Nature Methods最新文献

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BioNumPy: array programming for biology. BioNumPy：生物学数组编程。

IF 36.1 1区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Nature Methods

Pub Date : 2024-10-17 DOI: 10.1038/s41592-024-02483-4

Knut Dagestad Rand, Ivar Grytten, Milena Pavlović, Chakravarthi Kanduri, Geir Kjetil Sandve

引用次数: 0

In vivo imaging of the human brain with the Iseult 11.7-T MRI scanner 使用 Iseult 11.7-T 核磁共振成像扫描仪对人脑进行活体成像。

IF 36.1 1区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Nature Methods

Pub Date : 2024-10-17 DOI: 10.1038/s41592-024-02472-7

Nicolas Boulant, Franck Mauconduit, Vincent Gras, Alexis Amadon, Caroline Le Ster, Michel Luong, Aurélien Massire, Christophe Pallier, Laure Sabatier, Michel Bottlaender, Alexandre Vignaud, Denis Le Bihan

The understanding of the human brain is one of the main scientific challenges of the twenty-first century. In the early 2000s, the French Atomic Energy Commission launched a program to conceive and build a human magnetic resonance imaging scanner operating at 11.7 T. We have now acquired human brain images in vivo at such a magnetic field. We deployed parallel transmission tools to mitigate the radiofrequency field inhomogeneity problem and tame the specific absorption rate. The safety of human imaging at such high field strength was demonstrated using physiological, vestibular, behavioral and genotoxicity measurements on the imaged volunteers. Our technology yields T2 and T2*-weighted images reaching mesoscale resolutions within short acquisition times and with a high signal and contrast-to-noise ratio. In a technological tour de force, a whole-body 11.7-T MRI scanner has been developed. Here images of the human brain are presented while safety for the imaged human volunteers has been ascertained.

了解人类大脑是二十一世纪的主要科学挑战之一。本世纪初，法国原子能委员会启动了一项计划，构想并建造一台在 11.7 T 下工作的人体磁共振成像扫描仪。现在，我们已经在这样的磁场下获取了活体人脑图像。我们采用并行传输工具来缓解射频场不均匀性问题，并控制比吸收率。通过对成像志愿者进行生理、前庭、行为和遗传毒性测量，证明了在如此高的磁场强度下进行人体成像的安全性。我们的技术能在较短的采集时间内获得达到中尺度分辨率的 T2 和 T2* 加权图像，并具有较高的信号和对比度-噪声比。

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引用次数: 0

Image processing tools for petabyte-scale light sheet microscopy data. 百万亿字节级光片显微镜数据的图像处理工具。

IF 36.1 1区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Nature Methods

Pub Date : 2024-10-17 DOI: 10.1038/s41592-024-02475-4

Xiongtao Ruan, Matthew Mueller, Gaoxiang Liu, Frederik Görlitz, Tian-Ming Fu, Daniel E Milkie, Joshua L Lillvis, Alexander Kuhn, Johnny Gan Chong, Jason Li Hong, Chu Yi Aaron Herr, Wilmene Hercule, Marc Nienhaus, Alison N Killilea, Eric Betzig, Srigokul Upadhyayula

Light sheet microscopy is a powerful technique for high-speed three-dimensional imaging of subcellular dynamics and large biological specimens. However, it often generates datasets ranging from hundreds of gigabytes to petabytes in size for a single experiment. Conventional computational tools process such images far slower than the time to acquire them and often fail outright due to memory limitations. To address these challenges, we present PetaKit5D, a scalable software solution for efficient petabyte-scale light sheet image processing. This software incorporates a suite of commonly used processing tools that are optimized for memory and performance. Notable advancements include rapid image readers and writers, fast and memory-efficient geometric transformations, high-performance Richardson-Lucy deconvolution and scalable Zarr-based stitching. These features outperform state-of-the-art methods by over one order of magnitude, enabling the processing of petabyte-scale image data at the full teravoxel rates of modern imaging cameras. The software opens new avenues for biological discoveries through large-scale imaging experiments.

光片显微镜是对亚细胞动态和大型生物标本进行高速三维成像的强大技术。然而，单次实验所产生的数据集往往从数百千兆字节到数百兆字节不等。传统的计算工具处理这些图像的速度远远慢于获取图像的时间，而且经常由于内存限制而完全失败。为了应对这些挑战，我们推出了 PetaKit5D，这是一种可扩展的软件解决方案，用于高效处理 PB 级光片图像。该软件集成了一套常用的处理工具，并对内存和性能进行了优化。显著的进步包括快速图像读写器、快速且内存效率高的几何变换、高性能理查森-卢西去卷积和可扩展的基于 Zarr 的拼接。这些功能比最先进的方法高出一个数量级以上，能够以现代成像相机的全太象素速率处理 PB 级图像数据。该软件通过大规模成像实验为生物发现开辟了新途径。

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引用次数: 0

Scientists who marry scientists 科学家与科学家结婚。

IF 36.1 1区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Nature Methods

Pub Date : 2024-10-15 DOI: 10.1038/s41592-024-02490-5

Vivien Marx

When spouses are both scientists, they mix the typical research career decisions with some marriage-related ones.

当配偶双方都是科学家时，他们在做出典型的研究职业决定的同时，也会做出一些与婚姻有关的决定。

引用次数: 0

Interpretable spatially aware dimension reduction of spatial transcriptomics with STAMP 利用 STAMP 对空间转录组学进行可解释的空间感知降维。

IF 36.1 1区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Nature Methods

Pub Date : 2024-10-15 DOI: 10.1038/s41592-024-02463-8

Chengwei Zhong, Kok Siong Ang, Jinmiao Chen

Spatial transcriptomics produces high-dimensional gene expression measurements with spatial context. Obtaining a biologically meaningful low-dimensional representation of such data is crucial for effective interpretation and downstream analysis. Here, we present Spatial Transcriptomics Analysis with topic Modeling to uncover spatial Patterns (STAMP), an interpretable spatially aware dimension reduction method built on a deep generative model that returns biologically relevant, low-dimensional spatial topics and associated gene modules. STAMP can analyze data ranging from a single section to multiple sections and from different technologies to time-series data, returning topics matching known biological domains and associated gene modules containing established markers highly ranked within. In a lung cancer sample, STAMP delineated cell states with supporting markers at a higher resolution than the original annotation and uncovered cancer-associated fibroblasts concentrated on the tumor edge’s exterior. In time-series data of mouse embryonic development, STAMP disentangled the erythro-myeloid hematopoiesis and hepatocytes developmental trajectories within the liver. STAMP is highly scalable and can handle more than 500,000 cells. Spatial Transcriptomics Analysis with topic Modeling to uncover spatial Patterns (STAMP) is a versatile and scalable method for dimension reduction in spatially resolved transcriptomics that enables discovery of biologically relevant tissue domains.

空间转录组学产生了具有空间背景的高维基因表达测量数据。为这类数据获取具有生物学意义的低维表示对于有效解释和下游分析至关重要。在这里，我们介绍了利用主题建模揭示空间模式的空间转录组学分析（STAMP），这是一种可解释的空间感知降维方法，它建立在一个深度生成模型之上，可返回生物相关的低维空间主题和相关基因模块。STAMP 可以分析从单个切片到多个切片的数据，以及从不同技术到时间序列的数据，并返回与已知生物领域相匹配的主题和相关的基因模块，这些基因模块中包含已建立的高度排序的标记物。在肺癌样本中，STAMP 以比原始注释更高的分辨率划分了带有支持标记的细胞状态，并发现了集中在肿瘤边缘外部的癌症相关成纤维细胞。在小鼠胚胎发育的时间序列数据中，STAMP 分离了肝脏内红细胞-髓系造血和肝细胞的发育轨迹。STAMP 具有高度可扩展性，可处理超过 500,000 个细胞。

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引用次数: 0

Restoring protein glycosylation with GlycoShape 用 GlycoShape 恢复蛋白质糖基化。

IF 36.1 1区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Nature Methods

Pub Date : 2024-10-14 DOI: 10.1038/s41592-024-02464-7

Callum M. Ives, Ojas Singh, Silvia D’Andrea, Carl A. Fogarty, Aoife M. Harbison, Akash Satheesan, Beatrice Tropea, Elisa Fadda

Despite ground-breaking innovations in experimental structural biology and protein structure prediction techniques, capturing the structure of the glycans that functionalize proteins remains a challenge. Here we introduce GlycoShape ( https://glycoshape.org ), an open-access glycan structure database and toolbox designed to restore glycoproteins to their native and functional form in seconds. The GlycoShape database counts over 500 unique glycans so far, covering the human glycome and augmented by elements from a wide range of organisms, obtained from 1 ms of cumulative sampling from molecular dynamics simulations. These structures can be linked to proteins with a robust algorithm named Re-Glyco, directly compatible with structural data in open-access repositories, such as the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) and AlphaFold Protein Structure Database, or own. The quality, performance and broad applicability of GlycoShape is demonstrated by its ability to predict N-glycosylation occupancy, scoring a 93% agreement with experiment, based on screening all proteins in the PDB with a corresponding glycoproteomics profile, for a total of 4,259 N-glycosylation sequons. GlycoShape is an open-access web-based platform designed to supplement three-dimensional glycoprotein structures with missing structural information on glycans. To link them, the Re-Glyco algorithm evaluates the steric complementarity of glycans using their conformational ensemble with the protein surface.

尽管在实验结构生物学和蛋白质结构预测技术方面取得了突破性创新，但捕捉使蛋白质功能化的聚糖结构仍然是一项挑战。我们在此介绍 GlycoShape ( https://glycoshape.org )，它是一个开放存取的聚糖结构数据库和工具箱，可在数秒内将糖蛋白还原为原生的功能形式。迄今为止，GlycoShape 数据库已收录了 500 多个独特的聚糖，涵盖了人类聚糖结构，并通过分子动力学模拟 1 毫秒的累积采样获得了多种生物体的聚糖元素。这些结构可以通过一种名为 Re-Glyco 的强大算法与蛋白质连接起来，直接与结构生物信息学研究合作组织蛋白质数据库（RCSB PDB）和 AlphaFold 蛋白结构数据库等开放存取库中的结构数据兼容。GlycoShape 的质量、性能和广泛适用性体现在其预测 N-糖基化占有率的能力上，根据筛选 PDB 中所有蛋白质和相应的糖蛋白组学特征，共 4,259 个 N-糖基化序列，GlycoShape 与实验的吻合度达到 93%。

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引用次数: 0

Single-shot 20-fold expansion microscopy 单次 20 倍扩展显微镜。

IF 36.1 1区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Nature Methods

Pub Date : 2024-10-11 DOI: 10.1038/s41592-024-02454-9

Shiwei Wang, Tay Won Shin, Harley B. Yoder II, Ryan B. McMillan, Hanquan Su, Yixi Liu, Chi Zhang, Kylie S. Leung, Peng Yin, Laura L. Kiessling, Edward S. Boyden

Expansion microscopy (ExM) is in increasingly widespread use throughout biology because its isotropic physical magnification enables nanoimaging on conventional microscopes. To date, ExM methods either expand specimens to a limited range (~4–10× linearly) or achieve larger expansion factors through iterating the expansion process a second time (~15–20× linearly). Here, we present an ExM protocol that achieves ~20× expansion (yielding <20-nm resolution on a conventional microscope) in a single expansion step, achieving the performance of iterative expansion with the simplicity of a single-shot protocol. This protocol, which we call 20ExM, supports postexpansion staining for brain tissue, which can facilitate biomolecular labeling. 20ExM may find utility in many areas of biological investigation requiring high-resolution imaging. 20ExM achieves isotropic ~20× expansion of cells and tissues in a single shot for super-resolution imaging with <20-nm resolution on a conventional microscope.

膨胀显微镜（ExM）在生物学领域的应用日益广泛，因为它的各向同性物理放大率可在传统显微镜上进行纳米成像。迄今为止，ExM 方法要么将标本扩大到有限的范围（线性放大率约为 4-10 倍），要么通过第二次迭代扩大过程（线性放大率约为 15-20 倍）实现更大的扩大因子。在这里，我们提出了一种 ExM 方案，可实现 ~20 倍的扩展（产生

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引用次数: 0

A decade of neuroscience 神经科学的十年

IF 36.1 1区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Nature Methods

Pub Date : 2024-10-09 DOI: 10.1038/s41592-024-02448-7

Nina Vogt

For Nature Methods’ 20th anniversary, our current and past editors reminisce about their favorite papers, initiatives and projects at the journal.

在《自然-方法》创刊 20 周年之际，我们的现任和前任编辑回顾了他们最喜爱的论文、活动和项目。

引用次数: 0

Listening to an RNA orchestra and seeing CRISPR in action 聆听 RNA 管弦乐，观看 CRISPR 实际操作。

IF 36.1 1区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Nature Methods

Pub Date : 2024-10-09 DOI: 10.1038/s41592-024-02444-x

Nicole Rusk

For Nature Methods’ 20th anniversary, our current and past editors reminisce about their favorite papers, initiatives and projects at the journal.

在《自然-方法》创刊 20 周年之际，我们的现任和前任编辑回顾了他们最喜爱的论文、活动和项目。

引用次数: 0

Mapping the brain: an editor’s journey 绘制大脑地图：编辑之旅。

IF 36.1 1区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Nature Methods

Pub Date : 2024-10-09 DOI: 10.1038/s41592-024-02446-9

Erika Pastrana

For Nature Methods’ 20th anniversary, our current and past editors reminisce about their favorite papers, initiatives and projects at the journal.

在《自然-方法》创刊 20 周年之际，我们的现任和前任编辑回顾了他们最喜爱的论文、活动和项目。

引用次数: 0

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