染色质
计算生物学
生物
转录组
基因组学
基因表达
基因组
基因
遗传学
作者
Andrew Russell,Jackson A. Weir,Naeem Nadaf,Matthew Shabet,Vipin Kumar,Sandeep Kambhampati,Ruth Raichur,Giovanni J. Marrero,Sophia Liu,Karol S. Balderrama,Charles Vanderburg,Vignesh Shanmugam,Luyi Tian,J. Bryan Iorgulescu,Charles H. Yoon,Catherine J. Wu,Evan Z. Macosko,Fei Chen
出处
期刊:Nature
[Springer Nature]
日期:2023-12-13
卷期号:625 (7993): 101-109
被引量:35
标识
DOI:10.1038/s41586-023-06837-4
摘要
Abstract Recent technological innovations have enabled the high-throughput quantification of gene expression and epigenetic regulation within individual cells, transforming our understanding of how complex tissues are constructed 1–6 . However, missing from these measurements is the ability to routinely and easily spatially localize these profiled cells. We developed a strategy, Slide-tags, in which single nuclei within an intact tissue section are tagged with spatial barcode oligonucleotides derived from DNA-barcoded beads with known positions. These tagged nuclei can then be used as an input into a wide variety of single-nucleus profiling assays. Application of Slide-tags to the mouse hippocampus positioned nuclei at less than 10 μm spatial resolution and delivered whole-transcriptome data that are indistinguishable in quality from ordinary single-nucleus RNA-sequencing data. To demonstrate that Slide-tags can be applied to a wide variety of human tissues, we performed the assay on brain, tonsil and melanoma. We revealed cell-type-specific spatially varying gene expression across cortical layers and spatially contextualized receptor–ligand interactions driving B cell maturation in lymphoid tissue. A major benefit of Slide-tags is that it is easily adaptable to almost any single-cell measurement technology. As a proof of principle, we performed multiomic measurements of open chromatin, RNA and T cell receptor (TCR) sequences in the same cells from metastatic melanoma, identifying transcription factor motifs driving cancer cell state transitions in spatially distinct microenvironments. Slide-tags offers a universal platform for importing the compendium of established single-cell measurements into the spatial genomics repertoire.
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