Nature MethodsPub Date : 2025-01-15DOI: 10.1038/s41592-024-02584-0
Do-Hyeon Kim, Hong Minh Triet, Sun Hyeok Lee, Sina Jazani, Seongjae Jang, Syed Ali Abbas Abedi, Xiaogang Liu, Jongcheol Seo, Taekjip Ha, Young-Tae Chang, Sung Ho Ryu
{"title":"Super-photostable organic dye for long-term live-cell single-protein imaging","authors":"Do-Hyeon Kim, Hong Minh Triet, Sun Hyeok Lee, Sina Jazani, Seongjae Jang, Syed Ali Abbas Abedi, Xiaogang Liu, Jongcheol Seo, Taekjip Ha, Young-Tae Chang, Sung Ho Ryu","doi":"10.1038/s41592-024-02584-0","DOIUrl":"10.1038/s41592-024-02584-0","url":null,"abstract":"Organic dyes play a crucial role in live-cell imaging because of their advantageous properties, such as photostability and high brightness. Here we introduce a super-photostable and bright organic dye, Phoenix Fluor 555 (PF555), which exhibits an order-of-magnitude longer photobleaching lifetime than conventional organic dyes without the requirement of any anti-photobleaching additives. PF555 is an asymmetric cyanine structure in which, on one side, the indole in the conventional Cyanine-3 is substituted with 3-oxo-quinoline. PF555 provides a powerful tool for long-term live-cell single-molecule imaging, as demonstrated by the imaging of the dynamic single-molecule interactions of the epidermal growth factor receptor with clathrin-coated structures on the plasma membrane of a live cell under physiological conditions. Phoenix Fluor 555 (PF555) is a bright dye with an exceptional, order-of-magnitude longer photobleaching lifetime than conventional organic dyes that enables extended live-cell single-molecule imaging without anti-photobleaching additives.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 3","pages":"550-558"},"PeriodicalIF":36.1,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143008713","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}
Nature MethodsPub Date : 2025-01-15DOI: 10.1038/s41592-024-02564-4
Hadi T Nia, Lance L Munn, Rakesh K Jain
{"title":"Probing the physical hallmarks of cancer.","authors":"Hadi T Nia, Lance L Munn, Rakesh K Jain","doi":"10.1038/s41592-024-02564-4","DOIUrl":"https://doi.org/10.1038/s41592-024-02564-4","url":null,"abstract":"<p><p>The physical microenvironment plays a crucial role in tumor development, progression, metastasis and treatment. Recently, we proposed four physical hallmarks of cancer, with distinct origins and consequences, to characterize abnormalities in the physical tumor microenvironment: (1) elevated compressive-tensile solid stresses, (2) elevated interstitial fluid pressure and the resulting interstitial fluid flow, (3) altered material properties (for example, increased tissue stiffness) and (4) altered physical micro-architecture. As this emerging field of physical oncology is being advanced by tumor biologists, cell and developmental biologists, engineers, physicists and oncologists, there is a critical need for model systems and measurement tools to mechanistically probe these physical hallmarks. Here, after briefly defining these physical hallmarks, we discuss the tools and model systems available for probing each hallmark in vitro, ex vivo, in vivo and in clinical settings. We finally review the unmet needs for mechanistic probing of the physical hallmarks of tumors and discuss the challenges and unanswered questions associated with each hallmark.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143008711","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}
Nature MethodsPub Date : 2025-01-15DOI: 10.1038/s41592-024-02574-2
Kyle Coleman, Amelia Schroeder, Melanie Loth, Daiwei Zhang, Jeong Hwan Park, Ji-Youn Sung, Niklas Blank, Alexis J. Cowan, Xuyu Qian, Jianfeng Chen, Jiahui Jiang, Hanying Yan, Laith Z. Samarah, Jean R. Clemenceau, Inyeop Jang, Minji Kim, Isabel Barnfather, Joshua D. Rabinowitz, Yanxiang Deng, Edward B. Lee, Alexander Lazar, Jianjun Gao, Emma E. Furth, Tae Hyun Hwang, Linghua Wang, Christoph A. Thaiss, Jian Hu, Mingyao Li
{"title":"Resolving tissue complexity by multimodal spatial omics modeling with MISO","authors":"Kyle Coleman, Amelia Schroeder, Melanie Loth, Daiwei Zhang, Jeong Hwan Park, Ji-Youn Sung, Niklas Blank, Alexis J. Cowan, Xuyu Qian, Jianfeng Chen, Jiahui Jiang, Hanying Yan, Laith Z. Samarah, Jean R. Clemenceau, Inyeop Jang, Minji Kim, Isabel Barnfather, Joshua D. Rabinowitz, Yanxiang Deng, Edward B. Lee, Alexander Lazar, Jianjun Gao, Emma E. Furth, Tae Hyun Hwang, Linghua Wang, Christoph A. Thaiss, Jian Hu, Mingyao Li","doi":"10.1038/s41592-024-02574-2","DOIUrl":"10.1038/s41592-024-02574-2","url":null,"abstract":"Spatial molecular profiling has provided biomedical researchers valuable opportunities to better understand the relationship between cellular localization and tissue function. Effectively modeling multimodal spatial omics data is crucial for understanding tissue complexity and underlying biology. Furthermore, improvements in spatial resolution have led to the advent of technologies that can generate spatial molecular data with subcellular resolution, requiring the development of computationally efficient methods that can handle the resulting large-scale datasets. MISO (MultI-modal Spatial Omics) is a versatile algorithm for feature extraction and clustering, capable of integrating multiple modalities from diverse spatial omics experiments with high spatial resolution. Its effectiveness is demonstrated across various datasets, encompassing gene expression, protein expression, epigenetics, metabolomics and tissue histology modalities. MISO outperforms existing methods in identifying biologically relevant spatial domains, representing a substantial advancement in multimodal spatial omics analysis. Moreover, MISO’s computational efficiency ensures its scalability to handle large-scale datasets generated by subcellular resolution spatial omics technologies. MISO (MultI-modal Spatial Omics) integrates two or more spatial omics modalities, despite differences in data quality and spatial resolution for improved feature extraction and clustering to reveal biologically meaningful tissue organization.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 3","pages":"530-538"},"PeriodicalIF":36.1,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143008712","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}
Nature MethodsPub Date : 2025-01-13DOI: 10.1038/s41592-024-02591-1
{"title":"Year in review 2024","authors":"","doi":"10.1038/s41592-024-02591-1","DOIUrl":"10.1038/s41592-024-02591-1","url":null,"abstract":"We highlight several standout papers published in Nature Methods in 2024.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 1","pages":"1-1"},"PeriodicalIF":36.1,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02591-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976684","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}
Nature MethodsPub Date : 2025-01-13DOI: 10.1038/s41592-024-02577-z
Vivien Marx
{"title":"Can stem cells save the animals?","authors":"Vivien Marx","doi":"10.1038/s41592-024-02577-z","DOIUrl":"10.1038/s41592-024-02577-z","url":null,"abstract":"Scientists in stem cell and conservation biology are exploring how they might rescue endangered species, and perhaps even de-extinct some. From cell to genetically diverse population is a trek.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 1","pages":"8-12"},"PeriodicalIF":36.1,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02577-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976658","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}
Nature MethodsPub Date : 2025-01-03DOI: 10.1038/s41592-024-02552-8
Sina Majidian, Yannis Nevers, Ali Yazdizadeh Kharrazi, Alex Warwick Vesztrocy, Stefano Pascarelli, David Moi, Natasha Glover, Adrian M. Altenhoff, Christophe Dessimoz
{"title":"Orthology inference at scale with FastOMA","authors":"Sina Majidian, Yannis Nevers, Ali Yazdizadeh Kharrazi, Alex Warwick Vesztrocy, Stefano Pascarelli, David Moi, Natasha Glover, Adrian M. Altenhoff, Christophe Dessimoz","doi":"10.1038/s41592-024-02552-8","DOIUrl":"10.1038/s41592-024-02552-8","url":null,"abstract":"The surge in genome data, with ongoing efforts aiming to sequence 1.5 M eukaryotes in a decade, could revolutionize genomics, revealing the origins, evolution and genetic innovations of biological processes. Yet, traditional genomics methods scale poorly with such large datasets. Here, addressing this, ‘FastOMA’ provides linear scalability for orthology inference, enabling the processing of thousands of eukaryotic genomes within a day. FastOMA maintains the high accuracy and resolution of the well-established Orthologous Matrix (OMA) approach in benchmarks. FastOMA is available via GitHub at https://github.com/DessimozLab/FastOMA/ . FastOMA achieves fast and accurate orthology inference, with linear scalability.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 2","pages":"269-272"},"PeriodicalIF":36.1,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02552-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142927640","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}
Nature MethodsPub Date : 2025-01-02DOI: 10.1038/s41592-024-02562-6
Bruno M. Saraiva, Inês Cunha, António D. Brito, Gautier Follain, Raquel Portela, Robert Haase, Pedro M. Pereira, Guillaume Jacquemet, Ricardo Henriques
{"title":"Efficiently accelerated bioimage analysis with NanoPyx, a Liquid Engine-powered Python framework","authors":"Bruno M. Saraiva, Inês Cunha, António D. Brito, Gautier Follain, Raquel Portela, Robert Haase, Pedro M. Pereira, Guillaume Jacquemet, Ricardo Henriques","doi":"10.1038/s41592-024-02562-6","DOIUrl":"10.1038/s41592-024-02562-6","url":null,"abstract":"The expanding scale and complexity of microscopy image datasets require accelerated analytical workflows. NanoPyx meets this need through an adaptive framework enhanced for high-speed analysis. At the core of NanoPyx, the Liquid Engine dynamically generates optimized central processing unit and graphics processing unit code variations, learning and predicting the fastest based on input data and hardware. This data-driven optimization achieves considerably faster processing, becoming broadly relevant to reactive microscopy and computing fields requiring efficiency. NanoPyx optimizes bioimage analysis workflows by dynamically selecting code variations based on input data and hardware. This ensures maximum computational efficiency, enabling fast analysis of large datasets and real-time reactive microscopy.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 2","pages":"283-286"},"PeriodicalIF":36.1,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02562-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142922244","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}
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