SEM to Simulation: Bringing Ultrastructural Detail to Multiscale Modeling
Introduction Just as neuron morphology influences spiking behavior and thus network interactions, so too does the 3D placement of spines affect interaction between spines [1] and thus cellular behavior. However fine spine details are not visible under the optical microscopy used for reconstructing neuron morphology and full-cell scanning electron microscopy (SEM) images are generally not feasible due to size constraints. To address these challenges, we developed a tool for the NEURON simulator [2] for importing and editing an SEM reconstruction of a portion of a dendrite, selecting spines, rotating them, and inserting them into a full-cell reconstruction for simulation, using our experimental support for reaction-diffusion multigridding in NEURON. Methods SEM images may be segmented to identify each spine using standard segmentation software then exported to a TIFF stack. We estimate key electrical properties: approximately equivalent length, diameter, volume, and surface area. Our tool loads the image stack and identifies the voxels forming each spine-dendrite boundary so that we can preserve the connection location after transformations. PySide6 is used to provide a graphical interface allowing spines to be selected and manipulated into position; this can also be done programmatically. An algorithm adds/removes voxels to connect the spine cleanly. Transformed spines can be exported to text files for easy editing, enabling iterative refinement. Results We present our graphical tool, examples of relevant data sets, and simulation results. The graphical tool allows visualization of both the loaded SEM data and the placed spines after transformations. The simulations leverage our previous work, allowing a synaptic source (e.g., of IP3) to be placed at a precise 3D location within a spine. We validate the multigrid simulation by comparing to a single unified 3D simulation and contrast it to simplified geometry approximations, illustrating their similarities and differences. In particular, our tool allows toggling between the two representations. Discussion Support for imported spine morphologies brings NEURON a step closer to capturing the intricacies of the human brain. The same tool described here can also directly be used for incorporating SEM data of a dendrite as well. It is not feasible to simulate full cells and networks at this level of detail, nor is that necessarily desirable -- simpler models are often more useful for insights -- but our approach allows us to explore localized behavior in detail in a multiscale context with full cell and network simulations. This tool can give us insight on which details model when and allow us to explore detailed biological questions of synaptic plasticity or the role of morphological changes in disease. Acknowledgments This research was funded by the National Institute of Mental Health, National Institutes of Health, grant number R01 MH086638. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. References [1] Huertas, M., Newton, A., McDougal, R., Sacktor, T., & Shouval, H. (2022). Conditions for Synaptic Specificity during the Maintenance Phase of Synaptic Plasticity. eneuro, 9(3), ENEURO.0064-22.2022. doi: 10.1523/ENEURO.0064-22.2022[2] Hines, M. & Carnevale, N. (1997). The NEURON Simulation Environment. Neural Computation, 9(6), 1179-1209. doi: 10.1162/neco.1997.9.6.1179