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Nature Genetics · Dec 03, 2025

ADAR1 editing is necessary for only a small subset of cytosolic dsRNAs to evade MDA5-mediated autoimmunity

Endogenous long double-stranded RNAs (dsRNAs), which are not edited by the RNA editing enzyme ADAR1, may activate the antiviral dsRNA receptor MDA5 to trigger interferon-mediated immune responses. Among the large number of endogenous long dsRNAs, the key substrates that activate MDA5—termed as immunogenic dsRNAs—remain largely unidentified. Here we reveal that human immunogenic dsRNAs constitute a surprisingly small fraction of all cellular dsRNAs. We found that these immunogenic dsRNAs were highly enriched in mRNAs and depleted of introns, consistent with their role as cytosolic MDA5 substrates. We validated the MDA5-dependent immunogenicity of these dsRNAs, which was dampened following ADAR1-mediated RNA editing. Notably, immunogenic dsRNAs were enriched at genetic susceptibility loci associated with common inflammatory diseases, implying their functional importance. We anticipate that a focused analysis of immunogenic dsRNAs will enhance our understanding and treatment of cancer and inflammatory diseases, where the roles of dsRNA editing and sensing are increasingly recognized.

Immunogenetics Transcriptomics biology
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Nature Biotechnology · Dec 03, 2025

Standardized metrics for assessment and reproducibility of imaging-based spatial transcriptomics datasets

Spatial transcriptomics lacks standardized metrics for evaluating imaging-based in situ hybridization technologies across sites. In this study, we generated the Spatial Touchstone (ST) dataset from six tissue types across several global sites with centralized sectioning, analyzed on both Xenium and CosMx platforms. These platforms were selected for their widespread use and distinct chemistries. We assessed reproducibility, sensitivity, dynamic ranges, signal-to-noise ratio, false discovery rates, cell type annotation and congruence with single-cell profiling. This study offers ST standardized operating procedures (STSOPs) and an open-source software, SpatialQM, enabling evaluation of samples across all technical metrics and direct imputation of cell annotations. The generated imaging-based spatial transcriptomics data repository comprises 254 spatial profiles, incorporating both public and newly generated ST datasets in a web-based application, which enables analysis and comparison of user data against an extensive collection of imaging-based datasets. Finally, we establish best practices and metrics to evaluate and integrate imaging-based multi-omics data from single cells into spatial transcriptomics to spatial proteomics.

Bioinformatics Quality control Standards Transcriptomics
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Nature Genetics · Nov 27, 2025

Passenger mutations link cellular origin and transcriptional identity in human lung adenocarcinomas

DNA damage is preferentially repaired in expressed genes; thus, genome-wide correlations between somatic mutation patterns and normal cell transcription may reflect tumor cell origins. Accordingly, we found that aggregate lung adenocarcinoma (LUAD) and squamous cancer (LUSC) somatic mutation density associated most strongly with distal (alveolar) and proximal (basal) lung cell-type-specific gene expression, respectively, consistent with presumed LUAD and LUSC cell origins. Analyzing individual genomes, 21% of LUADs bore mutational footprints of proximal airway origins, with 38% classified as ambiguous. Distal origin LUADs, enriched forKRASandSTK11drivers, occurred mainly in smokers; proximal origin LUADs, enriched forEGFRdrivers, were more common in never-smokers. Ambiguous origin LUADs showed APOBEC signatures andSMARCA4alterations.TP53mutant LUADs with non-distal cell origins preferentially exhibited non-distal transcriptional identity. Our study reveals a complex interplay between lineage and identity in LUAD evolution and offers a scalable strategy to infer tumor origins in human cancers.

Genomics Non-small-cell lung cancer Transcriptomics biology
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Nature Cell Biology · Nov 26, 2025

Smart spatial omics (S2-omics) optimizes region of interest selection to capture molecular heterogeneity in diverse tissues

Spatial omics technologies have transformed biomedical research by enabling high-resolution molecular profiling while preserving the native tissue architecture. These advances provide unprecedented insights into tissue structure and function. However, the high cost and time-intensive nature of spatial omics experiments necessitate careful experimental design, particularly in selecting regions of interest (ROIs) from large tissue sections. Currently, ROI selection is performed manually, which introduces subjectivity, inconsistency and a lack of reproducibility. Previous studies have shown strong correlations between spatial molecular patterns and histological features, suggesting that readily available and cost-effective histology images can be leveraged to guide spatial omics experiments. Here we present Smart Spatial omics (S2-omics), an end-to-end workflow that automatically selects ROIs from histology images with the goal of maximizing molecular information content in the ROIs. Through comprehensive evaluations across multiple spatial omics platforms and tissue types, we demonstrate that S2-omics enables systematic and reproducible ROI selection and enhances the robustness and impact of downstream biological discovery.

Biotechnology Computational biology and bioinformatics Transcriptomics biology
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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
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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
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Nature · Nov 13, 2025

Multi-omics analysis of a pig-to-human decedent kidney xenotransplant

Organ shortage remains a major challenge in transplantation, and gene-edited pig organs offer a promising solution1–3. Despite gene-editing, the immune reactions following xenotransplantation can still cause transplant failure4. To understand the immunological response of a pig-to-human kidney xenotransplantation, we conducted large-scale multi-omics profiling of the xenograft and the host’s blood over a 61-day procedure in a brain-dead human (decedent) recipient. Blood plasmablasts, natural killer (NK) cells, and dendritic cells increased between postoperative day (POD)10 and 28, concordant with expansion of IgG/IgA B-cell clonotypes, and subsequent biopsy-confirmed antibody-mediated rejection (AbMR) at POD33. Human T-cell frequencies increased from POD21 and peaked between POD33-49 in the blood and xenograft, coinciding with T-cell receptor diversification, expansion of a restricted TRBV2/J1 clonotype and histological evidence of a combined AbMR and cell-mediated rejection at POD49. At POD33, the most abundant human immune population in the graft wasCXCL9+ macrophages, aligning with IFN-γ-driven inflammation and a Type I immune response. In addition, we see evidence of interactions between activated pig-resident macrophages and infiltrating human immune cells. Xenograft tissue showed pro-fibrotic tubular and interstitial injury, marked byS100A65,SPP16(Osteopontin), andCOLEC117, at POD21–POD33. Proteomics profiling revealed human and pig complement activation, with decreased human component after AbMR therapy with complement inhibition. Collectively, these data delineate the molecular orchestration of human immune responses to a porcine kidney, revealing potential immunomodulatory targets for improving xenograft survival.

Predictive medicine Proteomics Transcriptomics Translational immunology Transplant immunology
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Nature Biotechnology · Nov 11, 2025

Multimodal learning enables chat-based exploration of single-cell data

Single-cell sequencing characterizes biological samples at unprecedented scale and detail, but data interpretation remains challenging. Here, we present CellWhisperer, an artificial intelligence (AI) model and software tool for chat-based interrogation of gene expression. We establish a multimodal embedding of transcriptomes and their textual annotations, using contrastive learning on 1 million RNA sequencing profiles with AI-curated descriptions. This embedding informs a large language model that answers user-provided questions about cells and genes in natural-language chats. We benchmark CellWhisperer’s performance for zero-shot prediction of cell types and other biological annotations and demonstrate its use for biological discovery in a meta-analysis of human embryonic development. We integrate a CellWhisperer chat box with the CELLxGENE browser, allowing users to interactively explore gene expression through a combined graphical and chat interface. In summary, CellWhisperer leverages large community-scale data repositories to connect transcriptomes and text, thereby enabling interactive exploration of single-cell RNA-sequencing data with natural-language chats.

Gene regulation in immune cells Machine learning Preclinical research Software Transcriptomics
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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
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Nature Biotechnology · Nov 04, 2025

KATMAP infers splicing factor activity and regulatory targets from knockdown data

Typical RNA sequencing (RNA-seq) experiments uncover hundreds of splicing changes, reflecting underlying changes in splicing factor (SF) activity. Understanding how SF activity influences transcriptomic variation requires elucidating how each SF impacts splicing. Here, we present an interpretable regression model, KATMAP, which models splicing changes throughout the transcriptome by analyzing changes in SF binding and the resulting alterations in RNA processing. To learn a regulatory model, KATMAP requires SF perturbation RNA-seq data and the SF’s binding motif as inputs, returning a description of the SF’s position-specific regulatory activity and predicted targets. The KATMAP software includes models pretrained on ENCODE SF knockdown data. Learned KATMAP models can be applied to predict SF regulation andcis-elements at individual exons, which can guide the design of splice-switching antisense oligonucleotides. KATMAP can also interpret RNA-seq data by uncovering the factors responsible for transcriptomic changes, distinguishing direct SF targets from indirect effects and inferring relevant SFs from clinical RNA-seq data.

Computational models Software Transcriptomics
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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
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Nature Microbiology · Oct 30, 2025

Human immunodeficiency virus and antiretroviral therapies exert distinct influences across diverse gut microbiomes

Human immunodeficiency virus (HIV) infection alters gut microbiota composition and function, but the impact of geography and antiretroviral therapy remains unclear. Here we determined gut microbiome alterations linked to HIV infection and antiretroviral treatment in 327 individuals with HIV and 260 control participants in cohorts from Uganda, Botswana and the USA via faecal metagenomics. We found that while HIV-associated taxonomic differences were mostly site specific, changes in microbial functional pathways were broadly consistent across the cohorts and exacerbated in individuals with acquired immunodeficiency syndrome. Microbiome perturbations associated with antiretroviral medications were also geography dependent. In Botswana and Uganda, use of the non-nucleoside reverse transcriptase inhibitor efavirenz was linked to depletion ofPrevotella, disruption of interspecies metabolic networks, exacerbation of systemic inflammation and atherosclerosis. Efavirenz-associatedPrevotelladepletion may occur through cross-inhibition of prokaryotic reverse transcriptases involved in antiphage defences, as shown by computational and in vitro experiments. These observations could inform future geography-specific and microbiome-guided therapy.

HIV infections Metagenomics Transcriptomics
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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
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Nature Biotechnology · Oct 30, 2025

High-plex spatial RNA imaging in one round with conventional microscopes using color-intensity barcodes

Spatial RNA imaging has not been widely adopted because conventional fluorescence microscopy is limited to only a few channels and the cyclic reactions needed to increase multiplexing in techniques such as sequential fluorescence in situ hybridization require sophisticated instrumentation. Here, we introduce ‘profiling of RNA in situ through single-round imaging’ (PRISM), a method that expands coding capacity through color intensity grading. Using a radius vector filtering strategy to ensure the distinguishability of codewords in color space, PRISM achieves up to 64-plex color-barcoded RNA imaging in a single imaging round with conventional microscopes. We validate PRISM’s versatility across various tissues by generating a three-dimensional (3D) atlas of mouse embryonic development from E12.5 to E14.5, a quasi-3D tumor–normal transition landscape of human hepatocellular carcinoma and a 3D cell atlas and subcellular RNA localization landscapes of mouse brain. Additionally, we show the critical role of cancer-associated fibroblasts in mediating immune infiltration and immune response heterogeneity within and between tumor microenvironments.

Fluorescence imaging Fluorescence in situ hybridization Transcriptomics
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Nature · Oct 29, 2025

Multi-omic profiling reveals age-related immune dynamics in healthy adults

The generation and maintenance of immunity is a dynamic process that is dependent on age1,2,3. Here, to better understand its progression, we profiled peripheral immunity in more than 300 healthy adults (25 to 90 years of age) using single-cell RNA sequencing, proteomics and flow cytometry, following 96 adults longitudinally across 2 years with seasonal influenza vaccination. The resulting resource generated a single-cell RNA-sequencing dataset of more than 16 million peripheral blood mononuclear cells with 71 immune cell subsets from our Human Immune Health Atlas and enabled us to interrogate how immune cell composition and states shift with age, chronic viral infection and vaccination. From these data, we demonstrate robust, non-linear transcriptional reprogramming in T cell subsets with age that is not driven by systemic inflammation or chronic cytomegalovirus infection. This age-related reprogramming led to a functional T helper 2 (TH2) cell bias in memory T cells that is linked to dysregulated B cell responses against highly boosted antigens in influenza vaccines. Collectively, this study reveals unique features of the immune ageing process that occur prior to advanced age and provides novel targets for age-related immune modulation. We provide interactive tools for exploring this extensive human immune health resource athttps://apps.allenimmunology.org/aifi/insights/dynamics-imm-health-age/.

Cellular immunity Systems biology Transcriptomics Vaccines
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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
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Nature Genetics · Oct 27, 2025

Maternal factor OTX2 regulates human embryonic genome activation and early development

Transcription factors (TFs) are instrumental in kickstarting embryonic genome activation (EGA) in many species, yet their regulatory roles in human embryos remain poorly understood. Here, we show that OTX2, a maternally provided PRD-like homeobox TF, is required for proper human EGA and early development. At the four-cell stage, OTX2 promotes activation of key EGA genes, includingTPRX1andTPRX2, and the EGA-associated repeat HERVL-int and MLT2A1. At EGA targets, OTX2 directly binds promoters and putative enhancers, many of which overlap with Alu and MaLR repetitive elements containing the OTX2 motif, and promotes chromatin accessibility. The transcriptome and developmental defects uponOTX2knockdown are partially rescued by overexpression ofTPRX1andTPRX2. Finally, joint knockdown ofOTX2andTPRXL, encoding another maternal PRD-like homeobox TF, exacerbates chromatin opening and EGA defects at the 8C stage. These findings establish OTX2 as a crucial maternal TF that awakes the genome at the beginning of human life.

DNA sequencing Embryogenesis Gene regulation Transcriptomics
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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
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Nature · Oct 22, 2025

MappingPlasmodiumtransitions and interactions in theAnophelesfemale

The human malaria parasite,Plasmodium falciparum, relies exclusively onAnophelesmosquitoes for transmission. Once ingested during blood feeding, most parasites die in the mosquito midgut lumen or during epithelium traversal1. How surviving ookinetes interact with midgut cells and form oocysts remains poorly understood, yet these steps are essential to initiate a remarkable growth process culminating in the production of thousands of infectious sporozoites2. Here, using single-cell RNA sequencing of both parasites and mosquito cells across different developmental stages and metabolic conditions, we unveil key transitions and mosquito–parasite interactions that occur in the midgut. Functional analyses uncover processes that regulate oocyst growth and identify thePlasmodiumtranscription factor PfSIP2 as essential for sporozoite infection of human hepatocytes. Combining shared mosquito–parasite barcode analysis with confocal microscopy, we reveal that parasites preferentially interact with midgut progenitor cells during epithelial crossing, potentially using their basal location as an exit landmark. Additionally, we show tight connections between extracellular late oocysts and surrounding muscle cells that may ensure parasite adherence to the midgut. We confirm our major findings in several mosquito–parasite combinations, including field-derived parasites. Our study provides fundamental insight into the molecular events that characterize previously inaccessible biological transitions and mosquito–parasite interactions, and identifies candidates for transmission-blocking strategies.

Entomology Malaria Parasitology Transcriptomics
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Nature Neuroscience · Oct 21, 2025

TDP-43 nuclear loss in FTD/ALS causes widespread alternative polyadenylation changes

In frontotemporal dementia and amyotrophic lateral sclerosis, the RNA-binding protein TDP-43 is depleted from the nucleus of neurons in the brain and spinal cord. A key function of TDP-43 has emerged as a repressor of cryptic exon inclusion during pre-mRNA splicing, but a role for TDP-43 in other RNA-processing events remains unresolved. Here we show that loss of TDP-43 from neuronal nuclei of human brain and disease-causing mutations in TDP-43 are associated with widespread changes in alternative polyadenylation (APA). Using high-resolution polyadenylation site mapping, we comprehensively defined TDP-43-regulated APA events in human stem cell-derived neurons and found that both the strength and position of TDP-43 binding influence polyA site usage. APA events caused by loss of TDP-43 impact expression of disease-relevant genes (for example,SFPQ,NEFLandTMEM106B). These findings provide evidence that, in addition to cryptic exon inclusion, APA changes are a new facet of TDP-43 pathology.

Alternative splicing Amyotrophic lateral sclerosis Transcriptomics
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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
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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
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Nature Metabolism · Oct 09, 2025

Early-life ketone body signalling promotes beige fat biogenesis through changes in histone acetylome and β-hydroxybutyrylome

Infants undergo distinct ketogenesis during the preweaning period, yet its physiological implications remain unclear. Here, we show that preweaning ketosis promotes beige fat biogenesis and improves health outcomes in adulthood. Loss of ketogenesis in neonatal mice by early weaning or ablation ofHmgcs2hinders beige adipogenesis, subsequently exacerbating metabolic dysregulation in high-fat diet-induced obesity. Enhanced ketogenesis during lactation through exogenous ketone supplements enhances energy expenditure, beige fat formation, and mitochondrial biogenesis and respiration. Using single-cell RNA sequencing, we identified a subset of β-hydroxybutyrate-responsive adipocyte progenitor cells (APCs) expressingCd81that showed high beige adipogenic potential. Enhanced ketogenesis promotes the recruitment of beige APCs and their differentiation into beige adipocytes. Mechanistically, ketogenesis-derived βHB induces a switch in the histone acetylome and β-hydroxybutyrylome for transcriptional activation of beige fat biogenesis genes. Notably, enhanced ketogenesis during lactation alleviates adverse metabolic effects predisposed by parental obesity. Our study highlights that targeting preweaning ketosis to drive beige adipogenesis may offer a therapeutic approach to combat obesity and metabolic diseases in adulthood.

Epigenetics Metabolism Mitochondria Obesity Transcriptomics
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Nature Genetics · Oct 03, 2025

Dissecting the impact of transcription factor dose on cell reprogramming heterogeneity using scTF-seq

Reprogramming often yields heterogeneous cell fates, yet the underlying mechanisms remain poorly understood. To address this, we developed single-cell transcription factor sequencing (scTF-seq), a single-cell technique that induces barcoded, doxycycline-inducible TF overexpression and quantifies TF dose-dependent transcriptomic changes. Applied to mouse embryonic multipotent stromal cells, scTF-seq generated a gain-of-function atlas for 384 mouse TFs, identifying key regulators of lineage specification, cell cycle control and their interplay. Leveraging single-cell resolution, we uncovered how TF dose shapes reprogramming heterogeneity, revealing both dose-dependent and stochastic cell state transitions. We classified TFs into low-capacity and high-capacity groups, with the latter further subdivided by dose sensitivity. Combinatorial scTF-seq demonstrated that TF interactions can shift from synergistic to antagonistic depending on the relative dose. Altogether, scTF-seq enables the dissection of TF function, dose and cell fate control, providing a high-resolution framework to understand and predict reprogramming outcomes, advancing gene regulation research and the design of cell engineering strategies. This study introduces single-cell transcription factor (TF) sequencing, a single-cell barcoded and doxycycline-inducible TF overexpression approach that reveals dose-sensitive functional classes of TFs and cellular heterogeneity by mapping TF dose-dependent transcriptomic changes during the reprogramming of mouse embryonic multipotent stromal cells.

Bioinformatics High-throughput screening RNA sequencing Stem cells Transcriptomics
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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
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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
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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
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Nature Genetics · Sep 05, 2025

Genetic variants affecting RNA stability influence complex traits and disease risk

Gene expression is modulated jointly by transcriptional regulation and messenger RNA stability, yet the latter is often overlooked in studies on genetic variants. Here, leveraging metabolic labeling data (Bru/BruChase-seq) and a new computational pipeline, RNAtracker, we categorize genes as allele-specific RNA stability (asRS) or allele-specific RNA transcription events. We identify more than 5,000 asRS variants among 665 genes across a panel of 11 human cell lines. These variants directly overlap conserved microRNA target regions and allele-specific RNA-binding protein sites, illuminating mechanisms through which stability is mediated. Furthermore, we identified causal asRS variants using a massively parallel screen (MapUTR) for variants that affect post-transcriptional mRNA abundance, as well as through CRISPR prime editing approaches. Notably, asRS genes were enriched significantly among a multitude of immune-related pathways and contribute to the risk of several immune system diseases. This work highlights RNA stability as a critical, yet understudied mechanism linking genetic variation and disease.

Gene expression Transcriptomics
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Nature Genetics · Sep 05, 2025

A multi-tissue single-cell expression atlas in cattle

Systematic characterization of the molecular states of cells in livestock tissues is essential for understanding the cellular and genetic mechanisms underlying economically and ecologically important physiological traits. Here, as part of the Farm Animal Genotype-Tissue Expression (FarmGTEx) project, we describe a comprehensive reference map including 1,793,854 cells from 59 bovine tissues in calves and adult cattle, spanning both sexes, which reveals intra-tissue and inter-tissue cellular heterogeneity in gene expression, transcription factor regulation and intercellular communication. Integrative analysis with genetic variants that underpin bovine monogenic and complex traits uncovers cell types of relevance, such as spermatocytes, responsible for sperm motility and excitatory neurons for milk fat yield. Comparative analysis reveals similarities in gene expression between cattle and humans, allowing for the detection of relevant cell types to study human complex phenotypes. This Cattle Cell Atlas will serve as a key resource for cattle genetics and genomics, selective breeding and comparative biology.

Data processing Transcriptomics
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Nature · Sep 03, 2025

Single-cell transcriptomic and genomic changes in the ageing human brain

Over time, cells in the brain and in the body accumulate damage, which contributes to the ageing process1. In the human brain, the prefrontal cortex undergoes age-related changes that can affect cognitive functioning later in life2. Here, using single-nucleus RNA sequencing (snRNA-seq), single-cell whole-genome sequencing (scWGS) and spatial transcriptomics, we identify gene-expression and genomic changes in the human prefrontal cortex across lifespan, from infancy to centenarian. snRNA-seq identified infant-specific cell clusters enriched for the expression of neurodevelopmental genes, as well as an age-associated common downregulation of cell-essential homeostatic genes that function in ribosomes, transport and metabolism across cell types. Conversely, the expression of neuron-specific genes generally remains stable throughout life. These findings were validated with spatial transcriptomics. scWGS identified two age-associated mutational signatures that correlate with gene transcription and gene repression, respectively, and revealed gene length- and expression-level-dependent rates of somatic mutation in neurons that correlate with the transcriptomic landscape of the aged human brain. Our results provide insight into crucial aspects of human brain development and ageing, and shed light on transcriptomic and genomic dynamics.

Neural ageing Sequencing Transcriptomics
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Nature Biotechnology · Aug 28, 2025

Skin metatranscriptomics reveals a landscape of variation in microbial activity and gene expression across the human body

Metatranscriptomics methods for the skin are hampered by low microbial biomass, contamination with host cells and low RNA stability. In this study, we developed a robust, clinically tractable skin metatranscriptomics workflow that provides high technical reproducibility of profiles, uniform coverage across gene bodies and strong enrichment of microbial mRNAs. Paired application of this protocol with metagenomics to five skin sites in a cohort of 27 healthy adults identifies a notable divergence between transcriptomic and genomic abundances. Specifically,Staphylococcusspecies and the fungiMalasseziahad an outsized contribution to metatranscriptomes at most sites, despite their modest representation in metagenomes. Species-level analysis shows signatures of microbial adaptation to their niches. Gene-level analysis identifies diverse antimicrobial genes transcribed by skin commensals in situ, including several uncharacterized bacteriocins. Correlation of microbial gene expression with organismal abundances uncovers more than 20 genes that putatively mediate interactions between microbes. This work highlights how skin metatranscriptomics identifies active species and microbial functions in situ.

Biochemical reaction networks Microbiome Transcriptomics
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Nature Medicine · Aug 21, 2025

Perioperative IDH inhibition in treatment-naive IDH-mutant glioma: a pilot trial

Mutant isocitrate dehydrogenase (mIDH) inhibition significantly improves progression-free survival in patients with mIDH WHO grade 2 glioma; however, a large proportion of patients will progress, and mechanisms of adaptation to mIDH inhibition remain poorly understood. Perioperative studies with evaluation of paired pre- and post-treatment samples enable detailed understanding of drug response, facilitating biomarker development, but are rare in glioma owing to safety and cost concerns. Here we conducted a single-arm, open-label feasibility perioperative trial in patients with mIDH1 low-grade glioma, treatment naive to radiation and chemotherapy, with safusidenib (AB-218/DS-1001b), an orally available small-molecule inhibitor of mIDH1. As of 8 November 2024, 10 patients were enrolled and have completed the perioperative component, with a median follow-up of 14 months. Patients continue postoperative safusidenib with ongoing follow-up for safety and efficacy. The primary endpoint showed the feasibility and acceptability of conducting a two-stage perioperative trial. One patient experienced a serious surgery-related adverse event, and ten reported safusidenib-related adverse events; most were grade 1, and one experienced grade 3 elevation of transaminases. Tumor 2-hydroxyglutarate quantification revealed on-target activity, associated with alterations in differentiation programs and neural excitability, functionally validated in post hoc analysis by patch-clamp electrophysiology. Taken together, these results provide a detailed investigation of observations associated with mIDH inhibition in glioma. The study shows the safety and feasibility of this perioperative approach, which can be applied broadly in clinical trial design, serving as proof of concept for advancing drug development in glioma. ClinicalTrials.gov registration:NCT05577416.

Cancer metabolism CNS cancer Transcriptomics Translational research
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Nature Genetics · Aug 20, 2025

Super-pangenome analyses across 35 accessions of 23Avenaspecies highlight their complex evolutionary history and extensive genomic diversity

Common oat, belonging to the genusAvenawith 30 recognized species, is a nutritionally important cereal crop and high-quality forage worldwide. Here, we construct a genus-level super-pangenome ofAvenacomprising 35 high-quality genomes from 14 cultivated oat accessions and 21 wild species. The fully resolved phylogenomic analysis unveils the origin and evolutionary scenario ofAvenaspecies, and the super-pangenome analysis identifies 26.62% and 59.93% specific genes and haplotypes in wild species. We delineate the landscape of structural variations (SVs) and the transcriptome profile based 1,401 RNA-sequencing (RNA-seq) samples from diverse abiotic stress treatments in oat. We highlight the crucial role of SVs in modulating gene expression and shaping adaptation to diverse stresses. Further combining SV-based genome-wide association studies (GWASs), we characterize 13 candidate genes associated with drought resistance such asAsARF7, validated by transgenic oat lines. Our study provides unprecedented genomic resources to facilitate genomic, evolution and molecular breeding research in oat.

Gene expression Genomics Plant breeding Transcriptomics
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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