A scalable and modular automated pipeline for stitching of large electron microscopy datasets

  1. Gayathri Mahalingam  Is a corresponding author
  2. Russel Torres
  3. Daniel Kapner
  4. Eric T Trautman
  5. Tim Fliss
  6. Shamishtaa Seshamani
  7. Eric Perlman
  8. Rob Young
  9. Samuel Kinn
  10. JoAnn Buchanan
  11. Marc M Takeno
  12. Wenjing Yin
  13. Daniel J Bumbarger
  14. Ryder P Gwinn
  15. Julie Nyhus
  16. Ed Lein
  17. Steven J Smith
  18. R Clay Reid
  19. Khaled A Khairy
  20. Stephan Saalfeld
  21. Forrest Collman
  22. Nuno Macarico da Costa  Is a corresponding author
  1. Allen Institute for Brain Science, United States
  2. Janelia Research Campus, United States
  3. Yikes LLC, United States
  4. Swedish Neuroscience Institute, United States
  5. St. Jude Children's Research Hospital, United States

Abstract

Serial-section electronmicroscopy (ssEM) is themethod of choice for studyingmacroscopic biological samples at extremely high resolution in three dimensions. In the nervous system, nanometer-scale images are necessary to reconstruct dense neural wiring diagrams in the brain, so called connectomes. In order to use this data, consisting of up to 108 individual EM images, it must be assembled into a volume, requiring seamless 2D stitching from each physical section followed by 3D alignment of the stitched sections. The high throughput of ssEM necessitates 2D stitching to be done at the pace of imaging, which currently produces tens of terabytes per day. To achieve this, we present a modular volume assembly software pipeline ASAP (Assembly Stitching and Alignment Pipeline) that is scalable to datasets containing petabytes of data and parallelized to work in a distributed computational environment. The pipeline is built on top of the Render (27) services used in the volume assembly of the brain of adult Drosophilamelanogaster (30). It achieves high throughput by operating on themeta-data and transformations of each image stored in a database, thus eliminating the need to render intermediate output. ASAP ismodular, allowing for easy incorporation of new algorithms without significant changes in the workflow. The entire software pipeline includes a complete set of tools for stitching, automated quality control, 3D section alignment, and final rendering of the assembled volume to disk. ASAP has been deployed for continuous stitching of several large-scale datasets of the mouse visual cortex and human brain samples including one cubic millimeter of mouse visual cortex (28; 8) at speeds that exceed imaging. The pipeline also has multi-channel processing capabilities and can be applied to fluorescence and multi-modal datasets like array tomography.

Data availability

The current manuscript describes is a software infrastructure resource that is being made publicly available. The manuscript is not a data generation manuscript. Nevertheless, one of the datasets used is already publicly available on https://www.microns-explorer.org/cortical-mm3#em-imagery with available imagery and segmentation (https://tinyurl.com/cortical-mm3).Moreover cloud-volume (https://github.com/seung-lab/cloud-volume) can be used to programmatically download EM imagery from either Amazon or Google with the cloud paths described below. The imagery was reconstructed in two portions, referred to internally by their nicknames 'minnie65' and 'minnie35' reflecting their relative portions of the total data. The two portions are aligned across an interruption in sectioning.minnie65:AWS Bucket: precomputed://https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/minnie65/emGoogle Bucket: precomputed://https://storage.googleapis.com/iarpa_microns/minnie/minnie65/emminnie35:AWS Bucket: precomputed://https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/minnie35/emGoogle Bucket: precomputed://https://storage.googleapis.com/iarpa_microns/minnie/minnie35/emWe have also made available in Dryad raw data of the remaining datasets https://doi.org/10.5061/dryad.qjq2bvqhr

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Gayathri Mahalingam

    Allen Institute for Brain Science, Seattle, United States
    For correspondence
    gayathrim@alleninstitute.org
    Competing interests
    No competing interests declared.
  2. Russel Torres

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2876-4382
  3. Daniel Kapner

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
  4. Eric T Trautman

    Scientific Computing, Janelia Research Campus, Ashburn, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8588-0569
  5. Tim Fliss

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
  6. Shamishtaa Seshamani

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
  7. Eric Perlman

    Yikes LLC, Baltimore, United States
    Competing interests
    Eric Perlman, has a competing interest in Yikes LLC.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5542-1302
  8. Rob Young

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
  9. Samuel Kinn

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
  10. JoAnn Buchanan

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
  11. Marc M Takeno

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8384-7500
  12. Wenjing Yin

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
  13. Daniel J Bumbarger

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
  14. Ryder P Gwinn

    Epilepsy Surgery and Functional Neurosurgery, Swedish Neuroscience Institute, Seattle, United States
    Competing interests
    No competing interests declared.
  15. Julie Nyhus

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
  16. Ed Lein

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
  17. Steven J Smith

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2290-8701
  18. R Clay Reid

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8697-6797
  19. Khaled A Khairy

    St. Jude Children's Research Hospital, Memphis, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9274-5928
  20. Stephan Saalfeld

    Saalfeld Lab, Janelia Research Campus, Ashburn, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4106-1761
  21. Forrest Collman

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0280-7022
  22. Nuno Macarico da Costa

    Allen Institute for Brain Science, Seattle, United States
    For correspondence
    nunod@alleninstitute.org
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2001-4568

Funding

IARPA (D16PC00004)

  • Gayathri Mahalingam
  • Russel Torres
  • Daniel Kapner
  • Tim Fliss
  • Shamishtaa Seshamani
  • Rob Young
  • Samuel Kinn
  • JoAnn Buchanan
  • Marc M Takeno
  • Wenjing Yin
  • Daniel J Bumbarger
  • R Clay Reid
  • Forrest Collman
  • Nuno Macarico da Costa

The funders had no role in study design and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Albert Cardona, University of Cambridge, United Kingdom

Ethics

Animal experimentation: All procedures were carried out in accordance with Institutional Animal Care and Use Committee approval at the Allen Institute for Brain Science with protocol numbers 1503, 1801 and 1808

Human subjects: Human surgical specimen was obtained from local hospital in collaboration with local neurosurgeon. The sample collection was approved by the Western Institutional Review Board (Protocol # SNI 0405). Patient provided informed consent and experimental procedures were approved by hospital institute review boards before commencing the study.

Version history

  1. Preprint posted: November 25, 2021 (view preprint)
  2. Received: December 20, 2021
  3. Accepted: July 10, 2022
  4. Accepted Manuscript published: July 26, 2022 (version 1)
  5. Version of Record published: August 30, 2022 (version 2)

Copyright

© 2022, Mahalingam et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Gayathri Mahalingam
  2. Russel Torres
  3. Daniel Kapner
  4. Eric T Trautman
  5. Tim Fliss
  6. Shamishtaa Seshamani
  7. Eric Perlman
  8. Rob Young
  9. Samuel Kinn
  10. JoAnn Buchanan
  11. Marc M Takeno
  12. Wenjing Yin
  13. Daniel J Bumbarger
  14. Ryder P Gwinn
  15. Julie Nyhus
  16. Ed Lein
  17. Steven J Smith
  18. R Clay Reid
  19. Khaled A Khairy
  20. Stephan Saalfeld
  21. Forrest Collman
  22. Nuno Macarico da Costa
(2022)
A scalable and modular automated pipeline for stitching of large electron microscopy datasets
eLife 11:e76534.
https://doi.org/10.7554/eLife.76534

Share this article

https://doi.org/10.7554/eLife.76534

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