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SPRITE: a genome-wide method for mapping higher-order 3D interactions in the nucleus using combinatorial split-and-pool barcoding

Sprite(计算机图形) 计算生物学 计算机科学 条形码 DNA DNA条形码 基因组 DNA测序 生物 基因 遗传学 进化生物学 人工智能 操作系统
作者
Sofia A. Quinodoz,Prashant Bhat,Peter Chovanec,Joanna W. Jachowicz,Noah Ollikainen,Elizabeth Detmar,Elizabeth Soehalim,Mitchell Guttman
出处
期刊:Nature Protocols [Nature Portfolio]
卷期号:17 (1): 36-75 被引量:42
标识
DOI:10.1038/s41596-021-00633-y
摘要

A fundamental question in gene regulation is how cell-type-specific gene expression is influenced by the packaging of DNA within the nucleus of each cell. We recently developed Split-Pool Recognition of Interactions by Tag Extension (SPRITE), which enables mapping of higher-order interactions within the nucleus. SPRITE works by cross-linking interacting DNA, RNA and protein molecules and then mapping DNA–DNA spatial arrangements through an iterative split-and-pool barcoding method. All DNA molecules within a cross-linked complex are barcoded by repeatedly splitting complexes across a 96-well plate, ligating molecules with a unique tag sequence, and pooling all complexes into a single well before repeating the tagging. Because all molecules in a cross-linked complex are covalently attached, they will sort together throughout each round of split-and-pool and will obtain the same series of SPRITE tags, which we refer to as a barcode. The DNA fragments and their associated barcodes are sequenced, and all reads sharing identical barcodes are matched to reconstruct interactions. SPRITE accurately maps pairwise DNA interactions within the nucleus and measures higher-order spatial contacts occurring among up to thousands of simultaneously interacting molecules. Here, we provide a detailed protocol for the experimental steps of SPRITE, including a video (https://youtu.be/6SdWkBxQGlg). Furthermore, we provide an automated computational pipeline available on GitHub that allows experimenters to seamlessly generate SPRITE interaction matrices starting with raw fastq files. The protocol takes ~5 d from cell cross-linking to high-throughput sequencing for the experimental steps and 1 d for data processing.

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