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Chang S, Li L, Hong B, Liu J, Xu Y, Pang K, Zhang L, Han H, Chen X. An intelligent workflow for sub-nanoscale 3D reconstruction of intact synapses from serial section electron tomography. BMC Biol 2023; 21:198. [PMID: 37743470 PMCID: PMC10519085 DOI: 10.1186/s12915-023-01696-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 09/06/2023] [Indexed: 09/26/2023] Open
Abstract
BACKGROUND As 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. RESULTS We 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. CONCLUSIONS Our 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.
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Affiliation(s)
- Sheng Chang
- Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, 100190, Beijing, China
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Linlin Li
- Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Bei Hong
- Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, 100190, Beijing, China
| | - Jing Liu
- Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Yuxuan Xu
- School of Software and Microelectronics, Peking University, 100871, Beijing, China
| | - Keliang Pang
- School of Pharmaceutical Sciences, Tsinghua University, 100084, Beijing, China
| | - Lina Zhang
- Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Hua Han
- Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, 101408, China.
| | - Xi Chen
- Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.
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Zhang Z, Bi X, Li P, Zhang C, Yang Y, Liu Y, Chen G, Dong Y, Liu G, Zhang Y. Automatic synchrotron tomographic alignment schemes based on genetic algorithms and human-in-the-loop software. JOURNAL OF SYNCHROTRON RADIATION 2023; 30:169-178. [PMID: 36601935 PMCID: PMC9814067 DOI: 10.1107/s1600577522011067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 11/18/2022] [Indexed: 06/17/2023]
Abstract
Tomography imaging methods at synchrotron light sources keep evolving, pushing multi-modal characterization capabilities at high spatial and temporal resolutions. To achieve this goal, small probe size and multi-dimensional scanning schemes are utilized more often in the beamlines, leading to rising complexities and challenges in the experimental setup process. To avoid spending a significant amount of human effort and beam time on aligning the X-ray probe, sample and detector for data acquisition, most attention has been drawn to realigning the systems at the data processing stages. However, post-processing cannot correct everything, and is not time efficient. Here we present automatic alignment schemes of the rotational axis and sample pre- and during the data acquisition process using a software approach which combines the advantages of genetic algorithms and human intelligence. Our approach shows excellent sub-pixel alignment efficiency for both tasks in a short time, and therefore holds great potential for application in the data acquisition systems of future scanning tomography experiments.
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Affiliation(s)
- Zhen Zhang
- National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, Anhui 230029, People’s Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
| | - Xiaoxue Bi
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
| | - Pengcheng Li
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
| | - Chenglong Zhang
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
| | - Yiming Yang
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
| | - Yu Liu
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
| | - Gang Chen
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
| | - Yuhui Dong
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
| | - Gongfa Liu
- National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, Anhui 230029, People’s Republic of China
| | - Yi Zhang
- University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
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Böhning J, Bharat TAM, Collins SM. Compressed sensing for electron cryotomography and high-resolution subtomogram averaging of biological specimens. Structure 2022; 30:408-417.e4. [PMID: 35051366 PMCID: PMC8919266 DOI: 10.1016/j.str.2021.12.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 10/21/2021] [Accepted: 12/22/2021] [Indexed: 11/07/2022]
Abstract
Cryoelectron tomography (cryo-ET) and subtomogram averaging (STA) allow direct visualization and structural studies of biological macromolecules in their native cellular environment, in situ. Often, low signal-to-noise ratios in tomograms, low particle abundance within the cell, and low throughput in typical cryo-ET workflows severely limit the obtainable structural information. To help mitigate these limitations, here we apply a compressed sensing approach using 3D second-order total variation (CS-TV2) to tomographic reconstruction. We show that CS-TV2 increases the signal-to-noise ratio in tomograms, enhancing direct visualization of macromolecules, while preserving high-resolution information up to the secondary structure level. We show that, particularly with small datasets, CS-TV2 allows improvement of the resolution of STA maps. We further demonstrate that the CS-TV2 algorithm is applicable to cellular specimens, leading to increased visibility of molecular detail within tomograms. This work highlights the potential of compressed sensing-based reconstruction algorithms for cryo-ET and in situ structural biology. Compressed sensing (CS-TV2) for cryo-ET using 3D second-order total variation CS-TV2 increases signal contrast while retaining high-resolution information Improved subtomogram averaging from CS-TV2 reconstructions of small datasets Increased contrast and detail in CS-TV2 reconstructions of cellular specimens
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Affiliation(s)
- Jan Böhning
- Sir William Dunn School of Pathology, University of Oxford, Oxford OX1 3RE, UK
| | - Tanmay A M Bharat
- Sir William Dunn School of Pathology, University of Oxford, Oxford OX1 3RE, UK; Structural Studies Division, MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK.
| | - Sean M Collins
- School of Chemical and Process Engineering & School of Chemistry, University of Leeds, Leeds LS2 9JT, UK.
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