From random to regular: Variation in the patterning of retinal mosaics

J Comp Neurol. 2020 Sep 1;528(13):2135-2160. doi: 10.1002/cne.24880. Epub 2020 Mar 3.

Abstract

The various types of retinal neurons are each positioned at their respective depths within the retina where they are believed to be assembled as orderly mosaics, in which like-type neurons minimize proximity to one another. Two common statistical analyses for assessing the spatial properties of retinal mosaics include the nearest neighbor analysis, from which an index of their "regularity" is commonly calculated, and the density recovery profile derived from autocorrelation analysis, revealing the presence of an exclusion zone indicative of anti-clustering. While each of the spatial statistics derived from these analyses, the regularity index and the effective radius, can be useful in characterizing such properties of orderly retinal mosaics, they are rarely sufficient for conveying the natural variation in the self-spacing behavior of different types of retinal neurons and the extent to which that behavior generates uniform intercellular spacing across the mosaic. We consider the strengths and limitations of these and other spatial statistical analyses for assessing the patterning in retinal mosaics, highlighting a number of misconceptions and their frequent misuse. Rather than being diagnostic criteria for determining simply whether a population is "regular," they should be treated as descriptive statistics that convey variation in the factors that influence neuronal positioning. We subsequently apply multiple spatial statistics to the analysis of eight different mosaics in the mouse retina, demonstrating conspicuous variability in the degree of patterning present, from essentially random to notably regular. This variability in patterning has both a developmental as well as a functional significance, reflecting the rules governing the positioning of different types of neurons as the architecture of the retina is assembled, and the distinct mechanisms by which they regulate dendritic growth to generate their characteristic coverage and connectivity.

Keywords: RRID AB_10013783; RRID AB_10064230; RRID AB_2079751; RRID AB_213554; RRID AB_2187701; RRID AB_309969; RRID AB_397958; RRID AB_90755; Voronoi domain; autocorrelation; density recovery profile; effective radius; nearest neighbor; packing factor; regularity index.

Publication types

  • Research Support, N.I.H., Extramural
  • Review

MeSH terms

  • Amacrine Cells / physiology*
  • Animals
  • Dendrites / physiology*
  • Humans
  • Mice
  • Retina / cytology*
  • Retina / physiology*