Nature Methods

Subcellular mass spectrometry imaging (MSI) has required specialized instrumentation and approaches. Additionally, data analysis has required expensive proprietary software, or code-based open-source tools. These barriers render spatial metabolomics less accessible than peer spatial omics techniques (transcriptomics, proteomics). Here we demonstrate subcellular MSI on standard commercial hardware, analyzed using code-free, open-source software. An inexpensive, fully reversible modification of a stock DESI source takes the platform past its typical 5-10 m pixels to routine 2x2 m pixels, and 1x1 m in proof-of-principle experiments. At a tissue-glass boundary imaged at 2x2 m, tissue-specific ions showed no measurable spillover onto the adjacent glass within the {+/-}4.4 m uncertainty of the edge position. To make these data interpretable, we extend MSI.EAGLE, an open-source, vendor-agnostic application with two new methods: i) Spatial UMAP preserves tissue architecture alongside subcellular detail, where conventional clustering breaks apart at high resolution; ii) Histology Fit performs landmark-free co-registration, and recovers synthetic misalignments of up to 8 m to within one pixel of a reference registration. Together, these enable previously high-burden spatial metabolomics experiments. In mouse brain tissue, we demonstrate genuine subcellular signal by resolving mitochondrial cardiolipin into perinuclear puncta, quantifying cytoplasmic enrichment across 6,073 cells. Cell identities are assigned to MSI pixels using histology-predicted transcriptomics, linking cell provenance to metabolism in 2D space directly. Finally, we recover cell-type-specific metabolomes consistent with the transcriptomic identities. This workflow allows cellular and subcellular spatial metabolomics linked to cell provenance, with only stock MSI hardware, open-source GUI-based software, and common histology techniques, putting it within reach of many researchers and laboratories.

Recent efforts have demonstrated that systematic analysis of the millions of metagenomes in the Sequence Read Archive (SRA) enables groundbreaking biological discoveries. However, SRA-scale comparison of whole metagenomes has remained elusive. Here, we introduce the most comprehensive resource to date for experiment-to-experiment analysis of all SRA metagenomes, comprising FracMinHash sketches (470GB), hypergen sketches (19GB), and an all-pairs Jaccard similarity matrix (90GB), which we release online at https://github.com/RolandFaure/SRA-metagenome-sketches-databases. These resources enable a wide range of analyses, which we demonstrate with several applications. First, we use the FracMinHash sketches to uncover temporal trends, finding that the amount of newly discovered microbial sequence continues to grow, rather than saturate or shrink. Second, we use the hypergen sketches, which enable nearest-neighbor search, to identify co-assembly candidates and thereby improve assembly quality. Third, we use the similarity matrix to analyze all SRA metagenomes, identifying duplicated submissions and over 50K candidates for metadata correction. Together, our resources unlock systematic analysis of metagenomic data at unprecedented scale.

Volumetric microscopy produces datasets too large to share as files and too rich to convey as still images, yet exploring them interactively means installing desktop software or building web infrastructure. Here we present Luxar, an open-source Python framework that compiles multidimensional data into compact archives that any modern browser renders from static file hosting. Fluorescence volumes are represented as mixtures of anisotropic Gaussians (Gaussian splats): the image becomes geometry that the GPU draws directly, with no voxel grid to rebuild. Splats share one scene with points, trajectories and surfaces. Blind-spot cross-validation sets the number of Gaussians without noise-free references. Across 17 volumes from 12 datasets and four modalities, this yields a median 99-fold (6- to 340-fold) size reduction at 26 to 67 dB peak signal-to-noise ratio (PSNR), in minutes per volume on one GPU. We demonstrate Luxar on a 500-timepoint embryogenesis recording, super-resolution localisations, single-cell atlases and lineage tracking, each explorable through a shareable link.

Focal adhesions (FAs) and actin stress fibers (SFs) are mechanically coupled but often analyzed separately. We present FilAdh, a unified framework for FA instance segmentation, SF instance reconstruction, morphometry, and FA-anchorage-based SF classification. Across 57 held-out HeLa, SUIT-2, and MRC-5 cell line images, FilAdh achieved the highest FA Dice, intersection over union (IoU), and recall and the highest SF clDice and skeleton precision among benchmarked methods; paired t tests with Holm correction indicated significant gains over all compared methods for FA Dice, IoU, and recall, and over classical methods for SF centerline metrics, with no significant differences detected relative to the deep-learning comparators. Conditional on expert-curated FA and SF masks, SFs were assigned to dorsal, ventral, and peripheral classes with 81.5%-87.3% accuracy. Controlled Poisson-Gaussian noise, Gaussian defocus, and point-spread-function blur revealed distinct, structure-specific errors in overlap and object-level measurements. By linking segmentation and reconstruction to count, area, length, apparent width, and structural class, FilAdh offers an interpretable view of the FA-actin structural system and makes degradation-dependent measurement biases explicit. BIGGER PICTURECells sense and respond to their physical environment through focal adhesions and actin stress fibers. The two form a mechanically coupled system, yet they are usually analyzed separately, with workflows re-tuned for each new set of images. FilAdh analyzes both within a single framework, tailoring segmentation, reconstruction, and classification to each structures geometry: compact adhesion patches versus thin, extended fiber networks. Testing on expert-annotated references and under controlled degradations that mimic low signal, focus errors, and optical blur shows that these common imaging imperfections distort adhesion and fiber measurements in different ways. The same design principle--matching the analysis to each structures shape and judging methods by how errors propagate into the final biological measurements--may benefit other bioimage-analysis problems. Released code, trained weights, and fixed data splits make all comparisons reproducible and extensible. HIGHLIGHTSO_LIFilAdh jointly quantifies focal adhesions and actin stress fibers C_LIO_LIFilAdh achieves the highest benchmarked FA overlap and comparable SF topology C_LIO_LIMatched synthetic degradations reveal geometry-specific measurement errors C_LIO_LIFA-SF spatial context enables dorsal, ventral, and peripheral SF classification C_LI

Super-resolution methods, such as single molecule localisation microscopy (SMLM), can determine the distribution of specific proteins at the nanometre scale. However, three-dimensional (3D) SMLM is typically limited to thin sections and requires prohibitively long acquisition times to image complete cells. Here we introduce a 3D SMLM imaging modality, Intrinsic3D, that combines the speed and optical sectioning of oblique plane microscopy (OPM) with the nanometre resolution of SMLM to determine the position of single molecule emitters throughout complete volumes more than an order-of-magnitude faster over regular DNA-PAINT. By scanning the oblique light sheet across the volume and combining localization information from two or more adjacent planes, an emitter's x, y and z position is determined, eliminating the need for post-processing steps to stitch multiple image stacks together. We demonstrate resolution and speed by imaging nuclear pore complexes in U2OS cells and Drosophila egg chambers. As a proof-of-concept, we map the 3D distribution of the cell-adhesion protein DE-cadherin in the Drosophila follicular epithelium and show that spot adherens junctions are enriched near tricellular junctions and contain more E-cadherin than the apical adherens junctions, suggesting that spot junctions play a wider role in cell-cell adhesion than previously appreciated. Finally, we examine the organization of Gliotactin, a tricellular septate junction protein relative to E-cadherin using exchange-PAINT. These investigations demonstrate that Intrinsic3D delivers rapid 3D protein mapping in whole cells. Combining Intrinsic3D with existing multiplex and resolution enhancing methods (e.g. exchange-PAINT and RESI) will enable multicolour, 3D in situ spatial proteomics in tissue samples and bridge the gap between SMLM and systems-level cell biology.

Interactive exploration and sharing of hybridization-based spatial transcriptomics datasets remain limited by large file sizes, specialized software, and computationally intensive preprocessing, restricting rapid access to publicly available resources such as the Brain Image Library. Here we present MERFISHEYES (https://merfisheyes.com), a browser-based platform for interactive visualization of single-cell and single-molecule spatial transcriptomics data across commercial and emerging high-throughput platforms. MERFISHEYES enables marker gene exploration, cross-dataset and cross-species comparison, visualization of subcellular RNA localization, and collaborative sharing through browser-accessible links generated directly from drag-and-drop datasets. We use MERFISHEYES to enable browser-based visualization of all 158 spatial transcriptomics datasets in the Brain Image Library and demonstrate its capabilities in handling high-throughput 3D data with genome-scale imputation of gene expression and DNA accessibility. MERFISHEYES provides a scalable framework for rapid exploration, sharing, and collaborative analysis of spatial transcriptomics data.

Continuous protein conformations are essential for understanding fundamental biological processes and supporting drug discovery. Although cryo-EM can resolve individual states at high resolution, recovering continuous heterogeneity from 2D particle images remains challenging. High noise, motion blur, and limited structural priors make it difficult to accurately generate and validate high-resolution continuous conformations using raw particle data. Here, we introduce cryoMV, a framework that integrates structure-prior-guided modeling with real-particle validation for continuous conformational transitions. CryoMV uses reference density maps to establish structural anchors and motion priors, models candidate transition paths between selected conformations, and transfers the learned representation to raw 2D cryo-EM particle images. Each candidate conformation is subsequently evaluated using the estimated particle poses and contrast transfer functions. Supported conformations are reconstructed through raw particle back-projection and assessed using canonical half-maps and Fourier shell correlation. On EMPIAR-10516 and EMPIAR-10345, cryoMV achieves excellent performance in terms of robustness, verifiability, and reconstruction resolution. By incorporating structure-prior modeling and evidence from the raw particles, cryoMV offers an explicit mechanism for assessing whether generated conformations are supported by experimental data and provides a practical approach to reducing model-induced artifacts in continuous cryo-EM heterogeneity analysis.

Most human genes can end their messenger RNAs at more than one position, and the choice shifts between cell types. Single-cell sequencing reads that choice in individual cells, but two questions are seldom asked: are the reported sites real, and does the test for a cell-type difference hold its error rate? Here we introduce our tool PeakATail, which calls polyadenylation sites from reads still carrying a piece of the tail, ranks them by how many distinct molecules carry that evidence, discards sites where adenosine-rich genomic sequence mimics a tail, and pairs them with a differential test whose error rate is measured, not assumed. The evidence is scarce, so the tool is built to be right rather than complete. Across four libraries from two species, 71 to 83 percent of its calls fall within 100 bases of a curated atlas, over thirty times the shuffled-control rate; long reads from different donors support 76.5 percent of the human calls. The cost is sensitivity. Only one of six test configurations held its false-positive rate under shuffled labels; with it, 15,942 cell-type switches replicated across twelve lung cancer patients and none in ten shuffled-label runs. One pre-registered target was missed and is reported.

Cell fate during development is directed by new gene expression states that replace prior ones. When and where a protein is first translated, and how long it will remain in the cell, dictate the timing and extent of a gene's phenotypic effect. Accessing protein synthesis and turnover information in the rapidly developing embryo remains a major challenge. Here we present MEMBRYO: a method for continuous stable isotope labeling (SILAC) of newly synthesized proteins throughout the live embryo at the hour-timescales of developmental phenotypes. We extend embryo culture into the late stages of organogenesis, thereby accessing the proteome of neurogenesis in the forebrain ex utero. Variations in protein synthesis and turnover activity lead to expression patterns that diverge from the corresponding transcripts, impacting molecular pathways and key milestones of cortical neuron differentiation. The high labeling depth enabled both spatial and single-cell SILAC proteomics analysis of cortical cells, revealing distinct layers of post-transcriptional gene expression regulation in progenitors and neurons. By continuous metabolic labeling in the live embryo, MEMBRYO tracks the past, present, and future of the developmental proteome.

Mass spectrometry generates data faster than it can be read, and the exchange standard, mzML, is text-based and must be parsed in full before any spectrum is accessible. Binary alternatives are smaller but depend on storage engines such as HDF5, so access is dictated by the engine, not the file. We present Ionic (.ion), an open-source, compact, streamable binary format, and Quant{middle dot}ion, a processing toolkit built on the former. Ionic stores spectra, chromatograms and metadata as independently compressed, indexed blocks, so a reader retrieves only the bytes it needs, even inside a web browser, and converts losslessly to and from mzML. Ionic was smaller than compressed mzMLb on every acquisition type tested, and extracting one compound took under 40 ms, 35 to 90 times faster than an mzML reader. Quant{middle dot}ion exposes one core to R, Python, and JavaScript with identical results, and recovered 97% of true features on a ground-truth benchmark.

Graph-based protein function analysis is powerful, but protein-protein interaction (PPI) networks exist for only a small fraction of animal and plant genomes. We present LoGoPPI, which infers PPIs from sequence by combining bi-encoder global protein representation with local residue-level late interaction. LoGoPPI matches or exceeds state-of-the-art PLM-based cross-encoders while achieving orders-of-magnitude faster inference, up to ~1,500-fold, and its local branch provides residue-level signals associated with interaction interfaces and structurally flexible regions. This efficiency enables practical proteome-wide PPI reconstruction at scales prohibitive for cross-encoder models, potentially extending interactome mapping to tens of thousands of animal and plant species. LoGoPPI thus provides a scalable framework for comparative and functional analysis of protein networks across diverse taxa.

Cryo-electron microscopy (cryo-EM) has become a central method for determining structures of molecular assemblies, requiring reliable tools to interpret the density maps. Despite recent advances in AI-driven model building, at intermediate-to-low resolution (4-15 [a]) accurate rigid-body fitting of existing atomic structures into cryo-EM maps remains an essential step in the modelling process. Here we present an updated version of our software package PowerFit. It performs an exhaustive translational and rotational search against the cryo-EM density map, implementing sensitive correlation functions. The updated release preserves the fitting accuracy and sensitivity of the original method while substantially improving the computational performance through modernised CPU and GPU implementations. In addition, to facilitate the interpretation of results, it provides an HTML report with interactive 3D visualisation using MolViewSpec. The same HTML report is used to generate the result page when using PowerFit via its web service. The report enables immediate inspection and validation of the results. The software's accessibility has been enhanced by updating it from Python 2 to Python 3, enabling straightforward installation via the Python Package Index (PyPI) and simple integration into computational pipelines. PowerFit is freely available at https://github.com/haddocking/powerfit, as a web service at https://wenmr.science.uu.nl/powerfit or in JupyterLite directly in a web browser at https://www.bonvinlab.org/powerfit/jupyterlite.html.

Archived formalin-fixed, paraffin-embedded (FFPE) tissue represents a vast clinical resource, yet three-dimensional imaging and molecular profiling remain largely incompatible because current antigen retrieval and clearing can degrade nucleic acids. We developed REZCUE, a depth-invariant antigen retrieval chemistry based on in situ protein refolding that enables 3D multiplexed immunofluorescence imaging of intact FFPE tissue while preserving DNA, RNA, and protein for subsequent assays. REZCUE restores antigenicity in tissue archived for up to 30 years, supports 160 commercial antibodies across tissue types, and enables imaging to 4 mm depth. Imaging before destructive molecular profiling establishes a 3D spatial reference for registering molecular assays to intact tissue architecture. In a 25-year-archived breast cancer specimen, REZCUE integrated 3D image-guided clonal segmentation with spatial transcriptomics, mass spectrometry, miRNA sequencing, and whole-genome sequencing, identifying a luminalB/HER2-positive clone missed by conventional section-based spatial-omics. REZCUE transforms archived FFPE specimens into spatially registered substrates for sequential 3D multi-omic interrogation.

Two-photon calcium imaging enables measurements of neural activity from individual neurons and their subcellular processes in awake animals. Axial brain motion can change measured fluorescence independently of neural activity and move small structures out of the imaging plane, confounding two-photon calcium measurements in behaving animals. We developed two complementary post-acquisition methods, selected according to whether the imaged structure remains detectable across the encountered depth range. When it does, z-stack correction estimates imaging depth from a separately acquired reference z-stack and uses an axial fluorescence profile specific to each neural structure to rescale fluorescence to a common reference depth. When a structure can leave a single imaging plane, volume-based z-registration reconstructs a common-depth movie from repeated acquisitions of closely spaced planes. In awake mice, both methods tracked experimentally imposed axial displacements within the sampled range, with depth-estimation root mean squared errors (RMSEs) below 1 m for z-stack correction and 0.69-1.03 m for volume-based z-registration. Correction changed fluorescence substantially for a subset of somata, with larger effects in axonal boutons. Both methods are implemented in suite2p-in-depth, a Python package that integrates with Suite2p and provides diagnostics for depth estimation, correction quality and data reliability. Together, these methods enable axial-motion artifacts to be assessed and corrected within established Suite2p workflows.

Spatial transcriptomic super-resolution recovers tissue organization obscured by coarse sampling, but fitting a model to each section greatly increases computational cost and reduces practicality. We introduce ASTRA, a cross-platform pretrained model that reconstructs expression from histology and measured RNA by zero-shot inference. Training with arbitrarily shaped count-bearing regions, termed parents, enables one frozen model to accommodate dense bins and separated spots. Across six held-out Visium HD sections, ASTRA achieved higher correlations under dense-bin and pseudo-Visium sampling, lower dense-bin errors and substantially shorter processing times than existing methods. It improved colorectal tissue segmentation and tumor-boundary recovery. In real colorectal Visium, reconstruction spatially resolved a stromal transcriptional state supported by measured RNA. The same checkpoint supported regional and nucleus-associated analyses across 43 invasive lobular carcinoma sections and within-spot interpretation across 92 triple-negative breast cancer sections. ASTRA establishes a reusable framework for practical, cohort-scale spatial transcriptomic super-resolution across platforms.

Microscopy-based cell profiling has broad applications in biological discovery, disease characterization, and phenotypic drug screening. Modern microscopy continues to push the limits of resolution, speed, depth and throughput, but better imaging does not automatically lead to better biomedical discovery and translation. A key bottleneck is feature representation: existing features are either handcrafted or learned as black-box embeddings and often lack explicit biological meaning. Here we propose biological grounding as a first principle for cell profiling and implement it through MorphAgent, an AI agent framework in which each quantitative feature originates from a biological hypothesis and is anchored to a cellular structure or process. By integrating biological knowledge, multimodal reasoning and automated validation, MorphAgent makes biologically grounded feature design systematic and scalable. We show biological grounding fundamentally changes the properties of cell profiling features. Across three microscopy modalities, MorphAgent produces compact, expressive and transferable features for drug screening, cell state characterization and disease morphology analysis. In wide-field Cell Painting, a common assay for phenotypic drug screening, it improves perturbation-retrieval mean average precision by 48% over CellProfiler and 20% over DeepProfiler while using substantially fewer dimensions. In confocal mitochondrial imaging, biologically grounded features transfer across independently acquired datasets and imaging resolutions, supporting accurate aging-state classification. In structured illumination microscopy (SIM) imaging of Tau-labeled samples, biologically grounded super resolution features reveal nanoscale morphologies inaccessible at conventional resolution, enhancing the discrimination of disease-associated mutations. Moreover, biologically grounded features provide a hierarchical organization of cellular morphology, enabling multilevel alignment with transcriptomics data and facilitating mechanistic understanding. Biologically grounded cell profiling thus brings AI reasoning closer to biological mechanisms, advancing biomedical discovery and translation.

Capturing fast hemodynamics in deep organs is essential for understanding microvascular regulation of organ responses to physiological demands and pathological stress. However, non-invasive three-dimensional (3D) imaging of these microscale processes remains challenging due to trade-offs between spatial resolution, imaging speed, and penetration depth. Here, we present fluctuation-based high-order super-resolution acoustic microscope (FLAME), a tracking-free 3D ultrasound imaging technology capable of fast microvascular angiography and flow measurement. Using as few as 30 volumes, FLAME improves 3D ultrasound resolution by 8-fold (~50 m) and shortens data acquisition time by over two orders of magnitude, from tens of seconds to tens of milliseconds, compared with conventional tracking-based super-resolution approaches1-6. This high-throughput capability supports high-fidelity imaging of tissue functions under flexible experimental settings. FLAME achieves an unprecedented 3D super-resolution frame rate of ~40 Hz, capturing transient hemodynamic responses to various physiological and pathological stimuli in different mouse organs, from the microvascular level to the whole-body scale. With open-sourced implementation, FLAME provides an accessible platform for real-time, in-depth hemodynamic monitoring across diverse biomedical applications and clinical translation.

Tracing molecular cell state transitions through development, homeostasis and disease is still beyond reach at single-cell resolution. We present scReMeMber-Seq, a methylation-based cell-memory recorder connecting past transcriptional states with present transcriptome and DNA methylome in individual cells. scReMeMber-Seq leverages pulse induction of DCM-Polr2b (TimeMachine) and single-cell methylome-transcriptome (scMT) sequencing for endpoint readout. In mouse neural stem cells (NSCs) and pancreatic organoids, single-cell DCM methylation profiles (scDCMome) accurately discriminate cell types. Over time, scDCMome also record NSC quiescence-activation state changes across multiple cell divisions. In zebrafish embryos, scDCMome resolve individual transcriptional trajectories from progenitor to neuron. Together, scReMeMber-Seq enables real time reconstruction of cellular trajectories, connecting past and present molecular states in single cells, at genome scale, across experimental systems, in vitro and in vivo.

Single-cell multi-omics reveals complementary aspects of cellular identity, but heterogeneous assays produce incomplete modality combinations. Integrating these mosaic measurements while preserving biological structure and recovering missing molecular information remains challenging. We present MoTRUST, combining neural representations, adaptive semantic protection, molecular prediction and conditional residual diffusion. MoTRUST ranked first in overall integration across 30 tasks spanning six scenarios and surpassed the strongest evaluated controls on six of eleven development recovery endpoints. Conditional residual diffusion reduced aggregate marginal prediction scores by 4.4-5.1% across three ATAC-to-RNA data sources while preserving point predictions. Together, these capabilities enable accurate mosaic integration and molecular recovery for multimodal characterization of cellular heterogeneity.

Long-read sequencing captures full-length transcripts but suffers elevated error rates, and PCR duplicates can distort quantification unless accounted for with UMIs. We present Nailpolish, a fast reference-free tool that error-corrects and deduplicates UMI-tagged long reads by generating consensus sequences via partial order alignment, while separating false duplicates arising from UMI collisions. Across eleven Oxford Nanopore Technologies and Pacific Biosciences datasets spanning bulk, single-cell, spatial, and targeted protocols, Nailpolish reduced per-read error rates and outperformed existing deduplication tools.