An intelligent workflow for sub-nanoscale 3D reconstruction of intact synapses from serial section electron tomography

doi:10.1186/s12915-023-01696-x

CORC > 自动化研究所 > 中国科学院自动化研究所 > 脑图谱与类脑智能实验室

	An intelligent workflow for sub-nanoscale 3D reconstruction of intact synapses from serial section electron tomography
	Chang, Sheng 1,5,6; Li, Linlin 5; Hong, Bei 5,6; Liu, Jing 5; Xu, Yuxuan 2; Pang, Keliang 3; Zhang, Lina 5; Han, Hua 1,4,5; Chen, Xi 5
刊名	BMC BIOLOGY
	2023-09-25
卷号	21 期号:1 页码:14
关键词	Serial section electron tomography 3D EM Semi-auto locating Auto-alignment Missing-information restoration Semi-auto segmentation Workflow Synapse
DOI	10.1186/s12915-023-01696-x
通讯作者	Han, Hua(hua.han@ia.ac.cn) ; Chen, Xi(xi.chen@ia.ac.cn)
英文摘要	BackgroundAs an extension of electron tomography (ET), serial section electron tomography (serial section ET) aims to align the tomographic images of multiple thick tissue sections together, to break through the volume limitation of the single section and preserve the sub-nanoscale voxel size. It could be applied to reconstruct the intact synapse, which expands about one micrometer and contains nanoscale vesicles. However, there are several drawbacks of the existing serial section ET methods. First, locating and imaging regions of interest (ROIs) in serial sections during the shooting process is time-consuming. Second, the alignment of ET volumes is difficult due to the missing information caused by section cutting and imaging. Here we report a workflow to simplify the acquisition of ROIs in serial sections, automatically align the volume of serial section ET, and semi-automatically reconstruct the target synaptic structure.ResultsWe propose an intelligent workflow to reconstruct the intact synapse with sub-nanometer voxel size. Our workflow includes rapid localization of ROIs in serial sections, automatic alignment, restoration, assembly of serial ET volumes, and semi-automatic target structure segmentation. For the localization and acquisition of ROIs in serial sections, we use affine transformations to calculate their approximate position based on their relative location in orderly placed sections. For the alignment of consecutive ET volumes with significantly distinct appearances, we use multi-scale image feature matching and the elastic with belief propagation (BP-Elastic) algorithm to align them from coarse to fine. For the restoration of the missing information in ET, we first estimate the number of lost images based on the pixel changes of adjacent volumes after alignment. Then, we present a missing information generation network that is appropriate for small-sample of ET volume using pre-training interpolation network and distillation learning. And we use it to generate the missing information to achieve the whole volume reconstruction. For the reconstruction of synaptic ultrastructures, we use a 3D neural network to obtain them quickly. In summary, our workflow can quickly locate and acquire ROIs in serial sections, automatically align, restore, assemble serial sections, and obtain the complete segmentation result of the target structure with minimal manual manipulation. Multiple intact synapses in wild-type rat were reconstructed at a voxel size of 0.664 nm/voxel to demonstrate the effectiveness of our workflow.ConclusionsOur workflow contributes to obtaining intact synaptic structures at the sub-nanometer scale through serial section ET, which contains rapid ROI locating, automatic alignment, volume reconstruction, and semi-automatic synapse reconstruction. We have open-sourced the relevant code in our workflow, so it is easy to apply it to other labs and obtain complete 3D ultrastructures which size is similar to intact synapses with sub-nanometer voxel size.
资助项目	The authors would like to thank the Microscopic Technology amp; Analysis Center, Institute of Automation, where Xiaohui Dong for section cutting and collection and thank Limei Lin for image acquisition.
WOS关键词	3-DIMENSIONAL RECONSTRUCTION ; MICROSCOPY ; ULTRASTRUCTURE ; CONSENSUS ; NEURON ; MODEL
WOS研究方向	Life Sciences & Biomedicine - Other Topics
语种	英语
出版者	BMC
WOS记录号	WOS:001078911100002
资助机构	The authors would like to thank the Microscopic Technology amp; Analysis Center, Institute of Automation, where Xiaohui Dong for section cutting and collection and thank Limei Lin for image acquisition.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/53011]
专题	脑图谱与类脑智能实验室
通讯作者	Han, Hua; Chen, Xi
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 2.Peking Univ, Sch Software & Microelect, Beijing 100871, Peoples R China 3.Tsinghua Univ, Sch Pharmaceut Sci, Beijing 100084, Peoples R China 4.Univ Chinese Acad Sci, Sch Future Technol, Beijing 101408, Peoples R China 5.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 6.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Chang, Sheng,Li, Linlin,Hong, Bei,et al. An intelligent workflow for sub-nanoscale 3D reconstruction of intact synapses from serial section electron tomography[J]. BMC BIOLOGY,2023,21(1):14.
APA	Chang, Sheng.,Li, Linlin.,Hong, Bei.,Liu, Jing.,Xu, Yuxuan.,...&Chen, Xi.(2023).An intelligent workflow for sub-nanoscale 3D reconstruction of intact synapses from serial section electron tomography.BMC BIOLOGY,21(1),14.
MLA	Chang, Sheng,et al."An intelligent workflow for sub-nanoscale 3D reconstruction of intact synapses from serial section electron tomography".BMC BIOLOGY 21.1(2023):14.