Bi-channel Image Registration and Deep-learning Segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain
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
-
Brain 1 and 2Dryad Digital Repository.
-
Brain 5Dryad Digital Repository.
-
Brain 3 and 4Dryad Digital Repository.
Article and author information
Author details
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
-
- 6,261
- views
-
- 693
- downloads
-
- 18
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
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)
Further reading
-
- Computational and Systems Biology
Manifold-learning is particularly useful to resolve the complex cellular state space from single-cell RNA sequences. While current manifold-learning methods provide insights into cell fate by inferring graph-based trajectory at cell level, challenges remain to retrieve interpretable biology underlying the diverse cellular states. Here, we described MGPfactXMBD, a model-based manifold-learning framework and capable to factorize complex development trajectories into independent bifurcation processes of gene sets, and thus enables trajectory inference based on relevant features. MGPfactXMBD offers a more nuanced understanding of the biological processes underlying cellular trajectories with potential determinants. When bench-tested across 239 datasets, MGPfactXMBD showed advantages in major quantity-control metrics, such as branch division accuracy and trajectory topology, outperforming most established methods. In real datasets, MGPfactXMBD recovered the critical pathways and cell types in microglia development with experimentally valid regulons and markers. Furthermore, MGPfactXMBD discovered evolutionary trajectories of tumor-associated CD8+ T cells and yielded new subtypes of CD8+ T cells with gene expression signatures significantly predictive of the responses to immune checkpoint inhibitor in independent cohorts. In summary, MGPfactXMBD offers a manifold-learning framework in scRNA-seq data which enables feature selection for specific biological processes and contributing to advance our understanding of biological determination of cell fate.