Quantitative models of feline lumbosacral dorsal root ganglia neuronal cell density

Abstract

Background: Dorsal root ganglia (DRG) are spinal root components that contain the cell bodies of converging primary sensory neurons. DRG are becoming a therapeutic target for electrical neural interfaces. Our purpose was to establish methods for quantifying the non-random nature and distribution of neuronal cell bodies within DRG.

New method: We identified neuronal cell body locations in 26 feline lumbosacral DRG cross-section histological images and used computational tools to quantify spatial trends. We first analyzed spatial randomness using the nearest-neighbor distance method. Next we overlaid a 6×6 grid, modeling neuronal cellular density in each grid square and comparing regions statistically. Finally we transformed DRG onto a polar map and calculated neuronal cellular density in annular sectors. We used a recursive partition model to determine regions of high and low density, and validated the model statistically.

Results: We found that the arrangement of neuronal cell bodies at the widest point of DRG is distinctly non-random with concentration in particular regions. The grid model suggested a radial trend in density, with increasing density toward the outside of the DRG. The polar transformation model showed that the highest neuronal cellular density is in the outer 23.9% radially and the dorsal ±61.4° angularly.

Comparison with existing methods: To our knowledge, DRG neuronal cell distribution has not been previously quantified.

Conclusions: These results confirm and expand quantitatively on the existing understanding of DRG anatomy. Our methods can be useful for analyzing the distribution of cellular components of other neural structures or expanding to three-dimensional models.

Keywords: Cats; Cell density; Spatial trend; Spinal ganglia.