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Nature Methods
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A scientific journal publishing cutting-edge methods, tools, analyses, resources, reviews, news and commentary, supporting life sciences research. Posts by the editors.
CABLE: a method that uses information about cell or nuclear shape to infer fiber tracts in the brain.

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Whole-brain reconstruction of fiber tracts based on cytoarchitectonic organization - Nature Methods
CABLE harnesses cytoarchitectural information such as cell or nuclear shape to infer fiber tracts in the brain of, for example, marmosets, macaques or humans.
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November 7, 2025 at 3:00 PM
Squidiff: a diffusion-based model to predict transcriptome response to perturbations.

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Squidiff: predicting cellular development and responses to perturbations using a diffusion model - Nature Methods
Squidiff is a diffusion-based model to predict transcriptomic changes in response to perturbations.
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November 7, 2025 at 2:41 PM
ESPRESSO: a method that extracts functional information about organelles for deep spatiotemporal phenotyping at the single cell level.

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ESPRESSO: spatiotemporal omics based on organelle phenotyping - Nature Methods
ESPRESSO leverages functional information obtained from organelles for deep spatiotemporal phenotyping of single cells.
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November 6, 2025 at 5:40 PM
Cyclone, an acyclovir-controlled poison exon cassette, enables regulation of transgene and endogenous gene expression with minimal background.

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A portable poison exon for small-molecule control of mammalian gene expression - Nature Methods
This work introduces Cyclone, an acyclovir-controlled cassette that enables regulation of both transgene and endogenous gene expression with minimal background activity.
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November 6, 2025 at 4:29 PM
By learning a differentiation potential using an optimal transport-based approach, STORIES models and infers cell fate trajectories using spatiotemporal omics data. @gabrielpeyre.bsky.social @cantinilab.bsky.social

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STORIES: learning cell fate landscapes from spatial transcriptomics using optimal transport - Nature Methods
By learning a differentiation potential using an optimal transport-based approach, STORIES models and infers cell fate trajectories using spatiotemporal omics data.
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November 6, 2025 at 4:07 PM
By pretraining on SpatialCorpus-110M, a curated resource of vast and diverse transcriptomes of dissociated and spatially resolved cells from both human and mouse, Nicheformer advances building foundation models for spatial single-cell analysis.

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Nicheformer: a foundation model for single-cell and spatial omics - Nature Methods
Pretrained on SpatialCorpus-110M, a curated resource of vast and diverse transcriptomes of dissociated and spatially resolved cells from both human and mouse, Nicheformer advances toward building foun...
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November 6, 2025 at 4:02 PM
High-resolution imaging mass cytometry enables highly multiplexed imaging of nuclear foci and mitochondrial networks.

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October 30, 2025 at 3:41 PM
SegmentNT: a general segmentation model for genome annotation that can process DNA sequences up to 50-kb long and shows strong generalization across unseen species.

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Annotating the genome at single-nucleotide resolution with DNA foundation models - Nature Methods
By leveraging the power of pretrained DNA foundation models, SegmentNT achieves performant genome annotation through segmenting different genic and regulatory elements.
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October 29, 2025 at 3:41 PM
CytoTRACE 2: an interpretable deep-learning method that uses scRNA-seq data to predict absolute developmental potential across datasets.

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Improved reconstruction of single-cell developmental potential with CytoTRACE 2 - Nature Methods
CytoTRACE 2 is an interpretable deep learning framework that leverages single-cell RNA sequencing data to predict absolute developmental potential across datasets.
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October 29, 2025 at 3:14 PM
PHLOWER infers complex cell differentiation trajectories from single-cell multimodal data.

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PHLOWER leverages single-cell multimodal data to infer complex, multi-branching cell differentiation trajectories - Nature Methods
PHLOWER leverages single-cell multimodal data to infer complex, multi-branching cell differentiation trajectories.
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October 23, 2025 at 3:16 PM
scooby achieves DNA sequence-based single-cell level modeling of RNA-seq coverage and ATAC-seq insertion profiles by adapting a deep learning model that predicts bulk RNA-seq coverage. @johahi.bsky.social @lauradmartens.bsky.social @gagneurlab.bsky.social @tum.de

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scooby: modeling multimodal genomic profiles from DNA sequence at single-cell resolution - Nature Methods
scooby achieves DNA sequence-based single-cell level modeling of RNA-sequencing coverage and ATAC-sequencing insertion profiles by adapting a deep learning model that predicts bulk RNA-sequencing cove...
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October 22, 2025 at 4:48 PM
CELLECT learns contrastive embeddings to enable high-fidelity cell tracking.

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CELLECT: contrastive embedding learning for large-scale efficient cell tracking - Nature Methods
CELLECT learns contrastive embeddings to enable high-fidelity cell tracking.
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October 22, 2025 at 4:31 PM
gReLU advances deep learning based modeling and analysis of DNA sequences with comprehensive toolsets and versatile applications. @avantikalal.bsky.social @gokcen.bsky.social

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gReLU: a comprehensive framework for DNA sequence modeling and design - Nature Methods
gReLU advances deep-learning-based modeling and analysis of DNA sequences with comprehensive toolsets and versatile applications.
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October 16, 2025 at 3:26 PM
Our latest Creature Column features the gray mouse lemur, a model for primate brain evolution, cognition, aging and neurodegenerative diseases.

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October 14, 2025 at 5:49 PM
MrVI, based on deep generative modeling, is a unified framework for integrative, exploratory, and comparative analyses of large-scale (multi-sample) single-cell RNA-seq datasets. @justjhong.bsky.social @canergen.bsky.social @elhamazizi.bsky.social

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Deep generative modeling of sample-level heterogeneity in single-cell genomics - Nature Methods
MrVI, based on deep generative modelling, is a unified framework for integrative, exploratory and comparative analyses of large-scale (multi-sample) single-cell RNA-seq datasets.
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October 14, 2025 at 4:44 PM
In our latest Registered Report, researchers compare computational methods for single-cell multimodal omics data integration and provide recommendations for different tasks and scenarios.

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Multitask benchmarking of single-cell multimodal omics integration methods - Nature Methods
This Registered Report compares computational methods for single-cell multimodal omics integration and provides recommendations for different tasks and scenarios.
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October 14, 2025 at 4:28 PM
Read the Research Briefing highlighting this work here:

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October 10, 2025 at 2:49 PM
In our latest Editorial we discuss some team updates, new editorial projects and initiatives.

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What’s new at Nature Methods - Nature Methods
Team news, editorial projects and initiatives, plus a preview of what’s to come in 2026.
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October 10, 2025 at 2:19 PM
Our October issue is live! 🥳
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On the cover, single-cell DNA-RNA sequencing (SDR-seq) links genetic variants to gene expression. Artwork by Tobias Wuestefeld. Paper here: www.nature.com/articles/s41...
October 10, 2025 at 2:07 PM
Nimbus predicts the likelihood of marker positivity from multiplexed image data in single cells.

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Automated classification of cellular expression in multiplexed imaging data with Nimbus - Nature Methods
Nimbus, a deep learning model, uses a large multiplexed imaging dataset to predict the likelihood of marker positivity in single cells.
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October 8, 2025 at 4:37 PM
OrganoidTracker 2.0 accurately tracks cells in complex systems such as developing organoids.

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Cell tracking with accurate error prediction - Nature Methods
OrganoidTracker 2.0 enables fast and accurate cell tracking in complex systems such as developing organoids. A key aspect of the work is determining cell tracks with error probabilities for any tracking feature, from cell cycles to lineage trees.
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October 8, 2025 at 4:24 PM
Laser ablation of imaged surface layers under adaptive control enables volumetric imaging of heterogeneous tissue like skull and brain.

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October 3, 2025 at 1:31 PM
CaCoFold-R3D: a probabilistic model that predicts RNA 3D motifs and secondary structure using evolutionary information.

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All-at-once RNA folding with 3D motif prediction framed by evolutionary information - Nature Methods
CaCoFold-R3D is a probabilistic model that simultaneously predicts the RNA 3D motifs jointly with the secondary structure in a structural RNA using evolutionary information.
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October 3, 2025 at 1:25 PM
META-SiM: a foundation model for single-molecule fluorescence microscopy trace classification, segmentation, idealization and stepwise photobleaching analysis.

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Foundation model for efficient biological discovery in single-molecule time traces - Nature Methods
META-SiM brings foundation model power to single-molecule time traces, excelling across diverse analysis tasks. Paired with the web-based META-SiM Projector and entropy mapping, it rapidly reveals hidden molecular behaviors inaccessible by other means.
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October 3, 2025 at 12:54 PM