Abstract
Nerve tissue contains a high density of chemical synapses, about 1 per µm3 in the mammalian cerebral cortex. Thus, even for small blocks of nerve tissue, dense connectomic mapping requires the identification of millions to billions of synapses. While the focus of connectomic data analysis has been on neurite reconstruction, synapse detection becomes limiting when datasets grow in size and dense mapping is required. Here, we report SynEM, a method for automated detection of synapses from conventionally en-bloc stained 3D electron microscopy image stacks. The approach is based on a segmentation of the image data and focuses on classifying borders between neuronal processes as synaptic or non-synaptic. SynEM yields 97% precision and recall in binary cortical connectomes with no user interaction. It scales to large volumes of cortical neuropil, plausibly even whole-brain datasets. SynEM removes the burden of manual synapse annotation for large densely mapped connectomes.
Highlights
The ambition to map neuronal circuits in their entirety has spurred substantial methodological developments in large-scale 3-dimensional microscopy (Denk and Horstmann, 2004; Hayworth et al, 2006; Knott et al, 2008; Eberle et al, 2015), making the acquisition of datasets as large as 1 cubic millimeter of brain tissue or even entire brains of small animals at least plausible (Mikula et al, 2012; Mikula and Denk, 2015)
We developed and tested SynEM on a dataset from layer 4 (L4) of mouse primary somatosensory cortex (S1) acquired using SBEM
We report SynEM, a toolset for automated synapse detection in EM-based connectomics
Summary
The ambition to map neuronal circuits in their entirety has spurred substantial methodological developments in large-scale 3-dimensional microscopy (Denk and Horstmann, 2004; Hayworth et al, 2006; Knott et al, 2008; Eberle et al, 2015), making the acquisition of datasets as large as 1 cubic millimeter of brain tissue or even entire brains of small animals at least plausible (Mikula et al, 2012; Mikula and Denk, 2015). Neurite reconstruction was so slow, that synapse annotation comparably paled as a challenge (Figure 1c): when comparing the contouring of neurites (proceeding at 200–400 work hours per millimeter neurite path length) with synapse annotation by manually searching the volumetric data for synaptic junctions (Figure 1d, proceeding at about 0.1 hr per mm3), synapse annotation consumed at least 20-fold less annotation time than neurite reconstruction (Figure 1c). An alternative strategy for manual synapse detection is to follow reconstructed axons (Figure 1e) and annotate sites of vesicle accumulation and postsynaptic partners. This axon-focused synapse annotation reduces synapse annotation time by about 8-fold for dense reconstructions (proceeding at about 1 min per potential contact indicated by a vesicle accumulation, which occurs every about 4–10 mm along axons in mouse cortex)
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