[blog] We acquired a rapidly preserved human surgical sample from the temporal lobe of the cerebral cortex. We stained a 1 mm3 volume with heavy metals, embedded it in resin, cut more than 5000 slices at ~30 nm and imaged these sections using a high-speed multibeam scanning electron microscope. We used computational methods to render the 3-dimensional structure of 50,000 cells, hundreds of millions of neurites and 130 million synaptic connections. The 1.3 petabyte electron microscopy volume, the segmented cells, cell parts, blood vessels, myelin, inhibitory and excitatory synapses, and 100 manually proofread cells are available to peruse online.
Despite the incompleteness of the automated segmentation caused by split and merge errors, many interesting features were evident. Glia outnumbered neurons 2:1 and oligodendrocytes were the most common cell type in the volume. The E:I balance of neurons was 69:31%, as was the ratio of excitatory versus inhibitory synapses in the volume. The E:I ratio of synapses was statistically-significantly higher on pyramidal neurons than inhibitory interneurons.
We found that deep layer excitatory cell types can be classified into subsets based on structural and connectivity differences, that chandelier interneurons not only innervate excitatory neuron initial segments as previously described, but also each others’ initial segments, and that among the thousands of weak connections established on each neuron, there exist rarer highly powerful axonal inputs that establish multi-synaptic contacts (up to ~20 synapses) with target neurons. Our analysis indicates that these strong inputs are specific, and allow small numbers of axons to have an outsized role in the activity of some of their postsynaptic partners.
…This “digital tissue” (2017) is a ~660,000-fold scale up of an earlier saturated reconstruction from a small region of mouse cortex, published in 2015 ( et al 2015). Although this scaleup was difficult, it was not hundreds of thousands of times more difficult and took about the same amount of time as the previous data set (~4 years). This means that many of the technical hurdles with imaging and computer-based analysis have improved dramatically over the past few years. This improvement was in large part due to two noteworthy advances: fast imaging owing to multibeam scanning electron microscopy ( et al 2015) and the profound effect of AI on image processing and analysis ( et al 2018). The rapid improvements over the past few years ( et al 2011; et al 2011; et al 2013; et al 2013; et al 2016; et al 2019; et al 2020; et al 2020; et al 2020; et al 2021) argues that analyzing volumes that are even 3 orders of magnitude larger, such as an exascale whole mouse brain connectome, will likely be in reach within a decade ( et al 2020).
![Figure 1: Image acquisition for the human brain sample. A fresh surgical cerebral cortex sample was rapidly preserved, then stained, embedded in resin, and sectioned. More than 5000 sequential ~30 nm sections were collected on tape using an ATUM [Automatic Tape-collecting Ultra-Microtome] (upper left panel). Yellow box shows the site where the brain sample is cut with the diamond knife and thin sections are collected onto the tape. The tape was then cut into strips and imaged in a multibeam scanning electron microscope (mSEM). This large machine (see middle panel with person on chair as reference) uses 61 beams that image a hexagonal area of about ~10,000 μm2 simultaneously (see upper right). For each thin section, all the resulting tiles are then stitched together. One such stitched section is shown (bottom). This section is about 4 mm2 in area and was imaged with 4×4 nm2 pixels. Given the necessity of some overlap between the stitched tiles, this single section required collection of more than 300 GB of data.](/doc/cryonics/2021-shapesoncoe-figure1-scanninghumanbrainsample.png)