Nature Methods最新文献

Bottom-up proteomics relies predominantly on collision-induced dissociation (CID) for peptide sequencing, which has achieved remarkable sensitivity and efficiency now enabling single-cell analysis. However, CID shows limitations in characterizing post-translational modifications and complex proteoforms. Here we have developed an integrated mass spectrometry platform enabling automated collision-, electron- and photon-based fragmentation techniques. Using multi-enzyme deep proteomics workflows, we generated comprehensive datasets to train a unified Prosit deep learning model predicting spectra across all dissociation methods. This publicly available model, now integrated into FragPipe's MSBooster module, increased protein identifications by >10% on average for both data-dependent and data-independent acquisition across all fragmentation techniques. We demonstrate that alternative approaches, particularly electron-induced and ultraviolet photodissociation, which generate richer, more informative spectra, achieve identification efficiency competitive with CID while providing superior sequence coverage. This work establishes a framework enabling routine application of advanced fragmentation techniques in standard proteomics pipelines.

We present online three-dimensional (3D) structure-prediction tools at GLYCAM-Web ( www.glycam.org ) that can be used for generating experimentally consistent 3D structures of oligosaccharides for data interpretation, hypothesis generation, 3D visualization, molecular docking and further simulation. The tools support the modeling of an unlimited array of natural glycans and polysaccharides, glycosaminoglycans, engineered glycomaterials and glycoproteins. GLYCAM-Web is directly linked to external databases, such as the Protein Data Bank, facilitating comparison with experimental data.

Histopathological data are foundational in both biological research and clinical diagnostics but remain siloed from modern multimodal and single-cell frameworks. Here we introduce LazySlide, an open-source Python package built on the scverse ecosystem for efficient whole-slide image analysis and multimodal integration. By leveraging vision-language foundation models and adhering to scverse data standards, LazySlide bridges histopathology with omics workflows. It supports tissue and cell segmentation, feature extraction, cross-modal querying and zero-shot classification, with minimal setup.

Artificial intelligence (AI) has seen transformative breakthroughs in the life sciences, expanding possibilities to interpret biological information at an unprecedented capacity. To maximize return on growing investments and accelerate progress, it is urgent to address long-standing research challenges arising from the rapid adoption of AI methods. We review the erosion of trust in AI outputs driven by poor reusability and reproducibility, and highlight their impact on environmental sustainability. Furthermore, we discuss the fragmented components of the AI ecosystem and lack of guiding pathways to support open and sustainable AI model development. In response, this Perspective introduces practical open and sustainable AI recommendations mapped to over 300 ecosystem components and provides guiding implementation pathways. Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and reproducible AI. Built upon community consensus and aligned to existing efforts, these outputs will aid future policy development and structured pathways for guiding AI implementation.

Modern biology increasingly relies on complex, high-dimensional datasets such as single-cell RNA sequencing (scRNA-seq), which present a vast space of potential hypotheses. Systematically exploring this space is often impractical, as scRNA-seq analyses are time-consuming and require substantial computational and domain expertise. To address this challenge, we introduce CellVoyager, an AI agent built on large language models that autonomously generates and implements scRNA-seq analyses within a Jupyter notebook environment. We evaluate CellVoyager on CellBench, a benchmark of 76 published scRNA-seq studies, where it outperforms GPT-4o and o3-mini by up to 23% in predicting which analyses authors ultimately conducted, given only the papers' background sections. Across three in-depth case studies, CellVoyager generated novel findings in COVID-19, cell-cell communication and aging that experts consistently rated as creative and scientifically sound. These results demonstrate CellVoyager's potential to accelerate computational biology and uncover missing insights by autonomously analyzing biological data at scale.

Tissue clearing has been widely used for fluorescence imaging of fixed tissues, but its application to live tissues has been limited by toxicity. Here we develop minimally invasive optical clearing media for fluorescence imaging of live mammalian tissues. Light scattering is minimized by adding spherical polymers with low osmolarity to the extracellular medium. A clearing medium containing bovine serum albumin (SeeDB-Live) is compatible with live cells, enabling structural and functional imaging of live tissues, such as spheroids, organoids, acute brain slices and the mouse brains in vivo. SeeDB-Live minimally affects neuronal electrophysiological properties and sensory responses in vivo, and facilitates fluorescence imaging of deep cortical layers in live animals without detectable toxicity to neurons or behavior. We further demonstrate its utility to epifluorescence voltage imaging in acute brain slices and in vivo preparations. Thus, SeeDB-Live expands both the depth and modality range of fluorescence imaging in live mammalian tissues.