Nature MethodsPub Date : 2025-07-10DOI: 10.1038/s41592-025-02747-7
{"title":"Delivering intact, naive, synthetic megabase-scale human DNA into mammalian embryos using SynNICE.","authors":"","doi":"10.1038/s41592-025-02747-7","DOIUrl":"https://doi.org/10.1038/s41592-025-02747-7","url":null,"abstract":"","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144608891","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}
{"title":"Benchmarking single-cell multi-modal data integrations.","authors":"Shaliu Fu, Shuguang Wang, Duanmiao Si, Gaoyang Li, Yawei Gao, Qi Liu","doi":"10.1038/s41592-025-02737-9","DOIUrl":"https://doi.org/10.1038/s41592-025-02737-9","url":null,"abstract":"<p><p>Recent advances have enabled the generation of both unpaired (separate profiling) and paired (simultaneous measurement) single-cell multi-modal datasets, driving rapid development of single-cell multi-modal integration tools. Nevertheless, there is a pressing need for a comprehensive benchmark to assess algorithms under varying integrated dataset types, integrated modalities, dataset sizes and data quality. Here we present a systematic benchmark for 40 single-cell multi-modal integration algorithms involving modalities of DNA, RNA, protein and spatial omics for paired, unpaired and mosaic datasets (a mixture of paired and unpaired datasets). We evaluated usability, accuracy and robustness to assist researchers in selecting suitable integration methods tailored to their datasets and applications. Our benchmark provides valuable guidance in the ever-evolving field of single-cell multi-omics.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144608889","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}
{"title":"De novo assembly and delivery of synthetic megabase-scale human DNA into mouse early embryos.","authors":"Yue Liu, Jianting Zhou, Duo Liu, Xiaoyu Hu, Lin Yang, Xue-Ru Song, Xiao-Dong Jin, Wei Xie, Luhan Yang, Zichuan Liu, Ying-Jin Yuan","doi":"10.1038/s41592-025-02746-8","DOIUrl":"https://doi.org/10.1038/s41592-025-02746-8","url":null,"abstract":"<p><p>Epigenetic modifications on natural chromosomes are inherited and maintained in a default state, making it challenging to remove intrinsic marks to study the fundamental principles of their establishment and further influence on transcriptional regulation. In this study, we developed SynNICE, a method for assembling and delivering intact, naive, synthetic megabase (Mb)-scale human DNA into early mouse embryos, to study de novo epigenetic regulation. By assembling and delivering a 1.14-Mb human AZFa (hAZFa) locus, we observed the spontaneous incorporation of murine histones and the establishment of DNA methylation at the one-cell stage. Notably, DNA methylation from scratch strongly enriches at repeat sequences without H3K9me3 reinforcement. Furthermore, the transcription of hAZFa initiated at the four-cell stage is regulated by newly established DNA methylation. This method provides a unique platform for exploring de novo epigenomic regulation mechanisms in higher animals.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144608890","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 : 2025-07-07DOI: 10.1038/s41592-025-02751-x
Guy Baele, Xiang Ji, Gabriel W Hassler, John T McCrone, Yucai Shao, Zhenyu Zhang, Andrew J Holbrook, Philippe Lemey, Alexei J Drummond, Andrew Rambaut, Marc A Suchard
{"title":"BEAST X for Bayesian phylogenetic, phylogeographic and phylodynamic inference.","authors":"Guy Baele, Xiang Ji, Gabriel W Hassler, John T McCrone, Yucai Shao, Zhenyu Zhang, Andrew J Holbrook, Philippe Lemey, Alexei J Drummond, Andrew Rambaut, Marc A Suchard","doi":"10.1038/s41592-025-02751-x","DOIUrl":"https://doi.org/10.1038/s41592-025-02751-x","url":null,"abstract":"<p><p>Here we present the open-source and cross-platform BEAST X software that combines molecular phylogenetic reconstruction with complex trait evolution, divergence-time dating and coalescent demographics in an efficient statistical inference engine. BEAST X significantly advances the flexibility and scalability of evolutionary models supported. Novel clock and substitution models leverage a large variety of evolutionary processes; discrete, continuous and mixed traits with missingness and measurement errors; and fast, gradient-informed integration techniques that rapidly traverse high-dimensional parameter spaces.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144584369","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 : 2025-07-01Epub Date: 2025-06-06DOI: 10.1038/s41592-025-02713-3
Michael Totty, Stephanie C Hicks, Boyi Guo
{"title":"SpotSweeper: spatially aware quality control for spatial transcriptomics.","authors":"Michael Totty, Stephanie C Hicks, Boyi Guo","doi":"10.1038/s41592-025-02713-3","DOIUrl":"10.1038/s41592-025-02713-3","url":null,"abstract":"<p><p>Quality control (QC) is a crucial step to ensure the reliability of data obtained from RNA sequencing experiments, including spatially resolved transcriptomics (SRT). Existing QC approaches for SRT that have been adopted from single-cell or single-nucleus RNA sequencing methods are confounded by spatial biology and are inappropriate for SRT data. In addition, no methods currently exist for identifying histological tissue artifacts that are unique to SRT. Here, we introduce SpotSweeper, a spatially aware QC method that leverages local neighborhoods to correct for spatial confounding in order to identify both local outliers and regional artifacts in SRT. Using SpotSweeper on publicly available data, we identify a consistent set of Visium barcoded spots as systematically low quality and demonstrate that SpotSweeper accurately identifies two distinct types of regional artifacts. SpotSweeper represents a substantial advancement in spatially resolved transcriptomics QC for SRT, providing a robust, generalizable framework to ensure data reliability across diverse experimental conditions and technologies.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":"1520-1530"},"PeriodicalIF":36.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144248782","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 : 2025-07-01DOI: 10.1038/s41592-025-02705-3
{"title":"Krakencoder unifies diverse estimates of brain connectivity.","authors":"","doi":"10.1038/s41592-025-02705-3","DOIUrl":"10.1038/s41592-025-02705-3","url":null,"abstract":"","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":"1406-1407"},"PeriodicalIF":36.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144234587","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 : 2025-07-01DOI: 10.1038/s41592-025-02728-w
Vivien Marx
{"title":"How to spy on condensates.","authors":"Vivien Marx","doi":"10.1038/s41592-025-02728-w","DOIUrl":"10.1038/s41592-025-02728-w","url":null,"abstract":"","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":"1396-1400"},"PeriodicalIF":36.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144485244","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 : 2025-07-01Epub Date: 2025-06-06DOI: 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":"10.1038/s41592-025-02704-4","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":"1593-1602"},"PeriodicalIF":36.1,"publicationDate":"2025-07-01","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}