Key words
Q Exactive Focus; SIEVE software; biomarkers; drug discovery; metabolomics
purpose
Develop a new automated workflow and instrumental analysis method for the discovery of metabolome biomarkers.
Foreword
In the pharmaceutical industry, metabolomics is used to study the biochemical changes produced by the pharmacological response of potential drug candidates, and its ability to identify toxicity/drug markers can significantly accelerate drug development and help develop appropriate clinical plans. Metabolic profile experimental data from liquid chromatography-mass spectrometry (LC-MS), which contains a large amount of chemical noise, often interferes with the discovery of biomarkers. This article uses new mass spectrometry techniques and data processing software to reduce the chemical background in animal experiments, and to study the relationship between drug-induced changes and animal age and nutrition.
Conventional LC-MS metabolomics studies have many redundant (one component with multiple ions) and uncorrelated (chemical noise) data. External factors (age, nutrition) that affect the metabolic profile also increase biological variability. Since many chemicals are unknown, it is especially important to filter out false positive results before performing structural identification. The separation of ultra-high resolution instruments combined with ultra-high performance liquid chromatography (UHPLC) provides sufficient resolution to distinguish metabolites from chemical backgrounds, solving chemical noise and data redundancy problems. Accurate mass numbers ensure the granular data processing required to identify the relevant signals. This technology can greatly reduce the amount of data and improve the quantification of target metabolites. Biological factors have profound effects on metabolic profiles, and even small metabolic changes can mask drug-induced metabolic effects. Understanding the normal metabolic changes in rats minimizes "biological noise" and provides more reliable information about drug-related metabolic changes.
Experimental part
Sample preparation
Blood samples from male rats (satisfaction, acute and chronic fasting, different ages) were taken and analyzed by LC-MS. Take 50 μL of serum sample and add 100 μL of cold methanol solution containing 0.1% formic acid to remove protein. The sample was dried and reconstituted by adding 200 μL of 10% aqueous methanol. N-benzoyl-D5 glycine (tR = 4.27 min, m/z 185.0969) was added as an internal standard to each sample.
Liquid Chromatography
Chromatography was performed using a Thermo ScientificTM Open AccelaTM 1250 UHPLC system and a Thermo ScientificTM Hypersil GOLDTM aQ column (150 x 2.1 mm, 1.9 μm particle size). The injection volume is 3 μL. The chromatographic conditions are as follows:
Mass spectrometry
High resolution accurate mass (HRAM) data with a resolution of 70,000 (FWHM) was acquired using a Thermo ScientificTM Q ExactiveTM Focus Hybrid Quadrupole Orbitrap mass spectrometer (Figure 1) in positive and negative ion modes, respectively.
data processing
The data was extracted using the Component Extraction (CE) data processing algorithm of Thermo ScientificTM SIEVETM software to determine the metabolic effects of fasting in rats.
Results and discussion
Figure 2 shows high quality LC-MS data for the N-benzoyl-D5-glycine internal standard. Positive ion mode data for serum QC replicates were collected between 25 and 35 hours after mass axis calibration, demonstrating excellent peak area and mass stability measurements during UHPLC analysis. The peak width at the baseline was 3.6 s and 15 scans were obtained for each peak. Figure 3 shows the importance of a resolution of 70,000 when determining the elemental composition of endogenous metabolites. The unfolded graph near the A+2 isotope (m/z 313) shows the presence of a 34S, which is impossible to detect at 35,000 resolution (simulation) because the 13C2 isotope and 34S are indistinguishable at lower resolutions. .
The data processing workflow of the component extraction software is shown in Figure 4. The software parses the data like an analyst, processes each data file in batches instead of processing it separately, and uses the information from each run to verify the information obtained in the next run. In this way, since each component is defined at the maximum concentration point in the data set, the data missing value phenomenon is greatly reduced. The same components can be identified and quantified using a more targeted approach when in low concentration samples.
The experiment yielded highly simplified data. Data processing removes a large amount of noise generated by the system, resulting in more rigorous statistical grouping and more reliable difference analysis and component inference results.
The rat study experiments in Table 1 were used to monitor the effects of fasting on the metabolic process in rats. Figure 5 shows the results of high-quality cluster analysis of the control sample group, mixed QC-like, and fasted 4, 12, and 16-hour serum-like principal component analysis (PCA). The results clearly show the differences in serum metabolomes between fed and fasted rats, as well as metabolome differences at different fasting times. Figure 6 shows the metabolites that increase with fasting time (Met methionine, 20:4 FA eicosatetraenoic acid) and decrease (Pro valine, 18:2 LPC lysophosphatidylcholine). As shown in Figure 6, each of the metabolites in the mixed serum QC replicates showed excellent reproducibility. Therefore, there is no need to do technical duplication. Figure 7 illustrates that in actual LC-MS analysis, although the run time of the positive and negative ion modes is 30 hours apart, the determination of uric acid in serum is consistent in both modes. Research confirms that the LC-MS platform and method are extremely durable. It is thus concluded that biological variability is the main source of noise in these data.
in conclusion
The Q Exactive Focus mass spectrometer provides a sophisticated and durable platform for non-target metabolomics research. This platform features UHPLC-compatible ultra-fast scanning speeds that maintain excellent mass accuracy and response stability in both positive and negative ion modes for a significant period of time after calibration. No technical duplication is required. The resolution of up to 70,000 FWHM enables the determination of fine isotope profiles, which helps to clearly infer elemental composition. To eliminate the large amount of noise interference in the study, the intelligent data simplification tool in the SIEVE software can be used to achieve a significant reduction in chemical noise. In addition, systematic research contributes to the characterization of biological noise, while metabolomics pre-screening helps identify biological outliers, ensuring consistency across the study.
As shown in this study, fasting is a significant variable in model design, and fasting data can help analyze many metabolite changes caused by drugs. Studies have found that fasting in rats has a profound effect on metabolic profiling. Although most metabolic changes
To a certain extent, it is subtle, but fasting can highlight or mask the metabolic effects caused by some drugs.
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