Pub Date : 2024-08-09DOI: 10.1038/s41592-024-02391-7
Advanced artificial intelligence approaches are rapidly transforming how biological data are acquired and analyzed.
先进的人工智能方法正在迅速改变生物数据的获取和分析方式。
{"title":"Embedding AI in biology","authors":"","doi":"10.1038/s41592-024-02391-7","DOIUrl":"10.1038/s41592-024-02391-7","url":null,"abstract":"Advanced artificial intelligence approaches are rapidly transforming how biological data are acquired and analyzed.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02391-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141913332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-09DOI: 10.1038/s41592-024-02322-6
Oded Rotem, Assaf Zaritsky
The success of deep learning in analyzing bioimages comes at the expense of biologically meaningful interpretations. We review the state of the art of explainable artificial intelligence (XAI) in bioimaging and discuss its potential in hypothesis generation and data-driven discovery.
{"title":"Visual interpretability of bioimaging deep learning models","authors":"Oded Rotem, Assaf Zaritsky","doi":"10.1038/s41592-024-02322-6","DOIUrl":"10.1038/s41592-024-02322-6","url":null,"abstract":"The success of deep learning in analyzing bioimages comes at the expense of biologically meaningful interpretations. We review the state of the art of explainable artificial intelligence (XAI) in bioimaging and discuss its potential in hypothesis generation and data-driven discovery.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141913350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In situ cryo-electron tomography enables investigation of macromolecules in their native cellular environment. Samples have become more readily available owing to recent software and hardware advancements. Data collection, however, still requires an experienced operator and appreciable microscope time to carefully select targets for high-throughput tilt series acquisition. Here, we developed smart parallel automated cryo-electron tomography (SPACEtomo), a workflow using machine learning approaches to fully automate the entire cryo-electron tomography process, including lamella detection, biological feature segmentation, target selection and parallel tilt series acquisition, all without the need for human intervention. This degree of automation will be essential for obtaining statistically relevant datasets and high-resolution structures of macromolecules in their native context. Smart parallel automated cryo-electron tomography (SPACEtomo) uses deep learning to fully automate data collection from lamella detection to tilt series acquisition, driving the future of cryo-ET through improved throughput and statistics.
{"title":"Smart parallel automated cryo-electron tomography","authors":"Fabian Eisenstein, Yoshiyuki Fukuda, Radostin Danev","doi":"10.1038/s41592-024-02373-9","DOIUrl":"10.1038/s41592-024-02373-9","url":null,"abstract":"In situ cryo-electron tomography enables investigation of macromolecules in their native cellular environment. Samples have become more readily available owing to recent software and hardware advancements. Data collection, however, still requires an experienced operator and appreciable microscope time to carefully select targets for high-throughput tilt series acquisition. Here, we developed smart parallel automated cryo-electron tomography (SPACEtomo), a workflow using machine learning approaches to fully automate the entire cryo-electron tomography process, including lamella detection, biological feature segmentation, target selection and parallel tilt series acquisition, all without the need for human intervention. This degree of automation will be essential for obtaining statistically relevant datasets and high-resolution structures of macromolecules in their native context. Smart parallel automated cryo-electron tomography (SPACEtomo) uses deep learning to fully automate data collection from lamella detection to tilt series acquisition, driving the future of cryo-ET through improved throughput and statistics.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141907145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-08DOI: 10.1038/s41592-024-02376-6
Stanimir Asenov Tashev, Jonas Euchner, Klaus Yserentant, Siegfried Hänselmann, Felix Hild, Wioleta Chmielewicz, Johan Hummert, Florian Schwörer, Nikolaos Tsopoulidis, Stefan Germer, Zoe Saßmannshausen, Oliver T. Fackler, Ursula Klingmüller, Dirk-Peter Herten
Determining the label to target ratio, also known as the degree of labeling (DOL), is crucial for quantitative fluorescence microscopy and a high DOL with minimal unspecific labeling is beneficial for fluorescence microscopy in general. Yet robust, versatile and easy-to-use tools for measuring cell-specific labeling efficiencies are not available. Here we present a DOL determination technique named protein-tag DOL (ProDOL), which enables fast quantification and optimization of protein-tag labeling. With ProDOL various factors affecting labeling efficiency, including substrate type, incubation time and concentration, as well as sample fixation and cell type can be easily assessed. We applied ProDOL to investigate how human immunodeficiency virus-1 pathogenesis factor Nef modulates CD4 T cell activation measuring total and activated copy numbers of the adapter protein SLP-76 in signaling microclusters. ProDOL proved to be a versatile and robust tool for labeling calibration, enabling determination of labeling efficiencies, optimization of strategies and quantification of protein stoichiometry. Protein-tag degree of labeling (ProDOL) is a versatile reference-based approach for experimentally determining the degree of target labeling for improved protein counting and quantification and for optimizing labeling protocols in fixed and live cells.
{"title":"ProDOL: a general method to determine the degree of labeling for staining optimization and molecular counting","authors":"Stanimir Asenov Tashev, Jonas Euchner, Klaus Yserentant, Siegfried Hänselmann, Felix Hild, Wioleta Chmielewicz, Johan Hummert, Florian Schwörer, Nikolaos Tsopoulidis, Stefan Germer, Zoe Saßmannshausen, Oliver T. Fackler, Ursula Klingmüller, Dirk-Peter Herten","doi":"10.1038/s41592-024-02376-6","DOIUrl":"10.1038/s41592-024-02376-6","url":null,"abstract":"Determining the label to target ratio, also known as the degree of labeling (DOL), is crucial for quantitative fluorescence microscopy and a high DOL with minimal unspecific labeling is beneficial for fluorescence microscopy in general. Yet robust, versatile and easy-to-use tools for measuring cell-specific labeling efficiencies are not available. Here we present a DOL determination technique named protein-tag DOL (ProDOL), which enables fast quantification and optimization of protein-tag labeling. With ProDOL various factors affecting labeling efficiency, including substrate type, incubation time and concentration, as well as sample fixation and cell type can be easily assessed. We applied ProDOL to investigate how human immunodeficiency virus-1 pathogenesis factor Nef modulates CD4 T cell activation measuring total and activated copy numbers of the adapter protein SLP-76 in signaling microclusters. ProDOL proved to be a versatile and robust tool for labeling calibration, enabling determination of labeling efficiencies, optimization of strategies and quantification of protein stoichiometry. Protein-tag degree of labeling (ProDOL) is a versatile reference-based approach for experimentally determining the degree of target labeling for improved protein counting and quantification and for optimizing labeling protocols in fixed and live cells.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02376-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141907144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-07DOI: 10.1038/s41592-024-02379-3
Eric Betzig
{"title":"A Cell Observatory to reveal the subcellular foundations of life.","authors":"Eric Betzig","doi":"10.1038/s41592-024-02379-3","DOIUrl":"https://doi.org/10.1038/s41592-024-02379-3","url":null,"abstract":"","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141902416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-07DOI: 10.1038/s41592-024-02374-8
Philipp R. Steen, Eduard M. Unterauer, Luciano A. Masullo, Jisoo Kwon, Ana Perovic, Kristina Jevdokimenko, Felipe Opazo, Eugenio F. Fornasiero, Ralf Jungmann
DNA points accumulation for imaging in nanoscale topography (DNA-PAINT) is a super-resolution fluorescence microscopy technique that achieves single-molecule ‘blinking’ by transient DNA hybridization. Despite blinking kinetics being largely independent of fluorescent dye choice, the dye employed substantially affects measurement quality. Thus far, there has been no systematic overview of dye performance for DNA-PAINT. Here we defined four key parameters characterizing performance: brightness, signal-to-background ratio, DNA-PAINT docking site damage and off-target signal. We then analyzed 18 fluorescent dyes in three spectral regions and examined them both in DNA origami nanostructures, establishing a reference standard, and in a cellular environment, targeting the nuclear pore complex protein Nup96. Finally, having identified several well-performing dyes for each excitation wavelength, we conducted simultaneous three-color DNA-PAINT combined with Exchange-PAINT to image six protein targets in neurons at ~16 nm resolution in less than 2 h. We thus provide guidelines for DNA-PAINT dye selection and evaluation and an overview of performances of commonly used dyes. The dyes chosen for DNA-PAINT microscopy are pivotal for data quality. This Analysis shows a comprehensive comparison of 18 fluorescent dyes in DNA-PAINT and offers guidance for optimum dye selection in single-color and multiplexed imaging.
用于纳米尺度形貌成像的 DNA 点积累（DNA-PAINT）是一种超分辨率荧光显微镜技术，通过瞬时 DNA 杂交实现单分子 "闪烁"。尽管闪烁动力学在很大程度上与荧光染料的选择无关，但所使用的染料会对测量质量产生重大影响。迄今为止，还没有对 DNA-PAINT 染色剂性能的系统概述。在此，我们定义了表征性能的四个关键参数：亮度、信号-背景比、DNA-PAINT对接位点损伤和脱靶信号。然后，我们分析了三个光谱区域的 18 种荧光染料，并在 DNA 折纸纳米结构和细胞环境中对它们进行了检测，前者建立了参考标准，后者则以核孔复合体蛋白 Nup96 为目标。最后，在为每个激发波长确定了几种性能良好的染料后，我们同时进行了三色 DNA-PAINT 和 Exchange-PAINT，在不到 2 小时的时间内以 ~16 nm 的分辨率对神经元中的六个蛋白质靶点进行了成像。
{"title":"The DNA-PAINT palette: a comprehensive performance analysis of fluorescent dyes","authors":"Philipp R. Steen, Eduard M. Unterauer, Luciano A. Masullo, Jisoo Kwon, Ana Perovic, Kristina Jevdokimenko, Felipe Opazo, Eugenio F. Fornasiero, Ralf Jungmann","doi":"10.1038/s41592-024-02374-8","DOIUrl":"10.1038/s41592-024-02374-8","url":null,"abstract":"DNA points accumulation for imaging in nanoscale topography (DNA-PAINT) is a super-resolution fluorescence microscopy technique that achieves single-molecule ‘blinking’ by transient DNA hybridization. Despite blinking kinetics being largely independent of fluorescent dye choice, the dye employed substantially affects measurement quality. Thus far, there has been no systematic overview of dye performance for DNA-PAINT. Here we defined four key parameters characterizing performance: brightness, signal-to-background ratio, DNA-PAINT docking site damage and off-target signal. We then analyzed 18 fluorescent dyes in three spectral regions and examined them both in DNA origami nanostructures, establishing a reference standard, and in a cellular environment, targeting the nuclear pore complex protein Nup96. Finally, having identified several well-performing dyes for each excitation wavelength, we conducted simultaneous three-color DNA-PAINT combined with Exchange-PAINT to image six protein targets in neurons at ~16 nm resolution in less than 2 h. We thus provide guidelines for DNA-PAINT dye selection and evaluation and an overview of performances of commonly used dyes. The dyes chosen for DNA-PAINT microscopy are pivotal for data quality. This Analysis shows a comprehensive comparison of 18 fluorescent dyes in DNA-PAINT and offers guidance for optimum dye selection in single-color and multiplexed imaging.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02374-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141902417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-05DOI: 10.1038/s41592-024-02366-8
Rapid advancements in transcriptomics have enabled the quantification of individual transcripts for thousands of genes in millions of single cells. By coupling a machine learning inference framework with biophysical models describing the RNA life cycle, we can explore the dynamics driving RNA production, processing and degradation across cell types.
{"title":"Studying RNA dynamics from single-cell RNA sequencing snapshots","authors":"","doi":"10.1038/s41592-024-02366-8","DOIUrl":"10.1038/s41592-024-02366-8","url":null,"abstract":"Rapid advancements in transcriptomics have enabled the quantification of individual transcripts for thousands of genes in millions of single cells. By coupling a machine learning inference framework with biophysical models describing the RNA life cycle, we can explore the dynamics driving RNA production, processing and degradation across cell types.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141893823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-05DOI: 10.1038/s41592-024-02372-w
Raktim Mitra, Jinsen Li, Jared M. Sagendorf, Yibei Jiang, Ari S. Cohen, Tsu-Pei Chiu, Cameron J. Glasscock, Remo Rohs
Predicting protein–DNA binding specificity is a challenging yet essential task for understanding gene regulation. Protein–DNA complexes usually exhibit binding to a selected DNA target site, whereas a protein binds, with varying degrees of binding specificity, to a wide range of DNA sequences. This information is not directly accessible in a single structure. Here, to access this information, we present Deep Predictor of Binding Specificity (DeepPBS), a geometric deep-learning model designed to predict binding specificity from protein–DNA structure. DeepPBS can be applied to experimental or predicted structures. Interpretable protein heavy atom importance scores for interface residues can be extracted. When aggregated at the protein residue level, these scores are validated through mutagenesis experiments. Applied to designed proteins targeting specific DNA sequences, DeepPBS was demonstrated to predict experimentally measured binding specificity. DeepPBS offers a foundation for machine-aided studies that advance our understanding of molecular interactions and guide experimental designs and synthetic biology. DeepPBS is a deep-learning model designed to predict the binding specificity of protein–DNA interactions using physicochemical and geometric contexts. DeepPBS functions across protein families and on experimentally determined as well as predicted protein–DNA complex structures.
预测蛋白质-DNA 结合的特异性是了解基因调控的一项具有挑战性但又必不可少的任务。蛋白质-DNA 复合物通常表现为与选定的 DNA 目标位点结合，而蛋白质则以不同程度的结合特异性与多种 DNA 序列结合。单个结构无法直接获取这些信息。为了获取这些信息，我们提出了结合特异性深度预测模型（DeepPBS），这是一种几何深度学习模型，旨在从蛋白质-DNA 结构中预测结合特异性。DeepPBS 可应用于实验结构或预测结构。可以为界面残基提取可解释的蛋白质重原子重要性分数。在蛋白质残基水平上汇总后，这些分数可通过诱变实验进行验证。将 DeepPBS 应用于以特定 DNA 序列为靶标的设计蛋白质，证明它可以预测实验测定的结合特异性。DeepPBS 为机器辅助研究奠定了基础，这些研究可促进我们对分子相互作用的理解，并为实验设计和合成生物学提供指导。
{"title":"Geometric deep learning of protein–DNA binding specificity","authors":"Raktim Mitra, Jinsen Li, Jared M. Sagendorf, Yibei Jiang, Ari S. Cohen, Tsu-Pei Chiu, Cameron J. Glasscock, Remo Rohs","doi":"10.1038/s41592-024-02372-w","DOIUrl":"10.1038/s41592-024-02372-w","url":null,"abstract":"Predicting protein–DNA binding specificity is a challenging yet essential task for understanding gene regulation. Protein–DNA complexes usually exhibit binding to a selected DNA target site, whereas a protein binds, with varying degrees of binding specificity, to a wide range of DNA sequences. This information is not directly accessible in a single structure. Here, to access this information, we present Deep Predictor of Binding Specificity (DeepPBS), a geometric deep-learning model designed to predict binding specificity from protein–DNA structure. DeepPBS can be applied to experimental or predicted structures. Interpretable protein heavy atom importance scores for interface residues can be extracted. When aggregated at the protein residue level, these scores are validated through mutagenesis experiments. Applied to designed proteins targeting specific DNA sequences, DeepPBS was demonstrated to predict experimentally measured binding specificity. DeepPBS offers a foundation for machine-aided studies that advance our understanding of molecular interactions and guide experimental designs and synthetic biology. DeepPBS is a deep-learning model designed to predict the binding specificity of protein–DNA interactions using physicochemical and geometric contexts. DeepPBS functions across protein families and on experimentally determined as well as predicted protein–DNA complex structures.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02372-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141893821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-05DOI: 10.1038/s41592-024-02375-7
Run Zhang, Maribel Anguiano, Isak K. Aarrestad, Sophia Lin, Joshua Chandra, Sruti S. Vadde, David E. Olson, Christina K. Kim
Intracellular calcium (Ca2+) is ubiquitous to cell signaling across biology. While existing fluorescent sensors and reporters can detect activated cells with elevated Ca2+ levels, these approaches require implants to deliver light to deep tissue, precluding their noninvasive use in freely behaving animals. Here we engineered an enzyme-catalyzed approach that rapidly and biochemically tags cells with elevated Ca2+ in vivo. Ca2+-activated split-TurboID (CaST) labels activated cells within 10 min with an exogenously delivered biotin molecule. The enzymatic signal increases with Ca2+ concentration and biotin labeling time, demonstrating that CaST is a time-gated integrator of total Ca2+ activity. Furthermore, the CaST readout can be performed immediately after activity labeling, in contrast to transcriptional reporters that require hours to produce signal. These capabilities allowed us to apply CaST to tag prefrontal cortex neurons activated by psilocybin, and to correlate the CaST signal with psilocybin-induced head-twitch responses in untethered mice. CaST is a Ca2+-activated version of split-TurboID. The tool allows labeling active neurons quickly, simply by administration of exogenous biotin, thus enabling the study of behaviors that would be impaired by hardware required for the use of other, light-dependent tools.
{"title":"Rapid, biochemical tagging of cellular activity history in vivo","authors":"Run Zhang, Maribel Anguiano, Isak K. Aarrestad, Sophia Lin, Joshua Chandra, Sruti S. Vadde, David E. Olson, Christina K. Kim","doi":"10.1038/s41592-024-02375-7","DOIUrl":"10.1038/s41592-024-02375-7","url":null,"abstract":"Intracellular calcium (Ca2+) is ubiquitous to cell signaling across biology. While existing fluorescent sensors and reporters can detect activated cells with elevated Ca2+ levels, these approaches require implants to deliver light to deep tissue, precluding their noninvasive use in freely behaving animals. Here we engineered an enzyme-catalyzed approach that rapidly and biochemically tags cells with elevated Ca2+ in vivo. Ca2+-activated split-TurboID (CaST) labels activated cells within 10 min with an exogenously delivered biotin molecule. The enzymatic signal increases with Ca2+ concentration and biotin labeling time, demonstrating that CaST is a time-gated integrator of total Ca2+ activity. Furthermore, the CaST readout can be performed immediately after activity labeling, in contrast to transcriptional reporters that require hours to produce signal. These capabilities allowed us to apply CaST to tag prefrontal cortex neurons activated by psilocybin, and to correlate the CaST signal with psilocybin-induced head-twitch responses in untethered mice. CaST is a Ca2+-activated version of split-TurboID. The tool allows labeling active neurons quickly, simply by administration of exogenous biotin, thus enabling the study of behaviors that would be impaired by hardware required for the use of other, light-dependent tools.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02375-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141893822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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