基于 Python 的交互式单粒子和单细胞 ICP-MS 数据处理平台,Journal of Analytical Atomic Spectrometry

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基于 Python 的交互式单粒子和单细胞 ICP-MS 数据处理平台
Journal of Analytical Atomic Spectrometry ( IF 3.1 ) Pub Date : 2021-10-08 , DOI: 10.1039/d1ja00297j
Thomas E. Lockwood ₁ , Raquel Gonzalez de Vega ₁ , David Clases ₁

Affiliation

单粒子 (SP) 和单细胞 (SC) 电感耦合等离子体质谱 (ICP-MS) 在环境和医学科学中用于分析纳米粒子、微结构和单个细胞的势头越来越大。这项工作提出了一个基于 Python 的开源 SP/SC ICP-MS 数据处理平台，具有交互式图形用户界面。该程序引导用户分析大型数据集，并使用高效透明的算法。基于高斯和泊松的数据过滤能够根据目的对来自背景噪声的粒子信号进行阈值处理。窗口过滤器的实施将软件的适用性扩展到 SP 激光烧蚀-ICP-MS 和其他包含漂移或可变背景的数据集。用多个数据点记录的 SP 或 SC 信号被整合，并且几个不同的校准和处理途径可用于确定质量、大小和数量浓度，或计算细胞内浓度。包括平均值、中值、离子背景水平和分析极限在内的相关参数与原始和校准数据的直方图一起自动计算和可视化。作为原理证明，开发的数据处理平台用于表征二氧化钛离子背景水平和分析极限与原始和校准数据的直方图一起自动计算和可视化。作为原理证明，开发的数据处理平台用于表征二氧化钛离子背景水平和分析极限与原始和校准数据的直方图一起自动计算和可视化。作为原理证明，开发的数据处理平台用于表征二氧化钛₂地表水中的纳米颗粒、土壤中的微塑料颗粒和单个细胞中的 C 含量。

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An interactive Python-based data processing platform for single particle and single cell ICP-MS

Single particle (SP) and single cell (SC) inductively coupled plasma-mass spectrometry (ICP-MS) are gaining increasing momentum in environmental and medical sciences for the analysis of nanoparticles, microstructures, and individual cells. This work presents an open-source Python-based SP/SC ICP-MS data processing platform with an interactive graphical user interface. The program guides users through the analysis of large data sets and uses efficient and transparent algorithms. Gaussian and Poisson-based data filtering enables fit for purpose thresholding of particle signals from background noise. Implementation of windowed filters extends applicability of the software to SP laser ablation-ICP-MS and other data sets that contain drifting or variable backgrounds. SP or SC signals recorded with multiple data points are integrated and several distinct calibration and processing pathways may be used to determine masses, sizes, and number concentrations, or to calculate intracellular concentrations. Relevant parameters including means, medians, ionic background levels and limits of analysis are automatically calculated and visualised together with histograms of raw and calibrated data. As a proof of principle, the developed data processing platform was employed to characterise TiO₂ nanoparticles in surface water, microplastic particles in soil and the C content across individual cells.

更新日期：2021-10-13

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