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单蛋白分辨率下神经元中的空间蛋白质组学。

Spatial proteomics in neurons at single-protein resolution.

机构信息

Max Planck Institute of Biochemistry, Planegg, Germany; Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität, Munich, Germany.

Institute of AI for Health, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany; Data & Analytics, Roche Pharma Research and Early Development, Roche Innovation Center Munich, Munich, Germany; Department of Mathematics, Technical University of Munich, Munich, Germany.

出版信息

Cell. 2024 Mar 28;187(7):1785-1800.e16. doi: 10.1016/j.cell.2024.02.045.

Abstract

To understand biological processes, it is necessary to reveal the molecular heterogeneity of cells by gaining access to the location and interaction of all biomolecules. Significant advances were achieved by super-resolution microscopy, but such methods are still far from reaching the multiplexing capacity of proteomics. Here, we introduce secondary label-based unlimited multiplexed DNA-PAINT (SUM-PAINT), a high-throughput imaging method that is capable of achieving virtually unlimited multiplexing at better than 15 nm resolution. Using SUM-PAINT, we generated 30-plex single-molecule resolved datasets in neurons and adapted omics-inspired analysis for data exploration. This allowed us to reveal the complexity of synaptic heterogeneity, leading to the discovery of a distinct synapse type. We not only provide a resource for researchers, but also an integrated acquisition and analysis workflow for comprehensive spatial proteomics at single-protein resolution.

摘要

为了理解生物过程,有必要通过获取所有生物分子的位置和相互作用来揭示细胞的分子异质性。超分辨率显微镜取得了重大进展,但这些方法仍远未达到蛋白质组学的多重检测能力。在这里,我们引入了基于二级标签的无限制多重 DNA-PAINT(SUM-PAINT),这是一种高通量成像方法,能够以优于 15nm 的分辨率实现几乎无限的多重检测。我们使用 SUM-PAINT 在神经元中生成了 30 重单分子分辨数据集,并采用基于组学的分析方法进行数据探索。这使我们能够揭示突触异质性的复杂性,从而发现一种独特的突触类型。我们不仅为研究人员提供了一个资源,还提供了一个综合的获取和分析工作流程,用于在单蛋白分辨率下进行全面的空间蛋白质组学研究。

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