N Nature Methods · Dec 04, 2025 C-COMPASS: a user-friendly neural network tool profiles cell compartments at protein and lipid levels Systematic proteomic organelle profiling methods including protein correlation profiling and LOPIT have advanced our understanding of cellular compartmentalization. To manage the complexity of organelle profiling data, we introduce C-COMPASS, a user-friendly open-source software that employs a neural network-based regression model to predict the spatial cellular distribution of proteins. C-COMPASS handles complex multilocalization patterns and integrates protein abundance to model organelle composition changes across conditions. We apply C-COMPASS to mice with humanized livers to elucidate organelle remodeling during metabolic perturbations. Additionally, by training neural networks with co-generated marker protein profiles, C-COMPASS extends spatial profiling to lipids, overcoming the lack of organelle-specific lipid markers, allowing for determination of localization and tracking of lipid species across different compartments. This provides integrated snapshots of organelle lipid and protein compositions. Overall, C-COMPASS offers an accessible tool for multiomic studies of organelle dynamics without needing advanced computational skills, empowering researchers to explore new questions in lipidomics, proteomics and organelle biology. Computational platforms and environments Organelles Proteomics biology mouse experiments
N Nature Methods · Dec 04, 2025 Latent space-based network analysis for brain–behavior linking in neuroimaging We propose a latent space-based statistical network analysis (LatentSNA) method that implements network science in a generative Bayesian framework, preserves neurologically meaningful brain topology and improves statistical power for imaging biomarker detection. LatentSNA (1) addresses the lack of power and inflated type II errors in current analytic approaches when detecting imaging biomarkers, (2) allows unbiased estimation of the influence of biomarkers on behavioral variants, (3) quantifies uncertainty and evaluates the likelihood of estimated biomarker effects against chance and (4) improves brain–behavior prediction in new samples as well as the clinical utility of neuroimaging findings. LatentSNA is broadly applicable across multiple imaging modalities and outcome measures in developing, aging and transdiagnostic cohorts, totaling 8,003 to 11,861 participants. LatentSNA achieves substantial accuracy gains (averaging 110–150%) and replicability improvements (averaging 153%) over existing approaches in moderate to large datasets. As a result, LatentSNA elucidates how network topology is implicated in brain–behavior relationships. Network topology Statistical methods Neuroscience Machine Learning Human Clinical
N Nature Methods · Dec 04, 2025 Deep Imputation for Skeleton data (DISK) for behavioral science Pose estimation methods and motion capture systems have opened doors to quantitative measurements of animal kinematics. While animal behavior experiments are expensive and complex, tracking errors sometimes make large portions of the experimental data unusable. Here our deep learning method, Deep Imputation for Skeleton data (DISK), uncovers dependencies between keypoints and their dynamics to impute missing tracking data without the help of any manual annotations. We demonstrate the utility and performance of DISK on seven animal skeletons including multi-animal setups. The imputed recordings allow us to detect more episodes of motion, such as steps, and obtain more statistically robust results when comparing these episodes between experimental conditions. In addition, by learning to impute the missing content, DISK learns meaningful representations of the data capturing, for example, underlying actions. This stand-alone imputation package, available athttps://github.com/bozeklab/DISK.git/, is applicable to outputs of tracking methods (marker-based or markerless) and allows for varied types of downstream analysis. Computational neuroscience Machine learning biology
N Nature Methods · Dec 03, 2025 Atom-level enzyme active site scaffolding using RFdiffusion2 Designing new enzymes typically begins with idealized arrangements of catalytic functional groups around a reaction transition state, then attempts to generate protein structures that precisely position these groups. Current AI-based methods can create active enzymes but require predefined residue positions and rely on reverse-building residue backbones from side-chain placements, which limits design flexibility. Here we show that a new deep generative model, RoseTTAFold diffusion 2 (RFdiffusion2), overcomes these constraints by designing enzymes directly from functional group geometries without specifying residue order or performing inverse rotamer generation. RFdiffusion2 successfully generates scaffolds for all 41 active sites in a diverse benchmark, compared to 16 using previous methods. We further design enzymes for three distinct catalytic mechanisms and identify active candidates after experimentally testing fewer than 96 sequences in each case. These results highlight the potential of atomic-level generative modeling to create de novo enzymes directly from reaction mechanisms. Enzymes Protein design biology
N Nature Methods · Dec 02, 2025 Molecular-scale isotropic 3D super-resolution microscopy via interference localization Three-dimensional (3D) nanoscale imaging reveals the detailed morphology of subcellular structures; however, conventional single-molecule localization microscopy is constrained by limited axial resolution. Here we introduce ROSE-3D, an interferometric localization approach that enables isotropic 3D super-resolution imaging with uniform performance across the entire depth of field. Compared with conventional astigmatism-based methods, ROSE-3D improves lateral localization precision by 2–6 times and axial precision by 3.5–8 times over a depth of field of approximately 1.2 μm. Leveraging its multicolor and whole-cell imaging capabilities, ROSE-3D resolves, in situ, the nanoscale organization of nuclear lamins and the assemblies of mitochondrial fission-related protein DRP1. These results establish ROSE-3D as a powerful tool for interrogating nanoscale cellular architecture. Fluorescence imaging Super-resolution microscopy biology
N Nature Methods · Dec 02, 2025 Parallel stopped-flow interrogation of diverse biological systems at the single-molecule scale Single-molecule imaging techniques have provided unprecedented insights into functional changes in composition and conformation across diverse biological systems. As with other biophysical methods, single-molecule fluorescence and Förster resonance energy transfer investigations are typically limited to examination of one sample at a time. Consequently, experimental throughput is restricted, and experimental variances are introduced that can obscure functional distinctions in closely related systems. Here, to address these limitations, we introduce parallel rapid exchange single-molecule fluorescence and single-molecule Förster resonance energy transfer to enable simultaneous steady-state and pre-steady-state interrogations of diverse systems. Using this approach, we elucidate the timing of distinct conformational events underpinning β-arrestin1 activation, unmask antibiotic-induced impacts on messenger RNA decoding fidelity and demonstrate that endogenously encoded ribosomal RNA sequence variation modulates antibiotic sensitivity. This generalizable and scalable method promises to broaden the scope and reproducibility of quantitative single-molecule interrogations of biomolecular function. Fluorescence imaging Fluorescence resonance energy transfer Proteins Single-molecule biophysics Translation biology
N Nature Methods · Dec 02, 2025 CaBLAM: a high-contrast bioluminescent Ca2+indicator derived from an engineeredOplophorus gracilirostrisluciferase Monitoring intracellular calcium is central to understanding cell signaling across nearly all cell types and organisms. Fluorescent genetically encoded calcium indicators (GECIs) remain the standard tools for in vivo calcium imaging, but require intense excitation light, leading to photobleaching, background autofluorescence and phototoxicity. Bioluminescent GECIs, which generate light enzymatically, eliminate these artifacts but have been constrained by low dynamic range and suboptimal calcium affinities. Here we show that CaBLAM (‘calcium bioluminescence activity monitor’), an engineered bioluminescent calcium indicator, achieves an order-of-magnitude improvement in signal contrast and a tunable affinity matched to physiological cytosolic calcium. CaBLAM enables single-cell and subcellular activity imaging at video frame rates in cultured neurons and sustained imaging over hours in awake, behaving animals. These capabilities establish CaBLAM as a robust and general alternative to fluorescent GECIs, extending calcium imaging to regimes where excitation light is undesirable or infeasible. Bioluminescence imaging Cellular neuroscience biology
N Nature Methods · Nov 28, 2025 ExoSloNano: multimodal nanogold labels for identification of macromolecules in live cells and cryo-electron tomograms In situ cryo-electron microscopy (cryo-EM) enables the direct interrogation of structure–function relationships by resolving macromolecular structures in their native cellular environment. Recent progress in sample preparation, imaging and data processing has enabled the identification and determination of large biomolecular complexes. However, the majority of proteins are of a size that still eludes identification in cellular cryo-EM data, and most proteins exist in low copy numbers. Therefore, novel tools are needed for cryo-EM to identify macromolecules across multiple size scales (from microns to nanometers). Here we introduce nanogold probes for detecting specific proteins using correlative light and electron microscopy, cryo-electron tomography (cryo-ET) and resin-embedded electron microscopy. These nanogold probes can be introduced into live cells, in a manner that preserves intact molecular networks and cell viability. We use this ExoSloNano system to identify both cytoplasmic and nuclear proteins by room-temperature electron microscopy, and resolve associated structures by cryo-ET. By providing high-efficiency protein labeling in live cells and molecular specificity within cryo-ET tomograms, ExoSloNano expands the proteome available to electron microscopy. Cellular imaging Cryoelectron tomography Fluorescence imaging Sensors and probes biology
N Nature Methods · Nov 28, 2025 DeepCor: denoising fMRI data with contrastive autoencoders Functional magnetic resonance imaging (fMRI) allows noninvasive measurement of neural activity with high spatial resolution. However, fMRI data are affected by noise. Here we introduce and evaluate a denoising method (DeepCor) that utilizes deep generative models to disentangle and remove noise. The method is applicable to data from single participants. DeepCor outperforms other state-of-the-art denoising approaches on a variety of simulated datasets. In real fMRI data, DeepCor enhances BOLD signal responses to face stimuli, outperforming CompCor by 215%. Cognitive neuroscience Computational biology and bioinformatics Neuroscience Machine Learning fMRI Denoising
N Nature Methods · Nov 28, 2025 Assessment of computational methods in predicting TCR–epitope binding recognition T cell receptors (TCRs) play a vital role in immune recognition by binding specific epitopes. Accurate prediction of TCR–epitope interactions is fundamental for advancing immunology research. Although numerous computational methods have been developed, a comprehensive evaluation of their performance remains lacking. Here we assessed 50 state-of-the-art TCR–epitope prediction models using 21 datasets covering 762 epitopes and hundreds of thousands binding TCRs. Our analysis revealed that the source of negative TCRs substantially impacts model accuracy, with external negatives potentially introducing uncontrolled confounders. Model performance generally improved with more TCRs per epitope, highlighting the importance of large and diverse datasets. Models incorporating multiple features typically outperformed those using only complementarity-determining region 3β information, yet all struggle to generalize to unseen epitopes. The use of independent test sets proved crucial for unbiased assessment on both seen and unseen epitopes. These insights will guide the development of more accurate and generalizable TCR–epitope prediction models for real-world applications. Adaptive immunity Computational models biology
N Nature Methods · Nov 27, 2025 A comprehensive foundation model for cryo-EM image processing Cryogenic electron microscopy (cryo-EM) has become a premier technique for determining high-resolution structures of biological macromolecules. However, its broad application is constrained by the demand for specialized expertise. Here, to address this limitation, we introduce the Cryo-EM Image Evaluation Foundation (Cryo-IEF) model, a versatile tool pre-trained on ~65 million cryo-EM particle images through unsupervised learning. Cryo-IEF performs diverse cryo-EM processing tasks, including particle classification by structure, pose-based clustering and image quality assessment. Building on this foundation, we developed CryoWizard, a fully automated single-particle cryo-EM processing pipeline enabled by fine-tuned Cryo-IEF for efficient particle quality ranking. CryoWizard resolves high-resolution structures across samples of varied properties and effectively mitigates the prevalent challenge of preferred orientation in cryo-EM. Cryoelectron microscopy Machine learning Proteins biology
N Nature Methods · Nov 27, 2025 FX-Cell: a method for single-cell RNA sequencing on difficult-to-digest and cryopreserved plant samples Single-cell RNA sequencing in plants requires the isolation of high-quality protoplasts—cells devoid of cell walls. However, many plant tissues and organs are resistant to enzymatic digestion, posing a significant barrier to advancing single-cell multi-omics in plant research. Furthermore, for field-grown crops, the lack of immediate laboratory facilities presents another major challenge for timely protoplast preparation. Here, to address these limitations, we developed FX-Cell and its derivatives, FXcryo-Cell and cryoFX-Cell, to enable single-cell RNA sequencing with both difficult-to-digest and cryopreserved plant samples. By optimizing the fixation buffer and minimizing RNA degradation, our approach ensures efficient cell wall digestion at high temperatures while maintaining high-quality single cells, even after long-term storage at −80 °C, and circumvents use of nuclei, which are not representative of the pool of translatable messenger RNAs. We successfully constructed high-quality cell atlases for rice tiller nodes, rhizomes of wild rice and maize crown roots grown under field conditions. Moreover, these methods enable the accurate reconstruction of plant acute wounding responses at single-cell resolution. Collectively, these advancements expand the applicability of plant single-cell genomics across a wider range of species and tissues, paving the way for comprehensive Plant Cell Atlases for plant species. Plant development RNA sequencing biology
N Nature Methods · Nov 27, 2025 Nondestructive X-ray tomography of brain tissue ultrastructure Maps of biological tissues at subcellular detail are key for understanding how organs function. X-ray nanotomography is a promising alternative to volume electron microscopy: it has the potential to nondestructively image millimeter-sized samples at ultrastructural resolution within a few days. A fundamental barrier is that the intense X-rays required for imaging also deform and disintegrate the tissue samples. Here we show a combination of solutions that overcome this barrier: We used a cryogenic and stable sample stage, tailored nonrigid tomographic reconstruction algorithms and an epoxy resin developed for the nuclear and aerospace industry. Tissue samples were resistant to radiation doses exceeding 1.15 × 1010Gy, and sub-40 nm isotropic resolution allowed identifying axon bundles, dendrites and synapses in mouse brain tissue without physical sectioning. Using volume electron microscopy, we demonstrate that tissue ultrastructure remains intact after X-ray imaging. Together, this unlocks the potential of X-ray tomography for high-resolution tissue imaging. Imaging Neuroscience biology mouse experiments
N Nature Methods · Nov 26, 2025 A highly photostable monomeric red fluorescent protein for dual-color 3D STED and time-lapse 3D SIM imaging Highly photostable red fluorescent proteins (RFPs) are invaluable for dual-color fluorescence microscopy, including super-resolution microscopy. Here we present mScarlet3‑S2, an RFP that exhibits a 29-fold improvement in photostability over its predecessor, mScarlet3, and outperforms other existing RFPs. This high photostability enables prolonged 2D and 3D imaging using both structured illumination microscopy and stimulated emission depletion microscopy. Using mScarlet3‑S2, we achieved over 150Z-stacks in 3D STED imaging, revealing the architecture of the endoplasmic reticulum (ER) in detail. Key findings facilitated by mScarlet3‑S2 include nonplanar ER junctions, nuclear envelope (NE) invaginations, 3D maps of ER–NE contacts, diverse contact morphotypes (punctate, ribbon-like and branched) and polarized ER–NE junction distributions. These findings redefine our structural understanding of the ER–NE interface and demonstrate the value of mScarlet3‑S2 in revealing subcellular complexity. Fluorescence imaging Super-resolution microscopy biology
N Nature Methods · Nov 24, 2025 Helixer: ab initio prediction of primary eukaryotic gene models combining deep learning and a hidden Markov model The accurate identification of genes is vital for understanding biological function, yet this remains challenging across many newly sequenced or less-studied species. Here we present Helixer, an artificial intelligence-based tool for ab initio gene prediction that delivers highly accurate gene models across fungal, plant, vertebrate and invertebrate genomes. Unlike traditional methods, Helixer operates without requiring additional experimental data such as RNA sequencing, making it broadly applicable to diverse species. We show that Helixer’s pretrained models achieve accuracy on par with or exceeding current tools, producing gene annotations that closely match expert-curated references across multiple evaluation metrics. Its design enables immediate use on genomes without retraining, providing an efficient, accessible solution for genome annotation in both research and applied settings. The tool is available as an open-source software for local installation via GitHub. An online web interface is also available as well as through the Galaxy ToolShed. Computational biology and bioinformatics Genome informatics Machine learning biology
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 24, 2025 TIRTL-seq: deep, quantitative and affordable paired TCR repertoire sequencing The specificity of T cells is determined by T cell receptor (TCR) α and β chain sequences. While bulk TCR sequencing enables cost-effective repertoire profiling without chain pairing information, single-cell approaches provide paired data but are costly and limited in throughput. Here we present throughput-intensive rapid TCR library sequencing (TIRTL-seq), an experimental and computational methodology for paired TCR repertoire sequencing (TCR-seq). TIRTL-seq is based on the parallel generation of hundreds of TCR libraries in 384-well plates at less than US$200 per plate, allowing cohort-scale paired TCR-seq studies. We benchmarked TIRTL-seq against state-of-the-art bulk TCR-seq and 10x Genomics Chromium technologies on longitudinal samples and identified severe acute respiratory syndrome coronavirus 2- and Epstein–Barr virus-specific clonal expansions after infection with distinct dynamics. TIRTL-seq offers a universal protocol scalable from a single cell to millions of T cells per sample, simultaneously delivering both precise clonal frequency estimation and accurate TCR chain pairing, combining the strengths of bulk and single-cell TCR-seq. TIRTL-seq is a high-throughput method for paired T cell receptor sequencing at the cohort scale. Adaptive immunity Immunological techniques Sequencing Software Systems biology biology
N Nature Methods · Nov 20, 2025 4Pi-SIMFLUX: 4Pi single-molecule localization microscopy with structured illumination Single-molecule localization microscopy (SMLM) has transformed biological imaging by enabling nanoscale visualization of intricate subcellular structures. However, conventional three-dimensional SMLM techniques typically exhibit lower axial resolution than lateral resolution, hindering isotropic investigations. Interferometric approaches, such as 4Pi-SMLM, enhance axial resolution by approximately fivefold through dual-objective coherent fluorescence detection, surpassing lateral resolution. Here we present 4Pi-SIMFLUX, which integrates structured illumination into 4Pi-SMLM to double its lateral resolution, achieving near-isotropic three-dimensional localization precision of 2–3 nm. We demonstrate that 4Pi-SIMFLUX breaks the 10-nm resolution barrier in biological samples, resolving microtubule ultrastructure and nuclear pore complexes with exceptional detail and clarity, while accounting for label size and localization density. Furthermore, it enables simultaneous multicolor imaging for interrogating multiple cellular components and high-fidelity, whole-cell visualization that captures comprehensive spatial organization. 4Pi-SIMFLUX effectively bridges the axial–lateral resolution gap, establishing a robust tool for molecular-scale imaging in complex cellular environments. Organelles Super-resolution microscopy biology
N Nature Methods · Nov 18, 2025 Light-induced extracellular vesicle and particle adsorption The role of extracellular vesicles (EVs) and particles (EPs/EVPs) in human health and disease has garnered considerable attention over the past two decades. However, while several types of EVPs are known to interact dynamically with the extracellular matrix and there is great potential value in producing high-fidelity EVP micropatterns, there are currently no label-free, scalable and tunable platform technologies with this capability. We introduce light-induced extracellular vesicle and particle adsorption (LEVA) as a powerful solution to study surface-bound EVPs. The versatility of LEVA is demonstrated using GFP–EV standards, EVs from conventional and bioreactor cultures, DiFi exomeres and Escherichia coli EVs, with the resulting patterns used for single-EV fluorescence imaging, cell migration on migrasome-mimetic trails and bacterial EV-mediated neutrophil swarming. LEVA will rapidly advance our understanding of extracellular matrix protein- and surface-bound EVPs and should encourage researchers from many disciplines to create new biomimetic, immunoengineering and other assays. LEVA is a label-free immobilization method for studying surface-bound extracellular vesicles. Cell biology Fluorescence imaging Lab-on-a-chip Nanoparticles biology
N Nature Methods · Nov 18, 2025 ImmunoMatch learns and predicts cognate pairing of heavy and light immunoglobulin chains The development of stable antibodies formed by compatible heavy (H) and light (L) chain pairs is crucial in both in vivo maturation of antibody-producing cells and ex vivo designs of therapeutic antibodies. We present ImmunoMatch, a machine-learning framework trained on paired H and L sequences from human B cells to identify molecular features underlying chain compatibility. ImmunoMatch distinguishes cognate from random H–L pairs and captures differences associated withκandλlight chains, reflecting B cell selection mechanisms in the bone marrow. We apply ImmunoMatch to reconstruct paired antibodies from spatial VDJ sequencing data and study the refinement of H–L pairing across B cell maturation stages in health and disease. We find further that ImmunoMatch is sensitive to sequence differences at the H–L interface. These insights provide a computational lens into the broader biological principles governing antibody assembly and stability. Adaptive immunity Lymphocytes Machine learning Software biology
N Nature Methods · Nov 18, 2025 Multiplexed ultrasound imaging of gene expression Acoustic reporter genes (ARGs) have enabled imaging of gene expression with ultrasound, which provides high resolution access to deep, optically opaque living tissues. However, unlike their fluorescent counterparts, ARGs have so far been limited to a single ‘sound’, preventing multiplexed imaging of cellular states or populations. Here we use rational protein design and directed evolution to develop two new ARGs that can be distinguished from each other based on their acoustic pressure-response profiles, enabling ‘two-tone’ ultrasound imaging of gene expression. We demonstrate the utility of multiplexed ARGs for delineating bacterial cell species and cell states in vitro, and then apply them towards imaging distinct subpopulations of probiotics in the mouse gastrointestinal tract and of tumor-colonizing bacterial agents in vivo. Just as the first wavelength-shifted derivatives of fluorescent proteins opened a vivid world for optical microscopy, our next-generation acoustic proteins set the stage for a rich symphony of ultrasound signals from living subjects. Molecular imaging Ultrasound 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 13, 2025 Confocal Airy beam oblique light-sheet tomography for brain-wide cell type distribution and morphology Advanced brain-wide mapping is critical for addressing complex questions in neuroscience. However, current imaging methods are limited by throughput, resolution and signal-to-noise ratio, constraining their broader applicability. Here, we present confocal Airy beam integrated with single-photon oblique light-sheet tomography (CAB-OLST): a system that integrates single-photon excitation with a scanned Airy beam light sheet, virtual slit detection and automated mechanical sectioning. CAB-OLST enables high-throughput, high-resolution and high-signal-to-noise ratio volumetric imaging, achieving an optical resolution of 0.77 μm × 0.49 μm × 2.61 μm. This allows for mouse brain-wide cell type distribution mapping at a voxel size of 0.37 μm × 0.37 μm × 1.77 μm in 10 h and single-neuron projectome imaging with a voxel size of 0.26 μm × 0.26 μm × 1.06 μm over 58 h. Compared to existing light-sheet and point-scanning systems, CAB-OLST provides a scalable and robust platform for comprehensive neuronal morphology reconstruction and high-precision cell atlas generation. Confocal Airy beam integrated with single-photon oblique light-sheet tomography (CAB-OLST) is a high-throughput imaging approach for brain-wide mapping of neurons, as demonstrated in cleared mouse brains. Fluorescence imaging Light-sheet microscopy Mouse Neuroscience biology mouse experiments
N Nature Methods · Nov 13, 2025 Jaxley: differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics Biophysical neuron models provide insights into cellular mechanisms underlying neural computations. A central challenge has been to identify parameters of detailed biophysical models such that they match physiological measurements or perform computational tasks. Here we describe a framework for simulating biophysical models in neuroscience—Jaxley—which addresses this challenge. By making use of automatic differentiation and GPU acceleration,Jaxleyenables optimizing large-scale biophysical models with gradient descent.Jaxleycan learn biophysical neuron models to match voltage or two-photon calcium recordings, sometimes orders of magnitude more efficiently than previous methods.Jaxleyalso makes it possible to train biophysical neuron models to perform computational tasks. We train a recurrent neural network to perform working memory tasks, and a network of morphologically detailed neurons with 100,000 parameters to solve a computer vision task.Jaxleyimproves the ability to build large-scale data- or task-constrained biophysical models, creating opportunities for investigating the mechanisms underlying neural computations across multiple scales. Computational biophysics Computational neuroscience biology
N Nature Methods · Nov 13, 2025 Bin Chicken: targeted metagenomic coassembly for the efficient recovery of novel genomes The recovery of microbial genomes from metagenomic datasets has provided genomic representation for hundreds of thousands of species from diverse biomes. However, low-abundance microorganisms are often missed due to insufficient genomic coverage. Here we present Bin Chicken, an algorithm that substantially improves genome recovery through automated, targeted selection of metagenomes for coassembly based on shared marker gene sequences derived from raw reads. Marker gene sequences that are divergent from known reference genomes can be further prioritized, providing an efficient means of recovering highly novel genomes. Applying Bin Chicken to public metagenomes and coassembling 800 sample groups recovered 77,562 microbial genomes, including the first genomic representatives of 6 phyla, 41 classes and 24,028 species. These genomes expand the genomic tree of life and uncover a wealth of novel microbial lineages for further research. Data mining Genome informatics Metagenomics Microbial genetics Software biology
N Nature Methods · Nov 13, 2025 MISO: microfluidic protein isolation enables single-particle cryo-EM structure determination from a single cell colony Single-particle cryogenic electron microscopy (cryo-EM) enables reconstruction of atomic-resolution 3D maps of proteins by visualizing thousands to millions of purified protein particles embedded in vitreous ice. This corresponds to picograms of purified protein, which can potentially be isolated from a few thousand cells. Hence, cryo-EM holds the potential of a very sensitive analytical method for delivering high-resolution protein structure as a readout. In practice, millions of times more starting biological material is required to prepare cryo-EM grids. Here we show that using a micro isolation (MISO) method, which combines microfluidics-based protein purification with cryo-EM grid preparation, cryo-EM structures of soluble bacterial and eukaryotic membrane proteins can be solved starting from less than 1 µg of a target protein and progressing from cells to cryo-EM grids within a few hours. This scales down the amount of starting biological material hundreds to thousands of times, opening possibilities for the structural characterization of hitherto inaccessible proteins. Cryoelectron microscopy Membrane proteins Single-molecule biophysics biology
N Nature Methods · Nov 13, 2025 Stimulus-modulated approach to steady state (SASS): a flexible paradigm for event-related fMRI Functional magnetic resonance imaging (fMRI) studies discard the initial volumes acquired during the approach of the magnetization to its steady-state value. Here we leverage the higher temporal signal-to-noise ratio of these initial volumes to increase the sensitivity of event-related fMRI experiments. We introduce acquisition-free periods (AFPs) that permit the full recovery of the magnetization, followed by an acquisition block of fMRI volumes. An appropriately placed stimulus in the AFP produces a blood oxygenation level-dependent response that peaks during the initial high temporal signal-to-noise ratio phase of the acquisition block. Using humans and monkeys (Callithrix jacchus) at different field strengths, we demonstrate up to a ~50% reduction in the number of trials needed to achieve a given statistical threshold relative to conventional fMRI. The silent AFP can be exploited for the presentation of auditory stimuli or uncontaminated electrophysiological recording and its variable duration allows aperiodic stimulus or response-locked signal averaging as well as gating to physiology or motion. Neurophysiology Neuroscience Neuroscience fMRI Human Monkey
N Nature Methods · Nov 11, 2025 Universal consensus 3D segmentation of cells from 2D segmented stacks Cell segmentation is the foundation of a wide range of microscopy-based biological studies. Deep learning has revolutionized two-dimensional (2D) cell segmentation, enabling generalized solutions across cell types and imaging modalities. This has been driven by the ease of scaling up image acquisition, annotation and computation. However, three-dimensional (3D) cell segmentation, requiring dense annotation of 2D slices, still poses substantial challenges. Manual labeling of 3D cells to train broadly applicable segmentation models is prohibitive. Even in high-contrast images annotation is ambiguous and time-consuming. Here we develop a theory and toolbox, u-Segment3D, for 2D-to-3D segmentation, compatible with any 2D method generating pixel-based instance cell masks. u-Segment3D translates and enhances 2D instance segmentations to a 3D consensus instance segmentation without training data, as demonstrated on 11 real-life datasets, comprising >70,000 cells, spanning single cells, cell aggregates and tissue. Moreover, u-Segment3D is competitive with native 3D segmentation, even exceeding when cells are crowded and have complex morphologies. Cellular imaging Image processing Machine learning Software biology
N Nature Methods · Nov 07, 2025 nELISA: a high-throughput, high-plex platform enables quantitative profiling of the inflammatory secretome Existing high-plex protein measurement tools compromise on quantification, precision and cost efficiency. Here, to address this, we present nELISA, a platform that combines a DNA-mediated, bead-based sandwich immunoassay with advanced multicolor bead barcoding. Antibody pairs are preassembled on target-specific, barcoded beads, which ensures spatial separation between noncognate assays. Detection antibodies are tethered via flexible single-stranded DNA to enable efficient ternary sandwich formation. Detection is achieved through toehold-mediated strand displacement, where fluorescently labeled DNA oligos simultaneously untether and label detection antibodies. nELISA delivers sub-picogram-per-milliliter sensitivity across seven orders of magnitude. Using a 191-plex inflammation panel, we profiled cytokine responses in 7,392 peripheral blood mononuclear cell samples, generating ~1.4 million protein measurements and revealing over 440 robust cytokine responses, including previously unreported effects. nELISA thus provides a simple, scalable and cost-efficient solution for large-scale, high-fidelity phenotypic screening. Biosensors High-throughput screening Proteomic analysis Proteomics biology
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 Single-cell Genomics Cell Biology Machine Learning
