A multivariate approach to structural heterogeneity of retinal ganglion cells

Abstract

Knowing neuronal types is essential for understanding the structural and functional organization of the nervous system. It has long been recognized that neuronal types should be discovered and not defined. This can be done using cluster analysis (CA). Despite there being many studies using CA to classify neurons, only a few of them meet its formal prerequisites. In the present study, we provide an example of using CA in combination with other multivariate techniques for examining neuronal diversity. A special emphasis is put on formal prerequisites to the data and procedure. The data under scrutiny are a sample of ganglion cells projecting to the basal optic nucleus [accessory optic system-projecting ganglion cells (AOS GCs)] in the common frog. There is physiological evidence that these cells comprise at least two functional types but their structural heterogeneity has not been addressed. Cells were labeled with horseradish peroxidase in vivo and examined in whole-mounted retinae using light microscopy. A sample of well-stained cells was obtained and used to estimate 18 structural parameters. A variety of clustering algorithms were used to classify the cells. The joint polar distribution of dendrite mass was monomodal. CA did not reveal a statistically reliable cluster structure in the sample. The clusters were not cohesive and well isolated. ANOVA-on-Ranks revealed no significant between-cluster differences. Our formal conclusion is that functionally distinct frog AOS GCs do not differ in morphology or dendritic arbor orientation. The advantages and limitations of the adopted approach are discussed.