Reconstruction of motor control circuits in adult Drosophila using automated transmission electron microscopy

Phelps, Jasper S.; Hildebrand, David G. C.; Graham, Brett J.; Kuan, Aaron T.; Thomas, Logan A.; Nguyen, Tri; Buhmann, Julia; Azevedo, Anthony W.; Sustar, Anne; Agrawal, Sweta; Liu, Ming-Guan; Shanny, Brendan L.; Funke, Jan; Tuthill, John C.; Lee, Wei-Chung Allen

doi:10.1016/j.cell.2020.12.013

Cited by 190 publications

(256 citation statements)

References 90 publications

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“…Images were acquired using techniques described elsewhere (Yin et al 2020;MICrONS Consortium et al 2021). It took 12 days to cut the brain volume into over 27,000 sections, which were collected onto GridTape (Phelps et al 2021). GridTape contains a series of apertures, each of which is covered by a plastic film.…”

Section: Image Acquisition and Alignmentmentioning

confidence: 99%

Petascale neural circuit reconstruction: automated methods

Macrina

Lee

et al. 2021

Preprint

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3D electron microscopy (EM) has been successful at mapping invertebrate nervous systems, but the approach has been limited to small chunks of mammalian brains. To scale up to larger volumes, we have built a computational pipeline for processing petascale image datasets acquired by serial section EM, a popular form of 3D EM. The pipeline employs convolutional nets to compute the nonsmooth transformations required to align images of serial sections containing numerous cracks and folds, detect neuronal boundaries, label voxels as axon, dendrite, soma, and other semantic categories, and detect synapses and assign them to presynaptic and postsynaptic segments. The output of neuronal boundary detection is segmented by mean affinity agglomeration with semantic and size constraints. Pipeline operations are implemented by leveraging distributed and cloud computing. Intermediate results of the pipeline are held in cloud storage, and can be effortlessly viewed as images, which aids debugging. We applied the pipeline to create an automated reconstruction of an EM image volume spanning four visual cortical areas of a mouse brain. Code for the pipeline is publicly available, as is the reconstructed volume.

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Section: Image Acquisition and Alignmentmentioning

confidence: 99%

Petascale neural circuit reconstruction: automated methods

Macrina

Lee

et al. 2021

Preprint

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“…Recent technological advances have enabled huge increases in the number of neuron reconstructions that can be performed in a single study into the hundreds (Gouwens et al, 2019(Gouwens et al, , 2020Jiang et al, 2021;BRAIN Initiative Cell Census Network (BICCN) et al, 2020). Multispectral labeling (Shen et al, 2020;Li et al, 2020) and large-volumetric electron microscopy (Motta et al, 2019;Yin et al, 2020;Phelps et al, 2021), in principle, allow reconstructing many more neurons within one brain or within a single common coordinate system (Wang et al, 2020). As a result, analysis techniques must be developed which allow these data to be integrated, with specific focuses on the ability to customize, automate and quickly expand processing workflows to handle large batches of individual neurons, including those reconstructed from various methods.…”

Section: Introductionmentioning

confidence: 99%

nGauge: Integrated and extensible neuron morphology analysis in Python

Williams

et al. 2021

Preprint

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The study of neuron morphology requires robust and comprehensive methods to quantify the differences between neurons of different subtypes and animal species. Several software packages have been developed for the analysis of neuron tracing results stored in the standard SWC format. However, providing relatively simple quantifications and their non-extendable architecture prohibit their use for advanced data analysis and visualization. We developed nGauge, a Python toolkit to support the parsing and analysis of neuron morphology data. As an application programming interface (API), nGauge can be referenced by other popular open-source software to create custom informatics analysis pipelines and advanced visualizations. nGauge defines an extendable data structure that handles volumetric constructions (e.g. soma), in addition to the SWC linear reconstructions, while remaining light-weight. This greatly extends nGauge's data compatibility.

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“…This gap has arisen because anatomical connectivity and neuronal dynamics are typically studied separately and at vastly different spatial and temporal scales. We recently established methods to scale electron (Phelps et al, 2021) and X-ray microscopy (Kuan et al, 2020) and to combine these high-resolution structural imaging approaches with cellular resolution in vivo two-photon calcium imaging (Bock et al, 2011;Lee et al, 2016). This combination of imaging modalities at multiple scales enables us to directly bridge behavior, neuronal computation, and circuit structure; however, linking them remains a challenge.…”

mentioning

confidence: 99%

“…We use serial-section transmission EM (TEM) to acquire structural data at 4 nm resolution to analyze the synaptic connectivity between neurons. We recently developed GridTape, a technology that combines automated serial-section collection with automated high-throughput TEM (Phelps et al, 2021). The data quality afforded by GridTape-based, automated TEM allows dense, automated segmentation and synapse prediction using convolutional neural networks.…”

mentioning

confidence: 99%

Multi-Scale Imaging of Connectomes With Photons and Electrons

Lee

2021

Microsc Microanal

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Reconstruction of motor control circuits in adult Drosophila using automated transmission electron microscopy

Cited by 190 publications

References 90 publications

Petascale neural circuit reconstruction: automated methods

Petascale neural circuit reconstruction: automated methods

nGauge: Integrated and extensible neuron morphology analysis in Python

Multi-Scale Imaging of Connectomes With Photons and Electrons

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