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.

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.

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.

Metrics

  • 2,369
    views
  • 374
    downloads
  • 23
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

Share this article

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

Further reading

    1. Cell Biology
    2. Evolutionary Biology
    Paul Richard J Yulo, Nicolas Desprat ... Heather L Hendrickson
    Research Article

    Maintenance of rod-shape in bacterial cells depends on the actin-like protein MreB. Deletion of mreB from Pseudomonas fluorescens SBW25 results in viable spherical cells of variable volume and reduced fitness. Using a combination of time-resolved microscopy and biochemical assay of peptidoglycan synthesis, we show that reduced fitness is a consequence of perturbed cell size homeostasis that arises primarily from differential growth of daughter cells. A 1000-generation selection experiment resulted in rapid restoration of fitness with derived cells retaining spherical shape. Mutations in the peptidoglycan synthesis protein Pbp1A were identified as the main route for evolutionary rescue with genetic reconstructions demonstrating causality. Compensatory pbp1A mutations that targeted transpeptidase activity enhanced homogeneity of cell wall synthesis on lateral surfaces and restored cell size homeostasis. Mechanistic explanations require enhanced understanding of why deletion of mreB causes heterogeneity in cell wall synthesis. We conclude by presenting two testable hypotheses, one of which posits that heterogeneity stems from non-functional cell wall synthesis machinery, while the second posits that the machinery is functional, albeit stalled. Overall, our data provide support for the second hypothesis and draw attention to the importance of balance between transpeptidase and glycosyltransferase functions of peptidoglycan building enzymes for cell shape determination.

    1. Cell Biology
    2. Developmental Biology
    Pavan K Nayak, Arul Subramanian, Thomas F Schilling
    Research Article

    Mechanical forces play a critical role in tendon development and function, influencing cell behavior through mechanotransduction signaling pathways and subsequent extracellular matrix (ECM) remodeling. Here we investigate the molecular mechanisms by which tenocytes in developing zebrafish embryos respond to muscle contraction forces during the onset of swimming and cranial muscle activity. Using genome-wide bulk RNA sequencing of FAC-sorted tenocytes we identify novel tenocyte markers and genes involved in tendon mechanotransduction. Embryonic tendons show dramatic changes in expression of matrix remodeling associated 5b (mxra5b), matrilin1 (matn1), and the transcription factor kruppel-like factor 2a (klf2a), as muscles start to contract. Using embryos paralyzed either by loss of muscle contractility or neuromuscular stimulation we confirm that muscle contractile forces influence the spatial and temporal expression patterns of all three genes. Quantification of these gene expression changes across tenocytes at multiple tendon entheses and myotendinous junctions reveals that their responses depend on force intensity, duration and tissue stiffness. These force-dependent feedback mechanisms in tendons, particularly in the ECM, have important implications for improved treatments of tendon injuries and atrophy.