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 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 · 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
