Nature Methods最新文献

Scaling a foundational protein language model to 100 billion parameters.

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-04-03 DOI: 10.1038/s41592-025-02637-y

引用次数: 0

xTrimoPGLM: unified 100-billion-parameter pretrained transformer for deciphering the language of proteins.

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-04-03 DOI: 10.1038/s41592-025-02636-z

Bo Chen, Xingyi Cheng, Pan Li, Yangli-Ao Geng, Jing Gong, Shen Li, Zhilei Bei, Xu Tan, Boyan Wang, Xin Zeng, Chiming Liu, Aohan Zeng, Yuxiao Dong, Jie Tang, Le Song

{"title":"xTrimoPGLM: unified 100-billion-parameter pretrained transformer for deciphering the language of proteins.","authors":"Bo Chen, Xingyi Cheng, Pan Li, Yangli-Ao Geng, Jing Gong, Shen Li, Zhilei Bei, Xu Tan, Boyan Wang, Xin Zeng, Chiming Liu, Aohan Zeng, Yuxiao Dong, Jie Tang, Le Song","doi":"10.1038/s41592-025-02636-z","DOIUrl":"https://doi.org/10.1038/s41592-025-02636-z","url":null,"abstract":"Protein language models have shown remarkable success in learning biological information from protein sequences. However, most existing models are limited by either autoencoding or autoregressive pretraining objectives, which makes them struggle to handle protein understanding and generation tasks concurrently. We propose a unified protein language model, xTrimoPGLM, to address these two types of tasks simultaneously through an innovative pretraining framework. Our key technical contribution is an exploration of the compatibility and the potential for joint optimization of the two types of objectives, which has led to a strategy for training xTrimoPGLM at an unprecedented scale of 100 billion parameters and 1 trillion training tokens. Our extensive experiments reveal that (1) xTrimoPGLM substantially outperforms other advanced baselines in 18 protein understanding benchmarks across four categories. The model also facilitates an atomic-resolution view of protein structures, leading to an advanced three-dimensional structural prediction model that surpasses existing language model-based tools. (2) xTrimoPGLM not only can generate de novo protein sequences following the principles of natural ones, but also can perform programmable generation after supervised fine-tuning on curated sequences. These results highlight the substantial capability and versatility of xTrimoPGLM in understanding and generating protein sequences, contributing to the evolving landscape of foundation models in protein science. Trained weight for the xTrimoPGLM model, and downstream datasets are available at https://huggingface.co/biomap-research .","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143780656","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

Genetically encoded biosensor for fluorescence lifetime imaging of PTEN dynamics in the intact brain.

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-04-01 Epub Date: 2025-02-20 DOI: 10.1038/s41592-025-02610-9

Tomer Kagan, Matan Gabay, Aasha Meenakshisundaram, Yossi Levi, Sharbel Eid, Nikol Malchenko, Maya Maman, Anat Nitzan, Luca Ravotto, Ronen Zaidel-Bar, Britta Johanna Eickholt, Maayan Gal, Tal Laviv

{"title":"Genetically encoded biosensor for fluorescence lifetime imaging of PTEN dynamics in the intact brain.","authors":"Tomer Kagan, Matan Gabay, Aasha Meenakshisundaram, Yossi Levi, Sharbel Eid, Nikol Malchenko, Maya Maman, Anat Nitzan, Luca Ravotto, Ronen Zaidel-Bar, Britta Johanna Eickholt, Maayan Gal, Tal Laviv","doi":"10.1038/s41592-025-02610-9","DOIUrl":"10.1038/s41592-025-02610-9","url":null,"abstract":"The phosphatase and tensin homolog (PTEN) is a vital protein that maintains an inhibitory brake for cellular proliferation and growth. Accordingly, PTEN loss-of-function mutations are associated with a broad spectrum of human pathologies. Despite its importance, there is currently no method to directly monitor PTEN activity with cellular specificity within intact biological systems. Here we describe the development of a FRET-based biosensor using PTEN conformation as a proxy for the PTEN activity state, for two-photon fluorescence lifetime imaging microscopy. We identify a point mutation that allows the monitoring of PTEN activity with minimal interference to endogenous PTEN signaling. We demonstrate imaging of PTEN activity in cell lines, intact Caenorhabditis elegans and in the mouse brain. Finally, we develop a red-shifted sensor variant that allows us to identify cell-type-specific PTEN activity in excitatory and inhibitory cortical cells. In summary, our approach enables dynamic imaging of PTEN activity in vivo with unprecedented spatial and temporal resolution.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":"764-777"},"PeriodicalIF":36.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143468633","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

Cell2fate infers RNA velocity modules to improve cell fate prediction.

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-04-01 Epub Date: 2025-03-03 DOI: 10.1038/s41592-025-02608-3

Alexander Aivazidis, Fani Memi, Vitalii Kleshchevnikov, Sezgin Er, Brian Clarke, Oliver Stegle, Omer Ali Bayraktar

{"title":"Cell2fate infers RNA velocity modules to improve cell fate prediction.","authors":"Alexander Aivazidis, Fani Memi, Vitalii Kleshchevnikov, Sezgin Er, Brian Clarke, Oliver Stegle, Omer Ali Bayraktar","doi":"10.1038/s41592-025-02608-3","DOIUrl":"10.1038/s41592-025-02608-3","url":null,"abstract":"RNA velocity exploits the temporal information contained in spliced and unspliced RNA counts to infer transcriptional dynamics. Existing velocity models often rely on coarse biophysical simplifications or numerical approximations to solve the underlying ordinary differential equations (ODEs), which can compromise accuracy in challenging settings, such as complex or weak transcription rate changes across cellular trajectories. Here we present cell2fate, a formulation of RNA velocity based on a linearization of the velocity ODE, which allows solving a biophysically more accurate model in a fully Bayesian fashion. As a result, cell2fate decomposes the RNA velocity solutions into modules, providing a biophysical connection between RNA velocity and statistical dimensionality reduction. We comprehensively benchmark cell2fate in real-world settings, demonstrating enhanced interpretability and power to reconstruct complex dynamics and weak dynamical signals in rare and mature cell types. Finally, we apply cell2fate to the developing human brain, where we spatially map RNA velocity modules onto the tissue architecture, connecting the spatial organization of tissues with temporal dynamics of transcription.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":"698-707"},"PeriodicalIF":36.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143541276","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

A systematic benchmark of Nanopore long-read RNA sequencing for transcript-level analysis in human cell lines. 用于人类细胞系转录本水平分析的 Nanopore 长读程 RNA 测序系统基准。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-04-01 Epub Date: 2025-03-13 DOI: 10.1038/s41592-025-02623-4

Ying Chen, Nadia M Davidson, Yuk Kei Wan, Fei Yao, Yan Su, Hasindu Gamaarachchi, Andre Sim, Harshil Patel, Hwee Meng Low, Christopher Hendra, Laura Wratten, Christopher Hakkaart, Chelsea Sawyer, Viktoriia Iakovleva, Puay Leng Lee, Lixia Xin, Hui En Vanessa Ng, Jia Min Loo, Xuewen Ong, Hui Qi Amanda Ng, Jiaxu Wang, Wei Qian Casslynn Koh, Suk Yeah Polly Poon, Dominik Stanojevic, Hoang-Dai Tran, Kok Hao Edwin Lim, Shen Yon Toh, Philip Andrew Ewels, Huck-Hui Ng, N Gopalakrishna Iyer, Alexandre Thiery, Wee Joo Chng, Leilei Chen, Ramanuj DasGupta, Mile Sikic, Yun-Shen Chan, Boon Ooi Patrick Tan, Yue Wan, Wai Leong Tam, Qiang Yu, Chiea Chuan Khor, Torsten Wüstefeld, Alexander Lezhava, Ploy N Pratanwanich, Michael I Love, Wee Siong Sho Goh, Sarah B Ng, Alicia Oshlack, Jonathan Göke

{"title":"A systematic benchmark of Nanopore long-read RNA sequencing for transcript-level analysis in human cell lines.","authors":"Ying Chen, Nadia M Davidson, Yuk Kei Wan, Fei Yao, Yan Su, Hasindu Gamaarachchi, Andre Sim, Harshil Patel, Hwee Meng Low, Christopher Hendra, Laura Wratten, Christopher Hakkaart, Chelsea Sawyer, Viktoriia Iakovleva, Puay Leng Lee, Lixia Xin, Hui En Vanessa Ng, Jia Min Loo, Xuewen Ong, Hui Qi Amanda Ng, Jiaxu Wang, Wei Qian Casslynn Koh, Suk Yeah Polly Poon, Dominik Stanojevic, Hoang-Dai Tran, Kok Hao Edwin Lim, Shen Yon Toh, Philip Andrew Ewels, Huck-Hui Ng, N Gopalakrishna Iyer, Alexandre Thiery, Wee Joo Chng, Leilei Chen, Ramanuj DasGupta, Mile Sikic, Yun-Shen Chan, Boon Ooi Patrick Tan, Yue Wan, Wai Leong Tam, Qiang Yu, Chiea Chuan Khor, Torsten Wüstefeld, Alexander Lezhava, Ploy N Pratanwanich, Michael I Love, Wee Siong Sho Goh, Sarah B Ng, Alicia Oshlack, Jonathan Göke","doi":"10.1038/s41592-025-02623-4","DOIUrl":"10.1038/s41592-025-02623-4","url":null,"abstract":"The human genome contains instructions to transcribe more than 200,000 RNAs. However, many RNA transcripts are generated from the same gene, resulting in alternative isoforms that are highly similar and that remain difficult to quantify. To evaluate the ability to study RNA transcript expression, we profiled seven human cell lines with five different RNA-sequencing protocols, including short-read cDNA, Nanopore long-read direct RNA, amplification-free direct cDNA and PCR-amplified cDNA sequencing, and PacBio IsoSeq, with multiple spike-in controls, and additional transcriptome-wide N6-methyladenosine profiling data. We describe differences in read length, coverage, throughput and transcript expression, reporting that long-read RNA sequencing more robustly identifies major isoforms. We illustrate the value of the SG-NEx data to identify alternative isoforms, novel transcripts, fusion transcripts and N6-methyladenosine RNA modifications. Together, the SG-NEx data provide a comprehensive resource enabling the development and benchmarking of computational methods for profiling complex transcriptional events at isoform-level resolution.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":"801-812"},"PeriodicalIF":36.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143625346","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

Connectivity of single neurons classifies cell subtypes in mouse brains.

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-04-01 Epub Date: 2025-03-21 DOI: 10.1038/s41592-025-02621-6

Lijuan Liu, Zhixi Yun, Linus Manubens-Gil, Hanbo Chen, Feng Xiong, Hongwei Dong, Hongkui Zeng, Michael Hawrylycz, Giorgio A Ascoli, Hanchuan Peng

{"title":"Connectivity of single neurons classifies cell subtypes in mouse brains.","authors":"Lijuan Liu, Zhixi Yun, Linus Manubens-Gil, Hanbo Chen, Feng Xiong, Hongwei Dong, Hongkui Zeng, Michael Hawrylycz, Giorgio A Ascoli, Hanchuan Peng","doi":"10.1038/s41592-025-02621-6","DOIUrl":"10.1038/s41592-025-02621-6","url":null,"abstract":"Classification of single neurons at a brain-wide scale is a way to characterize the structural and functional organization of brains. Here we acquired and standardized a large morphology database of 20,158 mouse neurons and generated a potential connectivity map of single neurons based on their dendritic and axonal arbors. With such an anatomy-morphology-connectivity mapping, we defined neuron connectivity subtypes for neurons in 31 brain regions. We found that cell types defined by connectivity show distinct separation from each other. Within this context, we were able to characterize the diversity in secondary motor cortical neurons, and subtype connectivity patterns in thalamocortical pathways. Our findings underscore the importance of connectivity in characterizing the modularity of brain anatomy at the single-cell level. These results highlight that connectivity subtypes supplement conventionally recognized transcriptomic cell types, electrophysiological cell types and morphological cell types as factors to classify cell classes and their identities.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":"861-873"},"PeriodicalIF":36.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143677155","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

Life beyond labels.

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-04-01 DOI: 10.1038/s41592-025-02649-8

Caroline Seydel

引用次数: 0

Spotiphy enables single-cell spatial whole transcriptomics across an entire section.

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-04-01 Epub Date: 2025-03-12 DOI: 10.1038/s41592-025-02622-5

Jiyuan Yang, Ziqian Zheng, Yun Jiao, Kaiwen Yu, Sheetal Bhatara, Xu Yang, Sivaraman Natarajan, Jiahui Zhang, Qingfei Pan, John Easton, Koon-Kiu Yan, Junmin Peng, Kaibo Liu, Jiyang Yu

{"title":"Spotiphy enables single-cell spatial whole transcriptomics across an entire section.","authors":"Jiyuan Yang, Ziqian Zheng, Yun Jiao, Kaiwen Yu, Sheetal Bhatara, Xu Yang, Sivaraman Natarajan, Jiahui Zhang, Qingfei Pan, John Easton, Koon-Kiu Yan, Junmin Peng, Kaibo Liu, Jiyang Yu","doi":"10.1038/s41592-025-02622-5","DOIUrl":"10.1038/s41592-025-02622-5","url":null,"abstract":"Spatial transcriptomics (ST) has advanced our understanding of tissue regionalization by enabling the visualization of gene expression within whole-tissue sections, but current approaches remain plagued by the challenge of achieving single-cell resolution without sacrificing whole-genome coverage. Here we present Spotiphy (spot imager with pseudo-single-cell-resolution histology), a computational toolkit that transforms sequencing-based ST data into single-cell-resolved whole-transcriptome images. Spotiphy delivers the most precise cellular proportions in extensive benchmarking evaluations. Spotiphy-derived inferred single-cell profiles reveal astrocyte and disease-associated microglia regional specifications in Alzheimer's disease and healthy mouse brains. Spotiphy identifies multiple spatial domains and alterations in tumor-tumor microenvironment interactions in human breast ST data. Spotiphy bridges the information gap and enables visualization of cell localization and transcriptomic profiles throughout entire sections, offering highly informative outputs and an innovative spatial analysis pipeline for exploring complex biological systems.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":"724-736"},"PeriodicalIF":36.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143616100","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

Optimizing Xenium In Situ data utility by quality assessment and best-practice analysis workflows. 通过质量评估和最佳实践分析工作流程优化 Xenium 原位数据实用性。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-04-01 Epub Date: 2025-03-13 DOI: 10.1038/s41592-025-02617-2

Sergio Marco Salas, Louis B Kuemmerle, Christoffer Mattsson-Langseth, Sebastian Tismeyer, Christophe Avenel, Taobo Hu, Habib Rehman, Marco Grillo, Paulo Czarnewski, Saga Helgadottir, Katarina Tiklova, Axel Andersson, Nima Rafati, Maria Chatzinikolaou, Fabian J Theis, Malte D Luecken, Carolina Wählby, Naveed Ishaque, Mats Nilsson

{"title":"Optimizing Xenium In Situ data utility by quality assessment and best-practice analysis workflows.","authors":"Sergio Marco Salas, Louis B Kuemmerle, Christoffer Mattsson-Langseth, Sebastian Tismeyer, Christophe Avenel, Taobo Hu, Habib Rehman, Marco Grillo, Paulo Czarnewski, Saga Helgadottir, Katarina Tiklova, Axel Andersson, Nima Rafati, Maria Chatzinikolaou, Fabian J Theis, Malte D Luecken, Carolina Wählby, Naveed Ishaque, Mats Nilsson","doi":"10.1038/s41592-025-02617-2","DOIUrl":"10.1038/s41592-025-02617-2","url":null,"abstract":"The Xenium In Situ platform is a new spatial transcriptomics product commercialized by 10x Genomics, capable of mapping hundreds of genes in situ at subcellular resolution. Given the multitude of commercially available spatial transcriptomics technologies, recommendations in choice of platform and analysis guidelines are increasingly important. Herein, we explore 25 Xenium datasets generated from multiple tissues and species, comparing scalability, resolution, data quality, capacities and limitations with eight other spatially resolved transcriptomics technologies and commercial platforms. In addition, we benchmark the performance of multiple open-source computational tools, when applied to Xenium datasets, in tasks including preprocessing, cell segmentation, selection of spatially variable features and domain identification. This study serves as an independent analysis of the performance of Xenium, and provides best practices and recommendations for analysis of such datasets.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":"813-823"},"PeriodicalIF":36.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143625363","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

A computational pipeline for spatial mechano-transcriptomics.

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-04-01 Epub Date: 2025-03-17 DOI: 10.1038/s41592-025-02618-1

Adrien Hallou, Ruiyang He, Benjamin D Simons, Bianca Dumitrascu

引用次数: 0