Cross-modality synthesis of EM time series and live fluorescence imaging

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Abstract

Analyses across imaging modalities allow the integration of complementary spatiotemporal information about brain development, structure and function. However, systematic atlasing across modalities is limited by challenges to effective image alignment. We combine highly spatially resolved electron microscopy (EM) and highly temporally resolved time-lapse fluorescence microscopy (FM) to examine the emergence of a complex nervous system in C. elegans embryogenesis. We generate an EM time series at four classic developmental stages and create a landmark-based co-optimization algorithm for cross-modality image alignment, which handles developmental heterochrony among datasets to achieve accurate single-cell level alignment. Synthesis based on the EM series and time-lapse FM series carrying different cell-specific markers reveals critical dynamic behaviors across scales of identifiable individual cells in the emergence of the primary neuropil, the nerve ring, as well as a major sensory organ, the amphid. Our study paves the way for systematic cross-modality data synthesis in C. elegans and demonstrates a powerful approach that may be applied broadly.

Data availability

EM data has been made available on WebKnossos, and source code made available on Github. Links are provided in MS and on project website.

The following data sets were generated

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Anthony Santella

Molecular Cytology Core, Memorial Sloan Kettering Cancer Center, New York, United States

Competing interests
The authors declare that no competing interests exist.
Irina Kolotuev

Electron Microscopy Facility, University of Lausanne, Lausanne, Switzerland

For correspondence
irina.kolotueva@unil.ch

Competing interests
The authors declare that no competing interests exist.
Caroline Kizilyaprak

Electron Microscopy Facility, University of Lausanne, Lausanne, Switzerland

Competing interests
The authors declare that no competing interests exist.
Zhirong Bao

Developmental Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States

For correspondence
baoz@mskcc.org

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-2201-2745

Funding

National Institutes of Health (R01GM097576)

Zhirong Bao

National Institutes of Health (R24OD016474)

Zhirong Bao

National Institutes of Health (P30CA008748)

Zhirong Bao

Chan Zuckerberg Initiative (2019-198110 (5022))

Anthony Santella

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

Copyright

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.