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And the award goes to … 该奖项将颁发给....

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-24 DOI: 10.1038/s41592-025-02842-9

Vivien Marx

引用次数: 0

Cryogenic STEM of thick biological specimens 厚生物标本的低温STEM。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-23 DOI: 10.1038/s41592-025-02818-9

Carsten Sachse

引用次数: 0

Common to rare transfer learning (CORAL) enables inference and prediction for a quarter million rare Malagasy arthropods 共同稀有迁移学习（CORAL）能够对25万只稀有的马达加斯加节肢动物进行推理和预测。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-23 DOI: 10.1038/s41592-025-02823-y

Otso Ovaskainen, Steven Winter, Gleb Tikhonov, Nerea Abrego, Sten Anslan, Jeremy R. deWaard, Stephanie L. deWaard, Brian L. Fisher, Brendan Furneaux, Bess Hardwick, Deirdre Kerdraon, Mikko Pentinsaari, Dimby Raharinjanahary, Eric Tsiriniaina Rajoelison, Sujeevan Ratnasingham, Panu Somervuo, Jayme E. Sones, Evgeny V. Zakharov, Paul D. N. Hebert, Tomas Roslin, David Dunson

{"title":"Common to rare transfer learning (CORAL) enables inference and prediction for a quarter million rare Malagasy arthropods","authors":"Otso Ovaskainen, Steven Winter, Gleb Tikhonov, Nerea Abrego, Sten Anslan, Jeremy R. deWaard, Stephanie L. deWaard, Brian L. Fisher, Brendan Furneaux, Bess Hardwick, Deirdre Kerdraon, Mikko Pentinsaari, Dimby Raharinjanahary, Eric Tsiriniaina Rajoelison, Sujeevan Ratnasingham, Panu Somervuo, Jayme E. Sones, Evgeny V. Zakharov, Paul D. N. Hebert, Tomas Roslin, David Dunson","doi":"10.1038/s41592-025-02823-y","DOIUrl":"10.1038/s41592-025-02823-y","url":null,"abstract":"DNA-based biodiversity surveys result in massive-scale data, including up to millions of species—of which, most are rare. Making the most of such data for inference and prediction requires modeling approaches that can relate species occurrences to environmental and spatial predictors, while incorporating information about their taxonomic or phylogenetic placement. Even if the scalability of joint species distribution models to large communities has greatly advanced, incorporating hundreds of thousands of species has not been feasible to date, leading to compromised analyses. Here we present a ‘common to rare transfer learning’ (CORAL) approach, based on borrowing information from the common species to enable statistically and computationally efficient modeling of both common and rare species. We illustrate that CORAL leads to much improved prediction and inference in the context of DNA metabarcoding data from Madagascar, comprising 255,188 arthropod species detected in 2,874 samples. CORAL can infer the occurrence of rare species based on common species, using DNA metabarcoding data or other high-dimensional biodiversity data. The approach is illustrated on a large-scale biodiversity survey from Madagascar.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 10","pages":"2074-2082"},"PeriodicalIF":32.1,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41592-025-02823-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145131524","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}

引用次数: 0

Dose-efficient cryo-electron microscopy for thick samples using tilt- corrected scanning transmission electron microscopy 使用倾斜校正扫描透射电子显微镜对厚样品进行剂量有效的冷冻电子显微镜。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-23 DOI: 10.1038/s41592-025-02834-9

Yue Yu, Katherine A. Spoth, Michael Colletta, Kayla X. Nguyen, Steven E. Zeltmann, Xiyue S. Zhang, Mohammadreza Paraan, Mykhailo Kopylov, Charlie Dubbeldam, Daniel Serwas, Hannah Siems, David A. Muller, Lena F. Kourkoutis

{"title":"Dose-efficient cryo-electron microscopy for thick samples using tilt- corrected scanning transmission electron microscopy","authors":"Yue Yu, Katherine A. Spoth, Michael Colletta, Kayla X. Nguyen, Steven E. Zeltmann, Xiyue S. Zhang, Mohammadreza Paraan, Mykhailo Kopylov, Charlie Dubbeldam, Daniel Serwas, Hannah Siems, David A. Muller, Lena F. Kourkoutis","doi":"10.1038/s41592-025-02834-9","DOIUrl":"10.1038/s41592-025-02834-9","url":null,"abstract":"Cryogenic electron microscopy is a powerful tool in structural biology. In thick specimens, challenges arise as an exponentially larger fraction of the transmitted electrons lose energy from inelastic scattering and can no longer be properly focused as a result of chromatic aberrations in the post-specimen optics. Rather than filtering out the inelastic scattering at the price of reducing potential signal, as is done in energy-filtered transmission electron microscopy, we show how a dose-efficient and unfiltered image can be rapidly obtained using tilt-corrected bright-field scanning transmission electron microscopy data collected on a pixelated detector. Enhanced contrast and a 3–5× improvement in dose efficiency are observed for two-dimensional images of intact bacterial cells and large organelles using tilt-corrected bright-field scanning transmission electron microscopy compared to energy-filtered transmission electron microscopy for thicknesses beyond 500 nm. As a proof of concept for the technique’s performance in structural determination, we present a single-particle analysis map at sub-nanometer resolution for a highly symmetric virus-like particle determined from 789 particles. Tilt-corrected bright-field scanning transmission electron microscopy offers enhanced cryogenic electron microscopy contrast and substantial improvement in dose efficiency for thick samples such as bacterial cells and large organelles, while still being able to perform single-particle analysis.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 10","pages":"2138-2148"},"PeriodicalIF":32.1,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41592-025-02834-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145131560","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}

引用次数: 0

Kilohertz volumetric imaging of in vivo dynamics using squeezed light field microscopy 利用压缩光场显微镜对体内动力学进行千赫兹体积成像。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-23 DOI: 10.1038/s41592-025-02843-8

Zhaoqiang Wang, Ruixuan Zhao, Daniel A. Wagenaar, Diego Espino, Liron Sheintuch, Ohr Benshlomo, Wenjun Kang, Enbo Zhu, Calvin K. Lee, William C. Schmidt, Aryan Pammar, Jing Wang, Gerard C. L. Wong, Rongguang Liang, Peyman Golshani, Tzung K. Hsiai, Liang Gao

{"title":"Kilohertz volumetric imaging of in vivo dynamics using squeezed light field microscopy","authors":"Zhaoqiang Wang, Ruixuan Zhao, Daniel A. Wagenaar, Diego Espino, Liron Sheintuch, Ohr Benshlomo, Wenjun Kang, Enbo Zhu, Calvin K. Lee, William C. Schmidt, Aryan Pammar, Jing Wang, Gerard C. L. Wong, Rongguang Liang, Peyman Golshani, Tzung K. Hsiai, Liang Gao","doi":"10.1038/s41592-025-02843-8","DOIUrl":"10.1038/s41592-025-02843-8","url":null,"abstract":"Volumetric functional imaging of transient cellular signaling and motion dynamics is often limited by hardware bandwidth and the scarcity of photons under short exposures. To overcome these challenges, we introduce squeezed light field microscopy (SLIM), a computational imaging approach that rapidly captures high-resolution three-dimensional light signals using only a single, low-format camera sensor. SLIM records over 1,000 volumes per second across a 550-µm diameter field of view and 300-µm depth, achieving 3.6-µm lateral and 6-µm axial resolution. Here we demonstrate its utility in blood cell velocimetry within the embryonic zebrafish brain and in freely moving tails undergoing high-frequency swings. Millisecond-scale temporal resolution further enables precise voltage imaging of neural membrane potentials in the leech ganglion and hippocampus of behaving mice. Together, these results establish SLIM as a versatile and robust tool for high-speed volumetric microscopy across diverse biological systems. Squeezed light field microscopy (SLIM) combines ideas from tomography and compressed sensing with light field microscopy to enable volumetric imaging at kilohertz rates, as demonstrated in blood flow imaging in zebrafish and voltage imaging in leeches and mice.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 10","pages":"2194-2204"},"PeriodicalIF":32.1,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145131478","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}

引用次数: 0

RNA-stabilized coat proteins for sensitive and simultaneous imaging of distinct single mRNAs in live cells. rna稳定的外壳蛋白，用于活细胞中不同单一mrna的敏感和同时成像。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-22 DOI: 10.1038/s41592-025-02782-4

Christopher J Kuffner, Alexander M Marzilli, John T Ngo

引用次数: 0

GPU-accelerated homology search with MMseqs2 gpu加速同源搜索与MMseqs2。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-18 DOI: 10.1038/s41592-025-02819-8

Felix Kallenborn, Alejandro Chacon, Christian Hundt, Hassan Sirelkhatim, Kieran Didi, Sooyoung Cha, Christian Dallago, Milot Mirdita, Bertil Schmidt, Martin Steinegger

引用次数: 0

MIFA: Metadata, Incentives, Formats and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysis. MIFA：元数据、激励、格式和可访问性指南，以改善生物图像分析中人工智能数据集的重用。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-15 DOI: 10.1038/s41592-025-02835-8

Teresa Zulueta-Coarasa, Florian Jug, Aastha Mathur, Josh Moore, Arrate Muñoz-Barrutia, Liviu Anita, Kolawole Babalola, Peter Bankhead, Perrine Gilloteaux, Nodar Gogoberidze, Martin L Jones, Gerard J Kleywegt, Paul Korir, Anna Kreshuk, Aybüke Küpcü Yoldaş, Luca Marconato, Kedar Narayan, Nils Norlin, Bugra Oezdemir, Jessica L Riesterer, Craig Russell, Norman Rzepka, Ugis Sarkans, Beatriz Serrano-Solano, Christian Tischer, Virginie Uhlmann, Vladimír Ulman, Matthew Hartley

{"title":"MIFA: Metadata, Incentives, Formats and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysis.","authors":"Teresa Zulueta-Coarasa, Florian Jug, Aastha Mathur, Josh Moore, Arrate Muñoz-Barrutia, Liviu Anita, Kolawole Babalola, Peter Bankhead, Perrine Gilloteaux, Nodar Gogoberidze, Martin L Jones, Gerard J Kleywegt, Paul Korir, Anna Kreshuk, Aybüke Küpcü Yoldaş, Luca Marconato, Kedar Narayan, Nils Norlin, Bugra Oezdemir, Jessica L Riesterer, Craig Russell, Norman Rzepka, Ugis Sarkans, Beatriz Serrano-Solano, Christian Tischer, Virginie Uhlmann, Vladimír Ulman, Matthew Hartley","doi":"10.1038/s41592-025-02835-8","DOIUrl":"https://doi.org/10.1038/s41592-025-02835-8","url":null,"abstract":"Artificial intelligence (AI) methods are powerful tools for biological image analysis and processing. High-quality annotated images are key to training and developing new algorithms, but access to such data is often hindered by the lack of standards for sharing datasets. We discuss the barriers to sharing annotated image datasets and suggest specific guidelines to improve the reuse of bioimages and annotations for AI applications. These include standards on data formats, metadata, data presentation and sharing, and incentives to generate new datasets. We are sure that the Metadata, Incentives, Formats and Accessibility (MIFA) recommendations will accelerate the development of AI tools for bioimage analysis by facilitating access to high-quality training and benchmarking data.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":32.1,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145069964","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}

引用次数: 0

Integrating diverse experimental information to assist protein complex structure prediction by GRASP. 整合多种实验信息，利用GRASP辅助蛋白质复合体结构预测

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-15 DOI: 10.1038/s41592-025-02820-1

Yuhao Xie, Chengwei Zhang, Shimian Li, Xinyu Du, Yanjiao Lu, Min Wang, Yingtong Hu, Zhenyu Chen, Sirui Liu, Yi Qin Gao

{"title":"Integrating diverse experimental information to assist protein complex structure prediction by GRASP.","authors":"Yuhao Xie, Chengwei Zhang, Shimian Li, Xinyu Du, Yanjiao Lu, Min Wang, Yingtong Hu, Zhenyu Chen, Sirui Liu, Yi Qin Gao","doi":"10.1038/s41592-025-02820-1","DOIUrl":"10.1038/s41592-025-02820-1","url":null,"abstract":"Protein complex structure prediction is crucial for understanding of biological activities and advancing drug development. While various experimental methods can provide structural insights into protein complexes, the knowledge obtained is often sparse or approximate. A general tool is needed to integrate limited experimental information for high-throughput and accurate prediction. Here we introduce GRASP to efficiently and flexibly incorporate diverse forms of experimental information. GRASP outperforms existing tools in handling both simulated and real-world experimental restraints including those from crosslinking, covalent labeling, chemical shift perturbation and deep mutational scanning. For example, GRASP excels at predicting antigen-antibody complex structures, even surpassing AlphaFold3 when using experimental deep mutational scanning or covalent-labeling restraints. Beyond its accuracy and flexibility in restrained structure prediction, GRASP's ability to integrate multiple forms of restraints enables integrative modeling. We also showcase its potential in modeling protein structural interactome under near-cellular conditions using previously reported large-scale in situ crosslinking data for mitochondria.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":32.1,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145069985","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}

引用次数: 0

MSnLib: efficient generation of open multi-stage fragmentation mass spectral libraries MSnLib：高效生成开放的多级碎片质谱库。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-15 DOI: 10.1038/s41592-025-02813-0

Corinna Brungs, Robin Schmid, Steffen Heuckeroth, Aninda Mazumdar, Matúš Drexler, Pavel Šácha, Pieter C. Dorrestein, Daniel Petras, Louis-Felix Nothias, Václav Veverka, Radim Nencka, Zdeněk Kameník, Tomáš Pluskal

{"title":"MSnLib: efficient generation of open multi-stage fragmentation mass spectral libraries","authors":"Corinna Brungs, Robin Schmid, Steffen Heuckeroth, Aninda Mazumdar, Matúš Drexler, Pavel Šácha, Pieter C. Dorrestein, Daniel Petras, Louis-Felix Nothias, Václav Veverka, Radim Nencka, Zdeněk Kameník, Tomáš Pluskal","doi":"10.1038/s41592-025-02813-0","DOIUrl":"10.1038/s41592-025-02813-0","url":null,"abstract":"Untargeted high-resolution mass spectrometry is a key tool in clinical metabolomics, natural product discovery and exposomics, with compound identification remaining the major bottleneck. Currently, the standard workflow applies spectral library matching against tandem mass spectrometry (MS2) fragmentation data. Multi-stage fragmentation (MSn) yields more profound insights into substructures, enabling validation of fragmentation pathways; however, the community lacks open MSn reference data of diverse natural products and other chemicals. Here we describe MSnLib, a machine learning-ready open resource of >2 million spectra in MSn trees of 30,008 unique small molecules, built with a high-throughput data acquisition and processing pipeline in the open-source software mzmine. MSnLib is a large-scale, open MSn spectral library featuring >2.3 million MSn and >357,000 MS2 spectra for 30,008 unique small molecules.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 10","pages":"2028-2031"},"PeriodicalIF":32.1,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41592-025-02813-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145069975","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}

引用次数: 0