1. Computational and Systems Biology
  2. Neuroscience

Bi-channel Image Registration and Deep-learning Segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain

  1. Xuechun Wang
  2. Weilin Zeng
  3. Xiaodan Yang
  4. Chunyu Fang
  5. Yunyun Han  Is a corresponding author
  6. Peng Fei  Is a corresponding author
  1. Huazhong University of Science and Technology, China
  2. Tongji Medical College, Huazhong University of Science and Technology, China
https://doi.org/10.7554/eLife.63455
Share this article
Cite this article
  1. Xuechun Wang
  2. Weilin Zeng
  3. Xiaodan Yang
  4. Chunyu Fang
  5. Yunyun Han
  6. Peng Fei
(2021)
Bi-channel Image Registration and Deep-learning Segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain
eLife 10:e63455.
https://doi.org/10.7554/eLife.63455
  2. Download BibTeX
  3. Download .RIS

Abstract

We have developed an open-source software called BIRDS (bi-channel image registration and deep-learning segmentation) for the mapping and analysis of 3D microscopy data and applied this to the mouse brain. The BIRDS pipeline includes image pre-processing, bi-channel registration, automatic annotation, creation of a 3D digital frame, high-resolution visualization, and expandable quantitative analysis. This new bi-channel registration algorithm is adaptive to various types of whole-brain data from different microscopy platforms and shows dramatically improved registration accuracy. Additionally, as this platform combines registration with neural networks, its improved function relative to other platforms lies in the fact that the registration procedure can readily provide training data for network construction, while the trained neural network can efficiently segment incomplete/defective brain data that is otherwise difficult to register. Our software is thus optimized to enable either minute-timescale registration-based segmentation of cross-modality, whole-brain datasets or real-time inference-based image segmentation of various brain regions of interest. Jobs can be easily submitted and implemented via a Fiji plugin that can be adapted to most computing environments.

Data availability

The Allen CCF is open access and available with related tools at https://atlas.brain-map.org/The datasets (Brain1~5) have been deposited in Dryad at https://datadryad.org/stash/share/4fesXcJif0L2DnSj7YmjREe37yPm1bEnUiK49ELtALgThe code and plugin can be found at the following link：https://github.com/bleach1by1/BIRDS_pluginhttps://github.com/bleach1by1/birds_reghttps://github.com/bleach1by1/birds_dl.githttps://github.com/bleach1by1/BIRDS_demoAll data generated or analysed during this study are included in the manuscript. Source data files have been provided for Figures 1, 2, 3, 4, 5 and Figure 2-figure supplement 3,4; Figure 5-figure supplement 2,3

The following previously published data sets were used

Article and author information

Author details

  1. Xuechun Wang

    School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
    Competing interests
    The authors declare that no competing interests exist.

  2. Weilin Zeng

    School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
    Competing interests
    The authors declare that no competing interests exist.

  3. Xiaodan Yang

    School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
    Competing interests
    The authors declare that no competing interests exist.

  4. Chunyu Fang

    School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
    Competing interests
    The authors declare that no competing interests exist.

  5. Yunyun Han

    School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
    For correspondence
    yhan@hust.edu.cn
    Competing interests
    The authors declare that no competing interests exist.

  6. Peng Fei

    School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
    For correspondence
    feipeng@hust.edu.cn
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3764-817X

Funding

National Key R&D program of China (2017YFA0700501)

  • Peng Fei

National Natural Science Foundation of China (21874052)

  • Peng Fei

National Natural Science Foundation of China (31871089)

  • Yunyun Han

Innovation Fund of WNLO

  • Peng Fei

Junior Thousand Talents Program of China

  • Peng Fei

Junior Thousand Talents Program of China

  • Yunyun Han

The FRFCU (HUST:2172019kfyXKJC077)

  • Yunyun Han

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

Copyright

© 2021, Wang 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

  • 6,366
    views
  • 707
    downloads
  • 19
    citations

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

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Xuechun Wang
  2. Weilin Zeng
  3. Xiaodan Yang
  4. Chunyu Fang
  5. Yunyun Han
  6. Peng Fei
(2021)
Bi-channel Image Registration and Deep-learning Segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain
eLife 10:e63455.
https://doi.org/10.7554/eLife.63455

Share this article

https://doi.org/10.7554/eLife.63455

Categories and tags

Research organism

Further reading

    1. Computational and Systems Biology

    Redox regulation and dynamic control of brain-selective kinases BRSK1/2 in the AMPK family through cysteine-based mechanisms

    George N Bendzunas, Dominic P Byrne ... Natarajan Kannan
    Research Article

    In eukaryotes, protein kinase signaling is regulated by a diverse array of post-translational modifications, including phosphorylation of Ser/Thr residues and oxidation of cysteine (Cys) residues. While regulation by activation segment phosphorylation of Ser/Thr residues is well understood, relatively little is known about how oxidation of cysteine residues modulate catalysis. In this study, we investigate redox regulation of the AMPK-related brain-selective kinases (BRSK) 1 and 2, and detail how broad catalytic activity is directly regulated through reversible oxidation and reduction of evolutionarily conserved Cys residues within the catalytic domain. We show that redox-dependent control of BRSKs is a dynamic and multilayered process involving oxidative modifications of several Cys residues, including the formation of intramolecular disulfide bonds involving a pair of Cys residues near the catalytic HRD motif and a highly conserved T-loop Cys with a BRSK-specific Cys within an unusual CPE motif at the end of the activation segment. Consistently, mutation of the CPE-Cys increases catalytic activity in vitro and drives phosphorylation of the BRSK substrate Tau in cells. Molecular modeling and molecular dynamics simulations indicate that oxidation of the CPE-Cys destabilizes a conserved salt bridge network critical for allosteric activation. The occurrence of spatially proximal Cys amino acids in diverse Ser/Thr protein kinase families suggests that disulfide-mediated control of catalytic activity may be a prevalent mechanism for regulation within the broader AMPK family.

    1. Computational and Systems Biology
    2. Genetics and Genomics

    Loss of CTRP10 results in female obesity with preserved metabolic health

    Fangluo Chen, Dylan C Sarver ... G William Wong
    Research Article

    Obesity is a major risk factor for type 2 diabetes, dyslipidemia, cardiovascular disease, and hypertension. Intriguingly, there is a subset of metabolically healthy obese (MHO) individuals who are seemingly able to maintain a healthy metabolic profile free of metabolic syndrome. The molecular underpinnings of MHO, however, are not well understood. Here, we report that CTRP10/C1QL2-deficient mice represent a unique female model of MHO. CTRP10 modulates weight gain in a striking and sexually dimorphic manner. Female, but not male, mice lacking CTRP10 develop obesity with age on a low-fat diet while maintaining an otherwise healthy metabolic profile. When fed an obesogenic diet, female Ctrp10 knockout (KO) mice show rapid weight gain. Despite pronounced obesity, Ctrp10 KO female mice do not develop steatosis, dyslipidemia, glucose intolerance, insulin resistance, oxidative stress, or low-grade inflammation. Obesity is largely uncoupled from metabolic dysregulation in female KO mice. Multi-tissue transcriptomic analyses highlighted gene expression changes and pathways associated with insulin-sensitive obesity. Transcriptional correlation of the differentially expressed gene (DEG) orthologs in humans also shows sex differences in gene connectivity within and across metabolic tissues, underscoring the conserved sex-dependent function of CTRP10. Collectively, our findings suggest that CTRP10 negatively regulates body weight in females, and that loss of CTRP10 results in benign obesity with largely preserved insulin sensitivity and metabolic health. This female MHO mouse model is valuable for understanding sex-biased mechanisms that uncouple obesity from metabolic dysfunction.