Name: semi-targeted ultra-high-performance chromatography coupled to mass spectrometry analysis of phenolic metabolites in plasma of elderly adults
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Description: Watch this Scientific Journal Video about Semi-Targeted Ultra-High-Performance Chromatography Coupled to Mass Spectrometry Analysis of Phenolic Metabolites in Plasma of Elderly Adults at JoVE.com

The authors are grateful for the financial support from CONACYT, Mexico (CB- 2016-01-286449), and UACJ-PIVA (Projects 313-17-16 and 335-18-13). OAMB wishes to thank CONACYT for his Ph.D. scholarship. Technical support from the Multimedia Production office from UACJ is gratefully acknowledged.

The plasma samples used in this protocol were collected in a previous study following all the ethical guidelines and approved by the Institutional Ethics and Bioethics Committee (CIEB-2018-1-37) from the Universidad Aut&#243;noma de Ciudad Ju&#225;rez. The complete protocol for the extraction and identification of the phenolic compounds and metabolites in plasma by UPLC-MS/MS is represented in Figure 1.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/63164/63164fig01.jpg" /> 
Figure 1: Schematic representation of the extraction and identification of phenolic compounds and metabolites in plasma by the semi-targeted UPLC-MS/MS method. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/63164/63164fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
1. Sample preparation
<ol>
	<li>Store the plasma samples at -80 &#176;C until analysis.</li>
	<li>Defrost the plasma samples at room temperature for 15 min. 
	NOTE: The samples can be placed in a water bath at 37 &#176;C to accelerate the process (5 min).</li>
	<li>Place 200 &#181;L of plasma sample in a 2 mL microtube and mix with 1,000 &#181;L of pure ethanol. Vortex the plasma sample for 30 s. 
	NOTE: Always use gloves when working with plasma samples.</li>
	<li>Centrifuge the sample at 6,580 x g for 5 min. After centrifugation, collect the supernatant with a micropipette or Pasteur pipette and place it in a new microtube. Store the supernatant at 4 &#176;C.</li>
	<li>Mix the pellet from the previous step with 1,000 &#181;L of 100% ethanol, vortex for 30 s, and then centrifuge at 6,580 x g for 5 min. 
	NOTE: The pellet is strongly packed and needs to be resuspended well to ensure contact between the sample and pure ethanol. The use of a micropipette to flush the pellet with ethanol is recommended.</li>
	<li>After centrifugation, collect the supernatant and mix with the supernatant previously obtained from Step 1.4. in a 2 mL microtube.</li>
	<li>Remove ethanol from the sample by using pure nitrogen (99.997%) at 135 psi. Keep the needle 1 cm away from the top of the microtube to prevent sample loss and flush until the sample is dry. No heat is needed to evaporate the ethanol. 
	NOTE: The nitrogen flow must be low to prevent sample loss. Once the ethanol is dried, keep the nitrogen flow for at least 5 min to ensure sample dryness. The protocol can be paused at this point; samples must be stored at -20 &#176;C. Avoid storing the samples for more than 12 h.</li>
	<li>Resuspend the dry samples in 100 &#181;L of a mixture of acetonitrile: water at a proportion of 50:50 (v:v).</li>
	<li>Filter the sample through a 0.45 &#181;m nylon syringe membrane directly into an HPLC vial micro insert. 
	NOTE: The samples in the vial can be stored at -20 &#176;C before analysis. Store the samples for no more than 8 h. It is recommended to inject the samples into the UPLC system just after filtration.</li>
</ol>
2. UPLC-MS/MS analysis
<ol>
	<li>Inject 3 &#181;L of sample onto a UPLC equipped with a C18 reverse-phase column (50 mm x 2.1 mm; 1.8 &#181;m). Set the autosampler temperature at 20 &#176;C and the column thermostat at 25 &#176;C. Inject each sample in triplicate.</li>
	<li>Use 0.1% (v:v) formic acid in water as solvent A, and 100% acetonitrile as solvent B. Set the flow rate at 0.4 mL/min and a gradient program as follows: 0-1 min 10% B, 1-4 min 30% B, 4-6 min 38% B, 6-8 min 60% B, 8-8.5 min 60% B, 8.5-9 min 10% B (Table 1).</li>
	<li>Set the mass spectrometer to negative mode ionization. Use nitrogen as a drying gas at 340 &#176;C and a flow rate of 13 L/min. Set the nebulizer pressure at 60 psi. Set the capillary voltage at 4,000 V, the fragmentor voltage at 175 V, and the Skimmer voltage at 65 V. Use collision energy at 20 V (Table 2).</li>
	<li>Scan the masses between 100-1100 mass to charge ratio (m/z) and, for MS/MS, scan masses between 50-1000 m/z (Table 2). Set data acquisition to Auto MS/MS mode. Use the following reference mass: 119.036 and 966.0007.</li>
</ol>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Time (min)</td>
			<td>Solvent A (0.1 % formic acid in HPLC water)</td>
			<td>Solvent B (100 % acetonitrile)</td>
		</tr>
		<tr>
			<td>0 to 1</td>
			<td>90</td>
			<td>10</td>
		</tr>
		<tr>
			<td>1 to 4</td>
			<td>70</td>
			<td>30</td>
		</tr>
		<tr>
			<td>4 to 6</td>
			<td>62</td>
			<td>38</td>
		</tr>
		<tr>
			<td>6 to 8</td>
			<td>40</td>
			<td>60</td>
		</tr>
		<tr>
			<td>8 to 8.5</td>
			<td>40</td>
			<td>60</td>
		</tr>
		<tr>
			<td>8.5 to 9</td>
			<td>90</td>
			<td>10</td>
		</tr>
	</tbody>
</table>
Table 1: Mobile phase gradient used for the separation of phenolic compounds by UPLC.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Ionization mode</td>
			<td>Negative</td>
		</tr>
		<tr>
			<td>Drying gas</td>
			<td>Nitrogen at 340 &#176;C, flow rate 13 L/min</td>
		</tr>
		<tr>
			<td>Nebulizer pressure</td>
			<td>60 psi</td>
		</tr>
		<tr>
			<td>Capillary voltage</td>
			<td>175 V</td>
		</tr>
		<tr>
			<td>MS scan masses</td>
			<td>100-1100 m/z</td>
		</tr>
		<tr>
			<td>MS/MS scan masses</td>
			<td>50-1000 m/z</td>
		</tr>
	</tbody>
</table>
Table 2: Ionization parameters for the MS/MS analysis.
3. Database construction
<ol>
	<li>Search for phenolic compounds, phenolic metabolites, or other compounds of interest in the scientific literature.</li>
	<li>Open the database management software included in the UPLC system. Select File | New Personal Database Compound Library (PCDL) | Create New PCDL. Select the type of PCDL: LC/MS Metabolomics. Set a name for the PCDL. Then select Create.</li>
	<li>In the toolbar, select PCDL and then the Allow editing option. Then click the Find compounds button. 
	NOTE: Since it is a new PCDL, the table results will be empty. This will change once new compounds are added into the PCDL.
	<ol>
		<li>Add compounds to the specialized personal database compound library by copying them from the instrument&#39;s general library. Open the instrument&#39;s existing database included in the database management software. Click the button Find compounds. In the Single search option, enter the compound search criteria to find the compound of interest. 
		NOTE: Compounds can be found by name, molecular formula, exact mass, and retention time.</li>
		<li>In the compound results table, select the compound of interest. To select more than one compound, click the first compound, hold down the CTRL key, and then click each compound of interest. Then, right-click on all the highlighted compounds and select Append to PCDL.</li>
		<li>In the new window, search and select the specialized personal database file. Mark the boxes Include spectra for compounds if present and Include ion mobility info for compounds if present. Click the Append button. In the new dialog box, select Yes to check the new compounds added. Select No to keep searching for more compounds of interest.</li>
	</ol>
	</li>
	<li>If the compounds of interest are not available in the instrument&#39;s general library, add new compounds manually.
	<ol>
		<li>Open the specialized personal database. Once opened, follow Step 3.3. Select the Edit compounds option. Click the Add new button.</li>
		<li>In the upper section of the window, complete the information for the new compound. Fill in the formula, name, IUPAC name, CAS number, Chemspider ID, and other identifiers.</li>
		<li>Use the information available in the free online libraries (Chemspider, PubChem, and Phenol Explorer) to fill in the information for the new compound of interest. Once finished, click the button Save as new to save the new compound information in the specialized personal database. 
		NOTE: When adding information from free libraries, be sure to include the compound information without the presence of chloride or iodide ions. This may modify the exact mass and molecular formula of the compound of interest.</li>
	</ol>
	</li>
	<li>Repeat the process with all the compounds of interest to complete the specialized personal database.</li>
</ol>
4. Data analysis
<ol>
	<li>Use the instrument&#180;s qualitative manager software to identify the phenolic compounds and phenolic metabolites present in the samples.</li>
	<li>Open the sample file. In the Chromatogram panel, select Define Chromatograms and extract the total ion chromatogram (TIC), the extracted ion-chromatogram of MS (EIC), and the EIC of MS/MS. Select the integrate chromatogram option.</li>
	<li>In the Find Compounds panel, select Find by Formula-Options. In the new window, select Formula Source and then the Database/Library option. Find the personal database previously created and click on Open.</li>
	<li>Select the Formula Matching option and set a mass match tolerance of 5 parts per million (ppm). 
	NOTE: A different match tolerance of masses can be set at 10 ppm; this difference depends on the mass spectrometer used.</li>
	<li>Select the Negative Ions option and select only the -H dialog box. In the Results option, mark the Extract EIC, Extract Cleaned Spectrum, Extract Raw Spectrum, and include Structure dialog boxes.</li>
	<li>Select the Result Filters option. Mark Warn if score is and set the score match at 80.00%. Mark Do not match if the score is and set the score at 75.00%. 
	NOTE: The match/not match scores can be changed to lower values if needed. This will reduce the accuracy of identification.</li>
	<li>Click on the Find Compounds by Formula to identify compounds of interest in the sample.</li>
</ol>

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All authors declare no conflict of interest.

The identification and quantification of the bioactive phytochemicals that are absorbed after consumption of a food or food supplement are crucial for demonstrating and understanding the health benefits of these compounds and the foods containing them. In the present work, the UPLC-MS/MS method was developed, aimed only at the identification of the main phenolic compounds and their metabolites that increased in concentration in plasma after a 30-day nutritional intervention with two food products specially formulated for the elderly. It was assumed that, if one compound increased or appeared in plasma only after the intervention period, that compound had been absorbed and/or biotransformed from the foods used in the intervention. 
UPLC-MS/MS is one of the preferred technologies for metabolomic studies in which many low molecular weight compounds are analyzed simultaneously in complex samples to assess the effect of some treatment or environmental alteration13. This can be done through targeted and non-targeted (or global) methods. Targeted analyses focus on a known, small number of metabolites of interest. They are the best option for quantifying compounds with available standards, providing the best specificity and sensitivity; however, these methods must be thoroughly optimized for the selected metabolites and are incapable of detecting unexpected compounds in the samples12. In other studies, HPLC coupled with a UV-vis detector has been used to identify phenolic acids and metabolites in plasma, but this type of study has used analytical standards to identify and quantify the compound of interest15. In another study, the addition of an internal standard was proposed18; however, only ferulic and caffeic acids and their metabolites were analyzed in this work. On the other hand, the synthesis of internal standards of phenolic metabolites has been proposed as an alternative to the lack of commercial analytical standards19. Nevertheless, the synthesis of a large variety of phenolic compounds is difficult, time-consuming, and expensive. Non-targeted methods attempt to detect and identify as many molecular compounds as possible and are primarily hypothesis-generating12. They can identify several hundreds of compounds in one sample by assigning formulas to detectable chromatographic signals with characteristic features, such as m/z or measured accurate mass, chromatographic retention time, isotopic fingerprint, etc., so the data generated by these analyses are very abundant and can be difficult to interpret20. Since the objective of the present method was not to obtain a full metabolic profile of the plasma samples (which would be an untargeted approach) but to identify the major phenolic compounds and phenolic metabolites (previously unknown) in them, a semi-targeted approach was designed. For this, all the identified signals were extracted automatically with the instrument's software and then compared with a self-created database that contained 645 phenolic compounds and their metabolites, which were obtained from free online databases. The database included reference MS/MS fragmentation patterns of the compounds that were used for the assignment of the compound name and formula, which allows for more accurate identification of compounds in the sample12. 
The most critical steps in the protocol were 1) sample pretreatment (extraction and concentration of phenolic compounds and metabolites from plasma samples); 2) the construction of a complete and specific database for the semi-targeted analysis of the samples and identification of all the possible compounds belonging to a specific compound class; and 3) the selection of the parameters used by the instrument's qualitative software (mass match tolerance ppm, and score%) to accurately identify the compounds in the sample. In sample pretreatment, caution must be taken to avoid loss of the compounds of interest, recover them at a high final concentration, and be able to eliminate interfering compounds. In the creation of the specific database, it is critical to conduct a thorough literature search and then use free online databases to obtain specific chemical data that will be used for compound identification in the sample. The selection of the best values for mass match and score required previous validation of the analytical method using standards and known samples21,22. 
Once the protocol was implemented, the common problems and their recommended solutions were the following: 1) No compounds were identified in the samples. This was due to the low concentration of the compounds and could be solved by drying the sample and re-dissolving it in a smaller volume. 2) Signals appeared in the TIC, but compounds were not identified. This could be due to a failure in mass calibration, so the difference between experimental and theoretical masses was high. In this case, the instrument's mass calibration should be performed according to the manufacturer and model of the equipment. A second reason could be an incomplete personal database, so it is critical to constantly check and maintain the database. Masses of compounds suspected to be of interest can be checked and compared with freely available online databases or new scientific literature. 3) Signals appeared in blank runs. This indicates contamination and can be solved by cleaning the autosampler needle and column. The standard cleaning protocol is to perform some runs using methanol, then isopropanol, and finally acetonitrile as the mobile phase. Then the column is re-equilibrated, running the mobile phase for at least 30 min.
The major difficulties in the development of this kind of method are 1) the low bioavailability, in general terms, of all phytochemicals, including phenolic compounds, which translates to very low plasma concentrations16; 2) the high diversity of phenolic compounds present in foods, which is increased by their metabolism and biotransformation that occur in human enterocytes and hepatocytes and the colonic microbiota23; 3) the lack of standards (some standards exist but are very difficult to obtain) for many compounds, especially for the metabolites, and the existence of some unknown or uncharacterized compounds23; and 4) variable responses among individuals, both in absorption and metabolism of phytochemicals14. To overcome the problem of low plasma concentrations of phenolic metabolites, they should be extracted and concentrated. This can be achieved by micro solid-phase extraction14 or, as in the present work, by the addition of solvents that precipitate proteins and dissolve phenolic compounds and metabolites, followed by solvent evaporation and resuspension in a smaller volume19. This technique is simple, economic, and adequate for a small number of samples. However, the recovery of compounds of interest could be easily improved by using larger plasma volumes, so the final concentration of all the compounds would be higher. The high diversity of phenolic metabolites and lack of standards is the reason why a semi targeted approach was used to identify the compounds increased by the intervention before attempting to quantify them. By using a specially created database instead of a completely untargeted analysis, it was possible to ignore the numerous primary metabolites in plasma, focusing only on the phenolic compounds and their metabolites. An important limitation of this database, specific for phenolic compounds and their human metabolites, was the lack of mass spectral information for many compounds and their metabolites, which reduces the accuracy of the compound identification.
Large variability among individuals is regularly observed in studies evaluating absorption and metabolism of phenolic compounds, both quantitively and qualitatively. In the present results, one individual presented 18 compounds in the plasma sample taken after the intervention, while the others showed only 9 or 10. Moreover, many phenolic compounds and metabolites were found only in the baseline samples (before the intervention), and they also varied among individuals. Overall, more than half of the identified compounds showed higher concentrations (AUC) before than after the intervention or were found only in the pre-intervention samples. Therefore, it was necessary to compare individual samples taken before and after the intervention to understand which compounds were actually increased by it. The only metabolite that consistently increased after the treatment was phenylacetic acid, a microbial catabolite of flavan-3-ols24 that has been found in the plasma of individuals in acute studies of the consumption of phenolic-rich foods14,25. Most of the studies that have identified and quantified phenolic metabolites in plasma were acute studies, in which the compound concentrations were monitored during the 24 h after consumption of the phenolic-rich meal, observing that, after 24 h, their concentrations were near the basal value19,25. Therefore, it is understandable that the samples analyzed in the present work showed low numbers and concentrations of phenolic metabolites. Recently, Zhang et al.25 evaluated the consumption of red raspberry, both in acute studies and after 4 weeks of supplementation. They observed that both acute and chronic interventions increased phenolic compounds and their metabolites, while after the 4-week supplementation, the basal plasma concentration of some metabolites increased (urolithins, cinnamic, phenylpropionic, and hippuric acids) and of others decreased (conjugated anthocyanin derivatives).
A final examination of the method developed in the present paper shows that, in most parts, it was well suited for the objective for which it was created. The sample pretreatment was simple and effective, and the UPLC separation and semi-targeted MS/MS identification of phenolic metabolites were achieved. This semi-targeted method can be useful as a first screening approach to identify phenolic compounds that can be absorbed from different food matrixes, as well as the phenolic metabolites present in human plasma. Moreover, the protocol is useful because it allows identification when no standards from phenolic compounds or metabolites are available. Finally, the method uses a small plasma quantity, which is critical in most in vivo studies where plasma samples are scarce. However, it is recommended that, for future studies, an acute study should be carried out before the chronic intervention to better identify the main phenolic compounds and metabolites absorbed from the specially formulated foods.

Sarcopenia is a progressive skeletal disorder related to an accelerated loss of muscle in the elderly population. This condition increases the risk of falls and leads to limited activities of daily living. Sarcopenia is present in about 5%-10% of persons over 65 years old and about 50% of persons aged 80 years or older1. No specific drugs have been approved for the treatment of sarcopenia, so prevention with physical activity and a well-balanced diet is important1,2. Nutritional interventions with specially formulated foods enriched with dairy protein and essential amino acids have shown positive results in preventing sarcopenia2. In other studies, authors have included vitamins and antioxidants, like vitamin E and isoflavones, in the diet, increasing the benefits for muscle gain on the waist and hips3.
Brosimum alicastrum Sw. (Ram&#243;n) is a tree that grows in the Mexican tropical regions; it has been consumed by Mayan cultures due to its high nutritional value4. It is a good source of protein, fiber, minerals, and phenolic antioxidants, such as chlorogenic acid5. Since it can be ground into powder and used in baking products or consumed in beverages, recent studies have evaluated the incorporation of Ram&#243;n seed flour (RSF) into different foods to improve their nutritional value. An RSF-supplemented cappuccino-flavored beverage was formulated, which was high in dietary fiber and had more than 6 g of protein per serving, and was highly accepted by consumers; thus, it was considered a potential alternative for meeting special dietary requirements6. In a follow-up study, RSF was also used to formulate a muffin and a new beverage rich in protein, dietary fiber, micronutrients, and phenolic antioxidants. The muffin and beverage were used in a dietary intervention for elderly individuals, who consumed both products twice per day for 30 days. After this period, the nutritional and sarcopenic status of the participants improved, and the total phenolic content of plasma increased7. However, the determination of total phenolic compounds in plasma was carried out by a spectrophotometric method, so identification of the actual phenolic compounds that were absorbed was not possible; moreover, this method is not completely specific for phenolic compounds, so some overestimation may occur8.
Identification and quantification of the phenolic compounds that are absorbed after consumption of foods rich in these antioxidants is a difficult task but is necessary to demonstrate the biological activity of these phytochemicals. The bioavailability of most phenolic compounds is low; less than 5% of them can be found without structural transformation in plasma. Phenolic compounds undergo several biotransformations, such as methylation, sulfonation, or glucuronidation, which are carried out by enterocytes and hepatocytes9. Phenolic compounds are also biotransformed by the microbiota into bacterial catabolites that may exert their beneficial effects in the body after being absorbed into the plasma10. For example, phenylacetic acid is a product of the bacterial transformation of flavonoids and oligomeric proanthocyanidins, which can inhibit up to 40% of bacteria (Escherichia coli) adhesion in the urinary tract after cranberry consumption11.
The structural diversity of naturally occurring phenolic compounds, added to the diversity of their metabolites and their low bioavailability, makes their identification in plasma even more challenging. Metabolomic profiling, using spectroscopic analysis platforms like nuclear magnetic resonance (NMR) and tandem mass spectroscopy (MS/MS), is probably the best approach to achieve this goal; unfortunately, the equipment is not easily accessible, and the development of analysis protocols is still limited12. Several studies have reported MS/MS coupled with a separation system (such as liquid chromatography) as a strategy for reducing the complexity of mass spectra in metabolomic studies. The recent introduction of ultra-high-performance liquid chromatography (UPLC) separation methods has reduced the time of analysis and increased the resolution and sensitivity compared with conventional high-performance liquid protocols, so UPLC-MS/MS systems have rapidly been widely accepted by the analytical metabolomics community13. In this way, some studies have investigated phenolic metabolites and detected glucuronidated derivatives from caffeic acid, quercetin, and ferulic acid, as well as sulfonated derivatives from syringic and vanillic acid in the plasma of individuals after cranberry intake14. Previous protocols have intended to find phenolic compounds and phenolic metabolites in biofluids such as plasma. These protocols were based on identification and quantification by high-performance liquid chromatography (HPLC) coupled to a UV-vis detector15. Nevertheless, such protocols require the use of authentic standards to assess absolute identification and accurate quantification. A wide range of studies have identified the most common metabolites in biofluids (sulphonated, glucuronidated, and methylated forms) by UPLC-MS and UPLC-MS/MS; however, a large part of the bacterial metabolites has not been reported due to the lack of databases that contain their complete information16. Metabolite identification is complicated by the cost and commercial availability of metabolite standards. Therefore, the best strategy may be untargeted or semi-targeted MS/MS metabolite analysis, which relies on the use of molecular feature information (m/z, monoisotopic exact mass, isotopic distribution, and fragmentation pattern) to determine the chemical identity and compares it with freely available online databases that contain polyphenol metabolites identified in biofluids after the consumption of polypolyphenol-richts12. The most important databases used in UPLC-MS/MS studies for the identification of phenolic compounds and their metabolites are the Human Metabolome Database (HMDB), LipidBlast Library, METLIN Library, and other complementary databases, such as PubChem, ChemSpider, and Phenol Explorer17.
In the present study, a semi-targeted UPLC-MS/MS method was developed to analyze the plasma samples of the group of elderly persons involved in the RSF-containing muffin and beverage consumption study7. Data from different free online databases of plasma metabolites were collected and integrated into a specialized database. This database can be accessed automatically by the equipment software to identify the polyphenolic metabolites in the five plasma samples before and after the 30-day nutritional intervention. This is done to identify the main phenolic compounds, or their metabolites, that are absorbed from the specially formulated functional foods designed for the prevention of sarcopenia.

<table><tbody><tr><td>Acetonitrile</td><td>Tedia</td><td>Al1129-001</td><td>LC Mass spectrometry</td></tr><tr><td>Autosampler</td><td>Agilent Technologies</td><td>G4226A</td><td>1290 Infinity series</td></tr><tr><td>C18 reverse phase column</td><td>Agilent Technologies</td><td>959757-902</td><td>Zorbax Eclipse plus C18 2.1x50 mm, 1.8 &amp;mu;m; Rapid resolution HD</td></tr><tr><td>Centrifuge</td><td>Eppendorf</td><td>5452000018</td><td>Mini Spin; Rotor F-45-12-11</td></tr><tr><td>Column compartment with thermostat</td><td>Agilent Technologies</td><td>G1316C</td><td>1290 Infinity series</td></tr><tr><td>Diode Array Detector (UV-Vis)</td><td>Agilent Technologies</td><td>G4212B</td><td>1260 Infinity series</td></tr><tr><td>Electrospray ionnization source</td><td>Agilent Technologies</td><td>G3251B</td><td>Dual sprayer ESI source</td></tr><tr><td>Formic acid</td><td>J.T. Baker</td><td>0128-02</td><td>Baker reagent, ACS</td></tr><tr><td>Mass Hunter Data Acquisition</td><td>Agilent Technologies</td><td>G3338AA</td><td></td></tr><tr><td>Mass Hunter Personal Compound Datbase and Library Manager</td><td>Agilent Technologies</td><td>G3338AA</td><td></td></tr><tr><td>Mass Hunter Qualitative Analysis</td><td>Agilent Technologies</td><td>G3338AA</td><td></td></tr><tr><td>Microcentrifuge tube</td><td>Brand</td><td>BR780546</td><td>Microcentrifuge tube, 2 mL with lid</td></tr><tr><td>Pure ethanol</td><td>Sigma-Aldrich</td><td>E7023-1L</td><td>200 proof, for molecular biology</td></tr><tr><td>Q-TOF LC/MS</td><td>Agilent Technologies</td><td>G6530B</td><td>6530 Accurate Mass</td></tr><tr><td>Quaternary pump</td><td>Agilent Technologies</td><td>G4204A</td><td>1290 Infinity series</td></tr><tr><td>Syringe filter</td><td>Thermo Scientific</td><td>44514-NN</td><td>17 mm, 0.45 &amp;mu;m, nylon membrane</td></tr><tr><td>Thermostat</td><td>Agilent Technologies</td><td>G1330B</td><td>1290 Infinity series</td></tr><tr><td>Vial</td><td>Agilent Technologies</td><td>8010-0199</td><td>Amber, PFTE red silicone 2 mL with screw top and blue caps</td></tr><tr><td>Vial insert</td><td>Agilent Technologies</td><td>5183-2089</td><td>Vial insert 200 &amp;mu;L for 2mL standard opening, conical</td></tr><tr><td>Water</td><td>Tedia</td><td>WL2212-001</td><td>LC Mass spectrometry</td></tr></tbody></table>

semi-targeted ultra-high-performance chromatography coupled to mass spectrometry analysis of phenolic metabolites in plasma of elderly adults

A group of 23 elderly persons was given functional meals (a beverage and a muffin) specially formulated for the prevention of sarcopenia (age-related loss of muscle mass). Plasma samples were taken at the beginning of the intervention and after 30 days of consuming the functional meals. A semi-targeted ultra-high-performance chromatography coupled with tandem mass (UPLC-MS/MS) analysis was carried out to identify phenolic compounds and their metabolites. Plasma proteins were precipitated with ethanol and the samples were concentrated and resuspended in the mobile phase (1:1 acetonitrile: water) before injection into the UPLC-MS/MS instrument. Separation was carried out with a C18 reverse-phase column, and compounds were identified using their experimental mass, isotopic distribution, and fragment pattern. Compounds of interest were compared to those of data banks and the internal semi-targeted library. Preliminary results showed that the major metabolites identified after the intervention were phenylacetic acid, glycitin, 3-hydroxyphenylvaleric acid, and gomisin M2.

The step-by-step process for the identification of phenolic metabolites through the semi-targeted UPLC-MS/MS analysis, in negative mode, of plasma samples is depicted in Figure 2. First, the total ion chromatogram (TIC) from the plasma phenolics extract (obtained after protein precipitation of the total plasma sample) was obtained through the instrument&#39;s qualitative software. Then, the extracted ion chromatogram was used, and the exact mass and fragmentation pattern (MS/MS analysis) of each signal (or molecular feature) were compared with those of a specific personal database created also in the instrument&#39;s software. Signals with a mass match of less than 5 ppm were assigned a molecular formula from the database. Finally, the isotopic distribution of each signal was compared to that of the assigned molecular formula to achieve the final tentative identification. Compounds that a) were identified only in one replicate or b) presented an area lower than 10,000 were treated as false identifications. From this analysis, a total of 25 phenolic compounds and metabolites were identified in the plasma samples (Table 3). In this list, both phenolic compounds and their metabolites, such as 3-hidroxyphenylvaleric acid and isopropyl 3-(3,4-dihydroxyphenyl)-2-hydroxypropanoate, were found. Since the negative ionization mode is best suited for all classes of phenolic compounds, except anthocyanins, these compounds could not be detected with the present method. If anthocyanins are important components of the food matrix, the positive mode should be also used.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="2">Phenolic metabolites</td>
			<td>R.T. (min)</td>
			<td>Formula</td>
			<td>Precursor</td>
			<td>Experimental mass</td>
			<td>Theorical mass</td>
			<td>Difference (ppm)</td>
		</tr>
		<tr>
			<td colspan="2">2,3-Dihydroxybenzoic acid</td>
			<td>0.622</td>
			<td>C7H6O4</td>
			<td>153.0203</td>
			<td>154.0273</td>
			<td>154.0266</td>
			<td>4.2</td>
		</tr>
		<tr>
			<td colspan="2">2-Hydroxyhippuric acid</td>
			<td>8.631</td>
			<td>C18H33NO4</td>
			<td>410.1648</td>
			<td>411.1725</td>
			<td>411.1717</td>
			<td>1.8</td>
		</tr>
		<tr>
			<td colspan="2">3,4-Dihydroxytoluene</td>
			<td>2.239</td>
			<td>C7H8O2</td>
			<td>123.0451</td>
			<td>124.0524</td>
			<td>124.0524</td>
			<td>-0.25</td>
		</tr>
		<tr>
			<td colspan="2">3-Hydroxyphenylvaleric acid</td>
			<td>6.717</td>
			<td>C11H14O3</td>
			<td>193.0874</td>
			<td>194.0947</td>
			<td>194.0943</td>
			<td>2.12</td>
		</tr>
		<tr>
			<td colspan="2">5-(3&#39;,4&#39;-dihydroxyphenyl)-valeric acid</td>
			<td>4.293</td>
			<td>C11H14O4</td>
			<td>209.0823</td>
			<td>210.0894</td>
			<td>210.0892</td>
			<td>0.68</td>
		</tr>
		<tr>
			<td colspan="2">6-Hydroxyenterodiol</td>
			<td>9.201</td>
			<td>C18H22O5</td>
			<td>317.1387</td>
			<td>318.1465</td>
			<td>318.1467</td>
			<td>-0.65</td>
		</tr>
		<tr>
			<td colspan="2">Ajugol</td>
			<td>3.889</td>
			<td>C15H24O9</td>
			<td>347.134</td>
			<td>348.1418</td>
			<td>348.142</td>
			<td>-0.59</td>
		</tr>
		<tr>
			<td colspan="2">Benzoic acid</td>
			<td>3.915</td>
			<td>C7H6O2</td>
			<td>121.0296</td>
			<td>122.0367</td>
			<td>122.0368</td>
			<td>-0.28</td>
		</tr>
		<tr>
			<td colspan="2">Carnosic acid</td>
			<td>6.785</td>
			<td>C20H28O4</td>
			<td>331.1905</td>
			<td>332.1979</td>
			<td>332.1988</td>
			<td>-2.58</td>
		</tr>
		<tr>
			<td colspan="2">Carnosol</td>
			<td>6.347</td>
			<td>C20H26O4</td>
			<td>329.1764</td>
			<td>330.1842</td>
			<td>330.1831</td>
			<td>3.43</td>
		</tr>
		<tr>
			<td colspan="2">Catechol</td>
			<td>0.892</td>
			<td>C6H6O2</td>
			<td>109.0297</td>
			<td>110.037</td>
			<td>110.0368</td>
			<td>1.91</td>
		</tr>
		<tr>
			<td colspan="2">Glycitin</td>
			<td>6.01</td>
			<td>C22H22O10</td>
			<td>445.1155</td>
			<td>446.1228</td>
			<td>446.1213</td>
			<td>3.4</td>
		</tr>
		<tr>
			<td colspan="2">Hesperetin</td>
			<td>6.01</td>
			<td>C16H14O6</td>
			<td>301.0718</td>
			<td>302.0796</td>
			<td>302.079</td>
			<td>-1.81</td>
		</tr>
		<tr>
			<td colspan="2">Hippuric acid</td>
			<td>1.396</td>
			<td>C9H9NO3</td>
			<td>178.051</td>
			<td>179.058</td>
			<td>179.0582</td>
			<td>-1.16</td>
		</tr>
		<tr>
			<td colspan="2">Homovanillic acid</td>
			<td>0.823</td>
			<td>C9H10O4</td>
			<td>181.0503</td>
			<td>182.0576</td>
			<td>182.0579</td>
			<td>-1.88</td>
		</tr>
		<tr>
			<td colspan="2">Isopropyl 3-(3,4-Dihydroxyphenyl)-2-hydroxypropanoate</td>
			<td>6.177</td>
			<td>C12H16O5</td>
			<td>239.0926</td>
			<td>240.0999</td>
			<td>240.0998</td>
			<td>0.48</td>
		</tr>
		<tr>
			<td colspan="2">Phenylacetic acid</td>
			<td>5.666</td>
			<td>C8H8O2</td>
			<td>135.0444</td>
			<td>136.0518</td>
			<td>136.0524</td>
			<td>-4.92</td>
		</tr>
		<tr>
			<td colspan="2">Phloretic acid</td>
			<td>2.811</td>
			<td>C9H10O3</td>
			<td>165.0556</td>
			<td>166.0626</td>
			<td>166.063</td>
			<td>-2.41</td>
		</tr>
		<tr>
			<td colspan="2">Protocatechuic aldehyde</td>
			<td>1.094</td>
			<td>C7H6O3</td>
			<td>137.0247</td>
			<td>138.0311</td>
			<td>138.0317</td>
			<td>-4.5</td>
		</tr>
		<tr>
			<td colspan="2">Secoisolariciresinol</td>
			<td>8.837</td>
			<td>C20H26O6</td>
			<td>361.1656</td>
			<td>362.1729</td>
			<td>362.1729</td>
			<td>-0.23</td>
		</tr>
		<tr>
			<td colspan="2">Vanillin</td>
			<td>2.508</td>
			<td>C8H8O3</td>
			<td>151.04</td>
			<td>152.0471</td>
			<td>152.0473</td>
			<td>-1.82</td>
		</tr>
		<tr>
			<td colspan="2">Epicatechin 3&#39;-O-glucuronide</td>
			<td>9.342</td>
			<td>C21H22O12</td>
			<td>465.1024</td>
			<td>466.109</td>
			<td>466.1111</td>
			<td>-4.64</td>
		</tr>
		<tr>
			<td colspan="2">Gomisin M2</td>
			<td>5.234</td>
			<td>C22H26O6</td>
			<td>385.1676</td>
			<td>386.1746</td>
			<td>386.1729</td>
			<td>4.38</td>
		</tr>
		<tr>
			<td colspan="2">Irisolidone</td>
			<td>6.145</td>
			<td>C17H14O6</td>
			<td>313.0727</td>
			<td>314.0798</td>
			<td>314.079</td>
			<td>2.33</td>
		</tr>
		<tr>
			<td colspan="2">Urolithin C</td>
			<td>6.753</td>
			<td>C13H8O5</td>
			<td>243.0294</td>
			<td>244.0368</td>
			<td>244.0372</td>
			<td>-1.69</td>
		</tr>
	</tbody>
</table>
Table 3: Tentative identification of phenolic compounds and metabolites in plasma samples by the semi-targeted UPLC-MS/MS method.
To evaluate the effectiveness of the designed method for the identification of the main phenolic compounds, or their metabolites, that were absorbed from the RSF-containing muffin and beverage, five aleatory samples of the study participants, obtained before and after the 30-day intervention, were analyzed. The relative abundance of each compound was calculated by dividing the area under the curve (AUC) after treatment by the AUC before treatment. From this analysis, it was possible to observe that some compounds only appeared in the samples obtained before the treatment, others remained unchanged, while some of them increased after consumption of the functional foods. Table 4 shows the list of 12 phenolic compounds that showed an increase in plasma after the 30-day consumption of the RSF-containing foods. Phenylacetic acid was the only metabolite found consistently in higher concentrations after the treatment. Glycitin, a glycosylated isoflavone, and 3-hydroxyphenylvaleric acid (a phenolic metabolite) increased in three of the five samples but decreased in the other two. Gomisin M2, a lignan, was detected in three of the five samples only after the nutritional intervention. The other phenolic compounds (such as hesperetin, secoisolariciresinol, and vanillin) and metabolites (such as 2-hydroxyhippuric acid) were found only in one sample and only after the treatment.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="2"></td>
			<td></td>
			<td colspan="5">Sample (AUC after treatmet/AUC before treatment)</td>
		</tr>
		<tr>
			<td colspan="2">Compound</td>
			<td>Formula</td>
			<td>1</td>
			<td>2</td>
			<td>3</td>
			<td>4</td>
			<td>5</td>
		</tr>
		<tr>
			<td colspan="2">2-Hydroxyhippuric acid</td>
			<td>C18H33NO4</td>
			<td>T</td>
			<td>nd</td>
			<td>nd</td>
			<td>nd</td>
			<td>nd</td>
		</tr>
		<tr>
			<td colspan="2">3-Hydroxyphenylvaleric acid</td>
			<td>C11H14O3</td>
			<td>1.30</td>
			<td>2.69</td>
			<td>2.69</td>
			<td>0.62</td>
			<td>0.62</td>
		</tr>
		<tr>
			<td colspan="2">6-Hydroxyenterodiol</td>
			<td>C18H22O5</td>
			<td>nd</td>
			<td>nd</td>
			<td>T</td>
			<td>nd</td>
			<td>nd</td>
		</tr>
		<tr>
			<td colspan="2">Glycitin</td>
			<td>C22H22O10</td>
			<td>1.88</td>
			<td>1.07</td>
			<td>1.07</td>
			<td>0.43</td>
			<td>0.45</td>
		</tr>
		<tr>
			<td colspan="2">Hesperetin</td>
			<td>C16H14O6</td>
			<td>T</td>
			<td>nd</td>
			<td>nd</td>
			<td>nd</td>
			<td>nd</td>
		</tr>
		<tr>
			<td colspan="2">Phenylacetic acid</td>
			<td>C8H8O2</td>
			<td>4.06</td>
			<td>T</td>
			<td>T</td>
			<td>T</td>
			<td>1.28</td>
		</tr>
		<tr>
			<td colspan="2">Phloretic acid</td>
			<td>C9H10O3</td>
			<td>T</td>
			<td>nd</td>
			<td>nd</td>
			<td>nd</td>
			<td>nd</td>
		</tr>
		<tr>
			<td colspan="2">Protocatechuic aldehyde</td>
			<td>C7H6O3</td>
			<td>T</td>
			<td>nd</td>
			<td>nd</td>
			<td>nd</td>
			<td>nd</td>
		</tr>
		<tr>
			<td colspan="2">Secoisolariciresinol</td>
			<td>C20H26O6</td>
			<td>T</td>
			<td>nd</td>
			<td>nd</td>
			<td>nd</td>
			<td>nd</td>
		</tr>
		<tr>
			<td colspan="2">Vanillin</td>
			<td>C8H8O3</td>
			<td>T</td>
			<td>nd</td>
			<td>nd</td>
			<td>nd</td>
			<td>nd</td>
		</tr>
		<tr>
			<td colspan="2">Gomisin M2</td>
			<td>C22H26O6</td>
			<td>nd</td>
			<td>T</td>
			<td>T</td>
			<td>T</td>
			<td>nd</td>
		</tr>
	</tbody>
</table>
Table 4: List of phenolic compounds that increased in the plasma of elderly individuals after 30-day consumption of RSF-containing foods. Data are the ratios of the abundance (AUC) of each compound after treatment compared to their abundance before the treatment. T indicates that the compound was only identified in the sample after the treatment. Nd: not detected.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/63164/63164fig02.jpg" /> 
Figure 2: Protocol for the identification of phenolic compound metabolites by semi-targeted UPLC-MS/MS. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/63164/63164fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Semi-Targeted Ultra-High-Performance Chromatography Coupled to Mass Spectrometry Analysis of Phenolic Metabolites in Plasma of Elderly Adults at JoVE.com

Semi-Targeted Ultra-High-Performance Chromatography Coupled to Mass Spectrometry Analysis of Phenolic Metabolites in Plasma of Elderly Adults

The goal of this protocol is to detect phenolic metabolites in plasma using a semi-targeted chromatography-mass spectrometry method.

A group of 23 elderly persons was given functional meals (a beverage and a muffin) specially formulated for the prevention of sarcopenia (age-related loss ...

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Biochemistry

3.8K Views.  Universidad Autónoma de Ciudad Juárez. The goal of this protocol is to detect phenolic metabolites in plasma using a semi-targeted chromatography-mass spectrometry method.

Video: Semi-Targeted Ultra-High-Performance Chromatography Coupled to Mass Spectrometry Analysis of Phenolic Metabolites in Plasma of Elderly Adults

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)

Non-targeted metabolite profiling by ultra performance liquid chromatography coupled with mass spectrometry (UPLC-MS) is a powerful technique to investigate metabolism. The approach offers an unbiased and in-depth analysis that can enable the development of diagnostic tests, novel therapies, and further our understanding of disease processes. The inherent chemical diversity of the metabolome creates significant analytical challenges and there is no single experimental approach that can detect all metabolites. Additionally, the biological variation in individual metabolism and the dependence of metabolism on environmental factors necessitates large sample numbers to achieve the appropriate statistical power required for meaningful biological interpretation. To address these challenges, this tutorial outlines an analytical workflow for large scale non-targeted metabolite profiling of serum by UPLC-MS. The procedure includes guidelines for sample organization and preparation, data acquisition, quality control, and metabolite identification and will enable reliable acquisition of data for large experiments and provide a starting point for laboratories new to non-targeted metabolite profiling by UPLC-MS.

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Non-targeted metabolite profiling by ultra performance liquid chromatography coupled with mass spectrometry (UPLC-MS) is a powerful technique to investigate metabolism. This article outlines a typical workflow utilized for non-targeted metabolite profiling of serum including sample organization and preparation, data acquisition, data analysis, quality control, and metabolite identification.

Non-targeted metabolite profiling by ultra performance liquid chromatography coupled with mass spectrometry (UPLC-MS) is a powerful technique to ...

Chemistry

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry (UPLC-HRMS)

Here we present a workflow to analyze the metabolic profiles for biological samples of interest including; cells, serum, or tissue. The sample is first separated into polar and non-polar fractions by a liquid-liquid phase extraction, and partially purified to facilitate downstream analysis. Both aqueous (polar metabolites) and organic (non-polar metabolites) phases of the initial extraction are processed to survey a broad range of metabolites. Metabolites are separated by different liquid chromatography methods based upon their partition properties. In this method, we present microflow ultra-performance (UP)LC methods, but the protocol is scalable to higher flows and lower pressures. Introduction into the mass spectrometer can be through either general or compound optimized source conditions. Detection of a broad range of ions is carried out in full scan mode in both positive and negative mode over a broad m/z range using high resolution on a recently calibrated instrument. Label-free differential analysis is carried out on bioinformatics platforms. Applications of this approach include metabolic pathway screening, biomarker discovery, and drug development.

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Untargeted metabolomics provides a hypothesis generating snapshot of a metabolic profile. This protocol will demonstrate the extraction and analysis of metabolites from cells, serum, or tissue. A range of metabolites are surveyed using liquid-liquid phase extraction, microflow ultraperformance liquid chromatography/high-resolution mass spectrometry (UPLC-HRMS) coupled to differential analysis software.

Here we present a workflow to analyze the metabolic profiles for biological samples of interest including; cells, serum, or tissue. The sample is first ...

A Convenient Method for Extraction and Analysis with High-Pressure Liquid Chromatography of Catecholamine Neurotransmitters and Their Metabolites

The extraction and analysis of catecholamine neurotransmitters in biological fluids is of great importance in assessing nervous system function and related diseases, but their precise measurement is still a challenge. Many protocols have been described for neurotransmitter measurement by a variety of instruments, including high-pressure liquid chromatography (HPLC). However, there are shortcomings, such as complicated operation or hard-to-detect multiple targets, which cannot be avoided, and presently, the dominant analysis technique is still HPLC coupled with sensitive electrochemical or fluorimetric detection, due to its high sensitivity and good selectivity. Here, a detailed protocol is described for the pretreatment and detection of catecholamines with high pressure liquid chromatography with electrochemical detection (HPLC-ECD) in real urine samples of infants, using electrospun composite nanofibers composed of polymeric crown ether with polystyrene as adsorbent, also known as the packed-fiber solid phase extraction (PFSPE) method. We show how urine samples can be easily precleaned by a nanofiber-packed solid phase column, and how the analytes in the sample can be rapidly enriched, desorbed, and detected on an ECD system. PFSPE greatly simplifies the pretreatment procedures for biological samples, allowing for decreased time, expense, and reduction of the loss of targets. 
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The extraction and analysis of catecholamine neurotransmitters in biological fluids is of great importance in assessing nervous system function and ...

Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis

Metabolomics is an emerging field which enables profiling of samples from living organisms in order to obtain insight into biological processes. A vital aspect of metabolomics is sample preparation whereby inconsistent techniques generate unreliable results. This technique encompasses protein precipitation, liquid-liquid extraction, and solid-phase extraction as a means of fractionating metabolites into four distinct classes.&#160;Improved enrichment of low abundance molecules with a resulting increase in sensitivity is obtained, and ultimately results in more confident identification of molecules. This technique has been applied to plasma, bronchoalveolar lavage fluid, and cerebrospinal fluid samples with volumes as low as 50 &#181;l. &#160;Samples can be used for multiple downstream applications; for example, the pellet resulting from protein precipitation can be stored for later analysis. The supernatant from that step undergoes liquid-liquid extraction using water and strong organic solvent to separate the hydrophilic and hydrophobic compounds. Once fractionated, the hydrophilic layer can be processed for later analysis or discarded if not needed. The hydrophobic fraction is further treated with a series of solvents during three solid-phase extraction steps to separate it into fatty acids, neutral lipids, and phospholipids. This allows the technician the flexibility to choose which class of compounds is preferred for analysis. It also aids in more reliable metabolite identification since some knowledge of chemical class exists.

The reliability of results in metabolomics experiments depends on the effectiveness and reproducibility of the sample preparation. Described is a rigorous and in-depth method that enables extraction of metabolites from biological fluids with the option of subsequently analyzing up to thousands of compounds, or just the compound classes of interest.

Metabolomics is an emerging field which enables profiling of samples from living organisms in order to obtain insight into biological processes. A vital ...

Bioengineering

Quantitative Metabolomics of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

Metabolomics is a methodology used for the identification and quantification of many low-molecular-weight intermediates and products of metabolism within a cell, tissue, organ, biological fluid, or organism. Metabolomics traditionally focuses on water-soluble metabolites. The water-soluble metabolome is the final product of a complex cellular network that integrates various genomic, epigenomic, transcriptomic, proteomic, and environmental factors. Hence, the metabolomic analysis directly assesses the outcome of the action for all these factors in a plethora of biological processes within various organisms. One of these organisms is the budding yeast Saccharomyces cerevisiae, a unicellular eukaryote with the fully sequenced genome. Because S. cerevisiae is amenable to comprehensive molecular analyses, it is used as a model for dissecting mechanisms underlying many biological processes within the eukaryotic cell. A versatile analytical method for the robust, sensitive, and accurate quantitative assessment of the water-soluble metabolome would provide the essential methodology for dissecting these mechanisms. Here we present a protocol for the optimized conditions of metabolic activity quenching in and water-soluble metabolite extraction from S. cerevisiae cells. The protocol also describes the use of liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) for the quantitative analysis of the extracted water-soluble metabolites. The LC-MS/MS method of non-targeted metabolomics described here is versatile and robust. It enables the identification and quantification of more than 370 water-soluble metabolites with diverse structural, physical, and chemical properties, including different structural isomers and stereoisomeric forms of these metabolites. These metabolites include various energy carrier molecules, nucleotides, amino acids, monosaccharides, intermediates of glycolysis, and tricarboxylic cycle intermediates. The LC-MS/MS method of non-targeted metabolomics is sensitive and allows the identification and quantitation of some water-soluble metabolites at concentrations as low as 0.05 pmol/&#181;L. The method has been successfully used for assessing water-soluble metabolomes of wild-type and mutant yeast cells cultured under different conditions.

Quantitative Metabolomics of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

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Phenolic acids are a class of organic compounds that bear both a phenolic group, and a carboxylic group. They are found in grains and concentrate in the ...

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Chinese herbal medicine is complex and has numerous unknown compounds, making qualitative research crucial. Ultra-high-performance liquid chromatography ...

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Metabolite profiling has been a valuable asset in the study of metabolism in health and disease. However, current platforms have different limiting ...

Biology

Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia

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Hyperlipidemia has become a leading risk factor for cardiovascular diseases and liver injury worldwide. Fructus Phyllanthi (FP) is an effective drug ...

Semi-Targeted Ultra-High-Performance Chromatography Coupled to Mass Spectrometry Analysis of Phenolic Metabolites in Plasma of Elderly Adults

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Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry (UPLC-HRMS)

A Convenient Method for Extraction and Analysis with High-Pressure Liquid Chromatography of Catecholamine Neurotransmitters and Their Metabolites

Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis

Quantitative Metabolomics of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

A Generalized Method for Determining Free Soluble Phenolic Acid Composition and Antioxidant Capacity of Cereals and Legumes

Measurement of Protein Turnover Rates in Senescent and Non-Dividing Cultured Cells with Metabolic Labeling and Mass Spectrometry

Ultra High-Performance Liquid Chromatography-Mass Spectrometry and Self-established Database Analysis of Chinese Herbal Medicine Components

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia