Université de Bordeaux, Institut interdisciplinaire de Neurosciences, Bordeaux, France.
CNRS UMR 5297, Institut interdisciplinaire de Neurosciences, Bordeaux, France.
Nat Methods. 2017 Dec;14(12):1184-1190. doi: 10.1038/nmeth.4486. Epub 2017 Oct 30.
Single-molecule localization microscopy techniques have proven to be essential tools for quantitatively monitoring biological processes at unprecedented spatial resolution. However, these techniques are very low throughput and are not yet compatible with fully automated, multiparametric cellular assays. This shortcoming is primarily due to the huge amount of data generated during imaging and the lack of software for automation and dedicated data mining. We describe an automated quantitative single-molecule-based super-resolution methodology that operates in standard multiwell plates and uses analysis based on high-content screening and data-mining software. The workflow is compatible with fixed- and live-cell imaging and allows extraction of quantitative data like fluorophore photophysics, protein clustering or dynamic behavior of biomolecules. We demonstrate that the method is compatible with high-content screening using 3D dSTORM and DNA-PAINT based super-resolution microscopy as well as single-particle tracking.
单分子定位显微镜技术已被证明是在前所未有的空间分辨率下定量监测生物过程的重要工具。然而，这些技术的通量非常低，并且还不能与全自动、多参数细胞分析完全兼容。这一缺点主要是由于在成像过程中产生的大量数据，以及缺乏用于自动化和专用数据挖掘的软件。我们描述了一种自动化的定量单分子超分辨率方法，该方法在标准的多孔板中运行，并使用基于高内涵筛选和数据挖掘软件的分析。该工作流程与固定和活细胞成像兼容，并允许提取定量数据，如荧光团光物理、蛋白质聚集或生物分子的动态行为。我们证明该方法与基于 3D dSTORM 和 DNA-PAINT 的超分辨率显微镜以及单粒子追踪的高内涵筛选兼容。
