Accepted Manuscript Title: Recent trends in analytical methods for the determination of amino acids in biological samples Authors: Yanting Song, Chang Xu, Hiroshi Kuroki, Yiyi Liao, Makoto Tsunoda PII: DOI: Reference:

S0731-7085(17)31479-6 http://dx.doi.org/10.1016/j.jpba.2017.08.050 PBA 11496

To appear in:

Journal of Pharmaceutical and Biomedical Analysis

Received date: Revised date: Accepted date:

9-6-2017 25-8-2017 26-8-2017

Please cite this article as: Yanting Song, Chang Xu, Hiroshi Kuroki, Yiyi Liao, Makoto Tsunoda, Recent trends in analytical methods for the determination of amino acids in biological samples, Journal of Pharmaceutical and Biomedical Analysishttp://dx.doi.org/10.1016/j.jpba.2017.08.050 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Recent trends in analytical methods for the determination of amino acids in biological samples Yanting Songa, Chang Xua, Hiroshi Kurokib, Yiyi Liaoa, Makoto Tsunodab,* a

Key Laboratory of Tropic Biological Resources, Minister of Education, Department

of Pharmaceutical Sciences, College of Marine Science, Hainan University, Haikou 570228, China b

Graduate School of Pharmaceutical Sciences, University of Tokyo, Tokyo 1130033, Japan

* Corresponding author Email: [email protected] (M. Tsunoda)

Highlights 

Analytical methods of amino acids in biological fluids were summarized.



Measurement of amino acid concentrations in human biological fluids is important.



Relationship of amino acid concentration to several diseases is also summarized.

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Abstract Amino acids are widely distributed in biological fluids and involved in many biological processes, such as the synthesis of proteins, fatty acids, and ketone bodies. The altered levels of amino acids in biological fluids have been found to be closely related to several diseases, such as type 2 diabetes, kidney disease, liver disease, and cancer. Therefore, the development of analytical methods to measure amino acid concentrations in biological samples can contribute to research on the physiological actions of amino acids and the prediction, diagnosis and understanding of diseases. This review describes the analytical methods reported in 2012-2016 that utilized liquid chromatography and capillary electrophoresis coupled with ultraviolet, fluorescence, mass spectrometry, and electrochemical detection. Additionally, the relationship between amino acid concentrations and several diseases is also summarized. Keywords: Liquid chromatography; Capillary electrophoresis; Fluorescence; Mass spectrometry; Diagnosis

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Contents 1.

Introduction...................................................................................................................................... 4

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Analytical methods for amino acid determination ........................................................................... 4 2.1. Ultraviolet detection ...................................................................................................................... 5 2.1.1. Methods without derivatization ............................................................................................. 5 2.1.2. Methods with derivatization .................................................................................................. 5 2.2. Fluorescence detection .................................................................................................................. 6 2.3. Mass spectrometry ........................................................................................................................ 8 2.4. Electrochemical detection ............................................................................................................. 9 2.5. Miscellaous ................................................................................................................................. 10 2.6. On-chip analysis .......................................................................................................................... 10

3. Amino acids and their relationship to disease .................................................................................... 11 3.1. Diabetes mellitus ......................................................................................................................... 12 3.2. Kidney disease ............................................................................................................................ 13 3.3. Liver disease ............................................................................................................................... 13 3.4. Cancer ......................................................................................................................................... 14 3.5. Other diseases ............................................................................................................................. 14 4. Conclusion and Prospect .................................................................................................................... 16 Acknowledgement .................................................................................................................................. 16 References .............................................................................................................................................. 17

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1. Introduction Amino acids have several roles in cellular metabolism, as they are the structural units for proteins and intermediate metabolites that fuel other biosynthetic reactions. Some of amino acids, such as aspartic acid (Asp), glutamic acid (Glu), γ-aminobutyric acid (GABA), and taurine (Tau), act as neurotransmitters that regulate synaptic transmission and memory. Branched-chain amino acids (BCAAs) (leucine (Leu), isoleucine (Ile), and valine (Val)) participate in the modulation of protein synthesis and the reduction of protein catabolism. Phenylalanine (Phe) and tyrosine (Tyr) are involved in the biosynthesis of trace amines and catecholamines. As described, the importance of amino acids in vivo has resulted in an increasing demand for analytical methods for their determination. Most such methods use separation techniques, such as high-performance liquid chromatography (HPLC) and capillary electrophoresis (CE). The measurement of amino acid concentrations in human biological fluids, such as plasma, urine, and sweat, is important for predictions, diagnoses, and research on the mechanisms of diseases [1-3]. This review focuses on only the methods that can be applied to biological samples reported in the period from 2012 to 2016. First, we summarize the analytical methods, which are classified based on the detection method employed, including ultraviolet (UV), fluorescence (FL), electrochemical, and mass spectrometry (MS) detection. The detailed analytical conditions for amino acids analysis are summarized in Tables 1-5. Next, the relationship between the amino acid concentration and disease is briefly summarized (Tables 6-10). It should be noted that D-amino acids analysis is not included in this review article, and the readers refer to the recent review article [4]. 2. Analytical methods for amino acid determination Various separation methods, such as HPLC and CE, have been commonly used for the determination of amino acids in biological samples. Recently, new types of columns, such as sub-2 μm-particle packed columns, monolithic silica columns, and 4

core-shell columns, have been more widely applied in the LC analysis of amino acids, and the analysis time has been greatly shortened. The on-chip LC technique was also developed for amino acid analysis. These separation techniques have been coupled with various detection methods, including UV [5-19], FL [20-37], MS [38-73], and electrochemical [74-76] detection. Among them, MS has been more widely used and has become the most common detection method in amino acid analysis. 2.1. Ultraviolet detection 2.1.1. Methods without derivatization Most amino acids do not have sufficient UV signals for their detection in biological samples, and there are few studies on UV detection without derivatization. UV signals from aromatic amino acids were used. Forteschi et al. developed a CE-UV method for the simultaneous determination of aromatic amino acids (Phe, Tyr, and tryptophan (Trp)) in human blood samples with minimal sample quantification and no derivatization reaction [11]. A validated, dual-mode, gradient HPLC utilizing a lowcapacity, sulfo-functionalized poly(ethylstyrene-divinylbenzene) resin column for the simultaneous analysis of imidazole amino acids, aromatic amino acids, and creatinine in urine samples was developed [16]. Some researchers developed new concentration technologies for CE for the sensitive detection of amino acids in biological samples. Carrier-mediated single drop microextraction was applied and obtained 120-fold improvements in the detection sensitivity [5]. A dynamic pH junction-sweeping technique has been developed for the on-line concentration of Glu and Asp. The analysis of Glu and Asp in biological samples was achieved with 30- and 50-fold sensitivity enhancements, respectively [6]. 2.1.2. Methods with derivatization To increase the sensitivity of amino acids detection, the amino acids are usually derivatized before analysis. Many derivatization reagents, such as 2,4dinitrofluorobenzene (DNFB) [17], 9-fluorenylmethyl chloroformate (FMOC-Cl)

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[7,8], benzoyl chloride [9], and 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (AQC) [10, 13, 19], have been employed for the analysis of amino acids. FMOC-Cl reacts with both primary and secondary amino groups to form the corresponding carbamates. FMOC-amino acids were separated on a core-shell column and a silica-based monolithic column. The monolithic column provided better chromatographic performance, a shorter analysis time and milder chromatographic conditions and was applied for the analysis of citrulline and metabolically related amino acids in adult human plasma samples [7]. However, the derivatization reagent was directly mixed with the sample solution, and the derivatization efficiency might be easily influenced by the sample matrix. To overcome this problem, an extrapolative internal standard method was applied to quantitate citrulline and other metabolically related amino acids [8]. AQC rapidly reacts with primary and secondary amino acids, and the derivatives are stable at room temperature for several days. Peake et al. described a simple modified MassTrak kit that utilized the precolumn derivatization of amino acids with AQC [19]. A minimal sample volume and automated online precolumn derivatization of the amino acids with AQC were investigated. The analysis of the amino acids in Amur sturgeon was completed within 10 min by ultra-high-performance liquid chromatography (UHPLC) with good reproducibility [13]. Castellanos et al. used a new ethyl-bridged packing material for the reversed-phase separation of AQCderivatized amino acids. Compared with conventional C18 columns, the separation efficiency was improved, and the lifetime of the columns was increased (Fig. 1) [10]. 2.2. Fluorescence detection Fluorescence detection (FLD), with high sensitivity, has been proven to be one of the best methods for the analysis of trace biological compounds in complex biological samples. Amino acids, except for Trp, Tyr, and Phe, do not fluoresce, thereby necessitating derivatization before analysis. As shown in Table 2, many derivatization reagents, such as AQC [21-24], o-phthaldialdehyde (OPA) [25-28], 4-fluoro-7-nitro2,1,3-benzoxadiazole (NBD-F) [29-34], and 1,3,5,7-tetramethyl-8-(N6

hydroxysuccinimidyl butyric ester) difluoroboradiaza-s- indacene (TMBB-Su) [38], have been applied for FLD of amino acids. The chemical structures of the derivatization reagents for FLD are shown in Fig. 2. AQC-derivatized amino acids can be analyzed not only by UV detection but also by FLD. Wang et al. analyzed amino acids in plasma samples of healthy humans and patients with falciparum malaria by applying precolumn derivatization with AQC [24]. In another study, 26 amino acids were analyzed in human plasma by HPLC utilizing AQC as the derivatization reagent, and the developed method was then applied to metabolic research [21]. OPA reacts specifically with primary amines in the presence of thiols to produce the FL derivatives. Borowczyk et al. applied on-column derivatization with OPA to methionine (Met) and homocysteine (Hcy) (Fig. 3) in human and mouse plasma and urine samples [26]. The limit of detection (LOD) for the OPA-derivatized amino acids by the FLD was below 1 μM, which was much lower than that for UV detection [18]. NBD-F is a derivatization reagent along with benzofurazans and has several advantages, such as high reactivity with primary and secondary amines, fast reaction under mild conditions, and stable products. Recent studies have focused on the fast analysis of NBD-amino acids in biological samples. Lorenzo et al. applied a CE-LIF method for amino acid analysis in biological samples, and the derivatization and determination could be achieved in 30 min [29]. Several new types of reversed-phase columns, such as monolithic silica columns and core-shell columns [31], were applied for the fast determination of amino acids in biological samples. The analysis times were shortened to 7 min, and the representative chromatograms of 21 NBD-amino acids in the standards and in a mouse plasma sample are shown in Fig. 4. In addition, 4-(N,N-dimethylaminosulfonyl)-7-fluoro-2,1,3-benzoxadiazole (DBD-F) was also used for the determination of sulfur-containing amino acids (including Hcy, Met, and cysteine (Cys) in human plasma samples [35].

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Higher sensitivity was achieved with the fluorescent derivatization of amino acids, but problems still existed during the analysis. The derivatization reaction generates derivatized reagent peaks, such as the peak of NBD-OH, and these peaks can interfere with the analysis of the derivatized amino acids [31]. Because the peak of NBD-OH is large and near the NBD-amino acids, this analysis requires high separation efficiency. The development of a new derivatization reagent with by-products that have no FL is desirable. 2.3. Mass spectrometry MS is the most used detection technique for amino acid analyses because of its high sensitivity, high selectivity, and good ability to combine with separation techniques, such as LC, CE, and gas chromatography (GC), and because no analyte baseline separation is required. When utilizing MS for the detection of amino acids, derivatization is not necessary, and the sample preparation can be simplified. In addition, hydrophilic interaction liquid chromatography (HILIC) is useful for nonderivatized amino acid separation because of its retention improvement of polar compounds and because there is no need for ion-pairing reagents [44, 51, 54, 56]. Applying centrifugal micro-solid-phase extraction as the sample preparation, Delgado-Povedano et al. quantitated amino acids in human sweat samples in 20 min by LC-MS/MS [45]. CE-MS was also used for amino acid determination in biological samples without derivatization. For a sensitive analysis of underivatized amino acids, a high-durability, sheathless electrospray ionization (ESI) CE-MS interface with electrokinetic injection and multiple reaction monitoring mode with a triplequadrupole analyzer were utilized [49]. Micellar electrokinetic chromatography (MEKC) is a mode of CE in which electrokinetic migration and partitioning of the analytes between the micellar phases and bulk solution are combined to induce separation. However, the combination of MEKC with ESI-MS is problematic due to the presence of nonvolatile surfactants, which are used to increase the selectivity and chromatographic separation in CE. To improve the compatibility of MEKC and MS,

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ammonium perfluorooctanoate, a volatile surfactant, was employed in the MEKCESI-MS method for the analysis of amino acids in human urine samples [55]. To improve the sensitivity and specificity, derivatization was also performed when LC-MS was applied for the analysis of amino acids in biofluids. Some derivatization reagents, such as aTRAQ reagent [42, 47, 64], dansyl chloride [46, 59], and AQC [58, 71], were utilized (Fig. 5). aTRAQ is an isobaric tagging reagent that has been widely applied to LC-MS/MS to determine amino acids. Held et al. applied the aTRAQ reagent for the determination of amino acids in urine samples, and the technique showed attractive features such as decreased run time and increased specificity [47]. GC-MS has been a great tool for metabolite analysis. When it is used for quantitative measurements of amino acids, a derivatization reagent is necessary. Bis(trimethylsilyl)trifluoroacetamide (BSTFA) has been commonly used to derivatize amino acids [39, 43, 72]. de Paiva et al. developed a microwave-assisted derivatization method using BSTFA as the derivatizing reagent for the analysis of amino acids in human cerebrospinal fluid by GC-MS [43]. Rashaid et al. analyzed the amino acid profiles of scalp hair from healthy and type 2 diabetes mellitus (DM) patients by GC-MS using BSTFA as the derivatization agent [72]. 2.4. Electrochemical detection Electrochemical detection is a powerful analytical method that can detect electric currents generated from oxidative or reductive reactions in test compounds. Derivatization is sometimes necessary to increase the sensitivity. Kleparnik et al. developed an analytical method for the simultaneous determination of OPAderivatized asymmetric dimethylarginine, symmetric dimethylarginine, and arginine (Arg) in human plasma samples using HPLC with electrochemical detection. OPAderivatized amino acids can be analyzed by FLD and electrochemical detection. In this proposed analysis, a more sensitive coulometric variant of an electrochemical detector was applied [73].

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Electrochemical detection based on capacitively coupled contactless conductivity detection (C4D) is universal. It does not require sample derivatization and achieves LODs at micromolar or even sub-micromolar levels. Tuma et al. used a new variant of a large-volume sample stacking injection in CE with C4D for the analysis of neurotransmitters, GABA, glycine (Gly) and Glu in the microdialysates of periaqueductal gray matter (PAG), and the electropherogram is presented in Fig. 6 [75]. These authors also applied pressure-assisted CE with C4D for the rapid analysis of BCAAs in human blood plasma samples. The determination was performed within 140 s [74]. 2.5. Miscellaneous Other detection methods, such as a refractive index (RI) detecion [76] and flame ionization detection (FID) [77], were applied for the determination of amino acids in biological samples. The quantitative determination of derivatives of 14 of the 20 classical amino acids with N2O3 was also achieved by isocratic HPLC separation and RI detection. By investigating the nitrosation reaction conditions, 18 of the classical amino acids formed detectable nitrosation products, compared to 13 in a previous study [76]. GC-FID was applied for the analysis of amino acids in skin samples from psoriatic and arsenicosis patients using trifluoroacetylacetone and ethyl chloroformate as the derivatization reagents [77]. 2.6. On-chip analysis Recently, microfluidic devices have been attracting attention and have provided a powerful platform due to their high integration, automatic operation, and lower sample and reagent consumption. Microchip electrophoresis is a miniaturized format for CE on a chip and has a higher separation efficiency and sample throughput. Liang et al. designed a novel, economical, simple and environmentally friendly method based on an in situ chemically induced synthesis strategy and modified a poly(dimethylsiloxane) (PDMS) microchip channel with poly-dopamine/gold nanoparticles to create a hydrophilic and biofouling-resistant surface. The analysis of five amino acids, including Arg, proline (Pro), histidine (His), Val, and serine (Ser), 10

was achieved in 60 s [78]. Because of the simplicity of the interface, ESI was combined with microchip electrophoresis. Li et al. developed a fast microchip electrophoresis-nano-ESI-MS method for the analysis of Asp and Glu released from cells under chemical stimulations, and they found that ethanol stimulated the release [52]. A compact, microchip-based electrochromatography-chemiluminescence (CL) system was developed. The analysis of four amino acids (Gly, Glu, Arg, and Asp) was completed in 1 min, and a higher sensitivity (all LODs were below 0.50 μM) was achieved without any derivatization [79]. The separation modes of the studies mentioned above were electrically driven, not pressure driven, which is a more widely applied mode in chromatography. Tsunoda et al. fabricated a long pillar array column for pressure-driven LC by utilizing a low dispersion turn to achieve better separation on a chip, and the analysis time of the fluorescently labeled BCAAs was greatly shortened [80, 81]. With the help of an internal standard, BCAAs were quantified in sports drinks and human plasma samples utilizing the pillar array column. The chromatograms are shown in Fig. 7. The LODs and limits of quantification (LOQs) were much lower than those of conventional LC in terms of the injection amounts [32]. To separate compounds that exhibit great differences in polarity in biological samples, a gradient elution system for pressuredriven LC on the microchip was developed [82, 83] and later applied for the fast and simultaneous analysis of Phe and Tyr in biological samples; quantitative analysis was completed in 140 s [84]. 3. Amino acids and their relationship to disease Amino acids participate in essential biological activities, including protein synthesis and the regulation of metabolic pathways. The changes in amino acid concentrations in biological fluids have been found to be related to and provide information on many types of diseases and can be used to identify diseases by using the amino acids as biomarkers. In our review, the relationships between amino acids and diseases are summarized in Tables 6-10. 11

Analytical techniques can provide accurate and reliable quantification of amino acid concentrations in biological fluids; however, patient characteristics, sample handling, and sample storage conditions have great effects on the results of amino acid analysis. Takehana et al. found that hemolysis, temperature after blood collection, and long-term storage at -20 °C altered the levels of 11 amino acids in blood samples; in addition, platelet contamination, repeated freeze-thaw cycles, and time from blood collection to cooling affected the concentrations of 4 amino acids [86]. Cystine and Cys showed extreme instability when stored at room temperature for 24 h, suggesting that blood samples should be collected rapidly and stored at low temperature. In addition, clinical samples are selected considering the disease state, body mass index (BMI), age, and other biological factors [87]. 3.1. Diabetes mellitus DM is a chronic metabolic illness diagnosed by higher fasting blood glucose levels over a prolonged period. DM is divided into two types: type 1 and type 2. Type 1 DM results from an insulin deficiency because pancreatic β-cells fail to produce enough insulin. Type 2 DM is characterized by insulin resistance, wherein the target cells fail to properly respond to insulin. Several studies have focused on the relationship between plasma amino acid concentrations and DM [27, 29, 88-92]. Insulin inhibits the breakdown of protein and helps cells to absorb circulating amino acids from the blood. Thus, a lack of insulin or a resistance to insulin inhibits the absorption of amino acids. Meanwhile, some amino acids, including alanine (Ala), Glu, Leu, Ile, and Arg, can stimulate β-cell insulin secretion [74]. The plasma amino acid concentrations of 95 subjects (25 unrelated healthy volunteers and 70 early diagnosed type 2 DM patients) were measured, and five amino acids (Lys, Asp, threonine (Thr), Met, and Ala) were found to be potential biomarkers of type 2 DM [27]. In 126 type 2 DM and 100 non-diabetic participants, 42 amino acids in plasma were analyzed by LC-MS/MS. In the plasma of diabetic patients, sixteen amino acid concentrations increased, and 11 amino acids decreased [89]. 12

3.2. Kidney disease The kidneys are two bean-shaped organs that play an important role in the clearance of nitrogenous substances and in the regulation of amino acid levels in the blood [93]. Kidney disease, also known as renal disease or nephropathy, and the amino acids levels in blood are related in several ways [93-95]. Li et al. found that chronic kidney disease patients showed lower serum levels of aromatic amino acids (Tyr and Trp) compared with healthy control subjects using an HPLC-FLD method [94]. The quantification of 26 amino acids in serum from 189 renal cell carcinoma patients and 104 age- and sex-matched controls showed that the levels of 15 amino acids significantly changed in the patient serum samples: 13 decreased, and 2 increased. Their findings suggested that serum amino acid levels may be applied for the diagnosis of renal cell carcinoma [93].

3.3. Liver disease The liver is an important organ with a wide range of functions, including synthesis of proteins, detoxification of metabolites, and regulation of glycogen storage and hormone production. Relationships between liver disease and the amino acid levels in liver [96] or plasma [97] samples have been discussed. After living donor liver transplantation in patients, the Val, Leu, Ile, and Gln levels in plasma samples significantly decreased [97]. Lake et al. studied the profile changes of hepatic BCAA levels during the progression of human nonalcoholic fatty liver disease (normal, steatosis, nonalcoholic steatohepatitis (NASH) fatty livers and NASH non-fatty livers). During the progression from steatosis to NASH, the levels of Leu, Ile, and Val increased, reflecting the adaptive physiological responses to diseaseinduced hepatic stress in NASH patients [96]. 3.4. Cancer Amino acids have been found to have important roles in carcinogenesis and are promising cancer biomarkers [88, 99-102]. Significant changes in the amino acid 13

levels in blood and urine samples were reported for lung cancer (LC) [103, 104], gastric cancer (GC) [103], colorectal cancer (CRC) [103, 105], breast cancer (BC) [103, 106, 107], prostate cancer (PC) [103], and chronic myelogenous leukemia (CML) [108]. Gln is an important nitrogen and carbon source in cancer cells [109]; recently, Ser, Gly [99], Trp [101], and BCAAs [108, 110] were found to be important in cancer progression. Leichtle et al. utilized ESI-MS/MS to study the amino acid screening profile alterations in CRC. Decreased levels of Gly and Tyr were recognized as significant features of CRC. These alterations may serve as a source of new CRC biomarkers and broaden the pathophysiological understanding of CRC [105]. Miyagi et al. developed an HPLC-ESI-MS method for the analysis of amino acid levels in plasma samples from 200 patients with one of the five types of cancer (LC, GC, CRC, BC, and PC). Significant alterations in the amino acid profiles between the controls and the cancer patients were revealed by univariate analysis. These results indicate that plasma-free amino acid profiling has great potential for cancer screening and diagnosis and understanding disease mechanisms [103]. 3.5. Other diseases Changes in the amino acid levels in biological samples were also reported for several other diseases, such as rheumatoid arthritis (RA) [111], maple syrup urine disease (MSUD) [112], chronic obstructive pulmonary disease (COPD) [113], ischemic stroke (IS) [114], Alzheimer’s disease [115], premature birth and critical illness in the neonatal range [116], sepsis [117], and major depressive disorder [118]. RA is a chronic autoimmune disease characterized by joint damage and persistent inflammation. Smolenska et al. compared the plasma amino acid and nicotinamide metabolite levels between RA patients and a control group. Changes in the amino acid patterns may be used for the diagnosis and monitoring of the disease progress and therapy in RA [111].

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MSUD is an autosomal recessive metabolic disorder caused by a deficiency in the activity of the mitochondrial enzyme branched-chain α-keto acid dehydrogenase (BCKAD) complex. The metabolic block from BCKAD deficiency leads to the accumulation of BCAAs and keto acids. Chiong et al. studied the plasma amino acid profiles of 26 patients with MSUD and compared them with the profiles of 26 sexand age-matched controls. Low levels of Gln and Ala and high levels of Leu, Ile, Phe, and Thr were observed in the MSUD patients compared to the controls [112]. COPD is a progressive disease characterized by systemic, low-grade inflammation. It can increase the production of nitric oxide (NO), of which Arg is the sole precursor. Jonker et al. studied the plasma amino acid levels in a group of patients with COPD. The experimental results indicated the potential of using these levels for a mechanistic study of COPD [113]. IS, one of the leading causes of disability and death, is characterized by the sudden loss of blood circulation to an area of the brain, resulting in a corresponding loss of neurologic function. Amino acids are involved in the pathogenesis of acute IS. Szpetnar et al. studied the fluctuations in the free amino acid levels in serum samples during acute IS. The experiment consisted of 18 patients with acute IS and 12 sexand age-matched individuals as the control group. The results showed that a decrease in Pro and a simultaneous increase in Glu serum levels could be potential markers of acute IS [114].

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4. Conclusion and prospect In this review article, analytical methods for the determination of amino acids in biological samples were summarized. Most methods used LC and CE as separation techniques, and UV, FL, MS, and electrochemical techniques were applied as detection methods. FLD and MS have been commonly used after the derivatization of amino acids. The development of a facile, rapid and inexpensive derivatization method that can yield the derivative with high sensitivity and stability will be a research topic of considerable interest in the future study of amino acids analysis. Amino acids have been analyzed on microchips, but only several amino acids have been analyzed to date, which limits the study of amino acids in clinical research and diagnosis. With the development of microchip technologies, a more comprehensive amino acid analysis will be achieved in a short analysis time. Alterations in amino acid concentrations in human blood plasma samples have been found to be closely related with certain diseases, including DM, kidney disease, liver disease, cancer, and other diseases. Therefore, analytical methods for amino acid detection are potential tools for further disease diagnosis and mechanistic studies. Acknowledgement This work was partly supported by the Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research (C) (17K08234) and Kobayashi International Scholarship Foundation to M.T. and the National Natural Science Foundation of China [21505029], the Natural Science Foundation of Hainan Province [20158363], and the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry to Y.S.

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27

Table 1 Recent analyses of amino acids in biological samples with UV detection

Biologic al sample

Derivatiza tion reagent

Separati on method

Detecti on

Numb er of amino acids analyz ed

Human urine

No derivatizat ion

CE

UV 200 nm

4

Human serum

No derivatizat ion

CE

UV 214 nm

Human plasma

No derivatizat ion

CE

UV 200 nm

3

50 μL

16 min

4 μM for Phe; 1.5 μM for Tyr and Trp

Human serum

No derivatizat ion

HPLC

UV 210 nm

2

30 μL

12 min

0.04 μM

[12]

Human urine

No derivatizat ion

HPLC

UV 210 nm

7

100 μL

30 min

0.01-0.05 μM

[16]

Rat serum

DNFB

HPLC

UV 360 nm

23

200 μL

10 min

1 μM

[17]

Human plasma

FMOC-Cl

HPLC

UV 260 nm

6

20 μL

25 min

0.6-5.4 μM

[8]

0.25-1.81 μM

[7]

2

Amount of sample analyzed

Analysis time

LOD

Refere nce

2×104 μL

14 min

0.07-0.5 μM

[5]

12 min

0.41 μM for Glu; 0.24 μM for Asp

[6]

[11]

100 μL

Human plasma

FMOC-Cl

HPLC

UV 260 nm

6

50 μL

Core-shell column: 28 min; Silica monolithic column: 25 min

Rat brain tissue

Benzoyl chloride

HPLC

UV 210400 nm

1

500 μL

7 min

5 ×10-4 μM for GABA

[9]

13 min

3.19-63.6 μM

[14]

10 min

1.0-7.6 μM

[18]

Plasma and liver

PITC

HPLC

UV 200400 nm

12

100 μL (plasma) and 20 mL (liver homogen ate)

Human serum

OPA

UHPLC

UV 338 nm

20

50 μL

28

Rat hippoca mpi

NBD-F

HPLC

UV 472 nm

5

100 μL

15 min

0.02 μM for Asp, Glu; 0.05 μM for Gly; 0.15 μM for Tau, GABA

Plasma

AQC

UHPLC

UV 260 nm

4

200 μL

31 min

2 μM

[19]

Amur sturgeon

AQC

UHPLC

UV 260 nm

17

10 μL

10 min

0.94-4.04 μM

[13]

Human plasma

AQC

HPLC

UV 254 nm

18

200–250 μL

60 min

8-14 μM

[10]

DNFB, 2,4-dinitrofluorobenzene; FMOC-Cl, 9-fluorenylmethyl chloroformate; AQC, 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate; PITC, phenyl isothiocyanate; OPA, o-phthaldialdehyde; NBD-F, 4-fluoro-7-nitro-2,1,3-benzoxadiazole; Glu, glutamic acid; Asp, aspartic acid; Phe, phenylalanine; Trp, tryptophan; Tyr, tyrosine; GABA, γ-aminobutyric acid; Gly, glycine, Tau, taurine.

29

[15]

Table 2 Recent analyses of amino acids in biological samples by FLD

Biological sample

Derivatiza tion reagent

Separati on method

Detection

Number of amino acids analyzed

Amoun t of sample analyze d

Analy sis time

LOD

Refere nce

33

50 μL

185 min

0.178.06 μM

[24]

UV 250 nm; Human plasma

AQC

HPLC

Human plasma

AQC

HPLC

FL (Ex. 250 nm; Em. 395 nm)

26

100 μL

35 min

≥2×10-6 μM

[21]

Seeded cells

AQC

HPLC

FL (Ex. 250 nm; Em. 395 nm)

21

5×104 cells

45 min

-

[22]

Ovine blood Plasma

AQC

HPLC

FL (Ex. 250 nm; Em. 395 nm)

7

100 μL

70 min

0.02750.774 μM

[23]

Tear sample

Fluoresca mine

CE

LEDinduced fluorescence

3

2 μL

6 min

-

[20]

Rats cerebrospin al fluid

OPA

HPLC

FL (Ex. 357 nm; Em. 455 nm)

5

15 μL

46 min

-

[28]

HPLC

FL: Met (Ex. 348 nm; Em. 438 nm); Hcy (Ex. 370 nm; Em. 480 nm)

2

FL (Ex. 250 nm; Em. 395 nm)

1 μM for Met;

Human and mouse plasma and urine

OPA

Canine plasma

OPA

LC

FL (Ex. 350 nm; Em. 450 nm)

20

5 μL

14 min

-

[25]

Human plasma

OPA

HPLC

FL (Ex. 345 nm; Em. 455 nm)

21

100 μL

35 min

0.010.07 μM

[27]

Human urine

NBD-F

CE

FL (Ex. 488 nm; Em. 522 nm)

5

20 μL

20 min

0.0340.163 μM

[29]

Mouse plasma

NBD-F

HPLC

FL (Ex. 470 nm; Em. 530 nm)

21

40 μL

7 min

1.22.33×1 0-3 μM

[31]

30

10 μL

14 min

[26] 0.01 μM for Hcy

Mouse plasma and adrenal gland

NBD-F

HPLC

FL (Ex. 470 nm; Em. 530 nm)

Mouse plasma

NBD-F

HPLC

FL (Ex. 470 nm; Em. 530 nm)

21

40 μL (plasma) and 10 μL (tissue homogenat e)

10 min

0.325.72×1 0-3 μM

[30]

21

20 μL

11 min

0.1524.7×103 μM

[33]

[35]

Maternal plasma

DBD-F

HPLC

FL (Ex. 400 nm; Em. 570 nm)

3

20 μL

12 min

0.13 μM for Hcy, 0.02 μM for Met and 0.11 μM for Cys

Hippocamp al CA1 region of rats

Dansyl chloride

HPLC

FL (Ex. 340 nm; Em. 525 nm)

8

15 μL

22 min

0.21.0×103 μM

[36]

Rat and mouse spinal cords

CBQCA

HPLC

FL (Ex. 465 nm; Em. 550 nm)

4

10 μL

20 min

0.030.06 μM

[37]

Cerebral cortex of mice

TMBB-Su

HPLC

FL (Ex. 494 nm; Em. 504 nm)

7

500 μL

50 min

2.112×10-3 μM

[38]

AQC, 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate; FL, fluorescence; OPA, ophthaldialdehyde; NBD-F, 4-fluoro-7-nitro-2,1,3-benzoxadiazole; TMBB-Su, 1,3,5,7tetramethyl-8-(N-hydroxysuccinimidyl butyric ester) difluoroboradiaza-s- indacene; CBQCA, 3-(4-carboxybenzoyl)-2-quinolinecarboxaldehyde; DBD-F, 4-(N,Ndimethylaminosulfonyl)-7-fluoro-2,1,3-benzoxadiazole; Met, methionine; Hcy, homocysteine; Cys, cysteine.

31

Table 3 Recent analyses of amino acids in biological samples by MS Number of

Derivatization Separatio amino Biological sample Detection reagent n method acids analyzed

Amount of Analy sample sis analyzed time

LOD

Referen ce

Brain microdialysis and cerebrospinal fluid

No derivatization

HPLC

MS/MS

1

10 μL

5 min

Gly 0.05 μM

[66]

Rat hippocampus

No derivatization

HPLC

MS/MS

2

-

4 min

-

[70]

12 min

LOD in Human urine 0.0155.5 μM; LOD in Human plasma 0.00250.606 μM; LOD in mice PFC 5.3390×10-4 μM

[53]

Human urine, plasma and mice prefrontal cortex extracts

No derivatization

HPLC

MS/MS

7

100 μL (urine), 150 μL (plasma), and 75 μL (mice prefrontal cortex extracts)

Human sweat

No derivatization

HPLC

MS/MS

23

50 μL

20 min

3.3120×10-4 μM

[45]

Rats urine

No derivatization

HPLC

MS/MS

32

100 μL

28 min

1.4 μM, except Cys 16.3 μM

[62]

Dried blood spot

No derivatization

HPLC

MS

7

50 μL

3.1 min

-

[54]

Rat plasma

No derivatization

2.5 μM except Arg 13.5 μM

[63]

Human blood

Human urine

MS/MS

30

50 μL

28 min

No derivatization

UHPLC MS/MS

20

50 μL

13.5 min

-

[69]

No derivatization

UHPLC

18

100 μL

18 min

72940×10 -4 μM

[51]

HPLC

MS

32

Rat plasma; Cell extracts; Tissue biopsies extracts

No derivatization

Human plasma

No derivatization

Piglets plasma, liver, and muscle

No derivatization

HPLC

Latent fingerprint residue

No derivatization

Human serum

No derivatization

Dried blood spots

No derivatization

CE

Human urine

No derivatization

Cerebrospina l fluid

UHPLC

10 μL (plasma), 2× 106 cells (cell extracts), 3 min and 5 mg (tissue biopsies extracts)