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文库规模的多重单分子表征。

Multiplexed single-molecule characterization at the library scale.

作者信息

Panfilov M, Mao G, Guo J, Aguirre Rivera J, Sabantsev A, Deindl S

机构信息

Department of Cell and Molecular Biology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.

出版信息

Nat Protoc. 2025 Jun 4. doi: 10.1038/s41596-025-01198-w.

Abstract

Single-molecule techniques are exceptionally well suited for analyzing the complex dynamic behavior of macromolecules involved in fundamental biological processes. Nevertheless, time and cost usually restrict current single-molecule methods to examining a limited number of different samples. At the same time, a broad sequence or chemical space often needs to be investigated to gain a thorough understanding of complex biological phenomena. To address this urgent need, we have developed multiplexed single-molecule characterization at the library scale (MUSCLE), a method that combines single-molecule fluorescence microscopy with next-generation sequencing to enable highly multiplexed observations of complex dynamics on millions of individual molecules spanning thousands of distinct sequences or barcoded entities. In this protocol, we outline the implementation of MUSCLE and present examples from our recent research, such as the sequence-dependent dynamics of Cas9-induced target DNA unwinding and rewinding. This example demonstrates that MUSCLE can be applied to study protein-nucleic acid interactions, going beyond nucleic-acid-only model systems. We detail the sample and library design, high-throughput single-molecule data acquisition, next-generation sequencing, spatial registration of single-molecule fluorescence and sequencing data and downstream data analysis. The ligation-based surface immobilization approach of MUSCLE ensures high clustering efficiency (>40%), increasing throughput and simplifying registration. In addition, MUSCLE includes a 3D-printed flow cell adapter that enables liquid exchange during single-molecule fluorescence microscopy. The complete procedure typically spans 3-4 days and yields a dataset that comprehensively characterizes the dynamic behavior of a library of constructs.

摘要

单分子技术非常适合分析参与基本生物过程的大分子的复杂动态行为。然而,时间和成本通常限制了当前的单分子方法,使其只能检测有限数量的不同样本。与此同时,为了全面理解复杂的生物现象,往往需要研究广泛的序列或化学空间。为了满足这一迫切需求,我们开发了文库规模的多重单分子表征技术(MUSCLE),这是一种将单分子荧光显微镜与下一代测序相结合的方法,能够对数百万个跨越数千个不同序列或条形码实体的单个分子的复杂动力学进行高度多重观察。在本方案中,我们概述了MUSCLE的实施过程,并展示了我们近期研究中的实例,如Cas9诱导的靶DNA解旋和重新缠绕的序列依赖性动力学。这个例子表明,MUSCLE可以应用于研究蛋白质-核酸相互作用,而不仅仅局限于仅含核酸的模型系统。我们详细介绍了样本和文库设计、高通量单分子数据采集、下一代测序、单分子荧光与测序数据的空间配准以及下游数据分析。MUSCLE基于连接的表面固定方法确保了高聚类效率(>40%),提高了通量并简化了配准。此外,MUSCLE包括一个3D打印的流动池适配器,可在单分子荧光显微镜检查期间进行液体交换。整个过程通常需要3至4天,并产生一个全面表征构建体文库动态行为的数据集。

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