Unbiased Quantification of Synaptic Inputs Onto Single Neurons

Henny P, Brown MT, Micklem BR, Magill PJ, Bolam JP. Stereological and ultrastructural quantification of the afferent synaptome of individual neurons. Brain Struct Funct 2014;219(2):631-640. doi: 10.1007/s00429-013-0523-9.

Background: Understanding how neurons integrate information depends on knowing the number and distribution of their synaptic inputs. While classical neuroanatomy revealed neuronal morphology, unbiased quantitative data on synaptic organization remain limited. This study in the rat brain combined light and electron microscopy with stereological sampling to obtain precise, three-dimensional measurements of synaptic inputs onto single neurons, providing a foundation for linking structure and function.

Hypothesis: This study hypothesized that combining juxtacellular labeling, digital neuronal reconstruction and stereological sampling at the ultrastructural level allows unbiased and accurate estimation of the total number and somato-dendritic distribution of synaptic inputs received by individual neurons.

Methods: The authors labeled single neurons in vivo in rats, sectioned the brain at constant thickness and performed random systematic sampling. Neuronal morphology was digitally reconstructed using Neurolucida, and serial ultrathin sections were analyzed with electron microscopy. Synapses were identified and counted directly from the electron microscopic images using the Stereo Investigator Optical Fractionator probe. This integrated stereological and reconstruction approach enabled estimation of total synapse number and mapping of their distribution along the soma and dendrites.

Results: A representative neuron was estimated to receive 10,488 synapses with a coefficient of error of 0.09. Reduced sampling decreased precision. Synapses were most numerous on proximal, low-order dendrites but densest on the soma and distal branches. Estimates were consistent with previously reported stereological values in other brain regions.

Conclusions: This protocol provides a reliable and unbiased framework for quantifying and mapping the afferent synaptome of single neurons, enabling structure–function correlations at the cellular level and supporting realistic computational modeling of neuronal processing.