Nature Methods最新文献_第2页

Improved structure prediction of protein complexes is within GRASP. 改进的蛋白质复合物的结构预测是在GRASP。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-10-08 DOI: 10.1038/s41592-025-02821-0

引用次数: 0

Pushing the limits of automated cell tracking. 突破了手机自动追踪的极限。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-10-08 DOI: 10.1038/s41592-025-02761-9

Swetha Nagarajan, Sundar R Naganathan

引用次数: 0

All-at-once RNA folding with 3D motif prediction framed by evolutionary information 所有在一次RNA折叠与三维基序预测框架的进化信息。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-10-03 DOI: 10.1038/s41592-025-02833-w

Aayush Karan, Elena Rivas

{"title":"All-at-once RNA folding with 3D motif prediction framed by evolutionary information","authors":"Aayush Karan, Elena Rivas","doi":"10.1038/s41592-025-02833-w","DOIUrl":"10.1038/s41592-025-02833-w","url":null,"abstract":"Structural RNAs exhibit a vast array of recurrent short three-dimensional (3D) elements found in loop regions involving non-Watson–Crick interactions that help arrange canonical double helices into tertiary structures. Here we present CaCoFold-R3D, a probabilistic grammar that predicts these RNA 3D motifs (also termed modules) jointly with RNA secondary structure over a sequence or alignment. CaCoFold-R3D uses evolutionary information present in an RNA alignment to reliably identify canonical helices (including pseudoknots) by covariation. Here we further introduce the R3D grammars, which also exploit helix covariation that constrains the positioning of the mostly noncovarying RNA 3D motifs. Our method runs predictions over an almost-exhaustive list of over 50 known RNA motifs (‘everything’). Motifs can appear in any nonhelical loop region (including three-way, four-way and higher junctions) (‘everywhere’). All structural motifs as well as the canonical helices are arranged into one single structure predicted by one single joint probabilistic grammar (‘all-at-once’). Our results demonstrate that CaCoFold-R3D is a valid alternative for predicting the all-residue interactions present in a RNA 3D structure. CaCoFold-R3D is fast and easily customizable for novel motif discovery and shows promising value both as a strong input for deep learning approaches to all-atom structure prediction as well as toward guiding RNA design as drug targets for therapeutic small molecules. CaCoFold-R3D is a probabilistic model that simultaneously predicts the RNA 3D motifs jointly with the secondary structure in a structural RNA using evolutionary information.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 10","pages":"2094-2106"},"PeriodicalIF":32.1,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41592-025-02833-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145225635","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

Spatiotemporal focusing enables all-optical in situ histology of heterogeneous tissue 时空聚焦使异质组织的全光学原位组织学。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-10-03 DOI: 10.1038/s41592-025-02849-2

Xiang Ji, Sincheng Huang, Beth Friedman, David Kleinfeld

引用次数: 0

Machine learning for accelerating discovery from single-molecule data 加速单分子数据发现的机器学习。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-10-02 DOI: 10.1038/s41592-025-02840-x

引用次数: 0

Foundation model for efficient biological discovery in single-molecule time traces 在单分子时间轨迹中有效发现生物的基础模型。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-10-02 DOI: 10.1038/s41592-025-02839-4

Jieming Li, Leyou Zhang, Alexander Johnson-Buck, Nils G. Walter

{"title":"Foundation model for efficient biological discovery in single-molecule time traces","authors":"Jieming Li, Leyou Zhang, Alexander Johnson-Buck, Nils G. Walter","doi":"10.1038/s41592-025-02839-4","DOIUrl":"10.1038/s41592-025-02839-4","url":null,"abstract":"Single-molecule fluorescence microscopy (SMFM) can reveal important biological insights. However, uncovering rare but critical intermediates often demands manual inspection of time traces and iterative ad hoc approaches. To facilitate systematic and efficient discovery from SMFM time traces, we introduce META-SiM, a transformer-based foundation model pretrained on diverse SMFM analysis tasks. META-SiM rivals best-in-class algorithms on a broad range of tasks including trace classification, segmentation, idealization and stepwise photobleaching analysis. Additionally, the model produces embeddings that encapsulate detailed information about each trace, which the web-based META-SiM Projector ( https://www.simol-projector.org ) casts into lower-dimensional space for efficient whole-dataset visualization, labeling, comparison and sharing. Combining this Projector with the objective metric of local Shannon entropy enables rapid identification of condition-specific behaviors, even if rare or subtle. Applying META-SiM to an existing single-molecule Förster resonance energy transfer dataset, we discover a previously undetected intermediate state in pre-mRNA splicing. META-SiM removes bottlenecks, improves objectivity and both systematizes and accelerates biological discovery in single-molecule data. META-SiM brings foundation model power to single-molecule time traces, excelling across diverse analysis tasks. Paired with the web-based META-SiM Projector and entropy mapping, it rapidly reveals hidden molecular behaviors inaccessible by other means.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 10","pages":"2149-2160"},"PeriodicalIF":32.1,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145213187","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

Choose your human genome reference wisely 明智地选择你的人类基因组参考。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-10-02 DOI: 10.1038/s41592-025-02850-9

Vivien Marx

引用次数: 0

HippoMaps: multiscale cartography of human hippocampal organization 河马地图：人类海马组织的多尺度制图。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-10-01 DOI: 10.1038/s41592-025-02783-3

Jordan DeKraker, Donna Gift Cabalo, Jessica Royer, Alexander Ngo, Ali R. Khan, Bradley G. Karat, Oualid Benkarim, Raul Rodriguez-Cruces, Birgit Frauscher, Raluca Pana, Justine Y. Hansen, Bratislav Misic, Sofie L. Valk, Jonathan C. Lau, Matthias Kirschner, Andrea Bernasconi, Neda Bernasconi, Sascha E. A. Muenzing, Markus Axer, Katrin Amunts, Alan C. Evans, Boris C. Bernhardt

{"title":"HippoMaps: multiscale cartography of human hippocampal organization","authors":"Jordan DeKraker, Donna Gift Cabalo, Jessica Royer, Alexander Ngo, Ali R. Khan, Bradley G. Karat, Oualid Benkarim, Raul Rodriguez-Cruces, Birgit Frauscher, Raluca Pana, Justine Y. Hansen, Bratislav Misic, Sofie L. Valk, Jonathan C. Lau, Matthias Kirschner, Andrea Bernasconi, Neda Bernasconi, Sascha E. A. Muenzing, Markus Axer, Katrin Amunts, Alan C. Evans, Boris C. Bernhardt","doi":"10.1038/s41592-025-02783-3","DOIUrl":"10.1038/s41592-025-02783-3","url":null,"abstract":"The hippocampus has a specialized microarchitecture, is situated at the nexus of multiple macroscale functional networks, contributes to numerous cognitive as well as affective processes and is highly susceptible to brain pathology across common disorders. These features make the hippocampus a model to understand how brain structure covaries with function, in both health and disease. Here we introduce HippoMaps, an open access toolbox and online data warehouse for the mapping and contextualization of subregional hippocampal data in the human brain ( http://hippomaps.readthedocs.io ). HippoMaps capitalizes on a unified hippocampal unfolding approach as well as shape intrinsic registration capabilities to allow for cross-participant and cross-modal data aggregation. We initialize this repository with a combination of hippocampal data spanning three-dimensional ex vivo histology, ex vivo 9.4-Tesla magnetic resonance imaging (MRI), as well as in vivo structural MRI and resting-state functional MRI obtained at 3 Tesla and 7 Tesla, together with intracranial encephalography recordings in patients with epilepsy. All code, data and tools are openly available online, with the aim of fostering further community contributions. HippoMaps provides an open-source resource for studying the human hippocampus at different scales and with different modalities such as histology, fMRI, structural MRI and EEG.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 10","pages":"2211-2222"},"PeriodicalIF":32.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41592-025-02783-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145206926","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

Giotto Suite: a multiscale and technology-agnostic spatial multiomics analysis ecosystem Giotto Suite：一个多尺度和技术不可知的空间多组学分析生态系统。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-10-01 DOI: 10.1038/s41592-025-02817-w

Jiaji G. Chen, Joselyn C. Chávez-Fuentes, Matthew O’Brien, Junxiang Xu, Edward C. Ruiz, Wen Wang, Iqra Amin, Jeffrey P. Sheridan, Sujung C. Shin, Sanjana V. Hasyagar, Irzam Sarfraz, Pratishtha Guckhool, Adriana Sistig, Veronica Jarzabek, Guo-Cheng Yuan, Ruben Dries

{"title":"Giotto Suite: a multiscale and technology-agnostic spatial multiomics analysis ecosystem","authors":"Jiaji G. Chen, Joselyn C. Chávez-Fuentes, Matthew O’Brien, Junxiang Xu, Edward C. Ruiz, Wen Wang, Iqra Amin, Jeffrey P. Sheridan, Sujung C. Shin, Sanjana V. Hasyagar, Irzam Sarfraz, Pratishtha Guckhool, Adriana Sistig, Veronica Jarzabek, Guo-Cheng Yuan, Ruben Dries","doi":"10.1038/s41592-025-02817-w","DOIUrl":"10.1038/s41592-025-02817-w","url":null,"abstract":"Emerging spatial multiomics technologies provide an increasingly large amount of information content at multiple scales. However, it remains challenging to efficiently represent and harmonize diverse spatial datasets. Here we present Giotto Suite, a suite of modular packages that provides scalable and extensible end-to-end solutions for multiscale and multiomic data analysis, integration and visualization. At its core, Giotto Suite is centered around an innovative data framework, allowing the representation and integration of spatial omics data in a technology-agnostic manner. Giotto Suite integrates molecular, morphology, spatial and annotated feature information to create a responsive and flexible workflow, as demonstrated by applications to several state-of-the-art spatial technologies. Furthermore, Giotto Suite builds upon interoperable interfaces and data structures that bridge the established fields of genomics and spatial data science in R, thereby enabling independent developers to create custom-engineered pipelines. As such, Giotto Suite creates an immersive and multiscale ecosystem for spatial multiomic data analysis. Giotto Suite provides a comprehensive, flexible and scalable platform for technology-agnostic spatial omics analysis using R.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 10","pages":"2052-2064"},"PeriodicalIF":32.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41592-025-02817-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145206993","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

Fourier-based three-dimensional multistage transformer for aberration correction in multicellular specimens 基于傅里叶的三维多级变压器用于多细胞标本的像差校正。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-10-01 DOI: 10.1038/s41592-025-02844-7

Thayer Alshaabi, Daniel E. Milkie, Gaoxiang Liu, Cyna Shirazinejad, Jason L. Hong, Kemal Achour, Frederik Görlitz, Ana Milunovic-Jevtic, Cat Simmons, Ibrahim S. Abuzahriyeh, Erin Hong, Samara Erin Williams, Nathanael Harrison, Evan Huang, Eun Seok Bae, Alison N. Killilea, Ian A. Swinburne, David G. Drubin, Srigokul Upadhyayula, Eric Betzig

{"title":"Fourier-based three-dimensional multistage transformer for aberration correction in multicellular specimens","authors":"Thayer Alshaabi, Daniel E. Milkie, Gaoxiang Liu, Cyna Shirazinejad, Jason L. Hong, Kemal Achour, Frederik Görlitz, Ana Milunovic-Jevtic, Cat Simmons, Ibrahim S. Abuzahriyeh, Erin Hong, Samara Erin Williams, Nathanael Harrison, Evan Huang, Eun Seok Bae, Alison N. Killilea, Ian A. Swinburne, David G. Drubin, Srigokul Upadhyayula, Eric Betzig","doi":"10.1038/s41592-025-02844-7","DOIUrl":"10.1038/s41592-025-02844-7","url":null,"abstract":"High-resolution tissue imaging is often compromised by sample-induced optical aberrations that degrade resolution and contrast. Although wavefront sensor-based adaptive optics (AO) can measure these aberrations, such hardware solutions are typically complex, expensive to implement and slow when serially mapping spatially varying aberrations across large fields of view. Here we introduce AOViFT (adaptive optical vision Fourier transformer)—a machine learning-based aberration sensing framework built around a three-dimensional multistage vision transformer that operates on Fourier domain embeddings. AOViFT infers aberrations and restores diffraction-limited performance in puncta-labeled specimens with substantially reduced computational cost, training time and memory footprint compared to conventional architectures or real-space networks. We validated AOViFT on live gene-edited zebrafish embryos, demonstrating its ability to correct spatially varying aberrations using either a deformable mirror or postacquisition deconvolution. By eliminating the need for the guide star and wavefront sensing hardware and simplifying the experimental workflow, AOViFT lowers technical barriers for high-resolution volumetric microscopy across diverse biological samples. Adaptive optical vision Fourier transformer (AOViFT) is a machine learning-based framework for accurately inferring aberrations and restoring diffraction-limited performance in diverse biological specimens.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 10","pages":"2171-2179"},"PeriodicalIF":32.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41592-025-02844-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145206977","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