Journal of Analytical Methods in Chemistry

Journal of Analytical Methods in Chemistry / 2020 / Article

Research Article | Open Access

Volume 2020 |Article ID 4641709 |

Yang Yang, Feng Zhang, Shouhong Gao, Zhipeng Wang, Mingming Li, Hua Wei, Renqian Zhong, Wansheng Chen, "Simultaneous Determination of 34 Amino Acids in Tumor Tissues from Colorectal Cancer Patients Based on the Targeted UHPLC-MS/MS Method", Journal of Analytical Methods in Chemistry, vol. 2020, Article ID 4641709, 12 pages, 2020.

Simultaneous Determination of 34 Amino Acids in Tumor Tissues from Colorectal Cancer Patients Based on the Targeted UHPLC-MS/MS Method

Academic Editor: Boryana M. Nikolova Damyanova
Received17 Feb 2020
Revised22 Apr 2020
Accepted01 May 2020
Published01 Aug 2020


A targeted ultrahigh-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method was established and validated for the simultaneous determination of 34 amino acids in tissue samples from colorectal cancer (CRC) patients. The chromatographic separation was achieved on an Agilent ZORBAX SB-C18 column (3.0 × 150 mm, 5 μm) with a binary gradient elution system (A, 0.02% heptafluorobutyric acid and 0.2% formic acid in water, v/v; B, methanol). The run time was 10 min. The multiple reaction monitoring mode was chosen with an electrospray ionization source operating in the positive ionization mode for data acquisition. The linear correlation coefficients were >0.99 for all the analytes in their corresponding calibration ranges. The sample was pretreated based on tissue homogenate and protein precipitation with a 100 mg aliquot sample. The average recovery and matrix effect for 34 amino acids and 3 internal standards were 39.00%∼146.95% and 49.45%∼173.63%, respectively. The intra- and interday accuracy for all the analytes ranged from −13.52% to 14.21% (RSD ≤8.57%) and from −14.52% to 12.59% (RSD ≤10.31%), respectively. Deviations of stability under different conditions were within ±15% for all the analytes. This method was applied to simultaneous quantification of 34 amino acids in tissue samples from 94 CRC patients.

1. Introduction

Amino acids play an essential role in both metabolism and proteome. Proper amino acid management is critical for maintaining cell function and organismal viability, particularly under metabolic disturbance. With the alterations in tumor cell metabolism recognized as a hallmark of cancer, there have been more research studies on amino acids, which are involved in the primary tumor formation, growth, and metabolism. As shown in Table 1 [118], studies in the field of colorectal cancer (CRC) metabolism were routinely performed to elucidate the difference of amino acid profiles in blood, urine, and feces between CRC patients and healthy volunteers or between CRC tissue and normal tissue. Recent findings are related to (1) glycemic and/or ketogenic amino acids that are involved in the energy metabolism by entering tricarboxylic acid cycle pathway; (2) amino acids that are synthetic precursors of pyrimidine bases or provide nitrogen sources for the synthesis of purine bases; and (3) amino acids that are the decomposition products from cytosine, uracil, and thymine nucleotides and that can reflect the severity of malignant tumor and the therapeutic effect of chemotherapy for malignant tumor [19]. To sum up, details about the association of amino acids with the formation, growth, and metabolism of CRC are presented in Figure 1. Besides, as a higher level of sarcosine was observed in the urine of patients with prostate cancer [2022], sarcosine was believed to be closely related to cancer and also investigated in our study.

Amino acidsVariationsReferences
In bloodIn urineIn fecesIn tissue#

Gly[1, 2]
Ser[3, 6, 7]
Val[2, 6, 7]
Leu↓↑[3, 57]
Asp↓↑[3, 4, 6, 7, 9]
Gln[3, 8]
Glu[4, 8, 9]
Phe[3, 10]
Arg[6, 7]
Tyr[3, 10]
Pro[3, 11, 12]
Cys[4, 11]
His[3, 6, 7]
Aba[16, 17]
Hia[5, 18]
SDMA[14, 15]
Kyn[9, 18]

Note. CRC patients vs. healthy volunteers; ↑: increase; ↓: decrease; ↓↑: increase and decrease. #The cancerous tissue of CRC patients vs. the normal tissue of CRC patients.

Despite the overlap between the results of the targeted and nontargeted metabolomic assays, there were also substantial inconsistencies. Whenever the assessment of a specific pathway such as amino acids became the focus of interest, a targeted metabolomic assay would seem preferable to a nontargeted one [23]. Generally, gas chromatography-mass spectrometry (GC-MS) was available for the amino acid analysis in some laboratories [2426], but it required a derivatization step for nonvolatile analytes, which was important for the analyte detection and quantification, but could be avoided when a liquid chromatography-mass spectrometry (LC-MS) method was adopted. Amino acids, as a group of polar chemical compounds, should also be modified by using a derivatization reagent to make them volatile and thus accessible for GC-MS [27]. Over the past two decades, many researchers had described the ultrahigh-performance liquid chromatography-tandem mass spectrometry- (UHPLC-MS/MS-) based method for a targeted metabolomic study with increasing levels of sophistication [2830]. Out of these considerations, thirty-four endogenous amino acids in human biological samples were involved in our study by using the LC-MS technique.

Amino acid metabolomics in human serum has been studied on a larger scale due to its potential diagnostic value in patients with breast, lung, ovarian, head and neck, gastric, and pancreatic cancers or in CRC patients [31], suggesting that the amino acid profiling in plasma/serum or in other body fluids or selected tissue samples might be a new tool for early diagnosis of cancers [32]. As different cancer subtypes show distinct metabolic phenotypes, the present paper aimed to evaluate the amino acid profile in clinical samples from CRC patients by means of a targeted metabolomic assay based on a UHPLC-MS/MS method, which was established and validated for the quantitative analysis of 34 amino acids in tumor tissues. So far, no research has ever evaluated the ability of the amino acid metabolic phenotype of CRC patients. Additionally, this was the first experiment to show the amino acid profile differences between cancerous, paracancerous, and normal tissue of CRC patients, which may shed light on the metabolic stability of amino acids in humans and provide biological information to find and evaluate a new diagnostic tool for the further study of CRC.

2. Materials and Methods

2.1. Chemicals and Reagents

Thirty-four amino acids, including glycine (Gly), L-alanine (Ala), L-serine (Ser), L-valine (Val), L-threonine (Thr), L-leucine (Leu), L-isoleucine (Ile), L-aspartic acid (Asp), L-lysine (Lys), L-glutamine (Gln), L-glutamic acid (Glu), L-phenylalanine (Phe), L-arginine (Arg), L-tyrosine (Tyr), L-proline (Pro), L-asparagine (Asn), L-methionine (Met), L-tryptophan (Trp), L-cysteine (Cys), L-histidine (His), L-citrulline (Cit), asymmetric dimethylarginine (ADMA), L-cystine (Cyss), sarcosine (Sar), β-alanine (3-aminopropanoic acid, Apa), β-aminoisobutyric acid (3-amino-2-methylpropanoic acid, Amp), γ-aminobutyric acid (4-aminobutyric acid, Aba), 5-oxo-L-proline (Opr), 4-hydroxy-L-proline (Hpr), L-ornithine (Orn), 2-amino-L-hexanoic diacid (α-aminoadipic acid, Ahd), hippuric acid (Hia), symmetric dimethylarginine (SDMA), and L-kynurenine (Kyn) were purchased from the National Institutes for Food and Drug Control of China (Beijing, China), Dalian Meilun Biotech Co., Ltd., (Dalian, China), Shanghai Yuanye Biotech Co., Ltd., (Shanghai, China), and Sigma-Aldrich LLC. (Darmstadt, Germany). L-Alanine-d4 (Ala-d4), L-methionine-d3 (Met-d3), and L-phenylalanine-d5 (Phe-d5) were chosen as internal standards (ISs), which were all provided by Toronto Research Chemicals Inc., (Toronto, Canada). The information about the 34 standards and 3 ISs is shown in Supplementary Material Table S1.

Methanol and acetonitrile of HPLC grade were purchased from Merck Inc., (Darmstadt, Germany). Phosphate-buffered solution (PBS) (10x, namely, 0.1 mol/L) of the cell culture grade (Lot. MA0016-May-19B) was purchased from Dalian Meilun Biotech Co., Ltd., (Dalian, China). Heptafluorobutyric acid (HFBA) (Lot. P11933) was obtained from Adamas Reagents Co., Ltd., (Basel, Switzerland). Formic acid (FA) (Lot. C10009619) was gained from Shanghai Macklin Biochemical Co., Ltd., (Shanghai, China). Hydrochloric acid (HCl) of analytical grade was the product of Sinopharm Chemical Reagent Co., Ltd., (Shanghai, China). Sodium chloride injection (0.9% saline, 1000 ml/bag, Lot. 160613) was produced by the pharmaceutical preparation factory of Shanghai Changzheng Hospital, the Second Military Medical University (Shanghai, China). Water was deionized and further purified by a Milli-Q Plus water purification system (Darmstadt, Germany) in our laboratory. The other reagents and solvents were of analytical grade.

2.2. Liquid Chromatography and Mass Spectrometry Conditions

An Agilent 1290 UHPLC coupled to an Agilent 6460 triple-quadrupole tandem mass spectrometer (Agilent Inc, USA) was used to establish the method. Chromatographic separation was performed on an Agilent ZORBAX SB-C18 column (3.0 × 150 mm, 5 μm), whose temperature was maintained at 50°C. Binary gradient elution was used by mixing the mobile phase A (0.02% HFBA and 0.2% FA in water, v/v) and B (methanol) at a flow rate at 0.4 mL/min. The mobile phase elution procedure was as follows (A/B, v/v): 0 min, 98 : 2; 1 min, 85 : 15; 4 min, 85 : 15; 5 min, 80 : 20; and 10 min, 20 : 80. The run time was 10 min for each sample, and the post time was set at 4.0 min to equilibrate the column pressure. The autosampler temperature was maintained at 4°C. The injected volume was 2 μL with needle wash.

The ionization of analytes was performed based on an electrospray ionization (ESI) source under the positive ionization mode, and the data acquisition was carried out in a multiple reaction monitoring (MRM) mode. Optimized mass spectrometer conditions were as follows: capillary voltage, 5.0 kV; dwell time, 40 ms; collision gas (high-purity nitrogen) pressure, 0.2 MPa; and nebulizer gas (nitrogen) pressure, 50 psi. The dry gas temperature was 325°C and delivered at 10 L/min. The sheath gas temperature was 350°C at the flow rate of 12 L/min.

All data were acquired and processed using Agilent Mass Hunter workstation software (version B.07.00). The optimized MRM parameters of 34 amino acids and 3 ISs are summarized in Table 2.

AnalyteMolecular weightPrecursor ionProduct ionFragmentor (V)Collision energy (eV)


2.3. Preparation of Calibration Standards and Quality Control Samples

Stock solutions (2.5 mg/mL) for each analyte were prepared separately and stored at −80°C. Gly, Ala, Ser, Val, Thr, Leu, Ile, Lys, Phe, Arg, Pro, Met, His, Cit, Sar, Apa, Amp, Aba, Opr, Hpr, Orn, Hia, and Kyn were dissolved in 5% methanol aqueous solutions, and Gln, Glu, and Trp were prepared in 0.2% FA aqueous solutions. Asp, Tyr, Asn, Cys, Cyss, Ahd, ADMA, and SDMA were in 4% HCl aqueous solutions. Stock solutions were further diluted with 5% methanol aqueous solution to obtain the following 4 groups of working solutions. Group A included Ala, Val, Thr, Leu, Ile, Lys, Glu, Phe, Arg, and Tyr (250 μg/mL for every analyte). Group B included Gly, Ser, Asp, and Gln (250 μg/mL for every analyte) and Pro, Asn, Met, and Trp (125 μg/mL for every analyte). Group C included Cys, His, Cit, ADMA, and Cyss (250 μg/mL for every analyte). Group D included Sar, Apa, Amp, Aba, Opr, Hpr, Orn, Ahd, Hia, SDMA, and Kyn (125 μg/mL for each analyte).

The highest calibration standard solution was prepared by adding appropriate volumes of working solutions group A∼D into PBS (1x, namely, 0.01 mol/L) using previously reported methods [3335]. Then, the other 8 calibration standard solutions were obtained by diluting the highest calibration standard solution with PBS. The final concentrations of calibration standard solutions were 1000, 2000, 4000, 8000, 10000, 20000, 40000, 60000, and 80000 ng/mL for Gly, Ala, Ser, Val, Thr, Leu, Ile, Asp, Lys, Gln, Glu, Phe, Arg, and Tyr; 500, 1000, 2000, 4000, 5000, 10000, 20000, 30000, and 40000 ng/mL for Pro, Asn, Met, and Trp; 100, 200, 400, 800, 1000, 2000, 4000, 6000, and 8000 ng/mL for Cys, His, Cit, ADMA, and Cyss; and 50, 100, 200, 400, 500, 1000, 2000, 3000, and 4000 ng/mL for Sar, Apa, Amp, Aba, Opr, Hpr, Orn, Ahd, Hia, SDMA, and Kyn, respectively.

Quality control (QC) samples were also separately prepared in the same way and at low, medium, and high concentrations (QC1∼3). The low, medium, and high concentrations of the QC samples were 2000, 10000, and 60000 ng/mL for Gly, Ala, Ser, Val, Thr, Leu, Ile, Asp, Lys, Gln, Glu, Phe, Arg, and Tyr; 1000, 5000, and 30000 ng/mL for Pro, Asn, Met, and Trp; 200, 1000, and 6000 ng/mL for Cys, His, Cit, ADMA, and Cyss; and 100, 500, and 3000 ng/mL for Sar, Apa, Amp, Aba, Opr, Hpr, Orn, Ahd, Hia, SDMA, and Kyn, respectively. All solutions were stored at −20°C.

2.4. Sample Pretreatment

Each tissue sample with a mass of about 100 mg was precisely weighed and added with a 5-fold mass of 0.9% saline before being homogenized by a superfine homogenizer at 15000 r/min for 2 min in the ice water bath to obtain tissue homogenate, and then the mixture was centrifuged for 15 min at 19060 ×g at 4°C after five minutes of ultrasonic treatment in the ice water bath. Then, a 50 μL aliquot of the supernatant was transferred to a microcentrifuge tube, and 150 μL of 0.2% FA acetonitrile solution (containing 400 ng/mL ISs) was added. The mixture was centrifuged again under the same conditions after being rested for 3 min and vortex-mixed for 2 min, and 2 μL of supernatant was injected into the UHPLC-MS/MS system for analysis.

2.5. Method Validation

Method validation was performed according to Chinese Pharmacopoeia [36] and US Food and Drug Administration (FDA) guidance [37] and with reference to our previous report [38].

The selectivity was evaluated by comparing six different batches of the blank matrix to the corresponding spiked samples, and the responses of interferents in the blank matrix less than 20% of the low limit of quantitation (LLOQ) samples and 5% of ISs were considered acceptable.

The calibration standards were prepared in triplicates and measured three times on different days (at least 2 days). The calibration curve was regressed from the IS-adjusted peak area versus the nominal concentration under a 1/X2 weighting factor. LLOQ was defined as the lowest concentration point of the calibration curve. A deviation of backcalculation for each calibration standard within ±15% was thought to be acceptable, and for LLOQ, the deviation should be within ±20%.

The recovery and matrix effect were assessed by preparing six replicates of the QC sample at low and high concentration levels. The matrix effect was the ratio of the peak area in the spiked postextraction samples to solvent-substituted samples at the same concentration, and the recovery was the ratio of the peak area in the spiked samples to spiked postextraction samples at the same concentration.

The intra- and interday accuracy and precision were assessed using the QC samples at LLOQ, low, medium, and high concentration levels (n = 5). Samples were analyzed in three analytical lots on separate days (at least 2 days), and the relative standard deviation (RSD) % for inter- and intraday precision not more than 15% was regarded as acceptable (for LLOQ, not more than 20%). For intra- and interday accuracy, the relative error (RE) % within ±15% (for LLOQ, within ±20%) was considered reasonable.

The stability of each analyte was assessed at three concentration levels (low, medium, and high) using the QC samples (n = 3) under four different conditions: room temperature stability was evaluated after exposing samples at room temperature for 6 h; three freeze-thaw cycles stability was evaluated after freeze and thaw of samples from −20°C to room temperature three times; short-term stability was assessed by analyzing samples kept in the autosampler (4°C) for 24 h; and long-term stability was evaluated after the samples were stored at −20°C for 90 days.

The dilution effect of all the analytes was assessed by diluting the sample with a blank matrix into the calibration range and comparing the measured concentrations to the nominal concentrations. Each dilution factor should be assessed at least five times, and the RSD% and RE% should be less than 15% and within ±15%, respectively.

2.6. Study Population and Sample Collection

The experimental protocol was reviewed and approved by the Ethical Committee of Changzheng Hospital prior to specimen collection, and it was conducted in accordance with the Helsinki Declaration of 1964, as revised in 2013, and according to regulatory guidance. Informed consent was obtained from all participants enrolled in this study.

Between July 2016 and December 2017, 94 patients (male 56, female 38) with CRC were enrolled from Changzheng Hospital. None of the patients received neoadjuvant treatment. The median age of these patients was 60 (ranging from 32 to 87). 10 patients were diagnosed with stage I CRC, 33 patients with stage II, 45 patients with stage III, and 6 patients with stage IV. The demographic and clinical chemistry characteristics of these CRC patients are shown in Table 3.


Number of patients945638
Age (median, range)60, (32∼87)58, (32∼87)61, (38∼80)
Number of patients with TNM stage
 Stage I1037
 Stage II331815
 Stage III453312
 Stage IV624

The sample set including cancerous, paracancerous, and normal tissue samples was collected from each of these CRC patients and was named Tc, Tp, and Tn, respectively. All samples were immediately washed using 0.9% icy saline solution, and the surfaces were subsequently dried by the filter tissue. The samples were stored at −80°C within cryotubes until analysis.

2.7. Data Analysis

Data were analyzed statistically, and graphs were generated by GraphPad Prism 6.01 for Windows (GraphPad Software, Inc., La Jolla, CA, USA). A nonparametric test (Friedman test) was performed to compare the content differences of 34 amino acids between sample sets. The value less than 0.05 was considered statistically significant.

3. Results and Discussion

3.1. Method Development

Many studies have published the quantitative analyses of amino acids in plasma [23, 24, 28, 31, 32], but these methods have never been applied in tissue homogenate. In this study, we developed a robust method for the quantitative analysis of underivatized amino acids in human tissue by UHPLC-MS/MS.

As for optimization of ESI-MS/MS conditions, this study highlighted the importance of quantifying the isomeric analytes using two strategies. For compounds that shared the same MRM transitions, such as Ala and Sar, modifications of the mobile phase and its gradient, as well as the column optimization, were tried to make sure they were completely separated in chromatography. The coelution of the other two pairs of standards, such as Leu, Ile, and Hpr and Lys and Gln, was also avoided at the same time. If the isomeric analytes had a similar retention time, another MRM transition was chosen to separate them on different mass spectrometer channels. For example, the MRM transition for ADMA was set at 203/46 instead of 203/70 and 203/172 for SDMA. The representative MRM chromatograms of Ala, Sar, Leu, Ile, Hpr, Lys, Gln, ADMA, and SDMA are shown in Figure 2.

During the selection of ISs, the ideal condition was an isotope-labeled internal standard for each analyte, but the major problem was the high expense and longer delivery time. Besides, those amino acids were of the same structure, so it was acceptable to use a structural analog as the internal standard for analytes. Furthermore, method verification in our experiment was currently acceptable as specified by Chinese Pharmacopoeia and FDA guidelines. Hence, the practical value was the availability of three isotopically labeled amino acids, which could facilitate application while making research less expensive.

In terms of optimization of chromatographic conditions, generally, analysis using sub-2 μm columns yielded a greater (S/N) due to the reduction in band broadening and thus an increase in sensitivity. We actually conducted the analysis by using reverse-phase LC columns with 1.8 and 5.0 μm packing materials, which generated a similar chromatographic peak resolution. Moreover, ion suppression from the coeluting peak was alleviated when the 5.0 μm column was used. Since gradual accumulation of small amounts of protein and/or particulates might occur and become noticeable after injection of a large number of samples owing to the incomplete efficiency of protein removal (only about 95%∼99%) [39], a wash step of the column was set between different analysis batches. Besides, the use of a column of 5 μm particle size was considered to be less vulnerable pollution than one of 1.8 μm particle size. Based on our previous reports [38], the adding of HFBA could lead to the best performance separation for amino acids. Therefore, we found that 0.02% HFBA and 0.2% FA aqueous solution with methanol could result in better separation of compounds and chromatographic peaks shapes and higher signal response (S/N) for most analytes. Also, great abundance and high ionization efficiency were obtained in the positive ionization mode for all the analytes.

The selection of an appropriate matrix for calibration samples and the QC samples preparation was an important part of methodological development when LC-MS was used for the quantitative analysis of endogenous compounds in biological samples. There were two main approaches to this problem: the first was to dissolve alternative analytes in the real matrix and the other was to use real analytes in an alternative matrix [35]. The ideal substitutive matrix should be completely analyte-free and identical to the real matrix in terms of analyte solubility and extractability, but it was unpractical for the detection of endogenous compounds. In our experiment, plasma was processed using neutral decolorizing carbon for stripping some endogenous carbohydrates [40]. This approach was effective for carbohydrates but not for all amino acids because the prepared plasma still contained a high concentration of amino acids. Therefore, the calibration and the QC samples could be prepared in an artificial matrix only as it was impossible to make an “analyte-free” matrix. As human biofluid usually contains a variety of proteins, fatty acids, and electrolytes, which is hard for simulation, some research studies documented that the PBS [33, 34] or mobile phase [41] could be treated as the matrix when the calibration and the QC samples were prepared. In addition, the pH value, osmotic pressure, and ion concentration of PBS were closer to those of the biofluid of humans than the mobile phase [35]. As such, PBS was applied as the “mimic tissue fluid” to prepare both the calibration and the QC samples.

When it came to optimization of sample preparation, treatment of the tissue homogenate commonly involved protein precipitation (PPT), liquid-liquid extraction, and solid phase extraction. The PPT method was considered the best method in that it was user-friendly, inexpensive, and suitable for high-throughput biological sample pretreatment. It was also validated in our previous study for the assay of 18 plasma amino acids by a UHPLC-MS/MS method [38]. By adding a 3-fold volume of precipitator (0.2% FA acetonitrile solution containing each IS of 400 ng/mL), stable and optimal recovery as well as matrix effect was achieved, so this pretreatment method was applied in this improved method.

In all, the quantification of amino acids in human plasma by both GC-MS- and LC-MS-based mass spectroscopy was well established [23, 24, 28, 31, 32] and would ideally suit our validation study after improvement. As the key differences for LC-MS were often in terms of sample preparation and separation parameters, the comparison of those published LC-MS methods with the UHPLC-MS/MS method established by us could find that (a) our analysis was time-saving and economical and without any derivatization process; (b) a unique chromatographic configuration minimized ion suppression and yielded excellent analytic performance, especially for the isomers; and (c) this method could easily be extended to different sample matrices, such as plasma (data not shown).

3.2. Method Validation Results
3.2.1. Specificity

The representative total ion current chromatograms and MRM chromatograms of blank sample, blank sample spiked with 34 amino acids and 3 ISs, and real CRC samples are shown in Supplementary Material Figure S1. The retention time of the 34 amino acids is shown in Table 4. No interfering peaks from endogenous matrix substances were shown at the retention time of 34 amino acids and 3 ISs, suggesting satisfactory separation and selectivity.

AnalyteRetention time (min)Regression equation (n = 9)Coefficient R2Calibration range (ng/ml)LLOQ (ng/ml)IS

Gly1.866y = 1.973405 × 10−5x + 0.0131710.994341000∼800001000Phe-d5
Ala2.118y = 0.002292x − 0.4711340.995381000∼800001000Ala-d4
Ser1.809y = 1.120434 × 10−4x + 0.4052180.992201000∼800001000Phe-d5
Val3.607y = 0.003533x − 0.5982120.994721000∼800001000Ala-d4
Thr2.026y = 0.001422x + 0.1298490.996041000∼800001000Met-d3
Leu6.402y = 0.021977x − 7.7840390.993751000∼800001000Met-d3
Ile5.863y = 0.022788x − 6.2709950.995351000∼800001000Met-d3
Asp1.787y = 4.507537 × 10−4x + 0.5223950.989231000∼800001000Met-d3
Lys2.994y = 5.106616 × 10−4x + 0.0216050.981571000∼800001000Phe-d5
Gln1.882y = 0.005491x + 4.1265050.995131000∼800001000Met-d3
Glu2.066y = 0.001872x + 0.8867730.998971000∼800001000Met-d3
Phe7.554y = 0.006928x − 3.1491610.992581000∼800001000Phe-d5
Arg3.329y = 0.001577x + 0.2985840.993701000∼800001000Met-d3
Tyr4.427y = 1.241443 × 10−4x + 0.0568220.992501000∼800001000Phe-d5
Pro2.146y = 0.004142x + 0.0528860.99646500∼40000500Ala-d4
Asn1.770y = 9.372446 × 10−4x + 0.4719970.99304500∼40000500Met-d3
Met3.636y = 0.001404x − 0.1796210.99310500∼40000500Met-d3
Trp8.755y = 3.881832 × 10−4x − 0.0346880.99746500∼40000500Phe-d5
Cys2.056y = 6.151545 × 10−4x − 0.0241060.99544100∼8000100Met-d3
His2.695y = 0.005120x + 0.1296740.98902100∼8000100Met-d3
Cit2.139y = 0.00316x + 0.1235140.99490100∼8000100Met-d3
ADMA3.610y = 2.22466 × 10−4x − 0.0080330.99537100∼8000100Phe-d5
Cyss1.843y = 3.122902 × 10−5x + 0.0078520.99611100∼8000100Phe-d5
Sar1.856y = 7.269948 × 10−4x + 0.0173120.9972450∼400050Phe-d5
Apa2.432y = 7.290745 × 10−4x − 0.0094280.9964950∼400050Met-d3
Amp3.181y = 1.459612 × 10−4x + 8.722727 × 10−40.9872950∼400050Phe-d5
Aba2.771y = 0.005955x − 0.0853800.9989850∼400050Met-d3
Opr2.567y = 1.048082 × 10−4x + 0.0359730.9963450∼400050Phe-d5
Hpr1.759y = 0.017388x + 0.0178640.9953350∼400050Met-d3
Orn2.676y = 0.001756x + 0.2364730.9940650∼400050Met-d3
Ahd2.577y = 0.003022x − 0.0393140.9990250∼400050Ala-d4
Hia7.210y = 0.001064x − 0.0028720.9930950∼400050Phe-d5
SDMA3.619y = 7.281465 × 10−5x − 0.0016140.9951450∼400050Phe-d5
Kyn6.298y = 3.698408 × 10−4x − 4.537424 × 10−40.9974450∼400050Phe-d5

3.2.2. Linearity of Calibration Curves and LLOQ

The linear equations were regressed to calculate the measured concentrations in all samples within the analytical runs. Good correlation coefficients (r > 0.99) were observed for all analytes in their corresponding calibration ranges. The regression equations, coefficients, calibration ranges, and LLOQ for the 34 amino acids and ISs are shown in Table 4.

3.2.3. Recovery and Matrix Effect

The average recovery results of the 34 amino acids and 3 ISs using QC1 and QC3 samples ranged from 39.00% to 146.95% (RSD 0.44%∼7.40%). The average matrix effect results using the same two samples ranged from 49.45% to 173.63% (RSD 0.61%∼12.97%), indicating that the extraction procedure was consistent and stable (Supplementary Material Table S2).

3.2.4. Inter- and Intraday Accuracy and Precision

The intra- and interday accuracy and precision of this method were assessed using the LLOQ, QC1, QC2, and QC3 samples. The deviations (RE%) of intraday ranged from −13.52% to 14.21%, and the RSD were less than 8.57%, while the deviations of interday ranged from −14.52% to 12.59%, and the RSD was not more than 10.31%. The intra- and interday accuracy of the LLOQ sample ranged from −18.03% to 16.99%, and precision was less than 13.25%. The results are shown in Supplementary Material Table S3.

3.2.5. Stability

The stability results of different conditions are shown in Supplementary Material Table S4. It demonstrated that all the analytes were stable with the accuracy within ±15% under different conditions.

3.2.6. Dilution Effect

The dilution effect results showed that the accuracy and precision for 8-time dilution were acceptable (RE ranged from −12.86% to 14.42%, RSD ≤5.46% for all the analytes). The results are shown in Supplementary Material Table S5.

3.3. Application in Determination of Clinical Samples

This targeted UHPLC-MS/MS method was successfully applied to simultaneously determine 34 amino acids in tumor tissue samples. Figure 3 and Table 5 depict the contents, median, and SD values of 34 amino acids in cancerous, paracancerous, and normal tissue from 94 CRC patients. As expected, the contents of some amino acids were significantly different between sample types. For example, Gly in Tc sample was markedly higher than that in Tp and Tn samples (each ). In addition, significantly increased contents were also found in Tc sample for the Asp, Glu, Pro, Cys, ADMA, Cyss, Kyn, Orn, Aba, Apa, and Hpr (each ). This supported the trait of altered metabolism of cancer, and it was encouraging that the candidate metabolites varied so much between cancer and normal tissue. However, it was not enough to diagnose the CRC by the amino acid profiles alone because the samples in our study were limited, and further research was needed to evaluate their potential as biomarkers for CRC diagnosis and treatment.

AnalyteCancerous tissue (μg/g)Paracancerous tissue (μg/g)Normal tissue (μg/g)


4. Conclusion

A simple, rapid, sensitive, and efficient targeted UHPLC-MS/MS method was developed for the determination of 34 amino acids with analytical time less than 10 min in tumor tissues, which was validated for selectivity, linearity, extraction recovery, matrix effect, intra- and interday accuracy and precision, and stability. The use of diluted PBS as the “mimic tissue fluid” could prevent serious interference from endogenous amino acids, which was proved to be simple and efficient. Enough retention was achieved for the highly polar amino acid analytes in the C18 column by using the HFBA, an ion-pairing reagent, as the mobile phase addictive, without any derivatization procedure. The one step PPT method for 100 mg cancer tissue supported the high-throughput testing. In summary, this UHPLC-MS/MS method was successfully applied to plot the profiles of 34 amino acids in cancerous, paracancerous, and normal tissue from CRC patients, which may be of help for the diagnosis and treatment of CRC in the future.


Aba:4-Aminobutyric acid
ADMA:Asymmetric dimethylarginine
Ahd:2‐Amino‐L‐hexanoic diacid
Amp:3-Amino-2-methylpropanoic acid
Apa:3-Aminopropanoic acid
Asp:L-Aspartic acid
CRC:Colorectal cancer
ESI:Electrospray ionization
FA:Formic acid
FDA:Food and drug administration
GC-MS:Gas chromatography-mass spectrometry
Glu:L-Glutamic acid
HCl:Hydrochloric acid
HFBA:Heptafluorobutyric acid
Hia:Hippuric acid
HPLC:High-performance liquid chromatography
IS:Internal standard
LC-MS:Liquid chromatography-mass spectrometry
LLOQ:Low limit of quantitation
MRM:Multiple reaction monitoring
PBS:Phosphate-buffered solution
PPT:Protein precipitation
QC:Quality control
RE:Relative error
RSD:Relative standard deviation
SDMA:Symmetric dimethylarginine
TCA:Tricarboxylic acid
UHPLC-MS/MS:Ultrahigh-performance liquid chromatography-tandem mass spectrometry

Conflicts of Interest

The authors declare that they have no conflicts of interest.


This study was supported by the International Science and Technology Cooperation and Communication Special Fund of China (2015DFA31810), the Scientific Research Fund of Shanghai Science and Technology Committee (17411972400), the Clinical Science and Technology Innovation Project of Shanghai Shenkang Hospital Development Center (SHDC12015120), the Science and Technology Development Fund of Shanghai (19QB1404500), the Important Weak Subject Construction Project of Shanghai Health Science Education (2016ZB0303), the Science and Technology Talent Development Project of CPLA Army (Zhenggan[2019]174), and the Xuzhou Medical Young Talent Project (Xuweikejiao[2015]7). The authors would like to acknowledge the kind support of Huan Man, Jing Chen, Weixiang Liu, Jianhua He, and Dongli Li and Agilent Technologies Co., Ltd., (Shanghai, China).

Supplementary Materials

TABLE S1: the control substances of 34 amino acids and 3 ISs. TABLE S2: the extraction recovery and matrix effect of 34 amino acids and 3 ISs (n = 6). TABLE S3: the intra- and interday accuracy and precision of 34 amino acids (n = 5). TABLE S4: the stability results of 34 amino acids (n = 3). TABLE S5: the dilution effect results of 34 amino acids (n = 3). FIGURE S1: the representative total ion current chromatograms and MRM chromatograms of 34 amino acids and 3 ISs—(a) blank matrix; (b) blank matrix spiked with 34 amino acid and 3 ISs; (c) cancerous tissue sample. (Supplementary Materials)


  1. E. C. Y. Chan, P. K. Koh, M. Mal et al., “Metabolic profiling of human colorectal cancer using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy and gas chromatography mass spectrometry (GC/MS),” Journal of Proteome Research, vol. 8, no. 1, pp. 352–361, 2009. View at: Publisher Site | Google Scholar
  2. Y. L. Ma, P. Zhang, F. Wang, W. J. Liu, J. J. Yang, and H. L. Qin, “An integrated proteomics and metabolomics approach for defining oncofetal biomarkers in the colorectal cancer,” Annals of Surgery, vol. 255, no. 4, pp. 720–730, 2012. View at: Publisher Site | Google Scholar
  3. Y. P. Qiu, “Metabonomics study on colorectal cancer using combined chromatography-mass spectrometry strategy,” Shanghai Jiao Tong University, Shanghai, China, 2008, Doctor dissertation. View at: Google Scholar
  4. Y. P. Qiu, G. X. Cai, B. S. Zhou et al., “A distinct metabolic signature of human colorectal cancer with prognostic potential,” Clinical Cancer Research, vol. 20, no. 8, pp. 2136–2146, 2014. View at: Publisher Site | Google Scholar
  5. J. J. Zhu, D. Djukovic, L. L. Deng et al., “Colorectal cancer detection using targeted serum metabolic profiling,” Journal of Proteome Research, vol. 13, no. 9, pp. 4120–4130, 2014. View at: Publisher Site | Google Scholar
  6. J. L. Chen, J. Fan, L. S. Yan et al., “Urine metabolite profiling of human colorectal cancer by capillary electrophoresis mass spectrometry based on MRB,” Gastroenterology Research and Practice, vol. 2012, Article ID 125890, 8 pages, 2012. View at: Publisher Site | Google Scholar
  7. J. Fan, “Metabolomics of gastric cancer, colorectal cancer, and pancreatic cancer urine by capillary electrophoresis mass spectrometry based on moving reaction boundary,” Suzhou University, Suzhou, China, 2012, Master dissertation. View at: Google Scholar
  8. F. Farshidfar, A. M. Weljie, K. Kopciuk et al., “Serum metabolomic profile as a means to distinguish stage of colorectal cancer,” Genome Medicine, vol. 4, no. 5, p. 42, 2012. View at: Publisher Site | Google Scholar
  9. S. Nishiumi, T. Kobayashi, A. Ikeda et al., “A novel serum metabolomics-based diagnostic approach for colorectal cancer,” PLoS One, vol. 7, no. 7, Article ID e40459, 2012. View at: Publisher Site | Google Scholar
  10. B. Jiménez, R. Mirnezami, J. Kinross et al., “1H HR-MAS NMR spectroscopy of tumor-induced local metabolic “Field-Effects” enables colorectal cancer staging and prognostication,” Journal of Proteome Research, vol. 12, no. 2, pp. 959–968, 2013. View at: Publisher Site | Google Scholar
  11. D. Monleón, J. M. Morales, A. Barrasa, J. A. López, C. Vázquez, and B. Celda, “Metabolite profiling of fecal water extracts from human colorectal cancer,” NMR in Biomedicine, vol. 22, no. 3, pp. 342–348, 2009. View at: Publisher Site | Google Scholar
  12. L. C. Phua, X. P. Chue, P. K. Koh et al., “Noninvasive fecal metabonomic detection of colorectal cancer,” Cancer Biology & Therapy, vol. 15, no. 4, pp. 389–397, 2014. View at: Publisher Site | Google Scholar
  13. H. Li, Y. Zhou, A. Zhao et al., “Asymmetric dimethylarginine attenuates serum starvation-induced apoptosis via suppression of the Fas (APO-1/CD95)/JNK (SAPK) pathway,” Cell Death & Disease, vol. 4, no. 10, Article ID e830, 2013. View at: Publisher Site | Google Scholar
  14. D. Owczarek, D. Cibor, and T. Mach, “Asymmetric dimethylarginine (ADMA), symmetric dimethylarginine (SDMA), arginine, and 8-iso-prostaglandin F2α (8-iso-PGF2α) level in patients with inflammatory bowel diseases,” Inflammatory Bowel Diseases, vol. 16, no. 1, pp. 52–57, 2010. View at: Publisher Site | Google Scholar
  15. Y. Z. Yang and M. T. Bedford, “Protein arginine methyltransferases and cancer,” Nature Reviews Cancer, vol. 13, no. 1, pp. 37–50, 2013. View at: Publisher Site | Google Scholar
  16. Z. Kleinrok, M. Matuszek, J. Jesipowicz, B. Matuszek, A. Opolski, and C. Radzikowski, “GABA content and GAD activity in colon tumors taken from patients with colon cancer or from xenografted human colon cancer cells growing as s.c. tumors in athymic nu/nu mice,” Journal of Physiology & Pharmacology: An Official Journal of the Polish Physiological Society, vol. 49, no. 2, pp. 303–310, 1998. View at: Google Scholar
  17. L. H. Song, L. H. Song, A. L. Du et al., “γ-Aminobutyric acid inhibits the proliferation and increases oxaliplatin sensitivity in human colon cancer cells,” Tumor Biology, vol. 37, no. 11, pp. 14885–14894, 2016. View at: Publisher Site | Google Scholar
  18. Y. Cheng, G. X. Xie, T. L. Chen et al., “Distinct urinary metabolic profile of human colorectal cancer,” Journal of Proteome Research, vol. 11, no. 2, pp. 1354–1363, 2012. View at: Publisher Site | Google Scholar
  19. G. B. Zhu, “Metabolism of β-amino acids in mammals,” Amino Acids, vol. 12, no. 1, pp. 19–24, 1990. View at: Google Scholar
  20. B. Cavaliere, B. Macchione, M. Monteleone, A. Naccarato, G. Sindona, and A. Tagarelli, “Sarcosine as a marker in prostate cancer progression: a rapid and simple method for its quantification in human urine by solid-phase microextraction-gas chromatography-triple quadrupole mass spectrometry,” Analytical and Bioanalytical Chemistry, vol. 400, no. 9, pp. 2903–2912, 2011. View at: Publisher Site | Google Scholar
  21. Y. H. Ma, Y. X. Ding, Y. L. Zhou et al., “Application of urinary sarcosine measurement for the diagnosis of carcinoma of prostate,” International Journal of Laboratory Medicine, vol. 32, no. 12, pp. 1299-1300, 2011. View at: Google Scholar
  22. A. Sreekumar, L. M. Poisson, T. M. Rajendiran et al., “Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression,” Nature, vol. 457, no. 7231, pp. 910–914, 2009. View at: Publisher Site | Google Scholar
  23. J. Klepacki, J. Klawitter, J. Klawitter et al., “Amino acids in a targeted versus a nontargeted metabolomics LC-MS/MS assay. Are the results consistent?” Clinical Biochemistry, vol. 49, no. 13-14, pp. 955–961, 2016. View at: Publisher Site | Google Scholar
  24. T. Rosado, A. Gonçalves, A. Martinho et al., “Simultaneous quantification of antidepressants and metabolites in urine and plasma samples by GC-MS for therapeutic drug monitoring,” Chromatographia, vol. 80, no. 2, pp. 301–328, 2017. View at: Publisher Site | Google Scholar
  25. I. Willenberg, A. I. Ostermann, and N. H. Schebb, “Targeted metabolomics of the arachidonic acid cascade: current state and challenges of LC-MS analysis of oxylipins,” Analytical and Bioanalytical Chemistry, vol. 407, no. 10, pp. 2675–2683, 2015. View at: Publisher Site | Google Scholar
  26. O. Begou, O. Deda, A. Agapiou, I. Taitzoglou, H. Gika, and G. Theodoridis, “Urine and fecal samples targeted metabolomics of carobs-treated rats,” Journal of Chromatography B, vol. 1114-1115, pp. 76–85, 2019. View at: Publisher Site | Google Scholar
  27. A. T. Godoy, M. N. Eberlin, and A. V. C. Simionato, “Targeted metabolomics: liquid chromatography coupled to mass spectrometry method development and validation for the identification and quantitation of modified nucleosides as putative cancer biomarkers,” Talanta, vol. 210, p. 120640, 2020. View at: Publisher Site | Google Scholar
  28. M. J. Li, Z. M. Zhang, F. Fan, P. Ma, Y. Wang, and H. M. Lu, “Exploring asthenozoospermia seminal plasma amino acid disorder based on GC-SIM-MS combined with chemometrics methods,” Analytical Methods, vol. 11, no. 22, pp. 2895–2902, 2019. View at: Publisher Site | Google Scholar
  29. A. Quigley, D. Connolly, and W. Cummins, “Determination of selected amino acids in milk using dispersive liquid-liquid microextraction and GC-MS,” Analytical Methods, vol. 11, no. 28, pp. 3538–3545, 2019. View at: Publisher Site | Google Scholar
  30. J. M. Batista, M. J. Neves, A. G. Pereira, L. S. Gonçalves, H. C. Menezes, and Z. L. Cardeal, “Metabolomic studies of amino acid analysis in Saccharomyces cells exposed to selenium and gamma irradiation,” Analytical Biochemistry, vol. 597, p. 113666, 2020. View at: Publisher Site | Google Scholar
  31. F. Manig, K. Kuhne, C. von Neubeck et al., “The why and how of amino acid analytics in cancer diagnostics and therapy,” Journal of Biotechnology, vol. 242, pp. 30–54, 2017. View at: Publisher Site | Google Scholar
  32. E. Simińska and M. Koba, “Amino acid profiling as a method of discovering biomarkers for early diagnosis of cancer,” Amino Acids, vol. 48, no. 6, pp. 1339–1345, 2016. View at: Publisher Site | Google Scholar
  33. D. Z. S. Furtado, F. B. V. de Moura Leite, C. N. Barreto et al., “Profiles of amino acids and biogenic amines in the plasma of Cri-du-Chat patients,” Journal of Pharmaceutical and Biomedical Analysis, vol. 140, pp. 137–145, 2017. View at: Publisher Site | Google Scholar
  34. H. Sugimoto, M. Kakehi, and F. Jinno, “Bioanalytical method for the simultaneous determination of D- and L-serine in human plasma by LC/MS/MS,” Analytical Biochemistry, vol. 487, pp. 38–44, 2015. View at: Publisher Site | Google Scholar
  35. N. C. Van de Merbel, “Quantitative determination of endogenous compounds in biological samples using chromatographic techniques,” TrAC Trends in Analytical Chemistry, vol. 27, no. 10, pp. 924–933, 2008. View at: Publisher Site | Google Scholar
  36. National Pharmacopoeia Committee, Pharmacopoeia of the People’s Republic of China: 4th Part, China Medical Science Press, Beijing, China, 2015.
  37. US Department of Health and Human Services, Guidance for Industry, Bioanalytical Method Validation, US Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research, Center for Veterinary Medicine, Washington, DC, USA, 2001,
  38. Q. H. Wang, Y. Wen, T. Y. Xia et al., “Quantification of 18 amino acids in human plasma: application in renal transplant patient plasma by targeted UHPLC-MS/MS,” Bioanalysis, vol. 8, no. 13, pp. 1337–1351, 2016. View at: Publisher Site | Google Scholar
  39. D. A. Wells, “Chapter 6 protein precipitation: high throughput techniques and strategies for method development,” Progress in Pharmaceutical and Biomedical Analysis, vol. 5, pp. 199–254, 2003. View at: Google Scholar
  40. B. J. Zhu, F. Liu, X. T. Li et al., “Fast quantification of endogenous carbohydrates in plasma using hydrophilic interaction liquid chromatography coupled with tandem mass spectrometry,” Journal of Separation Science, vol. 38, no. 1, pp. 34–41, 2015. View at: Publisher Site | Google Scholar
  41. C. Roy, P.-Y. Tremblay, J.-F. Bienvenu, and P. Ayotte, “Quantitative analysis of amino acids and acylcarnitines combined with untargeted metabolomics using ultrahigh-performance liquid chromatography and quadrupole time-of-flight mass spectrometry,” Journal of Chromatography B, vol. 1027, pp. 40–49, 2016. View at: Publisher Site | Google Scholar

Copyright © 2020 Yang Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Related articles

No related content is available yet for this article.
 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder

Related articles

No related content is available yet for this article.

Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. Read the winning articles.