title: An empirical method for converting nucleolar counts to neuronal numbers.
abstract: Our goal is to estimate neuronal numbers from counts of nucleolar profiles. The primary difficulty is that a simple way to convert the counts to the numbers, especially when multiple nucleoli are present in a nucleus, is not available. In this paper, we propose a solution. The formula is N = n X [N(c.f.)/n(c.f.)] where N = the true number of neurons, n = the number of nucleolar profiles in these neurons, N(c.f.) = the number of neurons used to estimate the correction factor and n(c.f.) = the number of nucleolar profiles found in the neurons that make up N(c.f.). The constraints are that the neurons identified for N(c.f.) be representative of the entire population, N; that the nucleolar profiles be counted by the same criteria when n(c.f.) is determined as when n is determined, and that when the correction factor [N(c.f.)/n(c.f.)] is calculated, the nucleolar profiles in each neuron be counted only once. The advantages are simplicity, and generality; the latter resulting from the empirical nature of the correction factor which can calibrate for multiple nucleoli, split nucleoli, invisible fragments, nucleolar size changes, section thickness differences and any other factors that cause n to deviate from N.
mesh_terms: D000818:Animals; D002452:Cell Count / Q000379:methods*; D002466:Cell Nucleolus / Q000648:ultrastructure*; D005725:Ganglia, Autonomic / Q000166:cytology*; D009445:Neuroanatomy / Q000379:methods*; D009474:Neurons / Q000166:cytology* / Q000648:ultrastructure; D051381:Rats