Development of a HPLC-MS/MS Method for Assessment of Thiol Redox Status in Human Tear Fluids

Abstract

Oxidative stress is reported to be part of the pathology of many ocular diseases. For the diagnosis of ocular diseases, tear fluid has unique advantages. Although numerous analytical methods exist for the measurement of different types of biomolecules in tear fluid, few have been reported for comprehensive understanding of oxidative stress-related thiol redox signaling. In this study, a high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method was developed to determine a panel of twelve metabolites that systematically covered several thiol metabolic pathways. With optimization of MS/MS parameters and HPLC mobile phases, this method was sensitive (LOQ as low as 0.01 ng/mL), accurate (80–125% spike recovery) and precise (<10% RSD). This LC-MS/MS method combined with a simple tear fluid collection with Schirmer test strip followed by ultrafiltration allowed the high-throughput analysis for efficient determination of metabolites associated with thiol redox signaling in human tear fluids. The method was then applied to a small cohort of tear fluids obtained from healthy individuals. The method presented here provides a new technique to facilitate future work aiming to determine the complex thiol redox signaling in tear fluids for accurate assessment and diagnosis of ocular diseases.

Graphical Abstract

1. Introduction

Oxidative stress results from an overabundance of reactive species or decreased antioxidant defenses in biological systems. If left unchecked, reactive oxygen and nitrogen species (ROS/RNS) can damage cellular components, including proteins, lipids, and nucleic acids, leading to dysregulation and dysfunction of the affected systems. In humans, a variety of diseases are associated with oxidative stress [1, 2]. Thiols, or compounds carrying a sulfhydryl (–SH) group, aid in protection from oxidative damage. Of the thiols, glutathione (GSH) is the most abundant and significant endogenous antioxidant, with the GSH-dependent antioxidant system being essential for regulating cellular redox balance. Thus, levels of GSH, along with the ratio of reduced GSH to its corresponding disulfide, glutathione disulfide (GSSG), have been used for decades as sensitive indicators of oxidative stress [3–5]. In addition to GSH and GSSG, changes in levels of other thiols and their oxidized products are closely associated with disruption to antioxidant pathways. For example, cysteine, along with its corresponding disulfide, cystine, was reported to be a novel biomarker for increased risk of death in patients with coronary artery disease [2], a condition in which oxidative stress is strongly implicated. Similarly, homocysteine has been implicated in the pathogenesis of various diseases, particularly those affecting the cardiovascular system [6–8].

Several studies over the past few decades have established a strong link between oxidative stress and ocular diseases such as age-related macular degeneration, dry eye disease, glaucoma, diabetic retinopathy, and retinal vein occlusion [9–15]. Therefore, examining oxidative stress biomarkers in ocular tissues could drastically improve the prevention, diagnosis, and treatment of these conditions. For this purpose, tear fluid represents an ideal sample matrix for biomarker analysis. Composed of proteins [16–18], lipids [19], glycans [20, 21], water, and salts, tear fluid lubricates and protects the ocular surface. As a result, tear fluid can be sampled painlessly and noninvasively, unlike other ocular tissues. Further, tear fluid is less complex than serum or plasma [18] and thus presents a lower risk of matrix interference for analysis, which allows for more streamlined sample preparation. Thus, the examination of tear fluid provides a convenient, accessible method for investigating biomarkers of ocular diseases. Consequently, interest in tear fluid biomarkers has increased considerably over the last decade [16, 17, 22–30]. However, only a few studies have specifically examined levels of biological thiols in tear fluids and their connection to ocular diseases. Saijyothi et al [27] compared the levels of GSH and cysteine in tear fluids of healthy and keratoconus group. Compared to a single compound or two, integrated biomarker panels that monitor multiple disease-associated pathways may provide a more comprehensive understanding of ocular pathology and are in high demand.

Therefore, in this study, we propose a new high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS)-based technique for simultaneous determination of eleven sulfur-containing compounds in tear fluids that reflect several metabolic pathways closely associated with oxidative stress and thiol recycling (Figure 1). In this panel, N-Acetylcysteine is not a metabolite but a xenobiotic supplement of cysteine. Creatinine was also included in this method for two reasons: First, alteration in creatinine levels was previously observed in patients with ocular diseases [31]. Secondly, the sample creatinine level was used as the hydration normalization factor, a technique widely employed in blood and urine sample analysis [32]. This integrated biomarker panels may provide new opportunities and reliable ways to predict and diagnose ocular diseases.

Figure 1.

List of the analytes and their metabolic pathways.

2. Material and Methods

2.1. Materials

Homocysteine was purchased from Advanced ChemBlocks Inc (Burlingame, CA, USA); homocystine was purchased from ChemScene (Monmouth Junction, NJ, USA); γ-glutamyl-cysteine was purchased from United States Biological (Salem, MA, USA); methionine was purchased from Bioworld (Dublin, OH, USA); cystine and cysteine were purchased from Ambeed (Arlington Heights, IL, USA); GSH, GSSG, N-acetylcysteine, cysteinyl-glycine, methionine sulfoxide, and creatinine were purchased from Sigma Aldrich (St. Louis, MO, USA). Molecular weight cut-off (MWCO) membrane centrifugal filter units (3 kD) were purchased from VWR (Radnor, PA, USA). Schirmer test strips were purchased from Sport World Vision (Ballinteer, Dublin, Ireland). Ultrapure water (18.2 MΩ ∙ cm) was generated in-house by a Millipore Elix-3 purification system (Millipore, Billerica, MA, USA).

2.2. Standard preparation

Analytical standard stock solutions were prepared at 10 mg/mL in ultrapure water except for homocysteine and homocystine, which were dissolved in 0.1 M HCl, and cystine, which was dissolved in 1 M HCl. These stock standard solutions were stored at −80 °C. Secondary standard solutions of 100 μg/mL were prepared by diluting stock solutions with ultrapure water and maintained at −20 °C. Secondary standard solutions were serially diluted in mobile phase A (0.01% formic acid in water, v/v) and mixed to prepare calibration standards.

2.3. Tear sample collection and preparation

The study was approved by the Institutional Review Board of Missouri University of Science and Technology. Four volunteers (two males, one < 60 years old and one > 60 years old; and two females, one < 60 years old and one > 60 years old) without known ocular diseases were enrolled. Tear samples were collected with Schirmer test strips using the following procedure: Briefly, one 35 × 5 mm Schirmer test strip was placed in the cul-de-sac of each eye, and then each volunteer was directed to close their eyes. After 5 min, the strips were removed with the length of wet area recorded, placed in MWCO filter units, and extracted with 500 μL mobile phase A at 4 °C for 15 min, followed by centrifugation at 13000 × g for 30 min at 4 °C. Ultrafiltration was used to remove large molecule matrices from the tear samples, which reduced matrix effects, ensured accurate detection of the analytes, and prevented possible harm to the HPLC column. The strips were extracted again with the same procedure. The filtrates from the two extractions were combined for HPLC-MS/MS analysis.

Spike recovery was examined by spiking standard mixtures into tear samples. Briefly, 5 μL of tear fluid was loaded to the strips before 10 μL of standard mixture or pure water (blank) was added to the strips. The strips were then extracted twice as described above for tear sample extraction, and the combined extract was analyzed by LC-MS/MS.

2.4. Stability of reduced thiols

Standard mixtures of methionine and reduced thiols, including GSH, cysteine, homocysteine, γ-glutamyl-cysteine, and cysteinyl-glycine, were prepared at both 100 ng/mL and 10 ng/mL in mobile phase A (0.01% formic acid in water, v/v). The standard mixtures and one tear sample were placed in the autosampler and injected eight times in a 6-hour period, respectively, and the relative intensities were compared for the assessment of short-time stability of each analyte.

2.5. Calculation of tear fluid volume collected by Schirmer test strips

In order to establish a method for determining the volume of tear fluid absorbed onto each strip, we examined the relationship between the wetted length (length of moistened area on the Schirmer strip) and the volume of liquid adsorbed. Since tear fluid is mostly composed of water (approximately 98%), water was used to model the adsorption of tear fluid onto the strip. Aliquots of 5, 10, 13, 15 or 20 μL of water were deposited at the starting points of the strips. The strips were placed upright for 5 min, after which time the wetted length of each strip was recorded. The wetted length (n = 3) was plotted versus the volume of water added, and the equation of the best-fit line was used to interpolate the volumes of tear fluids collected from the volunteers.

2.6. HPLC-MS/MS method

A Shimadzu (Columbia, MD, USA) Prominence UFLC system coupled to a 4000Q TRAP tandem mass spectrometer system (AB SCIEX, Concord, ON, CA) was used as previously described [33]. A HydroRP column (4 μm, 250 × 2 mm) purchased from Phenomenex (Torrance, CA, USA) was employed for separation. HPLC was performed with binary flow at a rate of 0.3 mL/min with mobile phase A consisting of 0.01% formic acid in ultrapure water and mobile phase B consisting of 0.01% formic acid in ACN. The column oven temperature was set at 40 °C. After injection of 20 μL of sample, the method began with 100% A for 1 min, followed by a linear gradient of 0–30% B from 1 to 4 min and a linear gradient of 30–60% B from 4 to 5 min. After holding at 60% B from 5 to 6 min, the mobile phase was change back to 100% A from 6 to 6.5 min, followed by a 6 min re-equilibration with 100% A before next injection.

Positive electrospray ionization mode and scheduled multiple reaction monitoring (MRM) were used for quantification analysis. The mass transitions were monitored at the retention time of each analyte with a 1-min window (Table 1). The ion source temperature was set to 550 °C. The ion spray voltage was set to 4500 V. The curtain gas was set to 15 psi, and the ion source gases (GS1 and GS2) were at 35 and 45 psi, respectively. All mass spectrometer conditions were optimized for quantitative detection of the analytes.

Table 1.

Optimized MS/MS parameters of the twelve metabolites.

Analyte	Ion Pairs (m/z)	Rt* (min)	DP* (V)	CE* (V)	CXP* (V)
NAC (Q*)	164.1→121.9	5.70	46	13	10
NAC (C*)	164.1→58.7	5.70	46	43	10
GSH (Q)	308.0→75.9	3.80	46	37	12
GSH (C)	308.0→83.9	3.80	46	49	14
GSSG (Q)	613.2→355.2	5.60	91	33	10
GSSG (C)	613.2→231.0	5.60	91	47	14
Cys-Gly (Q)	179.2→75.8	2.10	31	23	12
Cys-Gly (C)	179.2→162.0	2.10	31	13	14
Glu-Cys (Q)	251.1→121.8	3.44	56	19	6
Glu-Cys (C)	251.1→83.8	3.44	56	37	8
Met (Q)	150.2→103.8	3.20	41	15	8
Met (C)	150.2→132.8	3.20	41	13	10
MeS (Q)	166.1→73.8	2.07	41	19	6
MeS (C)	166.1→56.1	2.07	41	35	4
Hcys (Q)	136.2→89.8	2.35	41	17	16
Hcys (C)	136.2→55.9	2.35	41	17	16
Hcyss (Q)	269.2→135.8	2.43	41	15	10
Hcyss (C)	269.2→87.9	2.43	41	49	16
Cys (Q)	121.9→58.8	2.05	51	33	10
Cys (C)	121.9→75.9	2.05	51	18	13
Cyss (Q)	241.2→151.8	1.86	46	19	14
Cyss (C)	241.2→73.6	1.86	46	43	12
Crt (Q)	114.0→43.9	1.96	56	29	6
Crt (C)	114.0→85.8	1.96	56	17	6

^*:Rt, retention time; DP, declustering potential; CE, collision energy; CXP, collision cell exit potential; Q, quantification ion pair; C, confirmation ion pair.

3. Results and Discussion

3.1. Optimization of MRM MS/MS parameters and HPLC mobile phase

Mass optimizations were performed by direct infusion of standards using a syringe pump operating at a flow rate of 0.6 mL/h. Infusion standards were prepared by dilution of the secondary standards with 0.01% formic acid. Precursor ions were scanned with a scan range from 100 m/z to 800 m/z. Each analyte, except GSSG, formed a singly charged molecular ion, with no significant adductive ions observed. GSSG forms both singly charged and doubly charged molecular ions. The singly charged molecular ion (m/z 613) was selected for GSSG to avoid ambiguous results because of the small mass difference between the doubly charged molecular ion of GSSG and the singly charged molecular ion of GSH (m/z 307 vs m/z 308). For each analyte, the two most abundant product ions were selected, and the collision cell parameters, e.g., declustering potential (DP), collision energy (CE), and collision cell exit potential (CXP), were optimized for each mass transition as shown in Table 1.

To lower the detection limits of low-abundance analytes, we examined the effects of mobile phase composition on the signal intensities of each analyte. Mobile phases (both A and B) containing 0.01%, 0.05%, and 0.1% formic acid were tested, and the relative signal intensities (normalized to results using 0.01% formic acid) are shown in Figure S1. For all analytes, using mobile phases containing 0.01% formic acid resulted in the best sensitivity. Thus, mobile phases containing 0.01% formic acid were selected and used for further experiments. A representative overlaid extracted ion chromatogram of a standard mixture from the final optimized method is shown in Figure 2A.

Figure 2.

Representative overlaid extracted ion chromatograms of: A, standards prepared at 20 ng/mL in mobile phase A, except for GSH and GSSG, which were prepared at 500 ng/mL; B, high abundance analytes in tear fluid sample; C, lower abundance analytes in tear fluid sample.

3.2. Stability of reduced thiols

Historically, the analysis of thiols was accompanied by pre-column derivatization using N-(1-pyrenyl)maleimide [34], N-ethylmaleimide [35] or other reagents. This reaction step, on one hand, allows the fluorescent detection of the thiols. On the other hand, it prevents the further oxidation of the reduced thiols, of which the concentrations decreased significantly in plasma/serum within just few minutes [35]. Unlike plasma/serum, tear fluid contains less matrix, leading to a lower possibility of thiol oxidation, thus providing an opportunity for direct detection of the thiols without derivatization. As shown in Figure 3, the relative intensities of reduced thiols and methionine (normalized to the intensities of first injection in each series, respectively) were in a range of 90–110% in both standard mixtures and real tear fluid within a 6-hour period, indicating the reduced thiols and methionine are stable under the current condition and the derivatization step is not necessary for tear fluid analysis.

Figure 3.

Assessment of stabilities of methionine and reduced thiols in standard solutions and human tear fluids. Homocystine (Hcyss) was below detection limit in tear fluid.

3.3. Method validation

The performance characteristics of the optimized method were determined, and the results are shown in Table 2. The calibration curves were linear within the concentration ranges for all analytes, with all of the regression coefficients (R²) greater than 0.99. The linearity was not examined beyond the given range. The limits of quantification (LOQ, S/N > 10) varied among the analytes, of which MeS and Cyss had the lowest LOQ at 0.01 ng/mL. The sensitivity for all analytes was comparable or superior to those reported in other analytical methods for the selected analytes in cultured cells and rat plasma [5, 36–38].

Table 2.

HPLC-MS/MS method performance parameters using standard mixtures prepared in mobile phase A.

Analyte	LOQ (ng/mL)	Linear range (ng/mL)	R2
GSH	0.5	0.5–1250	0.9948
GSSG	5	5–1250	0.9987
Cys-Gly	0.1	0.1–50	0.9990
Glu-Cys	0.2	0.2–50	0.9992
Met	0.5	0.5–50	0.9999
MeS	0.01	0.01–50	0.9970
Cys	0.2	0.2–50	0.9997
Cyss	0.01	0.01–50	0.9999
Crt	1	1–50	0.9933
Hcys	0.2	0.2–50	0.9995
Hcyss	0.02	0.02–50	0.9968
NAC	0.05	0.05–50	0.9997

Since the concentrations vary among analytes in tear fluids, the spike recovery was examined at two levels representative of the analytes’ native concentration range. For GSH and GSSG, spike recovery was examined at 50 ng/mL and 250 ng/mL. For all other analytes, spike recovery was examined at 2 ng/mL and 10 ng/mL. As shown in Table 3, the recovery of all analytes at both low and high concentrations was between 80% and 125%, except that of Hcyss, which is less relevant to this study since Hcyss was not at detectable level in any of the tear samples. Triplicate measurements of recovery values yielded RSD values <10%, indicating that the method performance was consistent and repeatable.

Table 3.

Spike recovery of human tear sample and relative standard deviation (RSD) (n =3)

Analyte	Recovery (%)	RSD (%)	Recovery (%)	RSD (%)
	Spike level of 50 ng/mL		Spike level of 250 ng/mL
GSH	117.7	4.8	118.5	5.7
GSSG	101.9	10.2	93.3	2.5
	Spike level of 2 ng/mL		Spike level of 10 ng/mL
Cys-Gly	86.8	9.6	108.9	4.3
Glu-Cys	106.4	3.2	116.9	1.8
Met	82.8	5.5	79.6	3.9
MeS	105.9	9.0	112.9	5.4
Cys	104.4	6.8	113.1	4.0
Cyss	102.6	2.8	115.9	9.3
Crt	83.9	2.0	87.8	2.4
Hcys	95.1	3.0	124.9	4.4
Hcyss	135.7	3.5	158.4	6.3
NAC	116.3	0.7	122.8	6.2

3.4. Application to human tear samples

Tear fluid is most commonly collected using Schirmer strips or microcapillary tubes [22]. However, differences in sampling methodology may interfere with the development of widely accepted guidelines for analyzing and reporting tear metabolite concentrations. For example, a previous study using Schirmer strips only reported the concentrations of analytes in the extracted liquids, not in tear, due to the challenge of accurately measuring tear volume collected by the strips [27]. Thus, the reported values were highly dependent on the volume of extractant used. In contrast, another study reported accurate measurement of tear volume collected in microcapillary tubes (anesthesia generally required), which enabled determination of metabolite levels in tears [28]. Although both methods successfully distinguished the disease group from healthy controls [27, 28], their results are not easily compared, as study one reported in the extractant while the other one reported concentration in tears. Therefore, to better understand the relationship between eye disease and tear metabolites, it was necessary to report results in terms of tear fluid concentrations. To accomplish this, we developed a correlation of the volume of tear fluid collected by Schirmer strip with the lengths of strip wetted by tear fluid. Different volumes of water were added to the Schirmer test strips, and the wetted lengths were measured, as shown in Figure 4. The volumes of tear fluids collected was then calculated using the equation of the best-fit line and the wetted length of strip.

Figure 4.

Relationship between volumes of water spiked and lengths of wet area on Schirmer test strips.

Four volunteers with no known eye disease were included in this study. Tear samples were collected from both eyes of each volunteer. As shown in Table 4 and Fig. 2B and C, nine analytes were detected and quantified in each tear fluid sample. In all tear samples, GSH and GSSG were the most abundant analytes, both by measured weight (Table S1) and by concentration (Table S2), indicating this pair of reduced/oxidized thiols plays a significant role in the redox balance of human tear fluids. Notably, the levels of GSH and GSSG in human tear fluids (mean, 83.89 μM and 17.02 μM) are much higher than those in human plasma [39, 40], which suggests that there may be greater demand for antioxidant activity at the ocular surface. Both cysteine and cystine were detected at levels lower than those in human plasma. Methionine was found at moderate concentrations, with a range of 3.35–20.37 μM, while the mean concentration of its oxidized form, methionine sulfoxide, was 195.0 nM. Moreover, Cys-Gly, the degradation product of GSH, and Glu-Cys, the precursor of GSH, were also quantified in this study. The levels of GSH and cysteine detected in this study were in the same order of magnitude as what has been found in previous study on small molecular antioxidant profiling of tear fluid (mean, GSH, 83.89 μM vs 50.9 μM; Cys, 4.08 μM vs 13.6 μM) [27] while the levels of methionine and cystine were consistent with the previous study on amino acid profiling of tear fluid (mean, methionine, 9.92 μM vs 2.2 μM; cystine, 0.56 μM vs 0.6 μM) [28]. The differences between values in this study and previous report were presumably due to the individual variance, which was observed in the present study and previous studies discussed above.

Table 4.

Concentrations of analytes in eight human tear fluid samples.

Analyte	Mass concentration		Molar concentration
	Range	Mean	Range	Mean
GSH	3.93 – 56.30 μg/ml	25.78 μg/ml	12.78–183.2 μM	83.89 μM
GSSG	5.78 – 21.95 μg/ml	10.43 μg/ml	9.44–35.83 μM	17.02 μM
Cys-Gly	<7.95 – 58.67 ng/ml	23.02 ng/ml	<44.61– 329.2 nM	129.2 nM
Glu-Cys	21.96 – 255.71 ng/ml	124.11 ng/ml	84.36 −982.4 nM	476.8 nM
Met	0.50 – 3.04 μg/ml	1.48 μg/ml	3.35 −20.37 μM	9.92 μM
MeS	0.52 – 59.89 ng/ml	32.22 ng/ml	3.15 −362.6 nM	195.0 nM
Cyss	43.68 – 274.33 ng/ml	135.31 ng/ml	181.8 −1142 nM	563.1 nM
Cys	0.05 – 1.92 μg/ml	0.49 μg/ml	0.43 – 15.82 μM	4.08 μM
Crt	0.48 – 1.92 μg/ml	1.03 μg/ml	4.29 – 16.95 μM	9.14 μM

The sensitivity of this method, as demonstrated by its ability to detect low-abundance metabolites (e.g. MeS, Cys-Gly and Glu-Cys) affords several advantages. First, all the biomarkers in the selected large panel are analyzed directly without derivatization; second, it enriched the thiol metabolic network and allowed for systematic understanding of thiol redox status using a large panel of metabolites, in which four pairs of reduced/oxidized metabolites (GSH/GSSG, Cys/Cyss, Hcys/Hcyss, Met/MeS) were included; third, the assessment of Cys-Gly and Glu-Cys may elucidate the contribution of GSH synthesis and degradation pathways to the development of different ocular diseases; moreover, creatinine was simultaneously detected in this method, and thus it did not need to be analyzed with a separate method. Overall, this well-developed biomarker panel can shed light on several key pathological processes affecting ocular redox status and metabolic function, including GSH synthesis, GSH degradation, and thiol recycling, leading to significant advances in prediction and precision medicine for ocular diseases.

3.5. Alternative methods for data normalization

In addition to determining their absolute levels in tears, the relative ratios of thiols, especially reduced form to corresponding disulfide, e.g. GSH/GSSG, may reveal patterns indicative of disease that are less apparent from their absolute levels in tissues. For example, it has been reported that the ratio of GSH/GSSG was significant lower in the keratoconus cells than in other groups in vitro [14]. The GSH/GSSG ratio was also lower in smokers than in nonsmokers [3]. More recently, the cysteine/cystine redox couple was found as a powerful predictor of cardiovascular mortality [2]. Thus, it may be also important to consider the ratios and the relative percentage of these metabolites. The results expressed as a weight percentage (%, w/w) are shown in Table S3. Moreover, it is also common to use the creatinine level to normalize the hydration in urine and blood. Although it is still unknown whether this is necessary for tear fluid, normalization methods may need to be considered for future studies. The results normalized to sample creatinine levels were shown in Table S4.

Principle component analysis (PCA) was employed to compare the results when expressed in different formats (Table S1–4). We did not expect to determine the best data format using the small sample size nor to see a clear separation between distinct groups (young vs old) in any of the PCA plots (Figure S2). However, we did observe that expressing tear fluid metabolite levels in different formats (weight, molar concentration, or others) greatly affected the results of the PCA, as demonstrated by the differences in distribution and clustering of the samples in the scores plots. Such differences may be greatly magnified in a larger cohort and thus, affecting the separation of distinct groups. Carefully selecting the format in which results are reported and analyzed may be essential for eliminating bias and accurately predicting ocular diseases in the future.

4. Conclusions

In this study, a simple, high-throughput HPLC-MS/MS method was developed to simultaneously determine twelve metabolites in human tear fluids. Since most of these analytes are closely associated with oxidative stress, a common factor in many ocular diseases, further application of method will significantly advance precision medicine approaches to prediction and diagnosis of these conditions.

Supplementary Material

2

Figure S1. Comparison of relative signal intensities of twelve metabolites using different mobile phases (n = 5).

Figure S2. Principle component analysis (PCA) using different result formats.

Table S1. Measured weights (ng) of the metabolites in eight tear samples.

Table S2. Measured concentrations of the metabolites in eight tear samples.

Table S3. Percentage distribution of metabolites (w/w, %) in eight tear samples.

Table S4. Levels of the metabolites normalized to creatinine (ng analyte per ng creatinine) in eight tear samples.

Highlights.

• An LC-MS/MS method is developed for thiol redox status assessment in tear.

• The method is sensitive, accurate, and precise.

• The volume of tear collected by Schirmer strip is derived for quantification.

• Oxidative stress related thiols and their metabolites are quantified.

Abbreviations:

Met

methionine

MeS

methionine sulfoxide

Hcys

homocysteine

Hcyss

homocystine

Cys

cysteine

Cyss

cystine

GSH

glutathione

GSSG

glutathione disulfide

NAC

N-acetylcysteine

Cys-Gly

cysteinyl-glycine

Glu-Cys

γ-glutamyl-cysteine

Crt

creatinine