Nature Methods最新文献

Author Correction: Segment Anything for Microscopy. 作者更正：切片任何显微镜。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-06-10 DOI: 10.1038/s41592-025-02745-9

Anwai Archit, Luca Freckmann, Sushmita Nair, Nabeel Khalid, Paul Hilt, Vikas Rajashekar, Marei Freitag, Carolin Teuber, Melanie Spitzner, Constanza Tapia Contreras, Genevieve Buckley, Sebastian von Haaren, Sagnik Gupta, Marian Grade, Matthias Wirth, Günter Schneider, Andreas Dengel, Sheraz Ahmed, Constantin Pape

引用次数: 0

SpotSweeper: spatially aware quality control for spatial transcriptomics. SpotSweeper：空间转录组学的空间感知质量控制。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-06-06 DOI: 10.1038/s41592-025-02713-3

Michael Totty, Stephanie C Hicks, Boyi Guo

引用次数: 0

Benchmarking methods for mapping functional connectivity in the brain. 绘制大脑功能连接的基准方法。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-06-06 DOI: 10.1038/s41592-025-02704-4

Zhen-Qi Liu, Andrea I Luppi, Justine Y Hansen, Ye Ella Tian, Andrew Zalesky, B T Thomas Yeo, Ben D Fulcher, Bratislav Misic

{"title":"Benchmarking methods for mapping functional connectivity in the brain.","authors":"Zhen-Qi Liu, Andrea I Luppi, Justine Y Hansen, Ye Ella Tian, Andrew Zalesky, B T Thomas Yeo, Ben D Fulcher, Bratislav Misic","doi":"10.1038/s41592-025-02704-4","DOIUrl":"https://doi.org/10.1038/s41592-025-02704-4","url":null,"abstract":"The networked architecture of the brain promotes synchrony among neuronal populations. These communication patterns can be mapped using functional imaging, yielding functional connectivity (FC) networks. While most studies use Pearson's correlations by default, numerous pairwise interaction statistics exist in the scientific literature. How does the organization of the FC matrix vary with the choice of pairwise statistic? Here we use a library of 239 pairwise statistics to benchmark canonical features of FC networks, including hub mapping, weight-distance trade-offs, structure-function coupling, correspondence with other neurophysiological networks, individual fingerprinting and brain-behavior prediction. We find substantial quantitative and qualitative variation across FC methods. Measures such as covariance, precision and distance display multiple desirable properties, including correspondence with structural connectivity and the capacity to differentiate individuals and predict individual differences in behavior. Our report highlights how FC mapping can be optimized by tailoring pairwise statistics to specific neurophysiological mechanisms and research questions.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144248779","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

Spotiflow: accurate and efficient spot detection for fluorescence microscopy with deep stereographic flow regression. Spotiflow：荧光显微镜精确、高效的斑点检测，具有深度立体流回归。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-06-06 DOI: 10.1038/s41592-025-02662-x

Albert Dominguez Mantes, Antonio Herrera, Irina Khven, Anjalie Schlaeppi, Eftychia Kyriacou, Georgios Tsissios, Evangelia Skoufa, Luca Santangeli, Elena Buglakova, Emine Berna Durmus, Suliana Manley, Anna Kreshuk, Detlev Arendt, Can Aztekin, Joachim Lingner, Gioele La Manno, Martin Weigert

{"title":"Spotiflow: accurate and efficient spot detection for fluorescence microscopy with deep stereographic flow regression.","authors":"Albert Dominguez Mantes, Antonio Herrera, Irina Khven, Anjalie Schlaeppi, Eftychia Kyriacou, Georgios Tsissios, Evangelia Skoufa, Luca Santangeli, Elena Buglakova, Emine Berna Durmus, Suliana Manley, Anna Kreshuk, Detlev Arendt, Can Aztekin, Joachim Lingner, Gioele La Manno, Martin Weigert","doi":"10.1038/s41592-025-02662-x","DOIUrl":"https://doi.org/10.1038/s41592-025-02662-x","url":null,"abstract":"Identification of spot-like structures in large, noisy microscopy images is a crucial step for many life-science applications. Imaging-based spatial transcriptomics (iST), in particular, relies on the precise detection of millions of transcripts in low signal-to-noise images. Despite recent advances in computer vision, most of the currently used spot detection techniques are still based on classical signal processing and require tedious manual tuning per dataset. Here we introduce Spotiflow, a deep learning method for subpixel-accurate spot detection that formulates spot detection as a multiscale heatmap and stereographic flow regression problem. Spotiflow supports 2D and 3D images, generalizes across different imaging conditions and is more time and memory efficient than existing methods. We show the efficacy of Spotiflow by extensive quantitative experiments on diverse datasets and demonstrate that its increased accuracy leads to meaningful improvements in biological insights obtained from iST and live imaging experiments. Spotiflow is available as an easy-to-use Python library as well as a napari plugin at https://github.com/weigertlab/spotiflow .","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144248781","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

CellNEST reveals cell-cell relay networks using attention mechanisms on spatial transcriptomics. CellNEST揭示了利用空间转录组学的注意机制的细胞-细胞中继网络。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-06-06 DOI: 10.1038/s41592-025-02721-3

Fatema Tuz Zohora, Deisha Paliwal, Eugenia Flores-Figueroa, Joshua Li, Tingxiao Gao, Faiyaz Notta, Gregory W Schwartz

{"title":"CellNEST reveals cell-cell relay networks using attention mechanisms on spatial transcriptomics.","authors":"Fatema Tuz Zohora, Deisha Paliwal, Eugenia Flores-Figueroa, Joshua Li, Tingxiao Gao, Faiyaz Notta, Gregory W Schwartz","doi":"10.1038/s41592-025-02721-3","DOIUrl":"https://doi.org/10.1038/s41592-025-02721-3","url":null,"abstract":"Dysregulation of communication between cells mediates complex diseases such as cancer and diabetes; however, detecting cell-cell communication at scale remains one of the greatest challenges in transcriptomics. Most current single-cell RNA sequencing and spatial transcriptomics computational approaches exhibit high false-positive rates, do not detect signals between individual cells and only identify single ligand-receptor communication. To overcome these challenges, we developed Cell Neural Networks on Spatial Transcriptomics (CellNEST) to decipher patterns of communication. Our model introduces a new type of relay-network communication detection that identifies putative ligand-receptor-ligand-receptor communication. CellNEST detects T cell homing signals in human lymph nodes, identifies aggressive cancer communication in lung adenocarcinoma and colorectal cancer, and predicts new patterns of communication that may act as relay networks in pancreatic cancer. Along with CellNEST, we provide a web-based, interactive visualization method to explore in situ communication. CellNEST is available at https://github.com/schwartzlab-methods/CellNEST .","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144248780","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

Krakencoder unifies diverse estimates of brain connectivity. Krakencoder统一了对大脑连接的不同估计。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-06-05 DOI: 10.1038/s41592-025-02705-3

引用次数: 0

Krakencoder: a unified brain connectome translation and fusion tool. Krakencoder：一个统一的脑连接体翻译和融合工具。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-06-05 DOI: 10.1038/s41592-025-02706-2

Keith W Jamison, Zijin Gu, Qinxin Wang, Ceren Tozlu, Mert R Sabuncu, Amy Kuceyeski

{"title":"Krakencoder: a unified brain connectome translation and fusion tool.","authors":"Keith W Jamison, Zijin Gu, Qinxin Wang, Ceren Tozlu, Mert R Sabuncu, Amy Kuceyeski","doi":"10.1038/s41592-025-02706-2","DOIUrl":"10.1038/s41592-025-02706-2","url":null,"abstract":"Brain connectivity can be estimated in many ways, depending on modality and processing strategy. Here, we present the Krakencoder, a joint connectome mapping tool that simultaneously bidirectionally translates between structural and functional connectivity, and between different atlases and processing choices via a common latent representation. These mappings demonstrate exceptional accuracy and individual-level identifiability; the mapping between structural and functional connectivity has identifiability 42-54% higher than existing models. The Krakencoder combines all connectome flavors via a shared low-dimensional latent space. This fusion representation better reflects familial relatedness, preserves age- and sex-relevant information, and enhances cognition-relevant information. The Krakencoder can be applied, without retraining, to new out-of-distribution data while still preserving inter-individual differences in the connectome predictions and familial relationships in the latent representations. The Krakencoder is a notable leap forward in capturing the relationship between multimodal brain connectomes in an individualized, behaviorally and demographically relevant way.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144234588","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

BiaPy: accessible deep learning on bioimages. BiaPy：基于生物图像的可访问深度学习。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-06-01 DOI: 10.1038/s41592-025-02699-y

Daniel Franco-Barranco, Jesús A Andrés-San Román, Ivan Hidalgo-Cenalmor, Lenka Backová, Aitor González-Marfil, Clément Caporal, Anatole Chessel, Pedro Gómez-Gálvez, Luis M Escudero, Donglai Wei, Arrate Muñoz-Barrutia, Ignacio Arganda-Carreras

引用次数: 0

Dark sectioning boosts segmentation accuracy and image quality in fluorescence microscopy. 暗切片提高了荧光显微镜的分割精度和图像质量。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-06-01 DOI: 10.1038/s41592-025-02668-5

引用次数: 0

Super-resolution imaging technique for precision in vivo neuronal activity mapping. 超分辨率成像技术用于精确的体内神经元活动映射。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-06-01 DOI: 10.1038/s41592-025-02691-6

引用次数: 0