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Zhen C, Wang Y, Geng J, Han L, Li J, Peng J, Wang T, Hao J, Shang X, Wei Z, Zhu P, Peng J. A review and performance evaluation of clustering frameworks for single-cell Hi-C data. Brief Bioinform 2022; 23:6712299. [PMID: 36151714 DOI: 10.1093/bib/bbac385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 07/31/2022] [Accepted: 08/09/2022] [Indexed: 12/14/2022] Open
Abstract
The three-dimensional genome structure plays a key role in cellular function and gene regulation. Single-cell Hi-C (high-resolution chromosome conformation capture) technology can capture genome structure information at the cell level, which provides the opportunity to study how genome structure varies among different cell types. Recently, a few methods are well designed for single-cell Hi-C clustering. In this manuscript, we perform an in-depth benchmark study of available single-cell Hi-C data clustering methods to implement an evaluation system for multiple clustering frameworks based on both human and mouse datasets. We compare eight methods in terms of visualization and clustering performance. Performance is evaluated using four benchmark metrics including adjusted rand index, normalized mutual information, homogeneity and Fowlkes-Mallows index. Furthermore, we also evaluate the eight methods for the task of separating cells at different stages of the cell cycle based on single-cell Hi-C data.
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Affiliation(s)
- Caiwei Zhen
- School of Computer Science, Northwestern Polytechnical University, 710072, Xi'an, China.,Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, 710072, Xi'an, China
| | - Yuxian Wang
- School of Computer Science, Northwestern Polytechnical University, 710072, Xi'an, China.,Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, 710072, Xi'an, China
| | - Jiaquan Geng
- School of Computer Science, Northwestern Polytechnical University, 710072, Xi'an, China.,Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, 710072, Xi'an, China
| | - Lu Han
- School of Computer Science, Northwestern Polytechnical University, 710072, Xi'an, China.,Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, 710072, Xi'an, China
| | - Jingyi Li
- School of Computer Science, Northwestern Polytechnical University, 710072, Xi'an, China.,Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, 710072, Xi'an, China
| | - Jinghao Peng
- School of Computer Science, Northwestern Polytechnical University, 710072, Xi'an, China.,Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, 710072, Xi'an, China
| | - Tao Wang
- School of Computer Science, Northwestern Polytechnical University, 710072, Xi'an, China.,Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, 710072, Xi'an, China
| | - Jianye Hao
- School of Computer Software, Tianjin University, 300350, Tianjin, China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, 710072, Xi'an, China.,Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, 710072, Xi'an, China
| | - Zhongyu Wei
- School of Computer Science, Northwestern Polytechnical University, 710072, Xi'an, China
| | - Peican Zhu
- School of Computer Science, Northwestern Polytechnical University, 710072, Xi'an, China
| | - Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, 710072, Xi'an, China.,Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, 710072, Xi'an, China
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Zhuang C, Li X, Zhang Y, Kong L, Xie H, Dai Q. Photobleaching Imprinting Enhanced Background Rejection in Line-Scanning Temporal Focusing Microscopy. Front Chem 2021; 8:618131. [PMID: 33392156 PMCID: PMC7773834 DOI: 10.3389/fchem.2020.618131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 11/20/2020] [Indexed: 11/13/2022] Open
Abstract
Compared with two-photon point-scanning microscopy, two-photon temporal focusing microscopy (2pTFM) provides a parallel high-speed imaging strategy with optical sectioning capability. Owing to out-of-focus fluorescence induced by scattering, 2pTFM suffers deteriorated signal-to-background ratio (SBR) for deep imaging in turbid tissue, Here, we utilized the photobleaching property of fluorophore to eliminate out-of-focus fluorescence. According to different decay rates in different focal depth, we extract the in-focus signals out of backgrounds through time-lapse images. We analyzed the theoretical foundations of photobleaching imprinting of the line-scanning temporal focusing microscopy, simulated implementation for background rejection, and demonstrated the contrast enhancement in MCF-10A human mammary epithelial cells and cleared Thy1-YFP mouse brains. More than 50% of total background light rejection was achieved, providing higher SBR images of the MCF-10A samples and mouse brains. The photobleaching imprinting method can be easily adapted to other fluorescence dyes or proteins, which may have application in studies involving relatively large and nontransparent organisms.
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Affiliation(s)
- Chaowei Zhuang
- Department of Automation, Tsinghua University, Beijing, China
| | - Xinyang Li
- Department of Automation, Tsinghua University, Beijing, China
| | - Yuanlong Zhang
- Department of Automation, Tsinghua University, Beijing, China
| | - Lingjie Kong
- Department of Precision Instrument, Tsinghua University, Beijing, China
| | - Hao Xie
- Department of Automation, Tsinghua University, Beijing, China
| | - Qionghai Dai
- Department of Automation, Tsinghua University, Beijing, China.,Beijing National Research Center for Information Science and Technology, Beijing, China.,Institute for Brain and Cognitive Science, Tsinghua University, Beijing, China.,Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Beijing, China
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Chen L, Chen X, Yang X, He C, Wang M, Xi P, Gao J. Advances of super-resolution fluorescence polarization microscopy and its applications in life sciences. Comput Struct Biotechnol J 2020; 18:2209-2216. [PMID: 32952935 PMCID: PMC7476067 DOI: 10.1016/j.csbj.2020.06.038] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2020] [Revised: 06/20/2020] [Accepted: 06/22/2020] [Indexed: 11/29/2022] Open
Abstract
Fluorescence polarization microscopy (FPM) analyzes both intensity and orientation of fluorescence dipole, and reflects the structural specificity of target molecules. It has become an important tool for studying protein organization, orientational order, and structural changes in cells. However, suffering from optical diffraction limit, conventional FPM has low orientation resolution and observation accuracy, as the polarization information is averaged by multiple fluorescent molecules within a diffraction-limited volume. Recently, novel super-resolution FPMs have been developed to break the diffraction barrier. In this review, we will introduce the recent progress to achieve sub-diffraction determination of dipole orientation. Biological applications, based on polarization analysis of fluorescence dipole, are also summarized, with focus on chromophore-target molecule interaction and molecular organization.
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Affiliation(s)
- Long Chen
- Department of Automation, Tsinghua University, 100084 Beijing, China.,MOE Key Laboratory of Bioinformatics; Bioinformatics Division, Center for Synthetic & Systems Biology, BNRist; Center for Synthetic & Systems Biology, Tsinghua University, 100084 Beijing, China
| | - Xingye Chen
- Department of Automation, Tsinghua University, 100084 Beijing, China
| | - Xusan Yang
- Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, China
| | - Chao He
- Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, UK
| | - Miaoyan Wang
- Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, China
| | - Peng Xi
- Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, China
| | - Juntao Gao
- Department of Automation, Tsinghua University, 100084 Beijing, China.,MOE Key Laboratory of Bioinformatics; Bioinformatics Division, Center for Synthetic & Systems Biology, BNRist; Center for Synthetic & Systems Biology, Tsinghua University, 100084 Beijing, China
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