Hacettepe University, Faculty of Pharmacy, Department of Analytical Chemistry, Ankara, Turkey.
Hacettepe University, Faculty of Pharmacy, Department of Analytical Chemistry, Ankara, Turkey.
Talanta. 2025 Jan 1;282:126953. doi: 10.1016/j.talanta.2024.126953. Epub 2024 Sep 26.
Establishing direct causal and functional links between genotype and phenotype requires thoroughly analyzing metabolites and lipids in systems biology. Tissue samples, which provide localized and direct information and contain unique compounds, play a significant role in objectively classifying diseases, predicting prognosis, and deciding personalized therapeutic strategies. Comprehensive metabolomic and lipidomic analyses in tissue samples need efficient sample preparation steps, optimized analysis conditions, and the integration of orthogonal analytical platforms because of the physicochemical diversities of biomolecules. Here, we propose simple, rapid, and robust high-throughput analytical protocols based on the design of experiment (DoE) strategies, with the various parameters systematically tested for comprehensively analyzing the heterogeneous brain samples. The suggested protocols present a systematically DoE-based strategy for performing the most comprehensive analysis for integrated GC-MS and LC-qTOF-MS from brain samples. The five different DoE models, including D-optimal, full factorial, fractional, and Box-Behnken, were applied to increase extraction efficiency for metabolites and lipids and optimize instrumental parameters, including sample preparation and chromatographic parameters. The superior simultaneous extraction of metabolites and lipids from brain samples was achieved by the methanol-water-dichloromethane (2:1:3, v/v/v) mixture. For GC-MS based metabolomics analysis, incubation time, temperature, and methoxyamine concentration (10 mg/mL) affected metabolite coverage significantly. For LC-qTOF-MS based metabolomics analysis, the extraction solvent (methanol-water; 2:1, v/v) and the reconstitution solvent (%0.1 FA in acetonitrile) were superior on the metabolite coverage. On the other hand, the ionic strength and column temperature were critical and significant parameters for high throughput metabolomics and lipidomics studies using LC-qTOF-MS. In conclusion, DoE-based optimization strategies for a three-in-one single-step extraction enabled rapid, comprehensive, high-throughput, and simultaneous analysis of metabolites, lipids, and even proteins from a 10 mg brain sample. Under optimized conditions, 475 lipids and 158 metabolites were identified in brain samples.
建立基因型和表型之间的直接因果和功能联系需要在系统生物学中彻底分析代谢物和脂质。组织样本提供局部和直接信息,包含独特的化合物,在客观地对疾病进行分类、预测预后和决定个性化治疗策略方面发挥着重要作用。由于生物分子的物理化学多样性,组织样本中的综合代谢组学和脂质组学分析需要有效的样品制备步骤、优化的分析条件和正交分析平台的整合。在这里,我们提出了基于实验设计 (DoE) 策略的简单、快速和稳健的高通量分析方案,系统地测试了各种参数,以全面分析异质脑样本。所提出的方案为基于系统 DoE 的策略提供了一个系统,用于对脑样本进行综合 GC-MS 和 LC-qTOF-MS 的最全面分析。应用了五种不同的 DoE 模型,包括 D-最优、完全析因、分数和 Box-Behnken,以提高代谢物和脂质的提取效率,并优化仪器参数,包括样品制备和色谱参数。甲醇-水-二氯甲烷(2:1:3,v/v/v)混合物实现了对脑样本中代谢物和脂质的同时提取。对于基于 GC-MS 的代谢组学分析,孵育时间、温度和甲氧基胺浓度(10 mg/mL)对代谢物覆盖率有显著影响。对于基于 LC-qTOF-MS 的代谢组学分析,提取溶剂(甲醇-水;2:1,v/v)和复溶溶剂(0.1%FA 在乙腈中的)对代谢物覆盖率更优。另一方面,离子强度和柱温是使用 LC-qTOF-MS 进行高通量代谢组学和脂质组学研究的关键和重要参数。总之,基于 DoE 的优化策略用于一种三合一的单步提取,实现了 10 mg 脑样本中代谢物、脂质甚至蛋白质的快速、全面、高通量和同时分析。在优化条件下,在脑样本中鉴定出 475 种脂质和 158 种代谢物。