Nature Methods offers a unique interdisciplinary forum for the publication of novel methods. Nature Methods focuses on the life sciences, combining practical, technique-driven subject matter with rigorous peer-review standards to ensure that readers are consistently presented with only the most valuable and highest quality methodological research. The journal offers its readers primary research papers as well as an array of opinions, reviews and short journalistic pieces to provide busy researchers with a broad, yet easily absorbed perspective of important methodological developments in the life sciences. http://feeds.nature.com/nmeth/rss/current Nature Publishing Group en © 2026 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. Nature Methods © 2026 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. permissions@nature.com https://www.nature.com/uploads/product/nmeth/rss.gif http://feeds.nature.com/nmeth/rss/current https://www.nature.com/articles/s41592-026-03119-5 Nature Methods, Published online: 09 June 2026; doi:10.1038/s41592-026-03119-5

The advantages of training foundation models for single-cell datasets on large (tens of millions of cells) datasets have not been systematically tested. We evaluated the role of the size and diversity of the training dataset in the performance of single-cell foundation models and found little gain in increasing dataset size beyond a set point.]]> doi:10.1038/s41592-026-03119-5 Nature Methods, Published online: 2026-06-09; | doi:10.1038/s41592-026-03119-5 2026-06-09 Nature Methods 10.1038/s41592-026-03119-5 https://www.nature.com/articles/s41592-026-03119-5 https://www.nature.com/articles/s41592-026-03120-y Nature Methods, Published online: 09 June 2026; doi:10.1038/s41592-026-03120-y

The performance of single-cell foundation models is dependent on many factors. This study assesses the effect of the pretraining dataset’s size and diversity, revealing potential challenges in pursuing consistent improvement by naively scaling up pretraining data.]]> Alan DenAdelMadeline HughesAkshaya ThoutamAnay GuptaAndrew W. NaviaNicolo FusiSrivatsan RaghavanPeter S. WinterAva P. AminiLorin Crawford doi:10.1038/s41592-026-03120-y Nature Methods, Published online: 2026-06-09; | doi:10.1038/s41592-026-03120-y 2026-06-09 Nature Methods 10.1038/s41592-026-03120-y https://www.nature.com/articles/s41592-026-03120-y https://www.nature.com/articles/s41592-026-03125-7 Nature Methods, Published online: 09 June 2026; doi:10.1038/s41592-026-03125-7

A lightweight head-mounted multiplane microscope allows simultaneous imaging from >1,800 neurons spread across the cortical layers in freely moving mice performing complex behavioral tasks, sampled over weeks.]]> Alexandr KlioutchnikovDamian J. WallaceCaleb BerdahlAdam SugiJuergen SawinskiJason N. D. Kerr doi:10.1038/s41592-026-03125-7 Nature Methods, Published online: 2026-06-09; | doi:10.1038/s41592-026-03125-7 2026-06-09 Nature Methods 10.1038/s41592-026-03125-7 https://www.nature.com/articles/s41592-026-03125-7 https://www.nature.com/articles/s41592-026-03123-9 Nature Methods, Published online: 09 June 2026; doi:10.1038/s41592-026-03123-9

m6A-ARTR-DBiT offers a spatial assay to profile the distribution of N6-methyladenosine (m6A) across the transcriptome in intact tissue sections.]]> 6A profiling using m⁶A-ARTR-DBiT]]> Yu XiaoZhiliang BaiZhuoning ZouChang YeBo TaoZhong ZhengYan-Ming ChenZhongyu ZouLiudan JiangLijie ZhaoYuhang FanYun GaoRong FanChuan He doi:10.1038/s41592-026-03123-9 Nature Methods, Published online: 2026-06-09; | doi:10.1038/s41592-026-03123-9 2026-06-09 Nature Methods 10.1038/s41592-026-03123-9 https://www.nature.com/articles/s41592-026-03123-9 https://www.nature.com/articles/s41592-026-03126-6 Nature Methods, Published online: 09 June 2026; doi:10.1038/s41592-026-03126-6

The updated OrthoFinder v3 software boosts accuracy and scalability in phylogenetic orthology inference with massive and diverse datasets.]]> David M. EmmsYi LiuLaurence BelcherJonathan HolmesSteven Kelly doi:10.1038/s41592-026-03126-6 Nature Methods, Published online: 2026-06-09; | doi:10.1038/s41592-026-03126-6 2026-06-09 Nature Methods 10.1038/s41592-026-03126-6 https://www.nature.com/articles/s41592-026-03126-6 https://www.nature.com/articles/s41592-026-03122-w Nature Methods, Published online: 08 June 2026; doi:10.1038/s41592-026-03122-w

We built an open-source library of wave optics models — Chromatix — that enables optics simulations to be scaled efficiently on modern computing hardware. Chromatix solves computational optics problems up to 22× faster than typical research code and enables researchers to share innovations as part of a standard library for wave optics models.]]> doi:10.1038/s41592-026-03122-w Nature Methods, Published online: 2026-06-08; | doi:10.1038/s41592-026-03122-w 2026-06-08 Nature Methods 10.1038/s41592-026-03122-w https://www.nature.com/articles/s41592-026-03122-w https://www.nature.com/articles/s41592-026-03121-x Nature Methods, Published online: 08 June 2026; doi:10.1038/s41592-026-03121-x

Based on JAX, Chromatix provides a standardized framework for developing, sharing and reusing wave-optics simulations. The capabilities are demonstrated in a number of computational microscopy applications.]]> Diptodip DebGert-Jan BothEric BezzamAmit KohliSiqi YangAmey ChawareCédric AllierChangjia CaiGeneva AnderbergM. Hossein EybposhMagdalena C. SchneiderRainer HeintzmannFabrizio A. Rivera-SanchezCorey SimmererGuanghan MengJovan Tormes-VaqueranoSeungYun HanSibi Chakravarthy ShanmugavelTeja MaruvadaXi YangYewon KimBenedict DiederichChulmin JooLaura WallerNicholas J. DurrNicolas C. PégardPatrick J. La RivièreRoarke HorstmeyerShwetadwip ChowdhurySrinivas C. Turaga doi:10.1038/s41592-026-03121-x Nature Methods, Published online: 2026-06-08; | doi:10.1038/s41592-026-03121-x 2026-06-08 Nature Methods 10.1038/s41592-026-03121-x https://www.nature.com/articles/s41592-026-03121-x https://www.nature.com/articles/s41592-026-03124-8 Nature Methods, Published online: 08 June 2026; doi:10.1038/s41592-026-03124-8

Modeling the effect of DNA sequence variation on phenotypes such as gene expression faces unique challenges when deciphering inter-individual variation. This study presents a scalable and efficient sequence-to-function modeling framework for personal genome analysis.]]> Anna E. SpiroXinming TuYilun ShengAlexander SasseRezwan HosseiniMaria ChikinaSara Mostafavi doi:10.1038/s41592-026-03124-8 Nature Methods, Published online: 2026-06-08; | doi:10.1038/s41592-026-03124-8 2026-06-08 Nature Methods 10.1038/s41592-026-03124-8 https://www.nature.com/articles/s41592-026-03124-8