A petascale automated imaging pipeline for mapping neuronal circuits with high-throughput transmission electron microscopy

Wenjing Yin (), Derrick Brittain, Jay Borseth, Marie E. Scott, Derric Williams, Jedediah Perkins, Christopher S. Own, Matthew Murfitt, Russel M. Torres, Daniel Kapner, Gayathri Mahalingam, Adam Bleckert, Daniel Castelli, David Reid, Wei-Chung Allen Lee, Brett J. Graham, Marc Takeno, Daniel J. Bumbarger, Colin Farrell, R. Clay Reid () and Nuno Macarico da Costa ()
Additional contact information
Wenjing Yin: Allen Institute
Derrick Brittain: Allen Institute
Jay Borseth: Allen Institute
Marie E. Scott: Allen Institute
Derric Williams: Allen Institute
Jedediah Perkins: Allen Institute
Christopher S. Own: Voxa
Matthew Murfitt: Voxa
Russel M. Torres: Allen Institute
Daniel Kapner: Allen Institute
Gayathri Mahalingam: Allen Institute
Adam Bleckert: Allen Institute
Daniel Castelli: Allen Institute
David Reid: Allen Institute
Wei-Chung Allen Lee: Harvard Medical School
Brett J. Graham: Harvard Medical School
Marc Takeno: Allen Institute
Daniel J. Bumbarger: Allen Institute
Colin Farrell: Allen Institute
R. Clay Reid: Allen Institute
Nuno Macarico da Costa: Allen Institute

Nature Communications, 2020, vol. 11, issue 1, 1-12

Abstract: Abstract Electron microscopy (EM) is widely used for studying cellular structure and network connectivity in the brain. We have built a parallel imaging pipeline using transmission electron microscopes that scales this technology, implements 24/7 continuous autonomous imaging, and enables the acquisition of petascale datasets. The suitability of this architecture for large-scale imaging was demonstrated by acquiring a volume of more than 1 mm3 of mouse neocortex, spanning four different visual areas at synaptic resolution, in less than 6 months. Over 26,500 ultrathin tissue sections from the same block were imaged, yielding a dataset of more than 2 petabytes. The combined burst acquisition rate of the pipeline is 3 Gpixel per sec and the net rate is 600 Mpixel per sec with six microscopes running in parallel. This work demonstrates the feasibility of acquiring EM datasets at the scale of cortical microcircuits in multiple brain regions and species.

Date: 2020
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DOI: 10.1038/s41467-020-18659-3

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