Nature Methods最新文献_第5页

CelloType: a unified model for segmentation and classification of tissue images. CelloType：用于组织图像分割和分类的统一模型。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-11-22 DOI: 10.1038/s41592-024-02513-1

Minxing Pang, Tarun Kanti Roy, Xiaodong Wu, Kai Tan

{"title":"CelloType: a unified model for segmentation and classification of tissue images.","authors":"Minxing Pang, Tarun Kanti Roy, Xiaodong Wu, Kai Tan","doi":"10.1038/s41592-024-02513-1","DOIUrl":"10.1038/s41592-024-02513-1","url":null,"abstract":"Cell segmentation and classification are critical tasks in spatial omics data analysis. Here we introduce CelloType, an end-to-end model designed for cell segmentation and classification for image-based spatial omics data. Unlike the traditional two-stage approach of segmentation followed by classification, CelloType adopts a multitask learning strategy that integrates these tasks, simultaneously enhancing the performance of both. CelloType leverages transformer-based deep learning techniques for improved accuracy in object detection, segmentation and classification. It outperforms existing segmentation methods on a variety of multiplexed fluorescence and spatial transcriptomic images. In terms of cell type classification, CelloType surpasses a model composed of state-of-the-art methods for individual tasks and a high-performance instance segmentation model. Using multiplexed tissue images, we further demonstrate the utility of CelloType for multiscale segmentation and classification of both cellular and noncellular elements in a tissue. The enhanced accuracy and multitask learning ability of CelloType facilitate automated annotation of rapidly growing spatial omics data.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142693311","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

Accurate RNA 3D structure prediction using a language model-based deep learning approach 使用基于语言模型的深度学习方法准确预测 RNA 3D 结构。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-11-21 DOI: 10.1038/s41592-024-02487-0

Tao Shen, Zhihang Hu, Siqi Sun, Di Liu, Felix Wong, Jiuming Wang, Jiayang Chen, Yixuan Wang, Liang Hong, Jin Xiao, Liangzhen Zheng, Tejas Krishnamoorthi, Irwin King, Sheng Wang, Peng Yin, James J. Collins, Yu Li

{"title":"Accurate RNA 3D structure prediction using a language model-based deep learning approach","authors":"Tao Shen, Zhihang Hu, Siqi Sun, Di Liu, Felix Wong, Jiuming Wang, Jiayang Chen, Yixuan Wang, Liang Hong, Jin Xiao, Liangzhen Zheng, Tejas Krishnamoorthi, Irwin King, Sheng Wang, Peng Yin, James J. Collins, Yu Li","doi":"10.1038/s41592-024-02487-0","DOIUrl":"10.1038/s41592-024-02487-0","url":null,"abstract":"Accurate prediction of RNA three-dimensional (3D) structures remains an unsolved challenge. Determining RNA 3D structures is crucial for understanding their functions and informing RNA-targeting drug development and synthetic biology design. The structural flexibility of RNA, which leads to the scarcity of experimentally determined data, complicates computational prediction efforts. Here we present RhoFold+, an RNA language model-based deep learning method that accurately predicts 3D structures of single-chain RNAs from sequences. By integrating an RNA language model pretrained on ~23.7 million RNA sequences and leveraging techniques to address data scarcity, RhoFold+ offers a fully automated end-to-end pipeline for RNA 3D structure prediction. Retrospective evaluations on RNA-Puzzles and CASP15 natural RNA targets demonstrate the superiority of RhoFold+ over existing methods, including human expert groups. Its efficacy and generalizability are further validated through cross-family and cross-type assessments, as well as time-censored benchmarks. Additionally, RhoFold+ predicts RNA secondary structures and interhelical angles, providing empirically verifiable features that broaden its applicability to RNA structure and function studies. RhoFold+ is an end-to-end language model-based deep learning method to predict RNA three-dimensional structures of single-chain RNAs from sequences.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 12","pages":"2287-2298"},"PeriodicalIF":36.1,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02487-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142687584","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

Large language modeling and deep learning shed light on RNA structure prediction 大语言建模和深度学习为 RNA 结构预测提供了启示。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-11-21 DOI: 10.1038/s41592-024-02488-z

引用次数: 0

First-timers at a huge meeting 第一次参加大型会议。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-11-19 DOI: 10.1038/s41592-024-02527-9

Vivien Marx

引用次数: 0

A benchmarked, high-efficiency prime editing platform for multiplexed dropout screening. 用于复用漏检筛选的基准高效素材编辑平台。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-11-19 DOI: 10.1038/s41592-024-02502-4

Ann Cirincione, Danny Simpson, Weihao Yan, Ryan McNulty, Purnima Ravisankar, Sabrina C Solley, Jun Yan, Fabian Lim, Emma K Farley, Mona Singh, Britt Adamson

{"title":"A benchmarked, high-efficiency prime editing platform for multiplexed dropout screening.","authors":"Ann Cirincione, Danny Simpson, Weihao Yan, Ryan McNulty, Purnima Ravisankar, Sabrina C Solley, Jun Yan, Fabian Lim, Emma K Farley, Mona Singh, Britt Adamson","doi":"10.1038/s41592-024-02502-4","DOIUrl":"10.1038/s41592-024-02502-4","url":null,"abstract":"Prime editing installs precise edits into the genome with minimal unwanted byproducts, but low and variable editing efficiencies have complicated application of the approach to high-throughput functional genomics. Here we assembled a prime editing platform capable of high-efficiency substitution editing suitable for functional interrogation of small genetic variants. We benchmarked this platform for pooled, loss-of-function screening using a library of ~240,000 engineered prime editing guide RNAs (epegRNAs) targeting ~17,000 codons with 1-3 bp substitutions. Comparing the abundance of these epegRNAs across screen samples identified negative selection phenotypes for 7,996 nonsense mutations targeted to 1,149 essential genes and for synonymous mutations that disrupted splice site motifs at 3' exon boundaries. Rigorous evaluation of codon-matched controls demonstrated that these phenotypes were highly specific to the intended edit. Altogether, we established a prime editing approach for multiplexed, functional characterization of genetic variants with simple readouts.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142676370","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

Precision mutational scanning: your multipass to the future of genetics. 精准突变扫描：通往遗传学未来的多重通道。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-11-19 DOI: 10.1038/s41592-024-02522-0

Jonathan F Roth, Francisco J Sánchez-Rivera

引用次数: 0

Repurposing large-format microarrays for scalable spatial transcriptomics. 将大型微阵列重新用于可扩展的空间转录组学。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-11-19 DOI: 10.1038/s41592-024-02501-5

Denis Cipurko, Tatsuki Ueda, Linghan Mei, Nicolas Chevrier

{"title":"Repurposing large-format microarrays for scalable spatial transcriptomics.","authors":"Denis Cipurko, Tatsuki Ueda, Linghan Mei, Nicolas Chevrier","doi":"10.1038/s41592-024-02501-5","DOIUrl":"https://doi.org/10.1038/s41592-024-02501-5","url":null,"abstract":"Spatiomolecular analyses are key to study tissue functions and malfunctions. However, we lack profiling tools for spatial transcriptomics that are easy to adopt, low cost and scalable in terms of sample size and number. Here, we describe a method, Array-seq, to repurpose classical oligonucleotide microarrays for spatial transcriptomics profiling. We generate Array-seq slides from microarrays carrying custom-design probes that contain common sequences flanking unique barcodes at known coordinates. Then we perform a simple, two-step reaction that produces mRNA capture probes across all spots on the microarray. We demonstrate that Array-seq yields spatial transcriptomes with high detection sensitivity and localization specificity using histological sections from mouse tissues as test systems. Moreover, we show that the large surface area of Array-seq slides yields spatial transcriptomes (i) at high throughput by profiling multi-organ sections, (ii) in three dimensions by processing serial sections from one sample, and (iii) across whole human organs. Thus, by combining classical DNA microarrays and next-generation sequencing, we have created a simple and flexible platform for spatiomolecular studies of small-to-large specimens at scale.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142676384","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

Overcoming the preferred-orientation problem in cryo-EM with self-supervised deep learning. 利用自我监督深度学习克服低温电子显微镜中的首选方向问题。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-11-18 DOI: 10.1038/s41592-024-02505-1

Yun-Tao Liu, Hongcheng Fan, Jason J Hu, Z Hong Zhou

{"title":"Overcoming the preferred-orientation problem in cryo-EM with self-supervised deep learning.","authors":"Yun-Tao Liu, Hongcheng Fan, Jason J Hu, Z Hong Zhou","doi":"10.1038/s41592-024-02505-1","DOIUrl":"10.1038/s41592-024-02505-1","url":null,"abstract":"While advances in single-particle cryo-EM have enabled the structural determination of macromolecular complexes at atomic resolution, particle orientation bias (the 'preferred' orientation problem) remains a complication for most specimens. Existing solutions have relied on biochemical and physical strategies applied to the specimen and are often complex and challenging. Here, we develop spIsoNet, an end-to-end self-supervised deep learning-based software to address map anisotropy and particle misalignment caused by the preferred-orientation problem. Using preferred-orientation views to recover molecular information in under-sampled views, spIsoNet improves both angular isotropy and particle alignment accuracy during 3D reconstruction. We demonstrate spIsoNet's ability to generate near-isotropic reconstructions from representative biological systems with limited views, including ribosomes, β-galactosidases and a previously intractable hemagglutinin trimer dataset. spIsoNet can also be generalized to improve map isotropy and particle alignment of preferentially oriented molecules in subtomogram averaging. Therefore, without additional specimen-preparation procedures, spIsoNet provides a general computational solution to the preferred-orientation problem.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142668352","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 foundation model unlocks unified biomedical image analysis. 一个基础模型开启了统一的生物医学图像分析。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-11-18 DOI: 10.1038/s41592-024-02519-9

Yuhao Huang, Haoran Dou, Dong Ni

引用次数: 0

Content-aware motion correction for multi-shot imaging 针对多镜头成像的内容感知运动校正。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-11-18 DOI: 10.1038/s41592-024-02520-2

Romain F. Laine

引用次数: 0