Pub Date : 2026-02-11DOI: 10.1038/s41592-026-03020-1
Generative AI technology is having substantial impacts across society, and scientific publishing is by no means immune. We highlight journal policies around the use of generative AI and discuss its responsible use in writing, peer reviewing and publishing scientific research.
{"title":"Using AI responsibly in scientific publishing","authors":"","doi":"10.1038/s41592-026-03020-1","DOIUrl":"10.1038/s41592-026-03020-1","url":null,"abstract":"Generative AI technology is having substantial impacts across society, and scientific publishing is by no means immune. We highlight journal policies around the use of generative AI and discuss its responsible use in writing, peer reviewing and publishing scientific research.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 2","pages":"271-271"},"PeriodicalIF":32.1,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41592-026-03020-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146162923","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 : 2026-02-10DOI: 10.1038/s41592-025-02976-w
Current single-cell metabolomics methods show low sensitivity and limited coverage of small-molecule metabolites. We developed an ion mobility-resolved mass cytometry technology that incorporates selective ion accumulation and cell superposition strategies to deliver high sensitivity and deep coverage, which captured over 5,000 metabolic peaks and about 800 metabolites from individual cells in a high-throughput manner.
{"title":"Deep-coverage, high-throughput single-cell metabolomics","authors":"","doi":"10.1038/s41592-025-02976-w","DOIUrl":"10.1038/s41592-025-02976-w","url":null,"abstract":"Current single-cell metabolomics methods show low sensitivity and limited coverage of small-molecule metabolites. We developed an ion mobility-resolved mass cytometry technology that incorporates selective ion accumulation and cell superposition strategies to deliver high sensitivity and deep coverage, which captured over 5,000 metabolic peaks and about 800 metabolites from individual cells in a high-throughput manner.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 3","pages":"499-500"},"PeriodicalIF":32.1,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146157754","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 : 2026-02-09DOI: 10.1038/s41592-025-02932-8
Nicola De Maio, Myrthe Willemsen, Samuel Martin, Zihao Guo, Abhratanu Saha, Martin Hunt, Nhan Ly-Trong, Bui Quang Minh, Zamin Iqbal, Nick Goldman
Phylogenetic analyses of genome sequences from infectious pathogens reveal essential information regarding their evolution and transmission, as seen during the coronavirus disease 2019 pandemic. Recently developed pandemic-scale phylogenetic inference methods reduce the computational demand of phylogenetic reconstruction from genomic epidemiological datasets, allowing the analysis of millions of closely related genomes. However, widespread homoplasies, due to recurrent mutations and sequence errors, cause phylogenetic uncertainty and biases. We present algorithms and models to substantially improve the computational performance and accuracy of pandemic-scale phylogenetics. In particular, we account for, and identify, mutation rate variation and recurrent sequence errors. We reconstruct a reliable and public sequence alignment and phylogenetic tree of >2 million severe acute respiratory syndrome coronavirus 2 genomes encapsulating the evolutionary history and global spread of the virus up to February 2023. Performing pandemic-scale phylogenetic analysis poses multifaceted challenges. This study develops methods for identifying and accounting for mutation rate variation and recurrent sequence errors, leading to an improved global phylogenetic tree of >2 million severe acute respiratory syndrome coronavirus 2 genomes.
{"title":"Rate variation and recurrent sequence errors in pandemic-scale phylogenetics","authors":"Nicola De Maio, Myrthe Willemsen, Samuel Martin, Zihao Guo, Abhratanu Saha, Martin Hunt, Nhan Ly-Trong, Bui Quang Minh, Zamin Iqbal, Nick Goldman","doi":"10.1038/s41592-025-02932-8","DOIUrl":"10.1038/s41592-025-02932-8","url":null,"abstract":"Phylogenetic analyses of genome sequences from infectious pathogens reveal essential information regarding their evolution and transmission, as seen during the coronavirus disease 2019 pandemic. Recently developed pandemic-scale phylogenetic inference methods reduce the computational demand of phylogenetic reconstruction from genomic epidemiological datasets, allowing the analysis of millions of closely related genomes. However, widespread homoplasies, due to recurrent mutations and sequence errors, cause phylogenetic uncertainty and biases. We present algorithms and models to substantially improve the computational performance and accuracy of pandemic-scale phylogenetics. In particular, we account for, and identify, mutation rate variation and recurrent sequence errors. We reconstruct a reliable and public sequence alignment and phylogenetic tree of >2 million severe acute respiratory syndrome coronavirus 2 genomes encapsulating the evolutionary history and global spread of the virus up to February 2023. Performing pandemic-scale phylogenetic analysis poses multifaceted challenges. This study develops methods for identifying and accounting for mutation rate variation and recurrent sequence errors, leading to an improved global phylogenetic tree of >2 million severe acute respiratory syndrome coronavirus 2 genomes.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 3","pages":"565-573"},"PeriodicalIF":32.1,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41592-025-02932-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146150223","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}
Current single-cell metabolomics approaches are limited by insufficient sensitivity, robustness and metabolite coverage. We present an ion mobility-resolved mass cytometry technology that integrates high-throughput single-cell injection with ion mobility–mass spectrometry for multidimensional metabolomic profiling. Ion mobility-enabled selective ion accumulation and cell superposition-based amplification strategies substantially enhance sensitivity, robustness and overall analytical performance. Combined with our computational tool, MetCell, this technology allows high-throughput analysis while achieving exceptional profiling depth, detecting over 5,000 metabolic peaks and annotating approximately 800 metabolites per cell—representing a 3-fold to 10-fold improvement over existing methods. It offers attomole-level sensitivity and captures a broad dynamic range of metabolites within individual cells. Applied to 45,603 primary liver cells from aging mice, it enabled accurate cell-type and cell-subtype annotation and revealed distinct metabolic states and heterogeneity in hepatocytes during aging. This platform sets a new benchmark for high-throughput single-cell metabolomics, advancing our understanding of metabolic heterogeneity at single-cell resolution. An ion mobility-resolved mass cytometry method for single-cell metabolomics enables multidimensional metabolomic profiling. The approach was used to curate a metabolic single-cell atlas containing 45,603 primary liver cells from aging mice.
{"title":"Deep-coverage single-cell metabolomics enabled by ion mobility-resolved mass cytometry","authors":"Mingdu Luo, Tianzhang Kou, Yandong Yin, Shengyi Zhou, Xiaolan Zhu, Xinhao Zeng, Junhao Hu, Zheng-Jiang Zhu","doi":"10.1038/s41592-025-02970-2","DOIUrl":"10.1038/s41592-025-02970-2","url":null,"abstract":"Current single-cell metabolomics approaches are limited by insufficient sensitivity, robustness and metabolite coverage. We present an ion mobility-resolved mass cytometry technology that integrates high-throughput single-cell injection with ion mobility–mass spectrometry for multidimensional metabolomic profiling. Ion mobility-enabled selective ion accumulation and cell superposition-based amplification strategies substantially enhance sensitivity, robustness and overall analytical performance. Combined with our computational tool, MetCell, this technology allows high-throughput analysis while achieving exceptional profiling depth, detecting over 5,000 metabolic peaks and annotating approximately 800 metabolites per cell—representing a 3-fold to 10-fold improvement over existing methods. It offers attomole-level sensitivity and captures a broad dynamic range of metabolites within individual cells. Applied to 45,603 primary liver cells from aging mice, it enabled accurate cell-type and cell-subtype annotation and revealed distinct metabolic states and heterogeneity in hepatocytes during aging. This platform sets a new benchmark for high-throughput single-cell metabolomics, advancing our understanding of metabolic heterogeneity at single-cell resolution. An ion mobility-resolved mass cytometry method for single-cell metabolomics enables multidimensional metabolomic profiling. The approach was used to curate a metabolic single-cell atlas containing 45,603 primary liver cells from aging mice.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 3","pages":"585-595"},"PeriodicalIF":32.1,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146150273","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 : 2026-02-09DOI: 10.1038/s41592-025-02947-1
Martin Hunt, Angie S. Hinrichs, Daniel Anderson, Lily Karim, Bethany L. Dearlove, Jeff Knaggs, Bede Constantinides, Philip W. Fowler, Gillian Rodger, Teresa Street, Sheila Lumley, Hermione Webster, Theo Sanderson, Christopher Ruis, Benjamin Kotzen, Nicola de Maio, Lucas N. Amenga-Etego, Dominic S. Y. Amuzu, Martin Avaro, Gordon A. Awandare, Reuben Ayivor-Djanie, Timothy Barkham, Matthew Bashton, Elizabeth M. Batty, Yaw Bediako, Denise De Belder, Estefania Benedetti, Andreas Bergthaler, Stefan A. Boers, Josefina Campos, Rosina Afua Ampomah Carr, Yuan Yi Constance Chen, Facundo Cuba, Maria Elena Dattero, Wanwisa Dejnirattisai, Alexander Dilthey, Kwabena Obeng Duedu, Lukas Endler, Ilka Engelmann, Ngiambudulu M. Francisco, Jonas Fuchs, Etienne Z. Gnimpieba, Soraya Groc, Jones Gyamfi, Dennis Heemskerk, Torsten Houwaart, Nei-yuan Hsiao, Matthew Huska, Martin Hölzer, Arash Iranzadeh, Hanna Jarva, Chandima Jeewandara, Bani Jolly, Rageema Joseph, Ravi Kant, Karrie Ko Kwan Ki, Satu Kurkela, Maija Lappalainen, Marie Lataretu, Jacob Lemieux, Chang Liu, Gathsaurie Neelika Malavige, Tapfumanei Mashe, Juthathip Mongkolsapaya, Brigitte Montes, Jose Arturo Molina Mora, Collins M. Morang’a, Bernard Mvula, Niranjan Nagarajan, Andrew Nelson, Joyce M. Ngoi, Joana Paula da Paixão, Marcus Panning, Tomas Poklepovich, Peter K. Quashie, Diyanath Ranasinghe, Mara Russo, James Emmanuel San, Nicholas D. Sanderson, Vinod Scaria, Gavin Screaton, October Michael Sessions, Tarja Sironen, Abay Sisay, Darren Smith, Teemu Smura, Piyada Supasa, Chayaporn Suphavilai, Jeremy Swann, Houriiyah Tegally, Bryan Tegomoh, Olli Vapalahti, Andreas Walker, Robert J. Wilkinson, Carolyn Williamson, Xavier Zair, IMSSC Laboratory Network Consortium, Tulio de Oliveira, Timothy EA Peto, Derrick Crook, Russell Corbett-Detig, Zamin Iqbal
The majority of SARS-CoV-2 genomes obtained during the pandemic were derived by amplifying overlapping windows of the genome (‘tiled amplicons’), reconstructing their sequences and fitting them together. This leads to systematic errors in genomes unless the software is both aware of the amplicon scheme and of the error modes of amplicon sequencing. Additionally, over time, amplicon schemes need to be updated as new mutations in the virus interfere with the primer binding sites at the end of amplicons. Thus, waves of variants swept the world during the pandemic and were followed by waves of systematic errors in the genomes, which had significant impacts on the inferred phylogenetic tree. Here we reconstruct the genomes from all public data as of June 2024 using an assembly tool called Viridian ( https://github.com/iqbal-lab-org/viridian ), developed to rigorously process amplicon sequence data. With these high-quality consensus sequences we provide a global phylogenetic tree of 4,471,579 samples, viewable at https://viridian.taxonium.org . We provide simulation and empirical validation of the methodology, and quantify the improvement in the phylogeny. This Resource paper presents a global SARS-CoV-2 phylogenetic tree of 4,471,579 high-quality genomes consistently constructed by Viridian, an efficient amplicon-aware assembler.
{"title":"Addressing pandemic-wide systematic errors in the SARS-CoV-2 phylogeny","authors":"Martin Hunt, Angie S. Hinrichs, Daniel Anderson, Lily Karim, Bethany L. Dearlove, Jeff Knaggs, Bede Constantinides, Philip W. Fowler, Gillian Rodger, Teresa Street, Sheila Lumley, Hermione Webster, Theo Sanderson, Christopher Ruis, Benjamin Kotzen, Nicola de Maio, Lucas N. Amenga-Etego, Dominic S. Y. Amuzu, Martin Avaro, Gordon A. Awandare, Reuben Ayivor-Djanie, Timothy Barkham, Matthew Bashton, Elizabeth M. Batty, Yaw Bediako, Denise De Belder, Estefania Benedetti, Andreas Bergthaler, Stefan A. Boers, Josefina Campos, Rosina Afua Ampomah Carr, Yuan Yi Constance Chen, Facundo Cuba, Maria Elena Dattero, Wanwisa Dejnirattisai, Alexander Dilthey, Kwabena Obeng Duedu, Lukas Endler, Ilka Engelmann, Ngiambudulu M. Francisco, Jonas Fuchs, Etienne Z. Gnimpieba, Soraya Groc, Jones Gyamfi, Dennis Heemskerk, Torsten Houwaart, Nei-yuan Hsiao, Matthew Huska, Martin Hölzer, Arash Iranzadeh, Hanna Jarva, Chandima Jeewandara, Bani Jolly, Rageema Joseph, Ravi Kant, Karrie Ko Kwan Ki, Satu Kurkela, Maija Lappalainen, Marie Lataretu, Jacob Lemieux, Chang Liu, Gathsaurie Neelika Malavige, Tapfumanei Mashe, Juthathip Mongkolsapaya, Brigitte Montes, Jose Arturo Molina Mora, Collins M. Morang’a, Bernard Mvula, Niranjan Nagarajan, Andrew Nelson, Joyce M. Ngoi, Joana Paula da Paixão, Marcus Panning, Tomas Poklepovich, Peter K. Quashie, Diyanath Ranasinghe, Mara Russo, James Emmanuel San, Nicholas D. Sanderson, Vinod Scaria, Gavin Screaton, October Michael Sessions, Tarja Sironen, Abay Sisay, Darren Smith, Teemu Smura, Piyada Supasa, Chayaporn Suphavilai, Jeremy Swann, Houriiyah Tegally, Bryan Tegomoh, Olli Vapalahti, Andreas Walker, Robert J. Wilkinson, Carolyn Williamson, Xavier Zair, IMSSC Laboratory Network Consortium, Tulio de Oliveira, Timothy EA Peto, Derrick Crook, Russell Corbett-Detig, Zamin Iqbal","doi":"10.1038/s41592-025-02947-1","DOIUrl":"10.1038/s41592-025-02947-1","url":null,"abstract":"The majority of SARS-CoV-2 genomes obtained during the pandemic were derived by amplifying overlapping windows of the genome (‘tiled amplicons’), reconstructing their sequences and fitting them together. This leads to systematic errors in genomes unless the software is both aware of the amplicon scheme and of the error modes of amplicon sequencing. Additionally, over time, amplicon schemes need to be updated as new mutations in the virus interfere with the primer binding sites at the end of amplicons. Thus, waves of variants swept the world during the pandemic and were followed by waves of systematic errors in the genomes, which had significant impacts on the inferred phylogenetic tree. Here we reconstruct the genomes from all public data as of June 2024 using an assembly tool called Viridian ( https://github.com/iqbal-lab-org/viridian ), developed to rigorously process amplicon sequence data. With these high-quality consensus sequences we provide a global phylogenetic tree of 4,471,579 samples, viewable at https://viridian.taxonium.org . We provide simulation and empirical validation of the methodology, and quantify the improvement in the phylogeny. This Resource paper presents a global SARS-CoV-2 phylogenetic tree of 4,471,579 high-quality genomes consistently constructed by Viridian, an efficient amplicon-aware assembler.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 3","pages":"653-662"},"PeriodicalIF":32.1,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41592-025-02947-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146150245","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 : 2026-01-26DOI: 10.1038/s41592-026-03002-3
Vivien Marx
In this field, scientists draw on ecology, population genomics, oceanography and biophysical modeling to assess and predict change. Their dynamic study object just never quite sits still.
{"title":"Take a dive into seascape genomics","authors":"Vivien Marx","doi":"10.1038/s41592-026-03002-3","DOIUrl":"10.1038/s41592-026-03002-3","url":null,"abstract":"In this field, scientists draw on ecology, population genomics, oceanography and biophysical modeling to assess and predict change. Their dynamic study object just never quite sits still.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 2","pages":"286-290"},"PeriodicalIF":32.1,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146053226","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 : 2026-01-22DOI: 10.1038/s41592-025-03001-w
Vivien Marx
Poster sessions are a staple at conferences. Some junior and senior scientists share some experiences and strategies about making and presenting posters.
海报会议是会议的主要内容。一些初级和高级科学家分享了制作和展示海报的经验和策略。
{"title":"It’s poster time","authors":"Vivien Marx","doi":"10.1038/s41592-025-03001-w","DOIUrl":"10.1038/s41592-025-03001-w","url":null,"abstract":"Poster sessions are a staple at conferences. Some junior and senior scientists share some experiences and strategies about making and presenting posters.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 2","pages":"272-273"},"PeriodicalIF":32.1,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146030412","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}
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