提高单颗粒/单细胞ICP-MS分析中的检测阈值和事件过滤能力

智能文字提取功能测试中

本工作中，介绍了用于单颗粒（sp）和单细胞（sc）电感耦合等离子体质谱分析法（ICP-MS）的模块化数据处理工作流程。为了实现更可靠的检测阈值，需要特别关注描述背景（BG）信号的高斯分布和泊松分布的参数估计。对于高斯模型，采用一种通过增加检验因子来调整不同BG水平检验的算法，改进了广泛使用的迭代离群值检验。通过仔细评估，sc-和sp-ICP-MS实验数据的标准差被用作测试收敛质量的稳健度量。此外，异常值分析与后续事件检测的分离比之前报道的更为严格。重要的是，开发了基于高斯和泊松建模的数据依赖决策准则，以有效地处理实验数据中的额外泊松方差。在第二部分中，开发了闸门滤波器减少sc-ICP-MS中过多的假阳性事件的数量。为此，使用基于信号峰值高度的二次滤波器来消除偶发的假阳性事件，而不影响正确检测到的事件的信号强度。提出了两种基于检测极限的数值近似以及高斯和泊松分布的临界值的方法计算闸门滤波器级别。文中讨论了假阳性事件的可能来源，其中一些是sc-ICP-MS特有的。应用组合处理流程分析莱茵衣藻细胞群中六种内生性元素的分布。闸门滤波器将细胞数量浓度校正高达44%（平均22%），将每个细胞的质量校正高达30%（平均17%）。In this work, a modular data processing workflow for single-particle (sp) and single-cell (sc) inductively coupled plasma-mass spectrometry (ICP-MS) is presented. To achieve more reliable detection thresholds, a special focus is placed on the parameter estimation of Gaussian and Poisson distributions that describe the background (BG) signal. For Gaussian models, the widely used iterative outlier test was improved by an algorithm that adjusts the test for different BG levels by incrementing the test factor. Through careful evaluation, the standard deviation of the experimental sc- and sp-ICP-MS data was applied as a robust measure of the convergence quality of the test. In addition, the outlier analysis was separated from the subsequent event detection more strictly than it is often reported. Importantly, a data-dependent decision criterion based on Gaussian and Poisson modeling was developed to effectively address extra-Poisson variance in experimental data. In the second part, a gate filter was developed to reduce the amount of excess false-positive events in sc-ICP-MS. To that end, a secondary filter based on the signal peak height is used to remove rare false-positive events without affecting the signal intensity of the events that are detected correctly. Two approaches, based on a numerical approximation via the detection limit, and critical values of the Gaussian and Poisson distribution are presented to calculate the gate filter level. Possible sources of false-positive events, some of which are specific to sc-ICP-MS, are discussed. The combined processing workflow was applied to analyze the distribution of six endogenous elements in Chlamydomonas reinhardtii cell populations. The gate filter corrected the cell number concentration by up to 44% (22% on average), and mass per cell by up to 30% (17% on average).