Nature Methods最新文献

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.

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.

Inducible protein switches are currently limited for use in tissues and organisms because common inducers cannot be controlled with precision in space and time in optically dense settings. Here, we introduce a protein that can be reversibly toggled with a small change in temperature, a stimulus that is both penetrant and dynamic. This protein, called Melt (Membrane localization using temperature) oligomerizes and translocates to the plasma membrane when temperature is lowered. We generated a library of Melt variants with switching temperatures ranging from 30 °C to 40 °C, including two that operate at and above 37 °C. Melt was a highly modular actuator of cell function, permitting thermal control over diverse processes including signaling, proteolysis, nuclear shuttling, cytoskeletal rearrangements and cell death. Finally, Melt permitted thermal control of cell death in a mouse model of human cancer. Melt represents a versatile thermogenetic module for straightforward, non-invasive and spatiotemporally defined control of mammalian cells with broad potential for biotechnology and biomedicine.

The use of single-cell combinatorial indexing sequencing via droplet microfluidics presents an attractive approach for balancing cost, scalability, robustness and accessibility. However, existing methods often require tailored protocols for individual modalities, limiting their automation potential and clinical applicability. To address this, we introduce UDA-seq, a universal workflow that integrates a post-indexing step to enhance throughput and systematically adapt existing droplet-based single-cell multimodal methods. UDA-seq was benchmarked across various tissue and cell types, enabling several common multimodal analyses, including single-cell co-assay of RNA and VDJ, RNA and chromatin, and RNA and CRISPR perturbation. Notably, UDA-seq facilitated the efficient generation of over 100,000 high-quality single-cell datasets from three dozen frozen clinical biopsy specimens within a single-channel droplet microfluidics experiment. Downstream analysis demonstrated the robustness of this approach in identifying rare cell subpopulations associated with clinical phenotypes and exploring the vulnerability of cancer cells.