Nature Methods最新文献_第10页

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. This work establishes a prime editing platform for high-throughput interrogation of small genetic variants (up to tens of thousands) with negative selection phenotypes.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 1","pages":"92-101"},"PeriodicalIF":36.1,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02502-4.pdf","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":"OA","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":"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. Array-seq repurposes microarray slides for high-sensitivity, scalable spatial transcriptomics.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 1","pages":"145-155"},"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. spIsoNet is an end-to-end self-supervised deep learning-based software to address the reconstruction and misalignment challenge in single-particle cryo-EM caused by the preferred-orientation problem. spIsoNet can also improve map isotropy and particle alignment of preferentially oriented molecules during subtomogram averaging in cryogenic electron tomography.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 1","pages":"113-123"},"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

A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities 九种模式生物医学对象联合分割、检测和识别的基础模型。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-11-18 DOI: 10.1038/s41592-024-02499-w

Theodore Zhao, Yu Gu, Jianwei Yang, Naoto Usuyama, Ho Hin Lee, Sid Kiblawi, Tristan Naumann, Jianfeng Gao, Angela Crabtree, Jacob Abel, Christine Moung-Wen, Brian Piening, Carlo Bifulco, Mu Wei, Hoifung Poon, Sheng Wang

{"title":"A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities","authors":"Theodore Zhao, Yu Gu, Jianwei Yang, Naoto Usuyama, Ho Hin Lee, Sid Kiblawi, Tristan Naumann, Jianfeng Gao, Angela Crabtree, Jacob Abel, Christine Moung-Wen, Brian Piening, Carlo Bifulco, Mu Wei, Hoifung Poon, Sheng Wang","doi":"10.1038/s41592-024-02499-w","DOIUrl":"10.1038/s41592-024-02499-w","url":null,"abstract":"Biomedical image analysis is fundamental for biomedical discovery. Holistic image analysis comprises interdependent subtasks such as segmentation, detection and recognition, which are tackled separately by traditional approaches. Here, we propose BiomedParse, a biomedical foundation model that can jointly conduct segmentation, detection and recognition across nine imaging modalities. This joint learning improves the accuracy for individual tasks and enables new applications such as segmenting all relevant objects in an image through a textual description. To train BiomedParse, we created a large dataset comprising over 6 million triples of image, segmentation mask and textual description by leveraging natural language labels or descriptions accompanying existing datasets. We showed that BiomedParse outperformed existing methods on image segmentation across nine imaging modalities, with larger improvement on objects with irregular shapes. We further showed that BiomedParse can simultaneously segment and label all objects in an image. In summary, BiomedParse is an all-in-one tool for biomedical image analysis on all major image modalities, paving the path for efficient and accurate image-based biomedical discovery. BiomedParse is a foundation model for image analysis that uses a joint learning approach to jointly conduct segmentation, detection and recognition and offer state-of-the-art performance across a wide range of datasets and nine modalities.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 1","pages":"166-176"},"PeriodicalIF":36.1,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142668253","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

Nanopore approaches for single-molecule temporal omics: promises and challenges 纳米孔方法用于单分子时空 omics：前景与挑战。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-11-18 DOI: 10.1038/s41592-024-02492-3

Meng-Yin Li, Jie Jiang, Jun-Ge Li, Hongyan Niu, Yi-Lun Ying, Ruijun Tian, Yi-Tao Long

{"title":"Nanopore approaches for single-molecule temporal omics: promises and challenges","authors":"Meng-Yin Li, Jie Jiang, Jun-Ge Li, Hongyan Niu, Yi-Lun Ying, Ruijun Tian, Yi-Tao Long","doi":"10.1038/s41592-024-02492-3","DOIUrl":"10.1038/s41592-024-02492-3","url":null,"abstract":"The great molecular heterogeneity within single cells demands omics analysis from a single-molecule perspective. Moreover, considering the perpetual metabolism and communication within cells, it is essential to determine the time-series changes of the molecular library, rather than obtaining data at only one time point. Thus, there is an urgent need to develop a single-molecule strategy for this omics analysis to elucidate the biosystem heterogeneity and temporal dynamics. In this Perspective, we explore the potential application of nanopores for single-molecule temporal omics to characterize individual molecules beyond mass, in both a single-molecule and high-throughput manner. Accordingly, recent advances in nanopores available for single-molecule temporal omics are reviewed from the view of single-molecule mass identification, revealing single-molecule heterogeneity and illustrating temporal evolution. Furthermore, we discuss the primary challenges associated with using nanopores for single-molecule temporal omics in complex biological samples, and present the potential strategies and notes to respond to these challenges. This Perspective highlights the need to develop methods for single-molecule temporal omics studies and discusses nanopores as a potential solution, as well as the challenges associated with using nanopores for the analysis of complex biological samples.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 2","pages":"241-253"},"PeriodicalIF":36.1,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142668334","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

Probe set selection for targeted spatial transcriptomics 定向空间转录组学的探针组选择。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-11-18 DOI: 10.1038/s41592-024-02496-z

Louis B. Kuemmerle, Malte D. Luecken, Alexandra B. Firsova, Lisa Barros de Andrade e Sousa, Lena Straßer, Ilhem Isra Mekki, Francesco Campi, Lukas Heumos, Maiia Shulman, Valentina Beliaeva, Soroor Hediyeh-Zadeh, Anna C. Schaar, Krishnaa T. Mahbubani, Alexandros Sountoulidis, Tamás Balassa, Ferenc Kovacs, Peter Horvath, Marie Piraud, Ali Ertürk, Christos Samakovlis, Fabian J. Theis

{"title":"Probe set selection for targeted spatial transcriptomics","authors":"Louis B. Kuemmerle, Malte D. Luecken, Alexandra B. Firsova, Lisa Barros de Andrade e Sousa, Lena Straßer, Ilhem Isra Mekki, Francesco Campi, Lukas Heumos, Maiia Shulman, Valentina Beliaeva, Soroor Hediyeh-Zadeh, Anna C. Schaar, Krishnaa T. Mahbubani, Alexandros Sountoulidis, Tamás Balassa, Ferenc Kovacs, Peter Horvath, Marie Piraud, Ali Ertürk, Christos Samakovlis, Fabian J. Theis","doi":"10.1038/s41592-024-02496-z","DOIUrl":"10.1038/s41592-024-02496-z","url":null,"abstract":"Targeted spatial transcriptomic methods capture the topology of cell types and states in tissues at single-cell and subcellular resolution by measuring the expression of a predefined set of genes. The selection of an optimal set of probed genes is crucial for capturing the spatial signals present in a tissue. This requires selecting the most informative, yet minimal, set of genes to profile (gene set selection) for which it is possible to build probes (probe design). However, current selections often rely on marker genes, precluding them from detecting continuous spatial signals or new states. We present Spapros, an end-to-end probe set selection pipeline that optimizes both gene set specificity for cell type identification and within-cell type expression variation to resolve spatially distinct populations while considering prior knowledge as well as probe design and expression constraints. We evaluated Spapros and show that it outperforms other selection approaches in both cell type recovery and recovering expression variation beyond cell types. Furthermore, we used Spapros to design a single-cell resolution in situ hybridization on tissues (SCRINSHOT) experiment of adult lung tissue to demonstrate how probes selected with Spapros identify cell types of interest and detect spatial variation even within cell types. Spapros is a probe set selection pipeline for targeted spatial transcriptomics that optimizes for both transcriptional and within-cell type variation.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 12","pages":"2260-2270"},"PeriodicalIF":36.1,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02496-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142668356","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