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SMALL-LABS: Measuring Single-Molecule Intensity and Position in Obscuring Backgrounds

机译:小实验室:在模糊的背景下测量单分子强度和位置

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摘要

Single-molecule and super-resolution imaging relies on successful, sensitive, and accurate detection of the emission from fluorescent molecules. Yet, despite the widespread adoption of super-resolution microscopies, single-molecule data processing algorithms can fail to provide accurate measurements of the brightness and position of molecules in the presence of backgrounds that fluctuate significantly over time and space. Thus, samples or experiments that include obscuring backgrounds can severely, or even completely, hinder this process. To date, no general data analysis approach to this problem has been introduced that is capable of removing obscuring backgrounds for a wide variety of experimental modalities. To address this need, we present the Single-Molecule Accurate LocaLization by LocAl Background Subtraction (SMALL-LABS) algorithm, which can be incorporated into existing single-molecule and super-resolution analysis packages to accurately locate and measure the intensity of single molecules, regardless of the shape or brightness of the background. Accurate background subtraction is enabled by separating the foreground from the background based on differences in the temporal variations of the foreground and the background (i.e., fluorophore blinking, bleaching, or moving). We detail the function of SMALL-LABS here, and we validate the SMALL-LABS algorithm on simulated data as well as real data from single-molecule imaging in living cells.
机译:单分子和超分辨率成像依赖于对荧光分子发射的成功,灵敏和准确的检测。然而,尽管超分辨率显微技术得到了广泛的采用,但单分子数据处理算法仍无法在存在随时间和空间剧烈波动的背景的情况下提供分子亮度和位置的准确测量。因此,包含模糊背景的样本或实验会严重甚至完全阻碍该过程。迄今为止,还没有针对该问题引入通用的数据分析方法,该方法能够为多种实验模式消除模糊的背景。为了满足这一需求,我们提出了通过LocAl背景减法实现单分子精确定位(SMALL-LABS)算法,该算法可以并入现有的单分子和超分辨率分析软件包中,以准确定位和测量单分子的强度,无论背景的形状或亮度如何。根据前景和背景随时间变化的差异(即荧光团闪烁,漂白或移动),通过将前景与背景分开来实现准确的背景扣除。我们在这里详细介绍了SMALL-LABS的功能,并在模拟数据以及活细胞中单分子成像的真实数据中验证了SMALL-LABS算法。

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