Comparison of Methods for Counting Neurons and Neuron Profiles in Brain Sections

Advances in high-resolution microscopy and cell staining techniques allow for quantifying well-stained cells and other microstructures in anatomically defined regions of interest (ROIs) in ex vivo brains from humans and animal models of human disease. The current best practice is the optical disector, an unbiased stereology method for counting individual cells or other tissue deposits while manually focusing a thin focal plane through a known z-axis volume (optical disector). By eliminating all known sources of methodological bias, the optical disector method ensures that the ex vivo count of the total number of neurons (∑NCells) converges on true in vivo values. A current limitation with all computer-assisted methods is the time, labor, and effort for counting (clicking) on a hundred or more cells as the counting process repeats at 100–200 x-y locations through each ROI. Faster data collection methods include semiquantitative approaches for sampling and counting 2D neuron profiles (∑NProf), though these methods can include unknown and unknowable amounts of systematic error (bias). Here we compare accuracy, reproducibility, and efficiency of counting total number of NeuN-immunostained neurons using the manual optical disector (gold standard) versus three techniques for counts of NeuN profiles. All counts of ∑NCells and ∑NProf were done to the same sampling intensity in the same high-power (100×) stacks of z-axis images (i.e., disector stacks) collected in a systematic-random manner through the entire neocortex (NCTX) in six mouse brains. The findings showed no statistical differences between ∑NCells by the gold standard and ∑NProf counts using three methods (fully manual, semiautomatic, fully automatic). Reproducibility (inter-rater error) by two similarly trained data collectors ranged from 0% for fully automatic counts of ∑NProf to ~5% for ∑NCells counts by gold standard with average data collection times between <1 min per case to ~12 min per case, respectively. Notably, all three methods for counting ∑NProf required a significant amount of unsupervised time, i.e., from 30 to 90 min per case, for preprocessing image stacks prior to data collection. Estimates of ∑NCells by the manual optical disector required no preprocessing. In summary, the manual optical disector remains the best practice due to the avoidance of all known sources of methodological bias, low inter-rater error, and moderate-to-high throughput. These results provide a baseline for comparisons with other methods for quantifying stained cells in tissue sections, including novel deep learning approaches for automatic counts of well-stained cells and other microstructures in the brain.