Integrative Analysis of Nontargeted LC-HRMS and High-Throughput Metabarcoding Data for Aquatic Environmental Studies Using Combined Multivariate Statistical Approaches

IF 6.7 1区 化学 Q1 CHEMISTRY, ANALYTICAL
Maryam Vosough, Felix Drees, Guido Sieber, Tom L. Stach, Daniela Beisser, Alexander J. Probst, Jens Boenigk, Torsten C. Schmidt
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Abstract

Significant progress in high-throughput analytical techniques has paved the way for novel approaches to integrating data sets from different compartments. This study leverages nontarget screening (NTS) via liquid chromatography-high-resolution mass spectrometry (LC-HRMS), a crucial technique for analyzing organic micropollutants and their transformation products, in combination with biological indicators. We propose a combined multivariate data processing framework that integrates LC-HRMS-based NTS data with other high-throughput data sets, exemplified here by 18S V9 rRNA and full-length 16S rRNA gene metabarcoding data sets. The power of data fusion is demonstrated by systematically evaluating the impact of treated wastewater (TWW) over time on an aquatic ecosystem through a controlled mesocosm experiment. Highly compressed NTS data were compiled through the implementation of the region of interest-multivariate curve resolution-alternating least-squares (MCR-ALS) method, known as ROIMCR. By integrating ANOVA-simultaneous component analysis with structural learning and integrative decomposition (SLIDE), the innovative SLIDE-ASCA approach enables the decomposition of global and partial common, as well as distinct variation sources arising from experimental factors and their possible interactions. SLIDE-ASCA results indicate that temporal variability explains a much larger portion of the variance (74.6%) than the treatment effect, with both contributing to global shared space variation (41%). Design structure benefits include enhanced interpretability, improved detection of key features, and a more accurate representation of complex interactions between chemical and biological data. This approach offers a greater understanding of the natural and wastewater-influenced temporal patterns for each data source, as well as reveals associations between chemical and biological markers in an exemplified perturbed aquatic ecosystem.

Abstract Image

应用多元统计方法综合分析非靶向LC-HRMS和高通量元条形码数据用于水生环境研究
高通量分析技术的重大进展为整合来自不同隔间的数据集的新方法铺平了道路。本研究利用液相色谱-高分辨率质谱法(LC-HRMS)进行非靶筛选(NTS),这是一项分析有机微污染物及其转化产物的关键技术,并结合生物指标。我们提出了一个组合的多元数据处理框架,将基于lc - hrms的NTS数据与其他高通量数据集集成在一起,例如18S V9 rRNA和全长16S rRNA基因元条形码数据集。数据融合的力量是通过一个受控的生态系统实验,系统地评估经过处理的废水(TWW)随时间对水生生态系统的影响。高度压缩的NTS数据是通过实现感兴趣区域的多元曲线分辨率-交替最小二乘(MCR-ALS)方法编译的,称为ROIMCR。通过将anova -同步成分分析与结构学习和综合分解(SLIDE)相结合,创新的SLIDE- asca方法能够分解由实验因素及其可能的相互作用引起的全局和部分共同以及不同的变化源。SLIDE-ASCA结果表明,与治疗效果相比,时间变异性解释了更大一部分的方差(74.6%),两者都对全球共享空间变化(41%)有贡献。设计结构的好处包括增强可解释性,改进关键特征的检测,以及更准确地表示化学和生物数据之间复杂的相互作用。这种方法有助于更好地了解每个数据源的自然和废水影响的时间模式,并揭示了举例说明的受扰动水生生态系统中化学和生物标记物之间的联系。
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来源期刊
Analytical Chemistry
Analytical Chemistry 化学-分析化学
CiteScore
12.10
自引率
12.20%
发文量
1949
审稿时长
1.4 months
期刊介绍: Analytical Chemistry, a peer-reviewed research journal, focuses on disseminating new and original knowledge across all branches of analytical chemistry. Fundamental articles may explore general principles of chemical measurement science and need not directly address existing or potential analytical methodology. They can be entirely theoretical or report experimental results. Contributions may cover various phases of analytical operations, including sampling, bioanalysis, electrochemistry, mass spectrometry, microscale and nanoscale systems, environmental analysis, separations, spectroscopy, chemical reactions and selectivity, instrumentation, imaging, surface analysis, and data processing. Papers discussing known analytical methods should present a significant, original application of the method, a notable improvement, or results on an important analyte.
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