Nature Methods最新文献

Phylogenetic analyses of genome sequences from infectious pathogens reveal essential information regarding their evolution and transmission, as seen during the coronavirus disease 2019 pandemic. Recently developed pandemic-scale phylogenetic inference methods reduce the computational demand of phylogenetic reconstruction from genomic epidemiological datasets, allowing the analysis of millions of closely related genomes. However, widespread homoplasies, due to recurrent mutations and sequence errors, cause phylogenetic uncertainty and biases. We present algorithms and models to substantially improve the computational performance and accuracy of pandemic-scale phylogenetics. In particular, we account for, and identify, mutation rate variation and recurrent sequence errors. We reconstruct a reliable and public sequence alignment and phylogenetic tree of >2 million severe acute respiratory syndrome coronavirus 2 genomes encapsulating the evolutionary history and global spread of the virus up to February 2023.

Current single-cell metabolomics approaches are limited by insufficient sensitivity, robustness and metabolite coverage. We present an ion mobility-resolved mass cytometry technology that integrates high-throughput single-cell injection with ion mobility-mass spectrometry for multidimensional metabolomic profiling. Ion mobility-enabled selective ion accumulation and cell superposition-based amplification strategies substantially enhance sensitivity, robustness and overall analytical performance. Combined with our computational tool, MetCell, this technology allows high-throughput analysis while achieving exceptional profiling depth, detecting over 5,000 metabolic peaks and annotating approximately 800 metabolites per cell-representing a 3-fold to 10-fold improvement over existing methods. It offers attomole-level sensitivity and captures a broad dynamic range of metabolites within individual cells. Applied to 45,603 primary liver cells from aging mice, it enabled accurate cell-type and cell-subtype annotation and revealed distinct metabolic states and heterogeneity in hepatocytes during aging. This platform sets a new benchmark for high-throughput single-cell metabolomics, advancing our understanding of metabolic heterogeneity at single-cell resolution.

The majority of SARS-CoV-2 genomes obtained during the pandemic were derived by amplifying overlapping windows of the genome ('tiled amplicons'), reconstructing their sequences and fitting them together. This leads to systematic errors in genomes unless the software is both aware of the amplicon scheme and of the error modes of amplicon sequencing. Additionally, over time, amplicon schemes need to be updated as new mutations in the virus interfere with the primer binding sites at the end of amplicons. Thus, waves of variants swept the world during the pandemic and were followed by waves of systematic errors in the genomes, which had significant impacts on the inferred phylogenetic tree.Here we reconstruct the genomes from all public data as of June 2024 using an assembly tool called Viridian ( https://github.com/iqbal-lab-org/viridian ), developed to rigorously process amplicon sequence data. With these high-quality consensus sequences we provide a global phylogenetic tree of 4,471,579 samples, viewable at https://viridian.taxonium.org . We provide simulation and empirical validation of the methodology, and quantify the improvement in the phylogeny.

Microbial community analysis typically involves determining which microbial features are associated with properties such as environmental or health phenotypes. This task is impeded by data characteristics, including sparsity (technical or biological) and compositionality. Here we introduce MaAsLin 3 (microbiome multivariable associations with linear models) to simultaneously identify both abundance and prevalence relationships in microbiome studies with modern, potentially complex designs. MaAsLin 3 can newly account for compositionality either experimentally (for example, quantitative PCR or spike-ins) or computationally, and it expands the range of testable biological hypotheses and covariate types. On a variety of synthetic and real datasets, MaAsLin 3 outperformed state-of-the-art differential abundance methods, and when applied to the Inflammatory Bowel Disease Multi-omics Database, MaAsLin 3 corroborated previously reported associations, identifying 77% with feature prevalence rather than abundance. In summary, MaAsLin 3 enables researchers to identify microbiome associations more accurately and specifically, especially in complex datasets.

Spatially mapping the transcriptome and proteome in the same tissue section can profoundly advance our understanding of cellular heterogeneity and function. Here we present Deterministic Barcoding in Tissue sequencing plus (DBiTplus), an integrative multimodal spatial omics approach combining sequencing-based spatial transcriptomics and multiplexed protein imaging on the same section, enabling both single-cell-resolution cell typing and transcriptome-wide interrogation of biological pathways. DBiTplus utilizes spatial barcoding and RNase H-mediated cDNA retrieval, preserving tissue architecture for multiplexed protein imaging. We developed computational pipelines to integrate these modalities, allowing imaging-guided deconvolution to generate single-cell-resolved spatial transcriptome atlases. We demonstrate DBiTplus across diverse samples including frozen mouse embryos, and formalin-fixed paraffin-embedded human lymph nodes and lymphoma tissues, highlighting its compatibility with challenging clinical specimens. DBiTplus uncovered mechanisms of lymphomagenesis, progression and transformation in human lymphomas. Thus, DBiTplus is a unified workflow for spatially resolved single-cell atlasing and unbiased exploration of biological mechanisms in a cell-by-cell manner at transcriptome scale.