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11. An efficient 3D extraction and reconstruction method for myelinated axons of mouse cerebral cortex based on mixed intelligence

Author

Yan Fang, Ren Jieji, Shao Zhifeng, and Li Xiaowei

Subjects
Microbiology, QR1-502, Physiology, QP1-981, Zoology, QL1-991
Abstract

Accurate reconstruction of the 3D morphology and spatial distribution of myelinated axons in mouse brains is very important for understanding the mechanism and dynamic behavior of long-distance information transmission between brain regions. However, it is difficult to segment and reconstruct myelinated axons automatically due to two reasons: the amount of it is huge and the morphology of it is different between brain regions. Traditional artificial labeling methods usually require a large amount of manpower to label each myelinated axon slice by slice, which greatly hinders the development of the mouse brain connectome. In order to solve this problem and improve the reconstruction efficiency, this paper proposes an annotation generation method that takes the myelinated axon as prior knowledge, which can greatly reduce the manual labeling time while reaching the level of manual labeling. This method consists of three steps. Firstly, the 3D axis equation of myelinated axons is established by sparse axon artificial center point labels on slices, and the region to be segmented is pre-extracted according to the 3D axis. Subsequently, the U-Net network was trained by a small number of artificially labeled myelinated axons and was used for precise segmentation of output by the last step, so as to obtain accurate axon 2D morphology. Finally, based on the segmentation results, the high-precision 3D reconstruction of axons was performed by imaris software, and the spatial distribution of myelinated axons in the mouse brain was reconstructed. In this paper, the effectiveness of this method was verified on the dataset of high-resolution X-ray microtomography of the mouse cortex. Experiments show that this method can achieve an average MIoU 81.57, and the efficiency can be improved by more than 1400x compared with the manual labeling method.

Published
2023
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18. Fast Background Removal Method for 3D Multi-channel Deep Tissue Fluorescence Imaging

Author

Li, Chenchen, Li, Xiaowei, Cao, Hongji, Jiang, He, Deng, Xiaotie, Chen, Danny Z., Yang, Lin, Shao, Zhifeng, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Descoteaux, Maxime, editor, Maier-Hein, Lena, editor, Franz, Alfred, editor, Jannin, Pierre, editor, Collins, D. Louis, editor, and Duchesne, Simon, editor

Published
2017
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35. Time‐resolved transcriptomics of mouse gastric pit cells during postnatal development reveals features distinct from whole stomach development.

Author

Wei, Ying, Xu, Zeqian, Hu, Miaomiao, Wu, Zhongqin, Liu, Axian, Czajkowsky, Daniel M., Guo, Yan, and Shao, Zhifeng

Subjects
STOMACH, MICE, TRANSCRIPTOMES, MICRODISSECTION, PHENOTYPES
Abstract

Whole‐organ transcriptomic analyses have emerged as a common method for characterizing developmental transitions in mammalian organs. However, it is unclear if all cell types in an organ follow the whole‐organ defined developmental trajectory. Recently, a postnatal two‐stage developmental process was described for the mouse stomach. Here, using laser capture microdissection to obtain in situ transcriptomic data, we show that mouse gastric pit cells exhibit four postnatal developmental stages. Interestingly, early stages are characterized by the up‐regulation of genes associated with metabolism, a functionality not typically associated with pit cells. Hence, beyond revealing that not all constituent cells develop according to the whole‐organ determined pathway, these results broaden our understanding of the pit cell phenotypic landscape during stomach development. [ABSTRACT FROM AUTHOR]

Published
2023
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37. Anion-binding aromatic pentaamide macrocycles: Synthesis, high-affinity binding, transmembrane transport, and catalysis

Author

Cao, Ruikai, primary, Rossdeutcher, Robert, additional, Shen, Yi, additional, Miller, Daniel, additional, Zhong, Yulong, additional, B., Laura Sánchez, additional, Ramcharan, Karishma, additional, Wu, Xiangxiang, additional, Zurek, Eva, additional, Szyperski, Thomas, additional, Shao, Zhifeng, additional, and Gong, Bing, additional

Published
2022
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47. Additional file 6 of Monocytic THP-1 cells diverge significantly from their primary counterparts: a comparative examination of the chromosomal conformations and transcriptomes

Author

Liu, Yulong, Li, Hua, Czajkowsky, Daniel M., and Shao, Zhifeng

Abstract

Additional file 6 : Supplementary Figure S1. Comparison of gene expression levels in different compartments. Supplementary Figure S2. Similarities/differences of compartments between primary cells and THP-1 cells. Supplementary Figure S3. Example of differentially expressed genes whose changed expression is associated with the changes of compartment. Supplementary Figure S4. Compartments identified with different software yield similar results at loci of immune-related genes. Supplementary Figure S5. Comparison of gene expression levels at regions that switched compartments. Supplementary Figure S6. Relationship between gene expression levels and compartment changes in primary, GM-CSF-induced macrophages and macrophagic THP-1 cells. Supplementary Figure S7. Similarity of TAD locations between the primary and down-sampled THP-1 datasets. Supplementary Figure S8. Numbers of loops in the down-sampled datasets in the monocytic and macrophagic THP-1 cells. Supplementary Figure S9. Hi-C heatmap showing loop-scale chromosomal structural differences. Supplementary Figure S10. Correlation between chromosomal structural changes and gene expression. Supplementary Figure S11. A/B compartments identified by different software exhibit high congruence.

Published
2021
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