neurons are remarkably electrotonically compact (estimated λ > 1.5 mm). Electrotonically compact structures, in consort with graded transmission, provide an elegant solution to observed morphological variability in the STG. DOI: http://dx.doi.org/10.7554/eLife.23508.00

Molecular profiling of single neurons of known identity in two ganglia from the crab Cancer borealis

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Northcutt, A. J., Kick, D. R., Otopalik, A. G., Goetz, B. M., Harris, R. M., Santin, J. M., Hofmann, H. A., Marder, E., & Schulz, D. J. Molecular profiling of single neurons of known identity in two ganglia from the crab Cancer borealis. Proceedings of the National Academy of Sciences of the United States of America, 116 (52) (2019): 26980-26990, doi: 10.1073/pnas.1911413116.Understanding circuit organization depends on identification of cell types. Recent advances in transcriptional profiling methods have enabled classification of cell types by their gene expression. While exceptionally powerful and high throughput, the ground-truth validation of these methods is difficult: If cell type is unknown, how does one assess whether a given analysis accurately captures neuronal identity? To shed light on the capabilities and limitations of solely using transcriptional profiling for cell-type classification, we performed 2 forms of transcriptional profiling—RNA-seq and quantitative RT-PCR, in single, unambiguously identified neurons from 2 small crustacean neuronal networks: The stomatogastric and cardiac ganglia. We then combined our knowledge of cell type with unbiased clustering analyses and supervised machine learning to determine how accurately functionally defined neuron types can be classified by expression profile alone. The results demonstrate that expression profile is able to capture neuronal identity most accurately when combined with multimodal information that allows for post hoc grouping, so analysis can proceed from a supervised perspective. Solely unsupervised clustering can lead to misidentification and an inability to distinguish between 2 or more cell types. Therefore, this study supports the general utility of cell identification by transcriptional profiling, but adds a caution: It is difficult or impossible to know under what conditions transcriptional profiling alone is capable of assigning cell identity. Only by combining multiple modalities of information such as physiology, morphology, or innervation target can neuronal identity be unambiguously determined.We thank members of the D.J.S., H.A.H., and E.M. laboratories for helpful discussions. We thank the Genomic Sequencing and Analysis Facility (The University of Texas [UT] at Austin) for library preparation and sequencing and the bioinformatics consulting team at the UT Austin Center for Computational Biology and Bioinformatics for helpful advice. This work was supported by National Institutes of Health grant R01MH046742-29 (to E.M. and D.J.S.) and the National Institute of General Medical Sciences T32GM008396 (support for A.J.N.) and National Institute of Mental Health grant 5R25MH059472-18 and the Grass Foundation (support for Neural Systems and Behavior Course at the Marine Biological Laboratory)