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Bayesian localization microscopy based on intensity distribution of fluorophores

Protein & Cell 2015年第3期6卷 211-220页

作者： Fan Xu Mingshu Zhang Zhiyong Liu Pingyong Xu Fa Zhang Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China Laboratory of Non Coding RNA Institute of Biophysics Chinese Academy of Sciences Beijing 100101 China

Super-resolution microscopy techniques have overcome the limit of optical diffraction. Recently, the Bayesian analysis of Bleaching and Blinking data （3B） method has emerged as an important tool to obtain super-resolution fluorescence images. 3B uses the change in information caused by adding or removing fiuorophores in the cell to fit the date. When adding a new fluorophore, 3B selects a random initial position, optimizes this position and then determines its reliability. However, the fluorophores are not evenly distributed in the entire image region, and the fluorescence intensilty at a given position positively correlates with the probability of observing a fluorophore at this position. In this paper, we present a Bayesian analysis of Bleaching and Blinking microscopy method based on fluorescence intensity distribution （FID3B）. We utilize the intensity distribution to select more reliable positions as the initial positions of fluorophores. This approach can improve the reconstruction results and significantly reduce the computational time. We validate the perfor. mance of our method using both simulated date and experimental date from cellular structures. The results confirm the effectiveness of our method.

关键词： super-resolution fluorescence image 3B,intensity distribution

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Fluo-Fluo translation based on deep learning

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Chinese Optics Letters 2022年第3期20卷 82-88页

作者： Zhengfen Jiang Boyi Li Tho N.H.T.Tran Jiehui Jiang Xin Liu Dean Ta School of Communication&Information Engineering Shanghai UniversityShanghai 200A44China Academy for Engineering&Technology Fudan UniversityShanghai 200433China State Key Laboratory of Medical Neurobiology Fudan UniversityShanghai 200433China Center for Biomedical Engineering Fudan UniversityShanghai 200433China

fluorescence microscopy technology uses fluorescent dyes to provide highly specific visualization of cell components,which plays an important role in understanding the subcellular ***,fluorescence microscopy has some limitations such as the risk of non-specific cross labeling in multi-labeled fluorescent staining and limited number of fluo-rescence labels due to spectral *** paper proposes a deep learning-based fluorescence to fluorescence[Flu0-Fluo]translation method,which uses a conditional generative adversarial network to predict a fluorescence image from another fluorescence image and further realizes the multi-label fluorescent *** cell types used include human motor neurons,human breast cancer cells,rat cortical neurons,and rat *** effectiveness of the method is verified by successfully generating virtual fluorescence images highly similar to the true fluorescence *** study shows that a deep neural network can implement Fluo-Fluo translation and describe the localization relationship between subcellular structures labeled with different fluorescent *** proposed Fluo-Fluo method can avoid non-specific cross labeling in multi-label fluorescence staining and is free from spectral *** theory,an unlimited number of fluorescence images can be predicted from a single fluorescence image to characterize cells.

关键词： deep learning conditional generative adversarial network fluorescence image image translation

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In situ Visualization of HMGB Protein Co-localizing with Cisplatin Damaged DNA at Single-Cell Level

In situ Visualization of HMGB Protein Co-localizing with Cis...

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中国化学会第十四届全国生物无机化学学术会议

作者： Yu Lin Kui Wu Feifei Jia Ling Chen 赵耀 Fuyi Wang CAS Key Laboratory of Analytical Chemistry for Living Biosystems National Centre for Mass Spectrometry in Beijing Institute of Chemistry Chinese Academy of Sciences University of Chinese Academy of Sciences

In situ observation of the interaction between proteins and damaged DNA at single cell level is a difficult task. We introduce here a novel approach to explore the recognition between protein and metallodrugs or drug-damaged DNA in single cells. In general, laser scanning confocal microscope（LSCM） was used to image the location of protein in the cells either by immunofluorescence staining or fusing yellow fluorescent protein（EYFP）, and time of flight secondary ion mass spectrometry（ToF-SIMS） was used to image the metallodrugs or drug-damaged DNA, respectively. The LSCM images of protein and ToF-SIMS images of metals were aligned and overlaid to produce the merged images, in which the co-localization of those two indicates their recognitions or interaction. In this study, high mobility group box 1（HMGB1） protein and cisplatin were used as model to verify this method. HMGB1 is a nucleoprotein, which has been demonstrated to bind specifically to 1,2-intrastrand crosslinked DNA by cisplatin, block the repair of this DNA-lesion, and as a consequence, induce cell apoptosis. Applying the method we developed, we observed the overlap of HMGB1 and platinum in cell nucleus, which indicated that the interaction of the protein and Pt-damaged DNA were successfully visualized in situ. Higher concentration of cisplatin and longer incubation time corresponds to more and stronger overlapped spots in the images of the cells. When the amino acid Phe 38 in the key binding site of HMGB1 was mutated to Ala, or other DNA binding proteins such as SMAD3 or SMAD7 were labeled with EGFP, no overlap in the images was found, because they did not recognize the Pt damaged DNA. These results suggest successful in situ visualization of protein binding to specific damaged DNA in single cell.

关键词： fluorescence image HMGB1 protein SIMS image cisplatin

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