Nature Methods
Nature Methods offers a unique interdisciplinary forum for the publication of novel methods. Nature Methods focuses on the life sciences, combining practical, technique-driven subject matter with rigorous peer-review standards to ensure that readers are consistently presented with only the most valuable and highest quality methodological research. The journal offers its readers primary research papers as well as an array of opinions, reviews and short journalistic pieces to provide busy researchers with a broad, yet easily absorbed perspective of important methodological developments in the life sciences.
The Human BioMolecular Atlas Program (HuBMAP) aims to construct a 3D Human Reference Atlas (HRA) of the healthy adult body. Experts from 20+ consortia collaborate to develop a Common Coordinate Framework (CCF), knowledge graphs and tools that describe the multiscale structure of the human body (from organs and tissues down to cells, genes and biomarkers) and to use the HRA to characterize changes that occur with aging, disease and other perturbations. HRA v.2.0 covers 4,499 unique anatomical structures, 1,195 cell types and 2,089 biomarkers (such as genes, proteins and lipids) from 33 ASCT+B tables and 65 3D Reference Objects linked to ontologies. New experimental data can be mapped into the HRA using (1) cell type annotation tools (for example, Azimuth), (2) validated antibody panels or (3) by registering tissue data spatially. This paper describes HRA user stories, terminology, data formats, ontology validation, unified analysis workflows, user interfaces, instructional materials, application programming interfaces, flexible hybrid cloud infrastructure and previews atlas usage applications. This Resource describes data, code and tools developed for the Human Reference Atlas and the Human BioMolecular Atlas Program for building and navigating a multiscale human atlas.
The human genome contains instructions to transcribe more than 200,000 RNAs. However, many RNA transcripts are generated from the same gene, resulting in alternative isoforms that are highly similar and that remain difficult to quantify. To evaluate the ability to study RNA transcript expression, we profiled seven human cell lines with five different RNA-sequencing protocols, including short-read cDNA, Nanopore long-read direct RNA, amplification-free direct cDNA and PCR-amplified cDNA sequencing, and PacBio IsoSeq, with multiple spike-in controls, and additional transcriptome-wide N6-methyladenosine profiling data. We describe differences in read length, coverage, throughput and transcript expression, reporting that long-read RNA sequencing more robustly identifies major isoforms. We illustrate the value of the SG-NEx data to identify alternative isoforms, novel transcripts, fusion transcripts and N6-methyladenosine RNA modifications. Together, the SG-NEx data provide a comprehensive resource enabling the development and benchmarking of computational methods for profiling complex transcriptional events at isoform-level resolution. This analysis provides a collection of sequencing datasets generated from long-read and short-read RNA sequencing, serving as a valuable resource for transcriptome profiling.
The Xenium In Situ platform is a new spatial transcriptomics product commercialized by 10x Genomics, capable of mapping hundreds of genes in situ at subcellular resolution. Given the multitude of commercially available spatial transcriptomics technologies, recommendations in choice of platform and analysis guidelines are increasingly important. Herein, we explore 25 Xenium datasets generated from multiple tissues and species, comparing scalability, resolution, data quality, capacities and limitations with eight other spatially resolved transcriptomics technologies and commercial platforms. In addition, we benchmark the performance of multiple open-source computational tools, when applied to Xenium datasets, in tasks including preprocessing, cell segmentation, selection of spatially variable features and domain identification. This study serves as an independent analysis of the performance of Xenium, and provides best practices and recommendations for analysis of such datasets. This study presents a comprehensive evaluation of Xenium In Situ datasets and provides recommendations on analysis workflows.
The availability of single-cell transcriptomics has allowed the construction of reference cell atlases, but their usefulness depends on the quality of dataset integration and the ability to map new samples. Previous benchmarks have compared integration methods and suggest that feature selection improves performance but have not explored how best to select features. Here, we benchmark feature selection methods for single-cell RNA sequencing integration using metrics beyond batch correction and preservation of biological variation to assess query mapping, label transfer and the detection of unseen populations. We reinforce common practice by showing that highly variable feature selection is effective for producing high-quality integrations and provide further guidance on the effect of the number of features selected, batch-aware feature selection, lineage-specific feature selection and integration and the interaction between feature selection and integration models. These results are informative for analysts working on large-scale tissue atlases, using atlases or integrating their own data to tackle specific biological questions. This Registered Report presents a benchmarking study evaluating the impact of feature selection on scRNA-seq integration.
Nature Methods - Optimal transport for single-cell genomics
Nature Methods - Strolling down rats’ memory lane
Nature Methods - Chemical space exploration with quantum computing
Nature Methods - Structural biology at the plasma membrane
In 2023, Nature Methods chose methods to model development as our Method of the Year. Here, we catch up on what has happened in this field since.
Spatial transcriptomics (ST) has advanced our understanding of tissue regionalization by enabling the visualization of gene expression within whole-tissue sections, but current approaches remain plagued by the challenge of achieving single-cell resolution without sacrificing whole-genome coverage. Here we present Spotiphy (spot imager with pseudo-single-cell-resolution histology), a computational toolkit that transforms sequencing-based ST data into single-cell-resolved whole-transcriptome images. Spotiphy delivers the most precise cellular proportions in extensive benchmarking evaluations. Spotiphy-derived inferred single-cell profiles reveal astrocyte and disease-associated microglia regional specifications in Alzheimer’s disease and healthy mouse brains. Spotiphy identifies multiple spatial domains and alterations in tumor–tumor microenvironment interactions in human breast ST data. Spotiphy bridges the information gap and enables visualization of cell localization and transcriptomic profiles throughout entire sections, offering highly informative outputs and an innovative spatial analysis pipeline for exploring complex biological systems. Spotiphy (spot imager with pseudo-single-cell-resolution histology) is a computational toolkit that transforms sequencing-based spatially resolved transcriptomics data into whole-transcriptome images with single-cell resolution.
Nature Methods - Author Correction: Resolving tissue complexity by multimodal spatial omics modeling with MISO
High-density microelectrode arrays have opened new possibilities for systems neuroscience, but brain motion relative to the array poses challenges for downstream analyses. We introduce DREDge (Decentralized Registration of Electrophysiology Data), a robust algorithm for the registration of noisy, nonstationary extracellular electrophysiology recordings. In addition to estimating motion from action potential data, DREDge enables automated, high-temporal-resolution motion tracking in local field potential data. In human intraoperative recordings, DREDge’s local field potential-based tracking reliably recovered evoked potentials and single-unit spike sorting. In recordings of deep probe insertions in nonhuman primates, DREDge tracked motion across centimeters of tissue and several brain regions while mapping single-unit electrophysiological features. DREDge reliably improved motion correction in acute mouse recordings, especially in those made with a recent ultrahigh-density probe. Applying DREDge to recordings from chronic implantations in mice yielded stable motion tracking despite changes in neural activity between experimental sessions. These advances enable automated, scalable registration of electrophysiological data across species, probes and drift types, providing a foundation for downstream analyses of these rich datasets. DREDge is a software tool for motion correction of high-density electrophysiology recordings. It can handle action potential or local field potential data and is demonstrated on a variety of acute or chronic recordings from humans, nonhuman primates and mice.
Advances in single-cell proteomics allow quantification of half of the expressed proteome in an individual cell.
Advances in high-throughput microscopy have enabled the rapid acquisition of large numbers of high-content microscopy images. Next, whether by deep learning or classical algorithms, image analysis pipelines commonly produce single-cell features. To process these single cells for downstream applications, we present Pycytominer, a user-friendly, open-source Python package that implements the bioinformatics steps key to image-based profiling. We demonstrate Pycytominer’s usefulness in a machine-learning project to predict nuisance compounds that cause undesirable cell injuries. Pycytominer is a user-friendly, open-source Python package that carries out key bioinformatics steps in image-based profiling.
RNA velocity exploits the temporal information contained in spliced and unspliced RNA counts to infer transcriptional dynamics. Existing velocity models often rely on coarse biophysical simplifications or numerical approximations to solve the underlying ordinary differential equations (ODEs), which can compromise accuracy in challenging settings, such as complex or weak transcription rate changes across cellular trajectories. Here we present cell2fate, a formulation of RNA velocity based on a linearization of the velocity ODE, which allows solving a biophysically more accurate model in a fully Bayesian fashion. As a result, cell2fate decomposes the RNA velocity solutions into modules, providing a biophysical connection between RNA velocity and statistical dimensionality reduction. We comprehensively benchmark cell2fate in real-world settings, demonstrating enhanced interpretability and power to reconstruct complex dynamics and weak dynamical signals in rare and mature cell types. Finally, we apply cell2fate to the developing human brain, where we spatially map RNA velocity modules onto the tissue architecture, connecting the spatial organization of tissues with temporal dynamics of transcription. Cell2fate improves RNA velocity analysis of single-cell and spatial transcriptomics data by module decomposition of realistic biophysical models of transcription dynamics.
Nellie is a user-friendly tool that streamlines organelle analysis by providing automated segmentation, tracking and feature extraction across any intracellular structure, without the need for deep-learning models or manual parameter adjustment.
Cellular organelles undergo constant morphological changes and dynamic interactions that are fundamental to cell homeostasis, stress responses and disease progression. Despite their importance, quantifying organelle morphology and motility remains challenging due to their complex architectures, rapid movements and the technical limitations of existing analysis tools. Here we introduce Nellie, an automated and unbiased pipeline for segmentation, tracking and feature extraction of diverse intracellular structures. Nellie adapts to image metadata and employs hierarchical segmentation to resolve sub-organellar regions, while its radius-adaptive pattern matching enables precise motion tracking. Through a user-friendly Napari-based interface, Nellie enables comprehensive organelle analysis without coding expertise. We demonstrate Nellie’s versatility by unmixing multiple organelles from single-channel data, quantifying mitochondrial responses to ionomycin via graph autoencoders and characterizing endoplasmic reticulum networks across cell types and time points. This tool addresses a critical need in cell biology by providing accessible, automated analysis of organelle dynamics. Nellie is a comprehensive automated pipeline for studying the structure and intracellular dynamics of diverse organelles that offers accurate segmentation, tracking and feature extraction on both 2D and 3D data.
The phosphatase and tensin homolog (PTEN) is a vital protein that maintains an inhibitory brake for cellular proliferation and growth. Accordingly, PTEN loss-of-function mutations are associated with a broad spectrum of human pathologies. Despite its importance, there is currently no method to directly monitor PTEN activity with cellular specificity within intact biological systems. Here we describe the development of a FRET-based biosensor using PTEN conformation as a proxy for the PTEN activity state, for two-photon fluorescence lifetime imaging microscopy. We identify a point mutation that allows the monitoring of PTEN activity with minimal interference to endogenous PTEN signaling. We demonstrate imaging of PTEN activity in cell lines, intact Caenorhabditis elegans and in the mouse brain. Finally, we develop a red-shifted sensor variant that allows us to identify cell-type-specific PTEN activity in excitatory and inhibitory cortical cells. In summary, our approach enables dynamic imaging of PTEN activity in vivo with unprecedented spatial and temporal resolution. A first-in-class fluorescence lifetime-based FRET sensor for phosphatase and tensin homolog (PTEN) enables the dynamic monitoring of PTEN activity in cultured cells and in vivo with high spatiotemporal resolution.
An ideal tool for the study of cellular biology would enable the measure of molecular activity nondestructively within living cells. Single-molecule localization microscopy (SMLM) techniques, such as single-molecule tracking (SMT), enable in situ measurements in cells but have historically been limited by a necessary tradeoff between spatiotemporal resolution and throughput. Here we address these limitations using oblique line scan (OLS), a robust single-objective light-sheet-based illumination and detection modality that achieves nanoscale spatial resolution and sub-millisecond temporal resolution across a large field of view. We show that OLS can be used to capture protein motion up to 14 μm2 s−1 in living cells. We further extend the utility of OLS with in-solution SMT for single-molecule measurement of ligand–protein interactions and disruption of protein–protein interactions using purified proteins. We illustrate the versatility of OLS by showcasing two-color SMT, STORM and single-molecule fluorescence recovery after photobleaching. OLS paves the way for robust, high-throughput, single-molecule investigations of protein function required for basic research, drug screening and systems biology studies. Oblique line scan microscopy achieves nanoscale spatial and sub-millisecond temporal resolution across a large field of view, enabling improved and robust single-molecule biophysical measurements and single-molecule tracking in both cells and solution.
These researchers put their labs’ philosophies into practice and find it empowers science and collaboration.
The MARBLE method addresses a critical challenge in neural population recordings: inferring expressive and interpretable latent representations that are comparable across experiments and animals. It achieves this by explicitly leveraging the low-dimensional structure of neural states through geometric deep learning to learn the dynamical flow fields in neural activity.
Vectors can be the ultimate vehicle for transporting material into cells. Except when they’re not so ultimate.
The dynamics of neuron populations commonly evolve on low-dimensional manifolds. Thus, we need methods that learn the dynamical processes over neural manifolds to infer interpretable and consistent latent representations. We introduce a representation learning method, MARBLE, which decomposes on-manifold dynamics into local flow fields and maps them into a common latent space using unsupervised geometric deep learning. In simulated nonlinear dynamical systems, recurrent neural networks and experimental single-neuron recordings from primates and rodents, we discover emergent low-dimensional latent representations that parametrize high-dimensional neural dynamics during gain modulation, decision-making and changes in the internal state. These representations are consistent across neural networks and animals, enabling the robust comparison of cognitive computations. Extensive benchmarking demonstrates state-of-the-art within- and across-animal decoding accuracy of MARBLE compared to current representation learning approaches, with minimal user input. Our results suggest that a manifold structure provides a powerful inductive bias to develop decoding algorithms and assimilate data across experiments. MARBLE uses geometric deep learning to map dynamics such as neural activity into a latent representation, which can then be used to decode the neural activity or compare it across systems.
Accurate segmentation of objects in microscopy images remains a bottleneck for many researchers despite the number of tools developed for this purpose. Here, we present Segment Anything for Microscopy (μSAM), a tool for segmentation and tracking in multidimensional microscopy data. It is based on Segment Anything, a vision foundation model for image segmentation. We extend it by fine-tuning generalist models for light and electron microscopy that clearly improve segmentation quality for a wide range of imaging conditions. We also implement interactive and automatic segmentation in a napari plugin that can speed up diverse segmentation tasks and provides a unified solution for microscopy annotation across different microscopy modalities. Our work constitutes the application of vision foundation models in microscopy, laying the groundwork for solving image analysis tasks in this domain with a small set of powerful deep learning models. Segment Anything for Microscopy (μSAM) builds on the vision foundation model Segment Anything for high-quality image segmentation over a wide range of imaging conditions including light and electron microscopy.
Generalist methods for cellular segmentation have good out-of-the-box performance on a variety of image types; however, existing methods struggle for images that are degraded by noise, blurring or undersampling, all of which are common in microscopy. We focused the development of Cellpose3 on addressing these cases and here we demonstrate substantial out-of-the-box gains in segmentation and image quality for noisy, blurry and undersampled images. Unlike previous approaches that train models to restore pixel values, we trained Cellpose3 to output images that are well segmented by a generalist segmentation model, while maintaining perceptual similarity to the target images. Furthermore, we trained the restoration models on a large, varied collection of datasets, thus ensuring good generalization to user images. We provide these tools as ‘one-click’ buttons inside the graphical interface of Cellpose as well as in the Cellpose API. Cellpose3 employs deep-learning-based approaches for image restoration to improve cellular segmentation and shows strong generalized performance even on images degraded by noise, blurring or undersampling.
Most biological processes, from development to pathogenesis, span multiple time and length scales. While light-sheet fluorescence microscopy has become a fast and efficient method for imaging organisms, cells and subcellular dynamics, simultaneous observations across all these scales have remained challenging. Moreover, continuous high-resolution imaging inside living organisms has mostly been limited to a few hours, as regions of interest quickly move out of view due to sample movement and growth. Here, we present a self-driving, multiresolution light-sheet microscope platform controlled by custom Python-based software, to simultaneously observe and quantify subcellular dynamics in the context of entire organisms in vitro and in vivo over hours of imaging. We apply the platform to the study of developmental processes, cancer invasion and metastasis, and we provide quantitative multiscale analysis of immune–cancer cell interactions in zebrafish xenografts. A self-driving multiresolution light-sheet microscope enables the simultaneous observation and quantification of cellular and subcellular dynamics in the context of intact and developing organisms over many hours of imaging.
The ideal technology for directly investigating the relationship between genotype and phenotype would analyze both RNA and DNA genome-wide and with single-cell resolution; however, existing tools lack the throughput required for comprehensive analysis of complex tumors and tissues. We introduce a highly scalable method for jointly profiling DNA and expression following nucleosome depletion (DEFND-seq). In DEFND-seq, nuclei are nucleosome-depleted, tagmented and separated into individual droplets for messenger RNA and genomic DNA barcoding. Once nuclei have been depleted of nucleosomes, subsequent steps can be performed using the widely available 10x Genomics droplet microfluidic technology and commercial kits. We demonstrate the production of high-complexity mRNA and gDNA sequencing libraries from thousands of individual nuclei from cell lines, fresh and archived surgical specimens for associating gene expression with both copy number and single-nucleotide variants. Building on a nucleosome-depletion strategy, DEFND-seq utilizes a droplet microfluidic platform to enable high-throughput co-profiling of DNA and RNA in single cells.
Nature Methods - Structural biology of heterogeneous RNAs
Nature Methods - A DREADD for the periphery
Nature Methods - RNA-seq coverage prediction
Nature Methods - Tackling in vivo screening complexity
Neuroimaging has entered the era of big data. However, the advancement of preprocessing pipelines falls behind the rapid expansion of data volume, causing substantial computational challenges. Here we present DeepPrep, a pipeline empowered by deep learning and a workflow manager. Evaluated on over 55,000 scans, DeepPrep demonstrates tenfold acceleration, scalability and robustness compared to the state-of-the-art pipeline, thereby meeting the scalability requirements of neuroimaging. DeepPrep is a preprocessing pipeline for functional and structural MRI data from humans. Deep learning-based modules and an efficient workflow allow DeepPrep to handle large datasets.
All multicellular systems produce and dynamically regulate extracellular matrices (ECMs) that play essential roles in both biochemical and mechanical signaling. Though the spatial arrangement of these extracellular assemblies is critical to their biological functions, visualization of ECM structure is challenging, in part because the biomolecules that compose the ECM are difficult to fluorescently label individually and collectively. Here, we present a cell-impermeable small-molecule fluorophore, termed Rhobo6, that turns on and red shifts upon reversible binding to glycans. Given that most ECM components are densely glycosylated, the dye enables wash-free visualization of ECM, in systems ranging from in vitro substrates to in vivo mouse mammary tumors. Relative to existing techniques, Rhobo6 provides a broad substrate profile, superior tissue penetration, non-perturbative labeling, and negligible photobleaching. This work establishes a straightforward method for imaging the distribution of ECM in live tissues and organisms, lowering barriers for investigation of extracellular biology. Rhobo6 is a cell-impermeable small-molecule fluorophore that displays reversible fluorogenic binding to glycans, making it a general, wash-free, and non-perturbative label for the extracellular matrix in living samples.
Advances in computational structure prediction will vastly augment the hundreds of thousands of currently available protein complex structures. Translating these into discoveries requires aligning them, which is computationally prohibitive. Foldseek-Multimer computes complex alignments from compatible chain-to-chain alignments, identified by efficiently clustering their superposition vectors. Foldseek-Multimer is 3–4 orders of magnitudes faster than the gold standard, while producing comparable alignments; this allows it to compare billions of complex pairs in 11 h. Foldseek-Multimer is open-source software available at GitHub via https://github.com/steineggerlab/foldseek/ , https://search.foldseek.com/search/ and the BFMD database. Foldseek-Multimer offers a fast strategy for complex-to-complex alignment to quickly identify compatible sets of chain-to-chain alignments by their superpositions. It can compare billions of complex pairs in 11 h.
As they study the emerging roles of RNA in disease and homeostasis, some scientists use RNA editing to direct precise RNA changes that shape cellular events.
Spatial-Mux-seq adds to a growing portfolio of innovative spatial omics technologies and enables simultaneous profiling of up to five omic modalities in situ.
A key challenge of the modern genomics era is developing empirical data-driven representations of gene function. Here we present the first unbiased morphology-based genome-wide perturbation atlas in human cells, containing three genome-wide genotype–phenotype maps comprising CRISPR–Cas9-based knockouts of >20,000 genes in >30 million cells. Our optical pooled cell profiling platform (PERISCOPE) combines a destainable high-dimensional phenotyping panel (based on Cell Painting) with optical sequencing of molecular barcodes and a scalable open-source analysis pipeline to facilitate massively parallel screening of pooled perturbation libraries. This perturbation atlas comprises high-dimensional phenotypic profiles of individual cells with sufficient resolution to cluster thousands of human genes, reconstruct known pathways and protein–protein interaction networks, interrogate subcellular processes and identify culture media-specific responses. Using this atlas, we identify the poorly characterized disease-associated TMEM251/LYSET as a Golgi-resident transmembrane protein essential for mannose-6-phosphate-dependent trafficking of lysosomal enzymes. In sum, this perturbation atlas and screening platform represents a rich and accessible resource for connecting genes to cellular functions at scale. An optical pooled cell profiling platform (PERISCOPE) based on Cell Painting and optical sequencing of molecular barcodes was used to develop the first unbiased genome-wide morphology-based perturbation atlas in human cells.
The phenotypic and functional states of cells are modulated by a complex interactive molecular hierarchy of multiple omics layers, involving the genome, epigenome, transcriptome, proteome and metabolome. Spatial omics approaches have enabled the study of these layers in tissue context but are often limited to one or two modalities, offering an incomplete view of cellular identity. Here we present spatial-Mux-seq, a multimodal spatial technology that allows simultaneous profiling of five different modalities: two histone modifications, chromatin accessibility, whole transcriptome and a panel of proteins at tissue scale and cellular level in a spatially resolved manner. We applied this technology to mouse embryos and mouse brains, generating detailed multimodal tissue maps that identified more cell types and states compared to unimodal data. This analysis uncovered spatiotemporal relationships among histone modifications, chromatin accessibility, gene expression and protein levels during neuron differentiation, and revealed a radial glia niche with spatially dynamic epigenetic signals. Collectively, the spatial multi-omics approach heralds a new era for characterizing tissue and cellular heterogeneity that single-modality studies alone could not reveal. Spatial-Mux-seq offers a multimodal spatial platform capable of profiling multiple molecular modalities, including the transcriptome, chromatin accessibility, histone modifications and targeted proteins.
Teravoxel-scale, cellular-resolution images of cleared rodent brains acquired with light-sheet fluorescence microscopy have transformed the way we study the brain. Realizing the potential of this technology requires computational pipelines that generalize across experimental protocols and map neuronal activity at the laminar and subpopulation-specific levels, beyond atlas-defined regions. Here, we present artficial intelligence-based cartography of ensembles (ACE), an end-to-end pipeline that employs three-dimensional deep learning segmentation models and advanced cluster-wise statistical algorithms, to enable unbiased mapping of local neuronal activity and connectivity. Validation against state-of-the-art segmentation and detection methods on unseen datasets demonstrated ACE’s high generalizability and performance. Applying ACE in two distinct neurobiological contexts, we discovered subregional effects missed by existing atlas-based analyses and showcase ACE’s ability to reveal localized or laminar neuronal activity brain-wide. Our open-source pipeline enables whole-brain mapping of neuronal ensembles at a high level of precision across a wide range of neuroscientific applications. The ACE pipeline utilized deep learning and advanced statistics for mapping neural activity at a granular level that is independent of atlas-defined regions.
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