高分辨率准确的基于质谱的非靶向代谢组学:数据依赖采集、数据独立采集和AcquireX的再现性和检测能力。

IF 4.4 2区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Computational and structural biotechnology journal Pub Date : 2025-05-30 eCollection Date: 2025-01-01 DOI:10.1016/j.csbj.2025.05.046
Hanane El Boudlali, Laura Lehmicke, Uta Ceglarek
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引用次数: 0

摘要

非靶向代谢组学旨在无偏见的代谢分析和生物标志物的发现,但需要高灵敏度和可重复性的方法。在这里,我们比较了三种采集模式——数据依赖采集(DDA)、数据独立采集(DIA)和AcquireX,以评估在复杂基质中检测低丰度代谢物的性能和可重复性。在进行非靶向代谢组学分析和监测长期系统性能之前,实施了基于14个类二十烷标准的系统适用性测试(SST),以评估我们的仪器设置的适用性。牛肝总脂提取物(TLE)加入逐渐降低水平(10-0.01 ng/mL)的类二十烷标准混合物(StdMix),比较每种模式的检测能力。在三个独立的测量中评估再现性,间隔一周。采用C18-Kinetex Core-Shell色谱柱进行色谱分离,使用Orbitrap Exploris 480获取HRAM-MS/MS数据。DIA检测并鉴定出最多的代谢特征(三次测量平均1036个代谢特征),其次是DDA(减少18 %)和AcquireX(减少37 %)。此外,DIA表现出优异的再现性,在三次测量中检测到的化合物的方差系数为10 %,而DDA和AcquireX的方差系数分别为17 %和15 %。DIA进一步表现出更好的化合物鉴定一致性,与DDA(43 %)和AcquireX(50 %)相比,两天之间有61 %的重叠。DIA再现了高一致性的破碎谱图,提高了化合物鉴定的再现性。在TLE基质中,10和1 ng/mL时,DIA对所有突发性二十烷类化合物的检测能力最好。在低峰值水平,0.1和0.01 ng/mL,观察到三种采集模式的一般截止。这些评估的获取模式都无法检测和/或识别生理相关浓度的类二十烷酸,这解释了它们在常规非靶向分析中经常被遗漏的原因。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
High-resolution accurate mass- mass spectrometry based- untargeted metabolomics: Reproducibility and detection power across data-dependent acquisition, data-independent acquisition, and AcquireX.

Untargeted metabolomics aims at the unbiased metabolic profiling and biomarker discovery but requires methods with high sensitivity and reproducibility. Here, we compare three acquisition modes-Data-Dependent Acquisition (DDA), Data-Independent Acquisition (DIA), and AcquireX -to evaluate performance and reproducibility in detecting low-abundance metabolites in a complex matrix. A system suitability test (SST) based on 14 eicosanoid standards was implemented to evaluate the suitability of our instrumental setup prior to conducting untargeted metabolomics analyses and monitor long-term system performance. Bovine liver total Lipid Extract (TLE) was spiked with decreasing levels (10-0.01 ng/mL) of the eicosanoid standard mix (StdMix) to compare the detection power of each mode. Reproducibility was evaluated over three independent measurements, spaced one week apart. Chromatographic separation was performed on a C18-Kinetex Core-Shell column and HRAM-MS/MS data were acquired using an Orbitrap Exploris 480. DIA detected and identified the highest number of metabolic features, (averaging 1036 metabolic features over three measurements), followed by DDA (18 % fewer) and AcquireX (37 % fewer). Moreover, DIA demonstrated superior reproducibility, with a coefficient of variance of 10 % across detected compounds over three measurements, compared to 17 % for DDA and 15 % for AcquireX. DIA further exhibited better compound identification consistency, with 61 % overlap between two days, compared to DDA (43 %) and AcquireX (50 %). DIA reproduced fragmentation spectra patterns with high consistency, contributing to higher reproducibility in compound identification. DIA showed the best detection power for all spiking eicosanoids at 10 and 1 ng/mL in TLE matrix. At low spiking levels, 0.1 and 0.01 ng/mL, a general cut-off was observed for the three acquisition modes. None of this assessed acquisition modes was able to detect and/or identify eicosanoids at physiologically relevant concentrations, explaining their frequent omission in routine untargeted analyses.

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来源期刊
Computational and structural biotechnology journal
Computational and structural biotechnology journal Biochemistry, Genetics and Molecular Biology-Biophysics
CiteScore
9.30
自引率
3.30%
发文量
540
审稿时长
6 weeks
期刊介绍: Computational and Structural Biotechnology Journal (CSBJ) is an online gold open access journal publishing research articles and reviews after full peer review. All articles are published, without barriers to access, immediately upon acceptance. The journal places a strong emphasis on functional and mechanistic understanding of how molecular components in a biological process work together through the application of computational methods. Structural data may provide such insights, but they are not a pre-requisite for publication in the journal. Specific areas of interest include, but are not limited to: Structure and function of proteins, nucleic acids and other macromolecules Structure and function of multi-component complexes Protein folding, processing and degradation Enzymology Computational and structural studies of plant systems Microbial Informatics Genomics Proteomics Metabolomics Algorithms and Hypothesis in Bioinformatics Mathematical and Theoretical Biology Computational Chemistry and Drug Discovery Microscopy and Molecular Imaging Nanotechnology Systems and Synthetic Biology
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