Dense connectomic reconstruction in layer 4 of the somatosensory cortex

Abstract

21The dense circuit structure of the mammalian cerebral cortex is still unknown. With 22 developments in 3-dimensional (3D) electron microscopy, the imaging of sizeable 23 volumes of neuropil has become possible, but dense reconstruction of connectomes 24 from such image data is the limiting step. Here, we report the dense reconstruction 25 of a volume of about 500,000 µm 3 from layer 4 of mouse barrel cortex, about 300 26 times larger than previous dense reconstructions from the mammalian cerebral 27 cortex. Using a novel reconstruction technique, FocusEM, we were able to 28 reconstruct a total of 0.9 meters of dendrites and about 1.8 meters of axons investing 29 only about 4,000 human work hours, about 10-25 times more efficient than previous 30 dense circuit reconstructions. We find that connectomic data alone allows the 31 definition of inhibitory axon types that show established principles of synaptic 32 specificity for subcellular postsynaptic compartments. We find that also a fraction of 33 excitatory axons exhibit such subcellular target specificity. Only about 34 35% of inhibitory and 55% of excitatory synaptic subcellular innervation can be 35 predicted from the geometrical availability of membrane surface, revoking coarser 36 models of random wiring for synaptic connections in cortical layer 4. We furthermore 37 find evidence for enhanced variability of synaptic input composition between neurons 38 at the level of primary dendrites in cortical layer 4. Finally, we obtain evidence for 39 Hebbian synaptic weight adaptation in at least 24% of connections; at least 35% of 40 connections show no sign of such previous plasticity. Together, these results 41 establish an approach to connectomic phenotyping of local dense neuronal circuitry 42 in the mammalian cortex. 43 44 109First, we reconstructed those neurons, which had their cell bodies in the tissue 110 volume ( Fig. 1h,i, Supplementary Material 1, n=125 cell bodies, of these 97 111 neuronal, of these 89 reconstructed with dendrites in the dataset) using a set of 112 simple growth rules for automatically connecting neurite pieces based on the 113 segment-to-segment neighborhood graph and the connectivity and neurite type 114 classifiers ( Fig. 1f, see Methods). As a result, we obtained fully automated 115 reconstructions of the neuron's soma and dendritic processes. Notably with a 116 minimal additional manual correction investment of 9.7 hours for 89 cells (54.5 mm 117 6 dendritic and 2.1 mm axonal path length), the dendritic shafts of these neurons could 118 be reconstructed without merge errors, but 37 remaining split errors, at 87.3% 119 dendritic length recall ( Fig. 1h,i, Supplementary Material 1, see Methods). This 120 reconstruction efficiency compares favorably to recent reports of automated 121 segmentation of neurons in 3D EM data from the bird brain obtained at about 2-fold 122 higher imaging resolution (Januszewski et al., 2018), which reports soma-based 123 neuron reconstruction at an error rate of beyond 100 errors per 66 mm dendritic 124 ...

Light microscopy based approach for mapping connectivity with molecular specificity

“…miriEx and Brainbow use commercially available, “off the shelf”, reagents, and LM is a prevalent imaging modality across many labs. Data sizes are an order of magnitude smaller compared to EM datasets despite containing multiple channels (~20 vs. ~200 GB for a similar sized volume 5 ). Targeted analysis makes it possible to map connectivity in a reasonable amount of time, and application of automated reconstruction and synapse segmentation methods will further simplify this process.…”

“…EM is a powerful tool that offers unparalleled nanometer resolution 3 , sufficient to observe synaptic structures. Recent works have used EM to define the adult Drosophila hemibrain connectome 4 , as well as curate a 92.6 × 94.8 × 61.8 μm 3 connectome from mouse somatosensory cortex 5 . Despite the recent technological and biological advances with EM, several challenges remain.…”

UCSF ChimeraX: Structure visualization for researchers, educators, and developers

“…Milled block-face electron microscopy of mouse barrel cortex showing neurons, dendritic spines, and synapses. 33 F I G U R E 6 Placing molecular complexes in EM tomography (EMDB 10780) of the Chlamydomonas thylakoid membrane using virtual reality: 34 photosystem I (green), photosystem II (blue), cytochrome b6f (orange), ATP synthase (magenta), ribosome (yellow) the conformation to a high-resolution reference model) are accessible via the ChimeraX command line.…”

Neuronal Subcompartment Classification and Merge Error Correction

“…Although trained human reviewers can classify many neuronal fragments with respect to subcompartment, the growing scale of connectomic reconstructions demands automated methods. A recent approach to this problem was based on training random forest classifiers on manually defined features extracted from neurite segments and separately detected organelles such as mitochondria or synapses [4,11]. A later extension improved accuracy by classifying 2d projections of neurites and their organelles with convolutional neural networks (CNNs), a technique called Cellular Morphology Networks (CMNs) [12].…”

“…to correct errors in automated reconstructions. Prior works proposed to detect merge errors through identification of morphologically unlikely cross-shaped fragments [11,13] or used a 3d CNN trained specifically to detect merge [14] or split errors [15]. Strong biological priors dictate that vertebrate neurons have only one major axonal branch extending from the soma, and that dendritic and axonal subcompartments do not typically intermingle within a neurite [10].…”