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Novel Derivatization Strategy for the Comprehensive Characterization of Endogenous Fatty Aldehydes Using HPLC-MRM Cai Tie, Ting Hu, Zhi-Xin Jia, and Jin-lan Zhang Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.6b01756 • Publication Date (Web): 09 Jul 2016 Downloaded from http://pubs.acs.org on July 10, 2016
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Analytical Chemistry
Novel Derivatization Strategy for the Comprehensive Characterization of Endogenous Fatty Aldehydes Using HPLC-MRM Cai Tie‡, Ting Hu‡, Zhi-Xin Jia, Jin-Lan Zhang* State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing 100050, PR China *Corresponding author: fax: +86 10 63017757; E-mail:
[email protected] ABSTRACT: Fatty aldehydes are crucial substances that mediate a wide range of vital physiological functions, particularly lipid peroxidation. Fatty aldehydes such as acrolein and 4-hydroxynonenal (4-HNE) are considered potential biomarkers of myocardial ischemia and dementia, but analytical techniques for fatty aldehydes are lacking. In the present study, a comprehensive characterization strategy with high sensitivity and facility for fatty aldehydes based on derivatization and high-performance liquid chromatography-multiple reaction monitoring (HPLC-MRM) was developed. The fatty aldehydes of a biosample were derivatized using 2,4-bis(diethylamino)-6-hydrazino-1,3,5-triazine under mild and efficient reaction conditions at 37°C for 15 min. The limit of detection (LOD) of the fatty aldehydes varied from 0.1-1 pg/ml, depending on the structures of these molecules. General MRM parameters were forged for the analysis of endogenous fatty aldehydes. “Heavy” derivatization reagents with 20 deuterium atoms were synthesized for both the discovery and comprehensive characterization of fatty aldehydes. More than 80 fatty aldehydes were detected in the biosamples. The new strategy was successfully implemented in global fatty aldehyde profiling of plasma and brain tissue of the bilateral common carotid artery (2VO) dementia rat model. Dozens of fatty aldehydes were significantly changed between the control and model groups. These findings further highlight the importance of endogenous fatty aldehydes.
INTRODUCTION A fatty aldehyde is an aldehyde with an aliphatic carbon chain. Depending on carbon chain length, fatty aldehydes are divided into three subclasses: long-, medium- and shortchain fatty aldehydes. Long-chain fatty aldehydes (C13 and longer) are generally catabolized from fatty acids, sphingolipids, plasmalogens and isoprenoid alcohols via α- or ωoxidation.1,2 Long-chain fatty aldehydes are intermediates in the fatty alcohol cycle1,3,4 and can be further converted into fatty acids by aldehyde dehydrogenase. Short- and mediumchain aliphatic aldehydes (C3-C10) and α,β-unsaturated aldehydes have been characterized as lipid peroxidation (LPO) intermediates.5-7 Although research on fatty aldehyde toxicity has been limited, solid evidence of neural toxicity of malondialdehyde (MDA), acrolein and 4-hydroxynonenal (4HNE) has been reported.8-11 The neurotoxicity of these compounds is mediated by covalent binding to proteins and nucleic acids12 and is typically observed in different types of nervous system diseases, including Parkinson’s disease (PD), Alzheimer’s disease (AD) and other types of dementia.10,11,13-15 Neural cell membranes are rich in polyunsaturated fatty acids, which are prone to LPO and produce toxic products, including α,β-unsaturated fatty aldehydes, that seriously damage brain tissue.16 Comprehensive monitoring of fatty aldehydes in the circulatory system and brain tissue is critical for clarifying the pathogenesis of nervous system diseases.
Gas chromatography (GC) and liquid chromatography (LC) coupled with mass spectrometry (MS) have been used for fatty aldehyde analysis.5,17-19 However, the poor stability of fatty aldehydes at high temperatures hinders reliable GC-MS analysis. LC-MS was developed as an alternative to GC-MS. The poor sensitivity of LC-MS for fatty aldehydes has limited the use of this technique for fatty aldehyde analysis. Various derivatization strategies have been developed to enhance the sensitivity and separation efficiency of LC-MS analysis of fatty aldehydes. Williams and coworkers developed an LCMS/MS method for the analysis of fatty aldehydes after derivatization using 5,5-dimethyl-1,3-cyclohexanedione (dimethyl CHD).5 The limit of quantitation (LOQ) for dimethyl CHD derivatives was greater than 5 pg. The molecule 2,4dinitrophenyl hydrazine (DNPH) is another widely applied derivatization reagent for the analysis of fatty aldehydes. LC/APCI-MS/MS can detect fatty aldehyde DNPH derivatives at concentrations as low as 1 nmol/l.19 Although derivatization significantly enhances sensitivity, the low abundance of endogenous fatty aldehydes hinders the comprehensive characterization of these molecules. Laborious and timeconsuming multiple reaction monitoring (MRM) parameters for the optimization of MS/MS are another challenge. Parameters such as collision energy must be optimized using standards for each fatty aldehyde. Adequate standards reflecting the high diversity of fatty aldehydes are a challenge for the MRM detection of these compounds. The selectivity of derivatization is also problematic in fatty aldehyde analy-
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sis. The wide presence of endogenous compounds with carbonyl groups that could be derivatized and detected as fatty aldehydes reduces the accuracy and reliability of fatty aldehyde characterization. Complex biomatrices also present a challenge for the characterization of fatty aldehydes because other endogenous compounds are also detected in these analyses. The use of internal standards (ISs), particularly stable isotope-labeled ISs, might greatly reduce matrix effects.20,21 Considering the high diversity of endogenous fatty aldehydes, it is impossible to synthesize ISs for every fatty aldehyde. In the present study, a novel derivatization strategy based on 2,4-bis(diethylamino)-6-hydrazino-1,3,5-triazine (T3) was developed for the HPLC-MRM characterization of endogenous fatty aldehydes. More than 98% of the fatty aldehydes were converted to T3 derivatives after incubation at 37°C for 15 min. Derivatization significantly increased the sensitivities for fatty aldehydes. General MRM parameters were developed for endogenous fatty aldehyde global characterization. Fatty aldehydes in biosamples were identified based on isotope-induced retention time shifts (IRS)22 and CID products. “Heavy” derivatization reagents (D3) containing 20 deuterium atoms were synthesized for both the discovery and characterization of fatty aldehydes. The new strategy was applied to evaluate fatty aldehydes in animals. More than 80 endogenous fatty aldehydes in plasma and tissue were characterized using this strategy. Significant differences in dozens of fatty aldehyde compounds were observed between the control group and bilateral common carotid artery (2VO) dementia rat model group.
EXPERIMENT Materials. MS-grade acetonitrile and methanol were purchased from Mallinckrodt Baker Inc. (Phillipsburg, NJ, USA). HPLC-grade formic acid, acetic acid and trifluoroacetic acid (TFA) were obtained from TEDIA Company, Inc. (Fairfield, OH, USA). Citric acid and all fatty aldehyde standards (acrolein, 4-HNE, cinnamaldehyde, hexanal, heptanal, octanal, nonanal, decanal, undecanal) were purchased from SigmaAldrich (St. Louis, MO, USA). All endogenous metabolite standards listed in Supporting Information Table S1 were purchased from Sigma-Aldrich (St. Louis, MO, USA). Ceramide (18:1/10:0) and ceramide (18:1/2:0) were purchased from Avanti Polar Lipids (Alabaster, AL, USA). “Normal” (T3) and “heavy” (D3) derivatization reagents were a gift from Prof. Zhang.23 Water was purified using a Milli-Q system (Millipore, Bedford, MA, USA). Construction of the 2VO Rat Model. Male Wistar rats (8 weeks old, 200±5 g body weight) were purchased from the Beijing Weitonglihua Experimental Animal Technology Co. Ltd (Beijing, China). The rats were randomly divided into two groups (control group, n=8; model group, n=8;) and acclimated for 2 weeks prior to modeling. The animals were housed under specific pathogen-free conditions (12 h light/12 h dark photoperiod, 23±2°C, 55±5% relative humidity). The animal experiments were conducted in accordance with institutional guidelines and ethics and were approved by the Laboratories Institutional Animal Care and Use Committee of the Chinese Academy of Medical Sciences and Peking Union Medical Col-
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lege. After acclimating the rats, the 2VO model was constructed for the animals in the model group, and a sham operation was performed for the rats in the control group, as previously reported.24 Learning and memory abilities were examined using the Morris water maze task at three weeks after ligation as previously described.25,26 The criteria for the evaluation of the 2VO model were as described by Yu et al.27 A total of 12 rats meeting the criteria (n=6 for both groups) were screened. On the 58th day, all rats were sacrificed, and blood samples were collected using heparin as an anticoagulant to obtain plasma by centrifugation (4,500 rpm, 15 min). Brain tissues were collected after blood collection and immediately frozen in liquid nitrogen. All biological samples were stored at -80°C until further analysis. Biosample Pretreatments. Brain tissue samples were homogenized in an 8-fold volume of saline. Ten microliters of plasma or brain tissue homogenate was mixed with 100 μl of acetonitrile containing 50 ng/ml IS (ceramide (18:1/2:0) for T3-tagged samples and ceramide (18:1/2:0) for D3-tagged samples). The ISs were added to evaluate the extraction efficiency. The mixture was vortexed for 2 min and centrifuged at 13,000 rpm for 5 min. The supernatant was collected and dried in a vacuum concentrator at room temperature, and the dried sample was stored at -20°C prior to derivatization. Fatty Aldehyde Derivatization. A 10 μl aliquot of derivatization solution containing 10 mg/ml derivatization reagent and 1% formic acid in methanol was added to each dried sample and incubated at 37°C for 15 min for derivatization as shown in scheme 1. The D3-tagged samples were prepared using a similar reaction initiated with D3 reagent. The D3tagged pool sample was mixed in equal proportions (v/v) with the T3-tagged samples as an internal standard prior to HPLCMRM analysis. Scheme 1. Fatty aldehyde derivatization mechanism.
* * * * *
*
*
*
The hydrogen atoms attached to the carbon atoms indicated with red stars were replaced with deuterium to generate the ‘heavy’ derivatization reagent D3.
HPLC-MRM Analysis. An Agilent 1290 series HPLC system and a 6490 triple-quadrupole mass spectrometer (Agilent Technologies, Inc. Santa Clara, CA) with an AJS electrospray ionization (AJS-ESI) device were used for the analysis of fatty aldehyde derivatives. An Agilent Poroshell 120 EC-C8 column (2.1×50 mm, 2.7 µm particle size) was applied for separation. Mobile phase A was water containing 0.2% formic acid, and mobile phase B was acetonitrile. Elution was initiated with 70% A and 30% B. Mobile phase B was gradually increased to 99% over 8.5 min and maintained for an additional 1.5 min. The elution conditions were returned to the initial state over a period of 2 min. The flow rate was 0.5 ml/min. The column temperature was controlled at 25°C. The injection volume was 5 μl. Positive-mode MRM was used for data acquisition.
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Analytical Chemistry
The following parameters were used for mass spectrometry: spray voltage=3,000 V, gas temperature=200°C, gas flow=14 l/min, nebulizer=20 psi, sheath gas temperature=250°C, sheath gas flow=11 l/min, fragmentor=380. To optimize the MRM conditions, derivatized fatty aldehyde standards were subjected to an HLB cartridge (60 mg, 1cc). The SPE cartridge was washed with 2 ml of 50% methanol to remove excess derivatization reagent. Finally, the derivatized aldehydes were eluted with 2 ml of methanol. The eluent was injected into the mass spectrometer using a syringe pump at a flow rate of 10 µl/min. The MRM ion pairs and collision energy were automatically optimized. Data Processing and Statistical Analysis. Agilent Mass Hunter Quantitative Analysis B.05.02 (Agilent Technologies, Inc. Santa Clara, CA) was employed for peak integration. The peak areas of the T3- and D3-tagged fatty aldehydes were normalized using the corresponding ceramide IS peaks to minimize the influence of the extraction process. A new value for each aldehyde was generated after dividing the T3 derivative peak area by its corresponding “heavy” peak areas. The newly generated values were subsequently used in the statistical analysis. SPSS 21 was used for the bilateral t-test. SIMICA-P+12.0.1 (Umetrics AB, Sweden) was employed for supervised orthogonal partial least squares discriminant analysis (OPLS-DA) to identify the most significantly changed compounds. The open-source tool Metaboanalyst 3.0 (HYPERLINK: http://www.metaboanalyst.ca/) was also employed for statistical analysis and graph drawing.
RESULTS AND DISCUSSION
superior separation speed and peak shape of fatty aldehydes5,19 This derivatization strategy for fatty aldehyde analysis is a promising environmentally friendly and highthroughput technique because the time, labor and amount of mobile phase required for fatty aldehyde analysis were reduced. General MRM Conditions for the Global Profiling of Fatty Aldehydes. MRM has long been applied in the analysis of low-abundance compounds with high sensitivity and specificity. However, the time and labor-consuming optimization of MRM parameters using specific standards has limited the use of MRM in the analysis of target fatty aldehydes. A set of general MRM conditions was therefore developed using a derivatization strategy. Constant structures of fatty aldehyde derivatives were produced by derivatization, suggesting that the same fragments would be generated under equal conditions. Six fatty aldehydes with different structures were used to examine this hypothesis. The MRM conditions were individually optimized for the six derivatives. As shown in Table 1, the MRM conditions were identical. The fragment m/z 209 ion related to the T3 reagent was generated from all derivatized fatty aldehydes under the same collision energy. Similar characteristic fragment ions were observed for both T3- and D3-tagged fatty aldehydes as shown in Figures 1A and 1B. These results suggested that general MRM conditions could be used for all fatty aldehyde derivatives. The global profiling of fatty aldehydes is no longer limited by the availability of standards, and the HPLC-MRM method based on derivatization developed in this study can be used to profile fatty aldehyde species in biogenic samples.
Derivatization Optimization. The instability of fatty aldehydes due to the presence of aldehyde groups, hydroxyl groups, and double bonds requires mild derivatization conditions. A series of experiments were designed for derivatization optimization. A standard mixture containing 1 μg/ml hexanal, heptanal, octanal, nonanal, decanal and undecanal was used to optimize the derivatization conditions. As shown in Supporting Information Figure S1, approximately 98% fatty aldehydes were converted in 15 min at 37°C with 10 mg/ml derivatization reagent methanol solution containing 1% formic acid as a catalyst. This reaction was quicker and milder than previously reported for dimethyl CHD, DNPH, and a modified Girard reagent5,19,28 due to the high nucleophilicity of the hydrazine group in the derivatization reagent. The risk of fatty aldehyde degradation was minimized by decreasing the reaction temperature and time, thus improving fatty aldehyde characterization. Improvements in Chromatographic Performance. The hydrophilicity and diversity of the fatty aldehydes induced poor separation efficiency and peak shapes on the reverse phase column, limiting the sensitivity of the fatty aldehyde analysis. Derivatization with T3 moderately increased the hydrophobicity of the fatty aldehydes, resulting in relatively strong retention on the reverse phase column and improved chromatographic performance. As shown in Supporting Information Figure S2, the analysis of the derivatized aldehydes was completed within 10 min, and the peak widths were all within 0.2 min on the C8 column. Compared with dimethyl-CHD or DNPH, derivatization with T3 resulted in
Figure 1. (A) MS/MS spectra of T3 aldehyde derivatives. (B) MS/MS spectra of D3 aldehyde derivatives. (C) MS/MS spectra of T3 steroid derivatives. (MS/MS spectra of other fatty aldehydes and steroid derivatives are shown in Supporting Information Figure S4)
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Improvements in Sensitivity. Fatty aldehydes exhibit low ionization efficiency, resulting in poor sensitivity. Derivatization converts the carbonyl groups in fatty aldehydes to hydrazones. The increased alkalinity of the derivatives plays a critical role in ionization enhancement. As shown in Table 1, the sensitivities of different fatty aldehydes were investigated. The LODs varied from 0.1 to 1 pg/ml depending on the structures of these compounds, in contrast to the LODs of greater than 100 pg/ml of previously reported methods.5,19,28 This excellent sensitivity is promising for higher coverage and reduced biogenic sample consumption, which are essential for both biomarker survey and metabolomics research. Table 1. MS/MS Detection of T3-Labeled Fatty Aldehydes Aldehydes
Precursor
Fragment
CE/ev
LOD/pg/ml
Hexanal
336
209
38
0.1
Heptanal
350
209
38
0.1
Octanal
364
209
38
0.1
Nonanal
378
209
38
0.1
Decanal
392
209
38
1
Undecanal
406
209
38
1
Endogenous Fatty Aldehyde Identification in Biosamples. Global profiling of fatty aldehydes in biosamples was implemented based on the established general MRM conditions according to a previous report on the compounds in the LIPID MAPS database.29 Two replicates of fatty aldehyde extractions were derivatized using T3 and D3 and analyzed by HPLC-MRM. Using a previously developed mathematical model, 91 peaks were identified as potential fatty aldehyde compounds from more than 300 peaks in rat plasma.22,30 The retention times of aldehyde derivatives with a saturated straight carbon chain exhibited a clear relationship with the length of the corresponding carbon chain. The retention time increased with an increasing number of carbon atoms (Supporting Information Figure S3). Based on the retention times of the saturated aldehydes, the structures of these molecules can be predicted using this relationship without standards. Using this strategy, a number of fatty aldehydes were identified in the biosamples. In addition to saturated and unsaturated straight-chain fatty aldehydes, aldehydes with modifications were easily identified. As shown in Figure 2, the fatty aldehyde compound 4-HNE, which plays critical roles in various nervous system diseases, was detected in biosamples and verified using standards. Minimizing Endogenous Matrix Metabolites. Similar to other endogenous metabolites, a complicated biomatrix complicates the global characterization of fatty aldehydes. Based on the selectivity of the derivatization, the developed analytical strategy for fatty aldehydes sharply minimized the matrix effects. Only metabolites with carbonyl groups were tagged and identified. To determine if other endogenous metabolites with carbonyl groups could produce background
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signals during the profiling of fatty aldehydes, metabolites (Supporting Information Table S1) from seven classes, including free fatty acids, organic acids, amino acids, carbohydrates, vitamins, nucleotides and steroids, were employed to examine potential reactions with T3 under the same conditions as fatty aldehydes derivatization. Most of the endogenous metabolites, including free fatty acids, organic acids, amino acids, vitamins and nucleotides, were not tagged with T3 and did not produce any background during the global profiling of fatty aldehydes by HPLC-MRM. The carbonyl groups of carbohydrates were tagged with high efficiency using T3, but carbohydrates with polymerization degrees greater than 1 were not retained on the C8 column. Although T3-tagged monosaccharides were retained on the C8 reverse phase column, the T3-tagged monosaccharides were distinguishable from fatty aldehydes based on their different CID fragments (Supporting Information Figure S5). Steroids with carbonyl groups can be labeled as fatty aldehydes. However, the characteristic fragments of steroid derivatives differ from those of fatty aldehyde derivatives, reflecting structural differences. As shown in Figure 1, under the given CID conditions, the fatty aldehyde derivatives primarily generated m/z 209 fragments, whereas steroid derivatives primarily generated m/z 239 fragments. The fragments patterns of these structures likely differ, and several fatty aldehydes and steroids with different structures were analyzed to examine this hypothesis (Supporting Information Figure S4), providing clues for the identification of fatty aldehydes in the biomatrix. The simultaneous monitoring of m/z 209 and 239 fragments revealed that only compounds primarily generated with m/z 209 were fatty aldehydes. Screens based on the intensities of these two fragments revealed that 7 of 91 potential fatty aldehydes were not fatty aldehydes. Thus, a total of 84 fatty aldehydes were confirmed in rat plasma (Supporting Information Table S2). In addition to these seven classes of metabolites, other endogenous metabolites with carbonyl groups, including gluconic acid and α-ketoglutaric acid, can be tagged with T3 under fatty aldehyde derivatization. Fragments of the T3 derivatives were analyzed (Supporting Information Figure S5). These fragments were distinguishable from fatty aldehydes based on differences in characteristics associating with structural differences. These compounds were barely generated at m/z 209 as fatty aldehyde derivatives. The integrated application of appropriate selective derivatization and specific fragments and the fatty aldehyde analysis strategy thus minimized complicated biomatrix effects and guaranteed reliability. Endogenous Fatty Aldehyde Comprehensive Characterization Using an Isotope-Labeled Derivatization Reagent. Matrix effects are the most critical challenge for the characterization of endogenous compounds, particularly lowabundance compounds. The accuracy and reliability of the fatty aldehyde characterization were markedly hindered by matrix effects. Isotopic ISs are clearly the best choice to eliminate matrix effects, but the challenges of applying isotopic ISs are also equally evident. It was impossible to build an isotopic IS library sufficient for the diversity of endogenous fatty aldehydes. We therefore developed a parallel IS strategy using isotope-labeled derivatization reagents. Derivatized
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Analytical Chemistry
using D3, ‘Heavy’-tagged fatty aldehyde derivatives generated by derivatization with D3 exhibited a 20-Da mass shift compared with ‘normal’-tagged aldehydes. Fatty aldehydes
from pooled samples were derivatized using D3 and spiked into each sample as ISs. Thus, any fatty aldehyde in these samples could be characterized using constant parallel
Figure 2. (A) 4-HNE identified by tandem mass data in biosamples. The peak indicated using a red star is 4-HNE. The peak indicated using a black star is T52, isomer of 4-HNE listed in Supporting information Table S2. (B) MS/MS spectra of “Heavy” and “Normal” 4-HNE derivatives.
isotopic IS. A mixture containing six different fatty aldehydes was applied to examine the characterization strategy. A mixture containing 1 μg/ml fatty aldehydes was equally divided into two vials and dried. These samples were derivatized separately with D3 and T3. Prior to HPLC-MS/MS analysis, the ‘heavy’ and ‘normal’-tagged fatty aldehydes were mixed in equal volumes. As shown in Figure 3A, only a slight change in the retention time was induced by “heavy” derivatization. “Normal” derivatives (biosample) and “heavy” derivatives (IS) of the fatty aldehydes thus co-eluted into MS. The “Normal” derivatives were ionized under constant conditions to isotope-labeled ISs. Thus, the matrix effect was sharply minimized. This optimization is critical for the analysis of biosamples and reflects the extremely complicated matrices of these samples. Using this strategy, parallel isotopic IS can be forged for each endogenous fatty aldehyde using an isotopelabeled derivatization reagent, guaranteeing the accuracy and reliability of this endogenous fatty aldehyde analysis strategy. This strategy was also applied to analyze changes in endogenous fatty aldehydes in biosamples. Various concentrations of ‘normal’-tagged standard mixtures were equally mixed with ’heavy’-tagged ISs of 10 ng/ml. These samples were analyzed to examine the accuracy of the analysis. As shown in Supporting Information Figure S6, the correlation coefficients between the expected and observed r (the ratio between heavy and normal labeled compounds) were greater than 0.999, ensuring the accuracy of the method and providing a powerful tool for endogenous fatty aldehyde characterization. Fatty Aldehyde Characterization of Plasma and Brain Tissue in a Dementia Rat Model. The novel strategy was used to characterize fatty aldehydes in both plasma and brain tissue samples harvested from animals. The EIC of the pooled biosample is shown in Figure 3B. To capture the most
significant relationship between fatty aldehydes and dementia, unsupervised cluster analysis was performed for both plasma and brain data using the Ward method. The results are shown in Figures 4A and D. The samples within the group segregated into tight clusters for both plasma and brain tissue, indicating that endogenous fatty aldehydes were obviously changed after 2VO modeling.
Figure 3. (A) EICs of the T3- and D3-derivatized fatty aldehyde standards. T3 (solid lines) and D3 (dotted lines) mixtures were analyzed using HPLC-MRM. (B) EICs of derivatized fatty aldehydes in the plasma samples.
OPLS-DA was initially performed to identify significant changes in fatty aldehydes between the control and model groups. Aldehydes with VIP values greater than 1 were considered the most obviously changed species after modeling and subsequently subjected to principal component analysis (PCA). As shown in Figures 4B and E, the control and model
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groups were clearly distinguishable, with good separation on the PCA scatter plot for both plasma and brain tissue. The statistical results were highly consistent with those of the cluster analysis, suggesting that significant changes in endogenous fatty aldehydes occurred in the 2VO model. To
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visualize the level of each fatty aldehyde in every sample, the changes in endogenous fatty aldehydes in both plasma and brain tissue were presented as heatmaps, as shown in Figures 4C and F. The vast majority of fatty aldehydes were significantly increased in plasma after 2VO modeling. However,
Figure 4. Statistical results of fatty aldehyde profiling in the plasma and brain. (A, D) Cluster analysis based on the plasma and brain fatty aldehyde contents (Pearson’s correlation was used as the distance measurement method). (Control: red; model: green) (B, E) PCA scatter plots of plasma and brain tissue based on aldehydes with VIP>1 using OPLS-DA. (Control: red triangles; model: green crosses) (C, F) Heatmaps of the fatty aldehydes quantified in plasma and brain tissue (control: red; model: green). (G) Fatty aldehydes that differed significantly between the control and model groups (*: P
