A Petascale Automated Imaging Pipeline for Mapping Neuronal Circuits with High-throughput Transmission Electron Microscopy

Yin, Wenjing; Brittain, Derrick; Borseth, Jay; Scott, Marie E.; Williams, Derric; Perkins, Jed; Own, Chris; Murfitt, Matthew F.; Torres, Russel; Kapner, Daniel; Bleckert, Adam; Castelli, Daniel; Reid, David; Lee, Wei-Chung Allen; Graham, Brett J.; Takeno, Marc; Bumbarger, Dan; Farrell, Colin; Reid, R. Clay; Costa, Nuno Maçarico da

doi:10.1101/791889

Cited by 24 publications

(25 citation statements)

References 27 publications

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“…While brain slices are thicker than our reconstructed volume, neurons recorded in brain slices can be affected by truncation because they are close to the surface of the slice, typically within tens of microns. Forthcoming EM reconstructions of much larger volumes (Yin et al ., 2019) should make it possible to eliminate the effects of truncation from motif statistics.…”

Section: Discussionmentioning

confidence: 99%

Multiscale and multimodal reconstruction of cortical structure and function

Turner

Macrina

Bae

et al. 2020

Preprint

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We present a semi-automated reconstruction of L2/3 mouse primary visual cortex from 3 million cubic microns of electron microscopic images, including pyramidal and inhibitory neurons, astrocytes, microglia, oligodendrocytes and precursors, pericytes, vasculature, mitochondria, and synapses. Visual responses of a subset of pyramidal cells are included. The data are being made publicly available, along with tools for programmatic and 3D interactive access. The density of synaptic inputs onto inhibitory neurons varies across cell classes and compartments. We uncover a compartment-specific correlation between mitochondrial coverage and synapse density. Frequencies of connectivity motifs in the graph of pyramidal cells are predicted quite accurately from node degrees using the configuration model of random graphs. Cells receiving more connections from nearby cells exhibit stronger and more reliable visual responses. These example findings illustrate the resource's utility for relating structure and function of cortical circuits as well as for neuronal cell biology.

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Section: Discussionmentioning

confidence: 99%

Multiscale and multimodal reconstruction of cortical structure and function

Turner

Macrina

Bae

et al. 2020

Preprint

Self Cite

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“…Transmission electron microscopy. We made several custom modifications to a JEOL-1200EXII 120kV transmission electron microscope (Yin et al , 2019) . A column extension and scintillator magnified the nominal field of view by tenfold with negligible loss of resolution.…”

Section: Methodsmentioning

confidence: 99%

“…Most large-scale ssEM reconstructions have been completely manual, while many large-scale bfEM reconstructions have been semiautomated (19/20 and 5/10 in Table 1 of (Kornfeld and Denk, 2018) ). On the other hand, the higher imaging throughput of ssEM (Nickell and Zeidler, 2019;Yin et al , 2019) makes it suitable for scaling up to volumes that are large enough to encompass the arbors of mammalian neurons.…”

Section: Handling Of Ssem Image Defectsmentioning

confidence: 99%

Binary and analog variation of synapses between cortical pyramidal neurons

Dorkenwald

Turner

Macrina

et al. 2019

Preprint

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129

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Learning from experience depends at least in part on changes in neuronal connections. We present the largest map of connectivity to date between cortical neurons of a defined type (L2/3 pyramidal cells), which was enabled by automated analysis of serial section electron microscopy images with improved handling of image defects. We used the map to identify constraints on the learning algorithms employed by the cortex. Previous cortical studies modeled a continuum of synapse sizes (Arellano et al. , 2007) by a log-normal distribution (Loewenstein, Kuras and Rumpel, 2011;de Vivo et al. , 2017;Santuy et al. , 2018) . A continuum is consistent with most neural network models of learning, in which synaptic strength is a continuously graded analog variable. Here we show that synapse size, when restricted to synapses between L2/3 pyramidal cells, is well-modeled by the sum of a binary variable and an analog variable drawn from a log-normal distribution. Two synapses sharing the same presynaptic and postsynaptic cells are known to be correlated in size (Sorra and Harris, 1993;Koester and Johnston, 2005;Bartol et al. , 2015;Kasthuri et al. , 2015;Dvorkin and Ziv, 2016;Bloss et al. , 2018;Motta et al. , 2019) . We show that the binary variables of the two synapses are highly correlated, while the analog variables are not. Binary variation could be the outcome of a Hebbian or other synaptic plasticity rule depending on activity signals that are relatively uniform across neuronal arbors, while analog variation may be dominated by other influences. We discuss the implications for the stability-plasticity dilemma. †Correspondence to svenmd@princeton.edu ,

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“…Looking forward, this problem will only get worse. Recent similar projects are generating petabytes worth of data [38], and a mouse brain of 500 mm 3 , at a typical FIB-SEM resolution of 8nm isotropic, would require almost 1000 PB.…”

Section: Data Representationmentioning

confidence: 99%

A Connectome of the AdultDrosophilaCentral Brain

Januszewski

Lǚ

et al. 2020

Preprint

119

128

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The neural circuits responsible for behavior remain largely unknown. Previous efforts have reconstructed the complete circuits of small animals, with hundreds of neurons, and selected circuits for larger animals. Here we (the FlyEM project at Janelia and collaborators at Google) summarize new methods and present the complete circuitry of a large fraction of the brain of a much more complex animal, the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses, and proofread such large data sets; new methods that define cell types based on connectivity in addition to morphology; and new methods to simplify access to a large and evolving data set. From the resulting data we derive a better definition of computational compartments and their connections; an exhaustive atlas of cell examples and types, many of them novel; detailed circuits for most of the central brain; and exploration of the statistics and structure of different brain compartments, and the brain as a whole. We make the data public, with a web site and resources specifically designed to make it easy to explore, for all levels of expertise from the expert to the merely curious. The public availability of these data, and the simplified means to access it, dramatically reduces the effort needed to answer typical circuit questions, such as the identity of upstream and downstream neural partners, the circuitry of brain regions, and to link the neurons defined by our analysis with genetic reagents that can be used to study their functions.Note: In the next few weeks, we will release a series of papers with more involved discussions. One paper will detail the hemibrain reconstruction with more extensive analysis and interpretation made possible by this dense connectome. Another paper will explore the central complex, a brain region involved in navigation, motor control, and sleep. A final paper will present insights from the mushroom body, a center of multimodal associative learning in the fly brain.

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A Petascale Automated Imaging Pipeline for Mapping Neuronal Circuits with High-throughput Transmission Electron Microscopy

Cited by 24 publications

References 27 publications

Multiscale and multimodal reconstruction of cortical structure and function

Multiscale and multimodal reconstruction of cortical structure and function

Binary and analog variation of synapses between cortical pyramidal neurons

A Connectome of the AdultDrosophilaCentral Brain

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