Introduction to special issue on single cell analysis (Part II)

Journal of Innovative Optical Health SciencesVol. 16, No. 02, 2302002 (2023) EditorialOpen AccessIntroduction to special issue on single cell analysisShuhua Yue, Xuantao Su, Minbiao Ji, Fu Wang, and Xunbin WeiShuhua YueSchool of Biological Science and Medical Engineering, Beihang University, 37 Xueyuan Road, Haidian District, Beijing 100191, ChinaE-mail Address: [email protected] Search for more papers by this author , Xuantao SuSchool of Microelectronics, Shandong University, 1500 Shunhua Road, Gaoxin District, Jinan 250101, ChinaE-mail Address: [email protected] Search for more papers by this author , Minbiao JiDepartment of Physics, Fudan University, 2005 Songhu Road, Yangpu District, Shanghai 200438, ChinaE-mail Address: [email protected] Search for more papers by this author , Fu WangSchool of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Xuhui District, Shanghai 200030, ChinaE-mail Address: [email protected] Search for more papers by this author , and Xunbin WeiDepartment of Biomedical Engineering, Peking University, 38 Xueyuan Road, Haidian District, Beijing 100191, ChinaE-mail Address: [email protected]Corresponding author. Search for more papers by this author https://doi.org/10.1142/S1793545823020029Cited by:0 Next This article is part of the issue: Special Issue on Single Cell AnalysisGuest Editors: Shuhua Yue, Xuantao Su, Minbiao Ji, Fu Wang and Xunbin Wei AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Librarian ShareShare onFacebookTwitterLinked InRedditEmail Increasing evidence has shown that cell populations are not homogeneous, but rather heterogeneous, even within very small cell populations. Bulk measurements based on the homogenized cell population do not account for the critical changes occurring in individual cells and are sometimes misleading. Cellular heterogeneity characteristics may be the key to address previously unsolved questions in disease development and progression. Therefore, it is crucial to develop novel single cell analysis techniques, including spectroscopy, imaging, sensing, manipulating, and sorting. This special issue, including three review articles and five original research articles, highlights recent progress in the development of single-cell optical analysis techniques and their applications in biological discovery, disease diagnosis, and treatment.A number of advanced optical imaging methods have been recently developed for single cell analysis. Stimulated Raman scattering (SRS) microscopy, an attractive imaging technique with advantages of high chemical selectivity, high spatial resolution, and high imaging speed, has become a powerful tool for biology and medicine. Jing Huang and Minbiao Ji reviewed the principles of SRS, discussed the technical developments and implementations of SRS microscopy, then highlighted and summarized its applications on biological cellular machinery, and finally shared visions of potential breakthroughs in the future.1 Light field microscopy (LFM) is an elegant non-scanning imaging tool to achieve real-time three-dimensional (3D) volumetric imaging of single cells with a single shot. Beibei Gao et al. reviewed the principle and development of LFM, discussed the improved approaches based on hardware designs and 3D reconstruction algorithms, and presented the applications in single-cell imaging.2 As an emerging single-cell imaging modality, microwave-induced thermoacoustic imaging (MTAI), with the assistance of functional nanoparticles, has shown broad prospects in biomedical and clinical applications. Xiaoyu Tang et al. reviewed the recent progress, challenges, and future directions of MTAI integrated with functional nanoparticles.3 Besides imaging instrumentation, processing algorithm is also very important for single-cell analysis. Shutong Liu et al. proposed a new feature extraction method in fluorescence imaging of nucleus to achieve automatic and accurate identification of apoptosis with low cost in terms of time,4 which may facilitate large-scale single-cell analysis.The advanced single-cell optical methods have been increasingly applied for biological discovery and disease diagnosis. Exosome has been considered as a promising biomarker for diagnosis of early-stage cancer. Xiao Ma et al. achieved label-free breast cancer detection and classification by convolutional neural network-based exosomes surface-enhanced Raman scattering.5 Actin cytoskeleton plays crucial roles in various cellular functions. Fulin Xing et al. reported the regulation of actin cytoskeleton via photolithographic micropatterning, which provides new insights into how geometry of extracellular matrix (ECM) regulates the organization of actin cytoskeleton.6 Siyi Qiu et al. revealed microbial spore responses to microwave radiation by single-cell analysis, providing a new perspective on the responses of living single cells to microwave radiation.7 Fanyi Kong et al. showed continuous infrared light suppressed transmembrane Na currents irrelevant to K channel on the freshly isolated hippocampal neuron cell, which could be explained by a membrane capacitance mediation process.8Finally, we would like to thank all the contributing authors for making this issue possible. More articles focused on Single Cell Analysis will be published in the next special issue. References 1. J. Huang, M. Ji, “Stimulated Raman scattering microscopy on biological cellular machinery,” J. Innov. Opt. Health Sci. 16(2), 2230010 (2023). Google Scholar2. B. Gao, L. Gao, F. Wang, “Single-cell volumetric imaging with light field microscopy: Advances in systems and algorithms,” J. Innov. Opt. Health Sci. 16(2), 2230008 (2023). Google Scholar3. X. Tang, J. Fu, H. Qin, “Microwave-induced thermoacoustic imaging with functional nanoparticles,” J. Innov. Opt. Health Sci. 16(2), 2230014 (2023). Google Scholar4. S. Liu, L. Su, H. Sun, T. Chen, M. Hu, Z. Zhuang, “Automated apoptosis identification in fluorescence imaging of nucleus based on histogram of oriented gradients of high-frequency wavelet coefficients,” J. Innov. Opt. Health Sci. 16(2), 2244003 (2023). Google Scholar5. X. Ma, H. Xiong, J. Guo, Z. Liu, Y. Han, M. Liu, Y. Guo, M. Wang, H. Zhong, Z. Guo, “Label-free breast cancer detection and classification by convolutional neural network-based on exosomes surface-enhanced Raman scattering,” J. Innov. Opt. Health Sci. 16(2), 2244001 (2023). Google Scholar6. F. Xing, H. Zhang, M. Li, H. Dong, X. Ma, S. Deng, F. Hu, I. Lee, L. Pan, J. Xu, “Regulation of actin cytoskeleton via photolithographic micropatterning,” J. Innov. Opt. Health Sci. 16(2), 2244005 (2023). Google Scholar7. S. Qiu, H. Fan, L. He, “Single-cell analysis reveals microbial spore responses to microwave radiation,” J. Innov. Opt. Health Sci. 16(2), 2244004 (2023). Google Scholar8. F. Kong, X. Li, R. Jiao, K. Liu, X. Han, C. Sun, C. Sun, “Suppression of transmembrane sodium currents on the freshly isolated hippocampal neuron cell with continuous infrared light,” J. Innov. Opt. Health Sci. 16(2), 2244002 (2023). Google Scholar FiguresReferencesRelatedDetails Recommended Vol. 16, No. 02 Metrics History Published: 13 March 2023 Information© The Author(s)This is an Open Access article. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC-BY) License. Further distribution of this work is permitted, provided the original work is properly cited.PDF download