N Nature Methods · Nov 24, 2025 Scalable spatial single-cell transcriptomics and translatomics in 3D thick tissue blocks Characterizing the transcriptional and translational gene expression patterns at the single-cell level within their three-dimensional (3D) tissue context is essential for revealing how genes shape tissue structure and function in health and disease. However, most existing spatial profiling techniques are limited to 5–20 µm thin tissue sections. Here, we developed Deep-STARmap and Deep-RIBOmap, which enable 3D in situ quantification of thousands of gene transcripts and their corresponding translation activities, respectively, within 60–200-µm thick tissue blocks. This is achieved through scalable probe synthesis, hydrogel embedding with efficient probe anchoring and robust cDNA crosslinking. We first utilized Deep-STARmap in combination with multicolor fluorescent protein imaging for simultaneous molecular cell typing and 3D neuron morphology tracing in the mouse brain. We also demonstrate that 3D spatial profiling facilitates comprehensive and quantitative analysis of tumor–immune interactions in human skin cancer. Gene expression Molecular neuroscience RNA Transcriptomics Tumour heterogeneity biology mouse experiments
N Nature Methods · Nov 17, 2025 A multi-omics molecular landscape of 30 tissues in aging female rhesus macaques A systematic investigation of aging patterns across virtually all major tissues in nonhuman primates, our evolutionarily closest relatives, can provide valuable insights into tissue aging in humans, which is still elusive largely due to the difficulty in sampling. Here, we generated and analyzed multi-omics data, including transcriptome, proteome and metabolome, from 30 tissues of 17 female rhesus macaques (Macacamulatta) aged 3–27 years. We found that certain molecular features, such as increased inflammation, are consistent across tissues and align with findings in mice and humans. We further revealed that tissue aging in macaques is asynchronous and can be classified into two distinct types, with one type exhibiting more pronounced aging degree, likely associated with decreased mRNA translation efficiency, and predominantly contributing to whole-body aging. This work provides a comprehensive molecular landscape of aging in nonhuman primate tissues and links translation efficiency to tissue-specific aging. Genetic models Proteomics Sequencing Transcriptomics Zoology biology mouse experiments
N Nature Methods · Nov 07, 2025 Monod: model-based discovery and integration through fitting stochastic transcriptional dynamics to single-cell sequencing data Single-cell RNA sequencing analysis centers on illuminating cell diversity and understanding the transcriptional mechanisms underlying cellular function. These datasets are large, noisy and complex. Current analyses prioritize noise removal and dimensionality reduction to tackle these challenges and extract biological insight. We propose an alternative, physical approach to leverage the stochasticity, size and multimodal nature of these data to explicitly distinguish their biological and technical facets while revealing the underlying regulatory processes. With the Python package Monod, we demonstrate how nascent and mature RNA counts, present in most published datasets, can be meaningfully ‘integrated’ under biophysical models of transcription. By using variation in these modalities, we can identify transcriptional modulation not discernible through changes in average gene expression, quantitatively compare mechanistic hypotheses of gene regulation, analyze transcriptional data from different technologies within a common framework and minimize the use of opaque or distortive normalization and transformation techniques. Computational biophysics Computational models Software Transcriptomics
N Nature Methods · Nov 03, 2025 STORIES: learning cell fate landscapes from spatial transcriptomics using optimal transport In dynamic biological processes such as development, spatial transcriptomics is revolutionizing the study of the mechanisms underlying spatial organization within tissues. Inferring cell fate trajectories from spatial transcriptomics profiled at several time points has thus emerged as a critical goal, requiring novel computational methods. Wasserstein gradient flow learning is a promising framework for analyzing sequencing data across time, built around a neural network representing the differentiation potential. However, existing gradient flow learning methods face challenges in analyzing spatially resolved transcriptomic data. Here, we propose STORIES, a method that uses an extension of Optimal Transport to learn a spatially informed potential. We benchmark our approach using three large Stereo-seq spatiotemporal atlases and demonstrate superior spatial coherence compared to existing approaches. Finally, we provide an in-depth analysis of axolotl neural regeneration and mouse gliogenesis, recovering gene trends for known markers such asNptx1in neuron regeneration andAldh1l1in gliogenesis and additional putative drivers. Computational models Differentiation Software Transcriptomics biology mouse experiments
N Nature Methods · Oct 30, 2025 Nicheformer: a foundation model for single-cell and spatial omics Tissue makeup depends on the local cellular microenvironment. Spatial single-cell genomics enables scalable and unbiased interrogation of these interactions. Here we introduce Nicheformer, a transformer-based foundation model trained on both human and mouse dissociated single-cell and targeted spatial transcriptomics data. Pretrained on SpatialCorpus-110M, a curated collection of over 57 million dissociated and 53 million spatially resolved cells across 73 tissues on cellular reconstruction, Nicheformer learns cell representations that capture spatial context. It excels in linear-probing and fine-tuning scenarios for a newly designed set of downstream tasks, in particular spatial composition prediction and spatial label prediction. Critically, we show that models trained only on dissociated data fail to recover the complexity of spatial microenvironments, underscoring the need for multiscale integration. Nicheformer enables the prediction of the spatial context of dissociated cells, allowing the transfer of rich spatial information to scRNA-seq datasets. Overall, Nicheformer sets the stage for the next generation of machine-learning models in spatial single-cell analysis. Computational models Machine learning Software Transcriptomics biology mouse experiments
N Nature Methods · Oct 27, 2025 Improved reconstruction of single-cell developmental potential with CytoTRACE 2 While single-cell RNA sequencing has advanced our understanding of cell fate, identifying molecular hallmarks of potency—a cell’s ability to differentiate into other cell types—remains a challenge. Here we introduce CytoTRACE 2, an interpretable deep learning framework for predicting absolute developmental potential from single-cell RNA sequencing data. Across diverse platforms and tissues, CytoTRACE 2 outperformed previous methods in predicting developmental hierarchies, enabling detailed mapping of single-cell differentiation landscapes and expanding insights into cell potency. Cancer genomics Machine learning Software Stem cells Transcriptomics biology
N Nature Methods · Oct 22, 2025 scooby: modeling multimodal genomic profiles from DNA sequence at single-cell resolution Understanding how regulatory sequences shape gene expression across individual cells is a fundamental challenge in genomics. Joint RNA sequencing and epigenomic profiling provides opportunities to build models capturing sequence determinants across steps of gene expression. However, current models, developed primarily for bulk omics data, fail to capture the cellular heterogeneity and dynamic processes revealed by single-cell multimodal technologies. Here, we introduce scooby, a framework to model genomic profiles of single-cell RNA-sequencing coverage and single-cell assay for transposase-accessible chromatin using sequencing insertions from sequence at single-cell resolution. For this, we leverage the pretrained multiomics profile predictor Borzoi and equip it with a cell-specific decoder. Scooby recapitulates cell-specific expression levels of held-out genes and identifies regulators and their putative target genes. Moreover, scooby allows resolving single-cell effects of bulk expression quantitative trait loci and delineating their impact on chromatin accessibility and gene expression. We anticipate scooby to aid unraveling the complexities of gene regulation at the resolution of individual cells. Computational models Machine learning Software Transcriptomics Genomics Single-cell Machine Learning Human
N Nature Methods · Oct 13, 2025 Deep generative modeling of sample-level heterogeneity in single-cell genomics Single-cell genomic studies were recently conducted on hundred of samples exhibiting complex designs. These data have tremendous potential for discovering how sample- or tissue-level phenotypes relate to cellular and molecular composition. However, current analyses are often based on simplified representations of these data by averaging information across cells. We present multi-resolution variational inference (MrVI), a deep generative model designed to realize the potential of cohort studies at the single-cell level. MrVI tackles two fundamental, intertwined problems: stratifying samples into groups and evaluating the cellular and molecular differences between groups, without requiring predefined cell states. Leveraging its single-cell perspective, MrVI detects clinically relevant stratifications of cohorts of people with COVID-19 or inflammatory bowel disease that are manifested in only certain cellular subsets, enabling new discoveries that would otherwise be overlooked. MrVI can de novo identify groups of small molecules with similar biochemical properties and evaluate their effects on cellular composition and gene expression in large-scale perturbation studies. MrVI is an open-source tool atscvi-tools.org. Machine learning Software Statistical methods Transcriptomics Single-cell Machine Learning Genomics Human Clinical
N Nature Methods · Oct 13, 2025 Multitask benchmarking of single-cell multimodal omics integration methods Single-cell multimodal omics technologies have empowered the profiling of complex biological systems at a resolution and scale that were previously unattainable. These biotechnologies have propelled the fast-paced innovation and development of data integration methods, leading to a critical need for their systematic categorization, evaluation and benchmarking. Navigating and selecting the most pertinent integration approach poses a considerable challenge, contingent upon the tasks relevant to the study goals and the combination of modalities and batches present in the data at hand. Understanding how well each method performs multiple tasks, including dimension reduction, batch correction, cell type classification and clustering, imputation, feature selection and spatial registration, and at which combinations will help guide this decision. Here we develop a much-needed guideline on choosing the most appropriate method for single-cell multimodal omics data analysis through a systematic categorization and comprehensive benchmarking of current methods. The stage 1 protocol for this Registered Report was accepted in principle on 30 July 2024. The protocol, as accepted by the journal, can be found athttps://springernature.figshare.com/articles/journal_contribution/Multi-task_benchmarking_of_single-cell_multimodal_omics_integration_methods/26789902. Computational models Data integration Software Transcriptomics Single-cell Machine Learning Genomics Proteomics
N Nature Methods · Oct 01, 2025 Giotto Suite: a multiscale and technology-agnostic spatial multiomics analysis ecosystem Emerging spatial multiomics technologies provide an increasingly large amount of information content at multiple scales. However, it remains challenging to efficiently represent and harmonize diverse spatial datasets. Here we present Giotto Suite, a suite of modular packages that provides scalable and extensible end-to-end solutions for multiscale and multiomic data analysis, integration and visualization. At its core, Giotto Suite is centered around an innovative data framework, allowing the representation and integration of spatial omics data in a technology-agnostic manner. Giotto Suite integrates molecular, morphology, spatial and annotated feature information to create a responsive and flexible workflow, as demonstrated by applications to several state-of-the-art spatial technologies. Furthermore, Giotto Suite builds upon interoperable interfaces and data structures that bridge the established fields of genomics and spatial data science in R, thereby enabling independent developers to create custom-engineered pipelines. As such, Giotto Suite creates an immersive and multiscale ecosystem for spatial multiomic data analysis. Computational platforms and environments Software Transcriptomics Machine Learning Genomics Single-cell
N Nature Methods · Sep 15, 2025 Scaling up spatial transcriptomics for large-sized tissues: uncovering cellular-level tissue architecture beyond conventional platforms with iSCALE Recent advances in spatial transcriptomics (ST) technologies have transformed our ability to profile gene expression while preserving crucial spatial context within tissues. However, existing ST platforms are constrained by high costs, long turnaround times, low resolution, limited gene coverage and inherently small tissue capture areas, which hinder their broad applications. Here we present iSCALE, a method that reconstructs large-scale, super-resolution gene expression landscapes and automatically annotates cellular-level tissue architecture in samples exceeding capture areas of current ST platforms. The performance of iSCALE was assessed by comprehensive evaluations involving benchmarking experiments, immunohistochemistry staining and manual annotations by pathologists. When applied to multiple sclerosis human brain samples, iSCALE uncovered lesion-associated cellular characteristics undetectable by conventional ST experiments. Our results demonstrate the utility of iSCALE in analyzing large tissues by enabling unbiased annotation, resolving cell type composition, mapping cellular microenvironments and revealing spatial features beyond the reach of standard ST analysis or routine histopathological assessment. Gene expression analysis Machine learning RNA sequencing Transcriptomics Neuroscience Single-cell Genomics Human Machine Learning
N Nature Methods · Sep 08, 2025 Scvi-hub: an actionable repository for model-driven single-cell analysis The growing availability of single-cell omics datasets presents new opportunities for reuse, while challenges in data transfer, normalization and integration remain a barrier. Here we present scvi-hub: a platform for efficiently sharing and accessing single-cell omics datasets using pretrained probabilistic models. It enables immediate execution of fundamental tasks like visualization, imputation, annotation and deconvolution on new query datasets using state-of-the-art methods, with massively reduced storage and compute requirements. We show that pretrained models support efficient analysis of large references, including the CZI CELLxGENE Discover Census. Scvi-hub is built within the scvi-tools open-source environment and integrated into scverse. Scvi-hub offers a scalable and user-friendly framework for accessing and contributing to a growing ecosystem of ready-to-use models and datasets, thus putting the power of atlas-level analysis at the fingertips of a broad community of users. Machine learning Software Statistical methods Transcriptomics Single-cell Machine Learning Genomics Human
N Nature Methods · Aug 18, 2025 High-throughput profiling of chemical-induced gene expression across 93,644 perturbations In this Resource, we present an extensive dataset of chemical-induced gene signatures (CIGS), encompassing expression patterns of 3,407 genes regulating key biological processes in 2 human cell lines exposed to 13,221 compounds across 93,664 perturbations. This dataset encompasses 319,045,108 gene expression events, generated through 2 high-throughput technologies: the previously documented high-throughput sequencing-based high-throughput screening (HTS2) and the newly developed highly multiplexed and parallel sequencing (HiMAP-seq). Our results show that HiMAP-seq is comparable to RNA sequencing, but can profile the expression of thousands of genes across thousands of samples in one single test by utilizing a pooled-sample strategy. We further illustrate CIGS’s utility in elucidating the mechanism of action of unannotated small molecules, like ligustroflavone and 2,4-dihydroxybenzaldehyde, and to identify perturbation-induced cell states, such as those resistant to ferroptosis. The full dataset is publicly accessible athttps://cigs.iomicscloud.com/. Chemical genetics Genetic techniques RNA sequencing Transcriptomics Genomics Human Drug Development Machine Learning
