A Computational Framework for Ultrastructural Mapping of Neural Circuitry

Abstract

Circuitry mapping of metazoan neural systems is difficult because canonical neural regions (regions containing one or more copies of all components) are large, regional borders are uncertain, neuronal diversity is high, and potential network topologies so numerous that only anatomical ground truth can resolve them. Complete mapping of a specific network requires synaptic resolution, canonical region coverage, and robust neuronal classification. Though transmission electron microscopy (TEM) remains the optimal tool for network mapping, the process of building large serial section TEM (ssTEM) image volumes is rendered difficult by the need to precisely mosaic distorted image tiles and register distorted mosaics. Moreover, most molecular neuronal class markers are poorly compatible with optimal TEM imaging. Our objective was to build a complete framework for ultrastructural circuitry mapping. This framework combines strong TEM-compliant small molecule profiling with automated image tile mosaicking, automated slice-to-slice image registration, and gigabyte-scale image browsing for volume annotation. Specifically we show how ultrathin molecular profiling datasets and their resultant classification maps can be embedded into ssTEM datasets and how scripted acquisition tools (SerialEM), mosaicking and registration (ir-tools), and large slice viewers (MosaicBuilder, Viking) can be used to manage terabyte-scale volumes. These methods enable large-scale connectivity analyses of new and legacy data. In well-posed tasks (e.g., complete network mapping in retina), terabyte-scale image volumes that previously would require decades of assembly can now be completed in months. Perhaps more importantly, the fusion of molecular profiling, image acquisition by SerialEM, ir-tools volume assembly, and data viewers/annotators also allow ssTEM to be used as a prospective tool for discovery in nonneural systems and a practical screening methodology for neurogenetics. Finally, this framework provides a mechanism for parallelization of ssTEM imaging, volume assembly, and data analysis across an international user base, enhancing the productivity of a large cohort of electron microscopists.

ON cone bipolar cell axonal synapses in the OFF inner plexiform layer of the rabbit retina

“…The rabbit retinal connectome volume RC1 is a serial section, 2 nm resolution, 16.4 terabyte TEM image collection assembled into a cylindrical data volume ≈ 0.25 mm wide and ≈ 0.025 mm high spanning the mid-inner nuclear layer through the GC layer (Figure 1 A), augmented by molecular channels capping and intercalated every 30 sections through it (Anderson et al, 2011a;Anderson et al, 2009;Anderson et al, 2011b). The CMP channels include aspartate, glutamate, 4-aminobutyrate (GABA), glycine, glutamine, taurine, and AGB as a marker of light-driven activity.…”

“…RC1 was acquired by ATEM at 2.18 nm resolution and assembled into a volume with the NCRToolset (Anderson et al, 2009). Molecular-ultrastructural registrations were generated with ir-tweak (Anderson et al, 2011a;Anderson et al, 2009;Anderson et al, 2011b)3D renderings are built from disk annotations in Vikingplot (Anderson et al, 2011b), allowing rendering of surfaces and characterization of areas and volumes. All cells rendered in this paper are publicly available as Google Collada *.dae files via the Connectome Viz application.…”

“…As described in our prior papers on connectomics (Anderson et al, 2009), display TEM images in this paper were produced by remapping RC1 volume tiles to gamma 1.3. Optical and TEM overlays used the TEM greyscale brightness combined with the hue, and saturation from the optical image as described in Anderson et al (2011a).…”

Elastic volume reconstruction from series of ultra-thin microscopy sections

“…Sections can be imaged as mosaics of overlapping image tiles, either manually or using a motorized stage, allowing the imaging of very large fields of view. In combination, these advantages render serial section microscopy particularly useful for large scale high resolution reconstructions, for example, of dense neuronal tissue, where the method employing Electron Microscopy (EM) recently experienced a renaissance [1][2][3][4][5][6] .…”

“…To recover the imaged volume and extract biologically interesting information such as the reconstruction of neuronal circuits 2,3,5 , sections need to be aligned and distortion must be removed.…”