Nature Methods最新文献_第9页

The physics of sculpting a living creature 雕刻生物的物理学。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-08-21 DOI: 10.1038/s41592-026-03208-5

Caroline Seydel

引用次数: 0

Amplifying multiplex spatial profiling with tyramide oligonucleotides 利用酪酰胺寡核苷酸扩增多重空间谱。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-08-17 DOI: 10.1038/s41592-026-03201-y

引用次数: 0

PASTA: versatile tyramide-oligonucleotide amplification for multimodal spatial biology 面食：多模态空间生物学的多用途酪酰胺寡核苷酸扩增。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-08-17 DOI: 10.1038/s41592-026-03200-z

Hendrik A. Michel, Paige McCallum, Wenrui Wu, Huaying Qiu, Jia Le Lee, Shuli Luo, Chi Ngai Chan, Johanna Schaffenrath, Yao Yu Yeo, Stephanie Pei Tung Yiu, Yang Wang, Lindsay Parmelee, Hongbo Wang, Tania Pannellini, Melinda Burgess, Nourhan El Ahmar, Zoe Xiaozhu Zhang, Colm Keane, Tony Kiat Hon Lim, Sabina Signoretti, Sonia Victoria Del Rincon, Bo Zhao, David R. McIlwain, Yunhao Bai, Fei Chen, Roberto Chiarle, Sizun Jiang

{"title":"PASTA: versatile tyramide-oligonucleotide amplification for multimodal spatial biology","authors":"Hendrik A. Michel, Paige McCallum, Wenrui Wu, Huaying Qiu, Jia Le Lee, Shuli Luo, Chi Ngai Chan, Johanna Schaffenrath, Yao Yu Yeo, Stephanie Pei Tung Yiu, Yang Wang, Lindsay Parmelee, Hongbo Wang, Tania Pannellini, Melinda Burgess, Nourhan El Ahmar, Zoe Xiaozhu Zhang, Colm Keane, Tony Kiat Hon Lim, Sabina Signoretti, Sonia Victoria Del Rincon, Bo Zhao, David R. McIlwain, Yunhao Bai, Fei Chen, Roberto Chiarle, Sizun Jiang","doi":"10.1038/s41592-026-03200-z","DOIUrl":"10.1038/s41592-026-03200-z","url":null,"abstract":"Spatial proteomics is limited by detection sensitivity, multiplexing and multimodal integration, leaving a gap between discovery and clinical assays. Here we present protein and nucleic acid serial tyramide amplification (PASTA), using horseradish peroxidase-mediated oligonucleotide deposition and cyclical imaging for high-plex, multimodal spatial profiling. Compatible with conjugated antibodies and in situ hybridization, PASTA enables simultaneous protein and RNA codetection from formalin-fixed, paraffin-embedded samples, providing a cost-effective bridge from discovery to clinical validation. PASTA is a platform-agnostic signal amplification approach for multiplexed imaging across modalities.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 9","pages":"1769-1774"},"PeriodicalIF":28.3,"publicationDate":"2026-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148795891","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

Disruption of the research software landscape through AI software generation 通过人工智能软件生成颠覆研究软件格局。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-08-17 DOI: 10.1038/s41592-026-03210-x

Nelson D. Medina, Joergen M. R. Kornfeld

引用次数: 0

Trainee advice for a future that seems uncertain 对前途未卜的实习生的建议。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-08-17 DOI: 10.1038/s41592-026-03204-9

Vivien Marx

引用次数: 0

Voltage imaging of neurons distributed across entire brains of larval zebrafish 斑马鱼幼体全脑分布神经元的电压成像。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-08-14 DOI: 10.1038/s41592-026-03179-7

Zeguan Wang, Jie Zhang, Panagiotis Symvoulidis, Wei Guo, Davy Deng, Adam Amsterdam, Lige Zhang, Takato Honda, Steven Roche, Matthew A. Wilson, Edward S. Boyden

{"title":"Voltage imaging of neurons distributed across entire brains of larval zebrafish","authors":"Zeguan Wang, Jie Zhang, Panagiotis Symvoulidis, Wei Guo, Davy Deng, Adam Amsterdam, Lige Zhang, Takato Honda, Steven Roche, Matthew A. Wilson, Edward S. Boyden","doi":"10.1038/s41592-026-03179-7","DOIUrl":"10.1038/s41592-026-03179-7","url":null,"abstract":"Neurons interact in networks distributed throughout the brain. While much effort has focused on whole-brain calcium imaging, advances in genetically encoded voltage indicators raise the question of whether it might be possible to image neuronal voltage across entire brains. Achieving this requires a microscope with high volumetric imaging rates and signal-to-noise ratio. Here we present a remote-scanning light-sheet microscope capable of imaging genetically encoded voltage indicator-expressing neurons distributed throughout much of the brain of larval zebrafish at a volumetric rate of 200.8 Hz. We measured voltage traces from approximately one-quarter of all brain neurons. We found that neurons firing at different times during a sequence occupied different locations: visually evoked sequences mapped across the optic tectum, whereas stimulus-independent bursts were mapped across the cerebellum and medulla. Imaging voltage of neurons distributed in many brain regions may open new frontiers for understanding fundamental neural system operations. Voltage imaging across much of the zebrafish brain is achieved at 200.8 Hz, by optimizing a remote-scanning light-sheet microscope.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 9","pages":"1895-1907"},"PeriodicalIF":28.3,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13541626/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148761479","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

From pixels to patterns: the AI revolution in stem cell-derived models 从像素到模式：干细胞衍生模型中的人工智能革命。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-08-14 DOI: 10.1038/s41592-026-03202-x

Luca Deininger, Paolo Caldarelli, Magdalena Zernicka-Goetz, Ralf Mikut

{"title":"From pixels to patterns: the AI revolution in stem cell-derived models","authors":"Luca Deininger, Paolo Caldarelli, Magdalena Zernicka-Goetz, Ralf Mikut","doi":"10.1038/s41592-026-03202-x","DOIUrl":"10.1038/s41592-026-03202-x","url":null,"abstract":"Artificial intelligence (AI) is rapidly transforming stem cell and developmental biology, offering new strategies to analyze, interpret and optimize complex, dynamic systems such as organoids and stem cell-derived embryo models. In this Perspective, we chart the integration of AI into image-based analysis of stem cell systems, highlighting how deep learning, convolutional neural networks and emerging foundation models enable automated classification, segmentation and phenotyping at increasing scale and precision. We showcase applications in phenotyping, drug screening and mechanistic discovery, including real-time fate prediction and the identification of hidden morphological signatures linked to differentiation and disease. Practical challenges, including limited annotated data, model interpretability and live imaging constraints, are examined alongside future opportunities, such as multimodal integration, real-time experimental steering and protocol optimization. Altogether, we argue that AI is not merely an analytical tool, but a discovery engine that enhances reproducibility, accelerates insight and brings us closer to a mechanistic understanding of self-organization in complex stem cell-derived systems. This Perspective examines the role of AI-based image analysis when applied to stem cell-derived models.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 9","pages":"1710-1723"},"PeriodicalIF":28.3,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148759990","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

Alignment with experimental data improves protein generative modeling 与实验数据的对齐改进了蛋白质生成模型。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-08-14 DOI: 10.1038/s41592-026-03138-2

引用次数: 0

Lessons learned from a Kaggle challenge for particle picking in cryo-electron tomography 从低温电子断层扫描中粒子拾取的Kaggle挑战中吸取的教训。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-08-14 DOI: 10.1038/s41592-026-03198-4

Ariana Peck, Joshua Hutchings, Jonathan Schwartz, Yue Yu, Utz H. Ermel, Saugat Kandel, Dari Kimanius, Zhuowen Zhao, Shawn Zheng, Brendan Artley, David List, Sergio A. Silva Jr, Walter Reade, Jeremy Asuncion, Kira Evans, Jessica Gadling, Kandarp Khandwala, Suzette McCanny, Dannielle G. McCarthy, Jun Xi Ni, Janeece Pourroy, Manasa Venkatakrishnan, Zun Shi Wang, David A. Agard, Clinton S. Potter, Bridget Carragher, Kyle I. S. Harrington, Mohammadreza Paraan

{"title":"Lessons learned from a Kaggle challenge for particle picking in cryo-electron tomography","authors":"Ariana Peck, Joshua Hutchings, Jonathan Schwartz, Yue Yu, Utz H. Ermel, Saugat Kandel, Dari Kimanius, Zhuowen Zhao, Shawn Zheng, Brendan Artley, David List, Sergio A. Silva Jr, Walter Reade, Jeremy Asuncion, Kira Evans, Jessica Gadling, Kandarp Khandwala, Suzette McCanny, Dannielle G. McCarthy, Jun Xi Ni, Janeece Pourroy, Manasa Venkatakrishnan, Zun Shi Wang, David A. Agard, Clinton S. Potter, Bridget Carragher, Kyle I. S. Harrington, Mohammadreza Paraan","doi":"10.1038/s41592-026-03198-4","DOIUrl":"10.1038/s41592-026-03198-4","url":null,"abstract":"The difficulty of particle picking in cryo-electron tomography remains a barrier to routine in situ structure determination. Machine learning is well suited to overcome this bottleneck with efficient algorithms that generalize across molecular species. To spur new algorithm development, we held a 3-month Kaggle challenge that tasked contestants with annotating five molecular species across hundreds of experimental tomograms. Here we analyze the results of this competition, which successfully engaged >1,000 participants and delivered particle pickers that outperformed existing state of the art. Systematic comparisons of the contestants’ submissions revealed the tolerance of subtomogram averaging to moderate but not severe over-picking and underscored the need for more robust measures of annotation quality. The winning models also highlighted the importance of data augmentation to overcome limited training data. All competition tomograms along with the ground truth and winning teams’ annotations have been released on the CryoET Data Portal as a resource to benchmark current and future particle picking algorithms. A Kaggle challenge for particle picking in experimental cryo-electron tomography data delivered new machine learning algorithms that outperformed state-of-the-art software and provides a critical reference point to benchmark future annotation tools.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 9","pages":"1908-1916"},"PeriodicalIF":28.3,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13541615/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148760620","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

Aligning protein-generative models to experimental fitness with ProteinDPO 蛋白质生成模型与ProteinDPO实验适应度的比对。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-08-14 DOI: 10.1038/s41592-026-03137-3

Talal Widatalla, Ashir A. Borah, Samuel H. King, Claudia L. Driscoll, Rafael Rafailov, Brian L. Hie

{"title":"Aligning protein-generative models to experimental fitness with ProteinDPO","authors":"Talal Widatalla, Ashir A. Borah, Samuel H. King, Claudia L. Driscoll, Rafael Rafailov, Brian L. Hie","doi":"10.1038/s41592-026-03137-3","DOIUrl":"10.1038/s41592-026-03137-3","url":null,"abstract":"Biological generative models can predict biological functions without task-specific training data but often under-perform specialized models. This is due to a fundamental ‘alignment gap’, where the rules learned during unsupervised training are not related to the function of interest. Here we demonstrate how to provide task-specific information without losing the general knowledge learned during pretraining by using direct preference optimization to align a structure-conditioned protein language model to preferentially generate stable protein sequences. Our aligned model, ProteinDPO, achieves stability prediction competitive to task-specific models and consistently outperforms unsupervised and fine-tuned versions of the model. Notably, ProteinDPO generalizes beyond its training data to enable stabilization and improved binding affinity prediction of large multichain protein complexes. When applied to stabilization of the hemagglutinin trimer, a primary component of influenza vaccines, ~80% of designs achieve increased or similar stability compared with the native hemagglutinin and up to 32 °C improvements from recently emerged mammalian strains. Our results demonstrate how to augment generative models with biophysical information and, more broadly, provide a general framework for the alignment of biological foundation models. This Article demonstrates that direct preference optimization (DPO) can be used to effectively align an unsupervised structure-conditioned language model with biophysical information. The aligned model, ProteinDPO, achieves stability prediction competitive with that of task-specific models and consistently outperforms unsupervised and fine-tuned versions of the model.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 9","pages":"1805-1813"},"PeriodicalIF":28.3,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13541614/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148760030","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