Coupling, membrane conductance, and ion channel mRNA profiles in the establishment and maintenance of network activity in the crustacean cardiac ganglion

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Neural networks underly the production of rhythmic behaviors and do so throughout an animal’s life despite changes in physiological state or environment. This necessitates both stability of cell and network properties to maintain output and plasticity to respond to disruptions. Here, we use the Jonah crab (Cancer borealis) cardiac ganglion to explore both stability and plasticity. This network consists of only four interneurons and five motoneurons. These motoneurons have been shown to rapidly respond to and ameliorate activity perturbations through cell specific (Ransdell et al., 2012) and network (i.e. electrical coupling) changes (Lane et al., 2016). This occurs despite variability in cell properties (Ransdell et al., 2013). Variability within cell properties could confound identification based on these properties. We examine the how separable neuron cell types are based on mRNA abundances. We apply supervised and unsupervised machine learning techniques to these data to sort neurons, finding that we can identify cells based on mRNA abundance, albeit imperfectly. Next, we explore which sets of cell properties (mRNA abundances, membrane properties, cell excitability) are changed by perturbation and on what time scale. We perturb cells for up to twenty-four hours and find changes in transcription, membrane properties, and excitability. Excitability is not maintained at twenty-four hours relative to control suggesting limits to the duration of a cell’s compensatory capacity. Finally, we explore activity dependent regulation at the network level, focusing on of electrical coupling. We control the voltage of electrically coupled motoneurons and measure the strength of coupling between them to determine the salient electrophysiological signal inducing plasticity. We find that timing of activity rather than the amplitude of activity elicits plasticity. 


A. J. Northcutt1, D. R. Kick1, A. G. Otopalik, B. M. Goetz, R. M. Harris, J. M. Santin, H. A. Hofmann, E. Marder, D. J. Schulz. Molecular profiling of single neurons of known identity in two ganglia from the crab Cancer borealis. Proc. Natl. Acad. Sci. U.S.A. (2019). 

D. R. Kick, D. J. Schulz, Studying gap junctions with PARIS. eLife. 8, e45207 (2019).

D. R. Kick, D. J. Schulz, Variability in neural networks. eLife. 7, e34153 (2018). 

B. J. Lane, D. R. Kick, D. K. Wilson, S. S. Nair, D. J. Schulz. Dopamine maintains network synchrony via direct modulation of gap junctions in the crustacean cardiac ganglion. eLife. 7, e39368 (2018). 

A. M. Willenbrink, M. K. Gronauer, L. F. Toebben, D. R. Kick, M. Wells, B. Zhang. The Hillary Climber trumps manual testing: an automatic system for studying Drosophila climbing. J. Neurogenet. 30, 205–211 (2016).


  • Dr. David Schulz (chair)
  • Dr. Johannes Schul
  • Dr. Bing Zhang
  • Dr. Satish Nair

Dr. Kick accepted a postdoctoral position with the US Department of Agriculture Agricultural Research Service's Plant Genetics Lab in the lab of Dr. Jacob Washburn. He will be working to develop and refine machine learning models to predict and explain plant phenotypes from genetic, environmental, and management factors and the interactions between them.

Speaker Information

Daniel Kick 
Ph.D. Candidate - Schulz lab
Division of Biological Sciences
University of Missouri