Skip to main content
NCBI home page
ACS Omega logoLink to ACS Omega
. 2025 Feb 27;10(9):9257–9265. doi: 10.1021/acsomega.4c09550

Detecting Changes in Reactive Oxygen Species in the Brain by GSH-Thiol-Weighted Imaging via Variable Delay Multipulse Chemical-Exchange Saturation Transfer

Zhihong Zhao †,‡, Yue Chen †, Xiaolei Zhang †, Shiyan Xie †, Jiechai Lin †, Yuanyu Shen †, Gang Xiao §, Jitian Guan †, Yan Lin †, Renhua Wu †,*
PMCID: PMC11904656  PMID: 40092805

Abstract

graphic file with name ao4c09550_0007.jpg

Purpose: Detecting changes in reactive oxygen species (ROS) is critical for understanding its role in brain health and diseases. We assumed that GSH-thiol-weighted imaging could potentially reflect subtle changes in the ROS in the microenvironment. In this study, we aimed to investigate the capability of GSH-thiol-weighted imaging via VDMP-CEST in detecting alterations of ROS within the brain. Methods: To develop a new technique to image GSH-thiol, phantoms of different GSH concentrations under acidic and weakly alkaline conditions were performed using MRI CEST scanning. To explore the ability of GSH-thiol-weighted VDMP-CEST imaging to detect changes in ROS, experiments were conducted in vitro and in vivo. Phantom imaging in different concentrations of GSH and H2O2 was performed. Eight normal rats underwent rat brain imaging, followed by in vitro ROS detection. Another four rats underwent rat brain imaging before and after sleep deprivation. Results: We found that VDMP-CEST could achieve GSH-thiol-weighted imaging under both acid and weakly alkaline conditions. This signal decreased with the mixing time. We also demonstrated that GSH-thiol-weighted VDMP-CEST imaging can reflect alterations in ROS in vitro and in vivo. In vitro, the GSH-thiol-weighted VDMP-CEST signal was sensitive to changes in H2O2 concentration. In vivo, the GSH-thiol-weighted VDMP-CEST signal has regional heterogeneity, which is positively correlated with ROS content in vitro (r = 0.7404, P < 0.0001). Furthermore, this signal significantly increased after sleep deprivation (whole brain: P < 0.05, hippocampus: P < 0.01). Conclusions: This study demonstrates that GSH-thiol-weighted imaging via VDMP-CEST can serve as a new method for detecting changes in ROS in the brain.

Introduction

Reactive oxygen species (ROS) are essential for various intracellular physiological processes, including cellular signaling and immune responses.1−4 However, excessive ROS production, which disturbs brain redox homeostasis, has been implicated in the pathogenesis of numerous neurodegenerative diseases, including Alzheimer’s and Parkinson’s diseases.5 Detecting changes in ROS is, therefore, critical for understanding its role in brain health and diseases. To date, most of our knowledge about the role of ROS in physiological and pathological conditions comes from the analysis of cells or tissues by in vitro methods such as carboxy-2′,7′-dichloro-dihydro-fluorescein diacetate (DCFH-DA) assay.6 In vivo methods for measuring ROS, such as fluorescence microscopy7 and electron paramagnetic resonance,8 have provided valuable insights but are limited by their spatial resolution, invasiveness, or the need for exogenous markers. There is a pressing need for noninvasive, high-resolution imaging techniques that can detect changes in ROS.

Recent advances in magnetic resonance imaging (MRI) have introduced a chemical-exchange saturation transfer (CEST) approach known as GSH-thiol-weighted imaging. This technique achieves imaging leveraging the inherent contrast at −2.5 ppm in the Z spectrum provided by the thiol groups of GSH,9 the principal intracellular antioxidant.10 According to the principle of the CEST technique,11 signal changes in GSH-thiol-weighted imaging may originate from changes in GSH concentration or the thiol proton-water exchange rate, both of which are closely associated with changes in ROS. When ROS levels escalate, GSH acts as a reducing agent and can directly neutralize ROS through its reactive thiol group, causing the decrease of GSH concentration.12 In the other perspective, increased ROS produces more hydroxyl free radicals, which may promote the exchange rate of GSH-thiol protons with water protons. Hence, we boldly assumed that GSH-thiol-weighted imaging can potentially reflect subtle changes in ROS in the microenvironment.

The challenge in GSH-thiol-weighted imaging is that GSH-thiol can only be detected under acidic conditions with the traditional continuous-wave (CW) CEST sequence.9 However, in some neurological diseases such as epilepsy13 and Alzheimer’s disease,14 the brain pH value was reported to be under weakly alkaline conditions. Variable delay multipulse (VDMP) is a novel CEST sequence, which was originally designed to separate pools of fast-exchanging protons and slow-exchanging protons.15 For instance, it can separate amine and hydroxyl from amide and the nuclear Overhauser effect (NOE) by varying the mixing time while fixing the number of saturation pulses. VDMP can also be used to monitor changes in CEST contrast agent concentration when the number of pulses and the mixing time are fixed.16 A high labeling efficiency for fast-exchanging protons can be achieved by applying a series of saturation–excitation pulses such as binomial saturation pulses.17 Moreover, the pulse power and length can be optimized to minimize direct water saturation (DS).17 Given these advantages, VDMP-CEST is expected to achieve GSH-thiol-weighted imaging over a wider range of pH values.

In this study, we aimed to investigate whether GSH-thiol-weighted imaging via VDMP-CEST can detect changes in ROS in the brain. To address this issue, we first developed a new technique to image GSH-thiol using VDMP-CEST18 and then explored the ability of GSH-thiol-weighted VDMP-CEST imaging to reflect changes in ROS in vitro and in vivo.

Methods

Phantom Preparation

Phantoms were prepared using different concentrations (40, 20, 10, 5 mM) of GSH solution with pH 5.4 and 7.4, different concentrations (2, 3, 4 mM) of GSH solution (Biotopped, China) with pH 7.4, and GSH solution (4 mM) with different concentrations (0.100, 0.050, 0.010%) of H2O2 (Phygene, China) with pH 7.4. All solutions were loaded in 2 mL test tubes.

Animal Preparation

This study was carried out under the approval of the Ethics Committee of Shantou University Medical College. We chose Sprague–Dawley (SD) rats as the animal model due to their well-established genetic background and docile temperament, which facilitate experimental manipulations and provide a robust basis for comparative studies in neuroscience. Adult female SD rats were placed in cages in the institutional animal room with a 12:12 light–dark cycle (lights on: 08:00–20:00 h) at 25 °C. The criteria for determining group size in our study were based on a combination of statistical power analysis, the 3Rs principle (Replacement, Reduction, and Refinement), and the literature search for similar systems and designs, ensuring rigorous ethical and scientific standards. Eight rats were prepared for in vivo imaging and in vitro validation. Another four rats were prepared for obtaining oxidative stress models through sleep deprivation.

MRI Scanning

All phantoms and animals were scanned with a 7.0 T horizontal bore MRI scanner (Agilent Technologies, CA) with a standard brain coil at constant room temperature (21 °C). For T2-weighted imaging, a fast spin echo sequence was used with the following parameters: repetition time = 2000 ms, echo time = 24.48 ms, slice thickness = 2.00 mm, slices = 8, gap = 0.02 mm, field of view = 35 mm × 35 mm, and data matrix = 128 × 128. Before CEST scanning, the B1 radiofrequency field was calibrated, and the B0 main magnetic field was shimmed. CW-CEST was acquired using an echo-planar sequence with a continuous-wave presaturating pulse. Parameters were as follows: repetition time = 6000 ms, echo time = 26.26 ms, slice thickness = 2.00 mm, slices = 1, field of view = 35 mm × 35 mm, data matrix = 64 × 64, and CEST RF duration = 2.00 s. The saturation frequency ranged from −6 to 6 ppm at increments of 0.1 ppm. VDMP-CEST was acquired using a fast spin echo with a train of 16 binomial RF pulses (RF duration: 0.021 s) and various mixing times (40, 60, and 80 ms) between pulse pairs. Parameters were as follows: repetition time = 3500 ms, echo time = 40 ms, slice thickness = 2.00 mm, slices = 1, field of view = 35 mm × 35 mm, and data matrix = 64 × 64. The saturation frequency ranged from −6 to 6 ppm with a nonuniform acquisition protocol to save scanning time. The increment between −3.0 and −2.0 ppm was 0.1 ppm, while the increment in the remaining frequency offset was 0.2 ppm. The saturation power was set at 2.0 μT in all CEST scanning.

Data Processing

MATLAB software (2018a version) was used for data processing. Three pools, including water, amine, and thiol pools, were applied for Lorentzian fitting of Z-spectra acquired from the GSH solution. Six pools, including water, hydroxyl, guanidine, amine, amide, and thiol, were applied for the Lorentzian fitting of the Z-spectra acquired from rat brains. To avoid the influence of metabolites in the downfield of the Z-spectra and minimize the magnetization transfer contrast (MTC) effect, we used a three-offset measurement approach19 for in vivo GSH-thiol-weighted mapping instead of using the traditional MTRasym algorithm. Three offset frequencies in the Z-spectra were selected, in which −2.5 ppm is the center frequency of the GSH-thiol pit, while −3.0 and −2.0 ppm are frequencies of the upper and lower boundaries of the GSH-thiol pit. The signal intensity with saturation (Ssat) was normalized by the signal intensity without saturation (S0). The GSH-thiol-weighted VDMP-CEST intensity was calculated using the following equation.

graphic file with name ao4c09550_m001.jpg

Detection of Intracellular ROS

ROS was detected by the DCFH-DA assay. Homogenate samples from the hippocampus, the cortex, and the thalamus (190 μL each sample) were loaded on 96-well plates and then incubated with a DCFH-DA probe at 37 °C for 30 min. Fluorescence was measured at 490 nm excitation wavelengths on a microplate reader (Biotex Elx 800).

Establishment of Sleep Deprivation Models

The rats were placed in an automated sleep deprivation apparatus (XR-XS108, Shanghai Xinruan Information Technology Co., Ltd.) to obtain the oxidative stress models. The animals were provided with food pellets and water ad libitum. The form of sleep deprivation was total sleep deprivation (24 h every day) for 2 days.

Statistical Analyses

Data were expressed as mean ± standard deviation (SD). Statistical analyses were performed using GraphPad Prism (version 8.3.0) software. One-way ANOVA followed by Tukey’s multiple comparisons test was used to compare variables in different brain regions. Spearman correlation analysis was used to assess the correlation between in vitro and in vivo variables. The paired t-test was employed to compare means between preintervention and postintervention conditions. Differences were considered statistically significant when P < 0.05.

Results

Potential of VDMP-CEST in GSH-Thiol-Weighted Imaging

Comparison of VDMP-CEST and CW-CEST under Acidic and Weakly Alkaline Conditions

Under pH 5.4, both CW-CEST and VDMP-CEST demonstrated the effect of amine at 3.0 ppm and thiol at −2.5 ppm in the Z-spectra (Figure 1, left). Under pH 7.4, while the CEST effect of amine existed in both CW-CEST and VDMP-CEST, the effect of thiol only appeared in VDMP-CEST (Figure 1, right). This result indicates that the GSH-thiol-weighted VDMP-CEST imaging can be achieved under both acid and weakly alkaline conditions, while the GSH-thiol-weighted CW-CEST imaging can only be achieved under acidic conditions.

Figure 1.

Figure 1

Open in a new tab

Z-spectra and CEST images of GSH-thiol-weighted CW-CEST and VDMP-CEST under pH 5.4 and pH 7.4. 40, 20, 10, and 5 mM represent the concentrations of the GSH solution. VDMP-CEST imaging was performed with B1 = 2.0 μT, Tm = 40 ms.

Dependence of GSH-Thiol-Weighted VDMP-CEST Signal on the Mixing Time

Our GSH phantom experiment showed that when the saturation power was fixed at 2.0 μT, the GSH-thiol-weighted VDMP-CEST effect in different concentrations (40, 20, 10, and 5 mM) of the GSH solution decreased with the mixing time (Figure 2).

Figure 2.

Figure 2

Open in a new tab

Dependence of GSH-thiol-weighted VDMP-CEST signal on the mixing time: (A) Z-spectra of the GSH solution with different mixing times and (B) relationship between VDMP-CEST intensity and the mixing time. Tm represents the mixing time.

Ability of GSH-Thiol-Weighted VDMP-CEST Imaging in Detecting Changes of ROS In Vitro and In Vivo

Phantoms Imaging in Different Concentrations of GSH and H2O2

In vitro, there was no obvious distinction among the GSH-thiol-weighted VDMP-CEST signals of the GSH solution at concentrations of 2, 3, and 4 mM (Figure 3A). Surprisingly, the GSH-thiol-weighted VDMP-CEST effect increased significantly with a slight increase in H2O2 concentration (0.01, 0.05, 0.10%) (Figure 3B). This result indicates that the GSH-thiol-weighted VDMP-CEST signal is sensitive to changes in ROS content.

Figure 3.

Figure 3

Open in a new tab

Sensitivity of GSH-thiol-weighted VDMP-CEST signal to changes in GSH concentration and ROS content: (A) GSH-thiol-weighted VDMP-CEST effect in different GSH concentrations and (B) GSH-thiol-weighted VDMP-CEST effect in different H2O2 concentrations. VDMP-CEST imaging was performed with B1 = 2.0 μT, Tm = 40 ms. The dotted line connects two boundaries of the GSH-thiol effect, and the double arrows represent the magnitude of the CEST effect.

Rat Brain Imaging and Validation

In vivo, the GSH-thiol-weighted VDMP-CEST signal decreased with the mixing time (Figure 4A) and has regional heterogeneity (Figure 4B). GSH-thiol-weighted VDMP-CEST signal from different brain regions is thalamus > hippocampus > cortex (Table 1 and Figure 5A), and the signal in the lateral thalamus is greater than that in the medial thalamus (Figure 4B). In vitro, DCF fluorescence intensity from different brain regions is thalamus > hippocampus > cortex (Table 1 and Figure 5A). GSH-thiol-weighted VDMP-CEST signal in vivo is positively correlated with DCF fluorescence intensity in vitro (r = 0.7404, P < 0.0001) (Figure 5A).

Figure 4.

Figure 4

Open in a new tab

GSH-thiol-weighted VDMP-CEST imaging of normal rat brains: (A) GSH-thiol-weighted VDMP-CEST imaging with different mixing times and (B) Z-spectra by analyzing different brain regions and different nuclei in the thalamus. Tm represents the mixing time.

Table 1. GSH-Thiol-Weighted VDMP-CEST Signal and DCF Fluorescence Intensity in the Hippocampus, Cortex, and Thalamus in Normal Rat Brains.
  hippocampus cortex thalamus
GSH-thiol-weighted VDMP-CEST signal 0.0458 ± 0.0028 0.0240 ± 0.0049 0.0505 ± 0.0021
DCF fluorescence intensity 2.618 ± 0.1954 2.274 ± 0.2515 3.012 ± 0.2016
Open in a new tab
Figure 5.

Figure 5

Open in a new tab

Correlation between intracellular ROS content and GSH-thiol-weighted VDMP-CEST signal. (A) Quantification of DCF fluorescence intensity from different brain regions, quantification of GSH-thiol-weighted VDMP-CEST signal from different brain regions, and correlation between DCF fluorescence intensity and GSH-thiol weighted VDMP-CEST signal. (B) Regions of interest for quantifying GSH-thiol-weighted VDMP-CEST signal. One-way ANOVA and Tukey’s multiple comparisons test (n = 8), *P < 0.05, **P < 0.01, and ****P < 0.0001. Spearman correlation coefficient (r), P-value (P).

Rat Brains Imaging before and after Sleep Deprivation

We observed that the GSH-thiol-weighted VDMP-CEST signal increased significantly after sleep deprivation in rat brains (Figure 6). The GSH-thiol-weighted VDMP-CEST intensity elevated from 0.0303 ± 0.0043 to 0.0385 ± 0.0052 in the whole brain (P = 0.0194) and from 0.0368 ± 0.0040 to 0.0473 ± 0.0063 in the hippocampus (P = 0.0042).

Figure 6.

Figure 6

Open in a new tab

GSH-thiol-weighted VDMP-CEST imaging of rat brains before and after sleep deprivation. (A) GSH-thiol-weighted VDMP-CEST imaging with B1 = 2.0 μT, Tm = 40 ms (upper), and enlarged images focusing on the hippocampus (lower). (B) Quantification of VDMP-CEST signal in the whole brain and the hippocampus. Paired t-test (n = 4).

Discussion

In this study, we demonstrated that the GSH-thiol effect was only detectable in VDMP-CEST but not in CW-CEST at pH 7.4, despite no superiority of VDMP-CEST for GSH-thiol at pH 5.4. Although the concentrations of GSH that we used in the phantoms were much higher than those in the brain, this experiment demonstrated the advantage of VDMP-CEST in detecting GSH-thiol under weakly alkaline conditions. The absence of the GSH-thiol effect in the CW-CEST under pH 7.4 may be due to the ultrafast exchange rate of thiol–water that is catalyzed by alkaline. There are two aspects for elucidating the mechanism. On one hand, ultrafast exchange rate leads to loss of saturation efficiency, resulting in decreased saturation transfer.19 On the other hand, the ultrafast exchange rate causes the loss of the difference in a distinct chemical shift between the exchangeable protons and water protons, leading to the thiol resonance being hardly resolved from the water resonance.19 VDMP-CEST uses a train of hard RF pulses, which makes the exchangeable protons nutate rapidly to obtain effective saturation before magnetization transfer with water protons.17 Furthermore, the pulses in VDMP-CEST labels the fast-exchanging protons with minimal perturbation of the water signal, hence sustaining the chemical shift difference of fast-exchanging protons and water protons.17 These advantages alleviate the poor sensitivity of CEST in detecting ultrafast exchanging protons, enabling VDMP to achieve GSH-thiol-weighted imaging under a wider range of pH values compared with CW-CEST. Notably, the frequency offset of NOE from aliphatic protons (−5.0 to −2.0 ppm) overlaps with that of GSH-thiol (−3.0 to −2.0 ppm) in the upfield of the Z-spectra. Fortunately, the effect from different solute pools can be modulated by B1 saturation power.20 Studies based on Bloch–McConnell equation simulations showed that slow-exchanging protons required a lower B1 to achieve the maximum CEST effect, while the requirement for the fast-exchanging protons was the opposite.21 Our previous study showed that NOE contrast diminished with increased saturation power when B1 exceeded 1.0 μT.22 Moreover, although increasing saturation power may further eliminate the NOE effect and achieve a better CEST effect for the fast-exchanging GSH-thiol protons, it can also increase the MTC effect since short T2 components in MTC are labeled together with the fast-exchanging protons by the binomial pulses.17 Therefore, a saturation power of 2.0 μT was chosen to achieve a good balance among these factors in this study.

The GSH-thiol-weighted VDMP-CEST effect decreased with the mixing time. This result is consistent with a human study using VDMP to access different CEST pools, which reveals that the fast-exchanging pools decrease in saturation with the mixing time.20 It was reported that VDMP is favorable for detecting fast-exchanging protons when a binomial saturation pulse is used.16 During the saturation pulse, both slow-exchange protons and fast-exchange protons are labeled.17 However, the trend of VDMP-CEST signal changing with the mixing time is very different between fast-exchanging protons and slow-exchanging protons.15 For the fast-exchanging protons, the saturation transfers completely during the pulse, and no additional increase in proton exchange occurs during the mixing time. Therefore, the VDMP-CEST signal of fast-exchanging protons depends on T1 relaxation and decays with the extension of mixing time.23 For the slow-exchanging protons, it takes more time to completely exchange with water protons, and the saturation transfer continues during the mixing time. Therefore, the VDMP-CEST signal increases with the mixing time within a range. When the mixing time is very long, the VDMP-CEST signal will decay with the continuous extension of mixing time because the signal gained from the increased saturation transfer cannot compensate for the signal loss caused by T1 relaxation.23

We investigated the GSH-thiol weighted VDMP-CEST effect in different concentrations of GSH solution ranging from 2 to 4 mM, which is the range of GSH concentration in the brain quantified by 1H-MRS using the MEGA-PRESS method.24 There was no obvious distinction of the GSH-thiol-weighted signal among the different concentrations (2–4 mM), hinting that the GSH-thiol-weighted imaging may be difficult to reflect changes in brain GSH level directly. However, this signal is sensitive to changes in H2O2 concentration, which is one of the most important reactive oxygen species (ROS) in the cells. One possible explanation is that hydroxyl free radicals produced by H2O2 promote the proton exchange rate.25 This may be comparable to the base-catalyzed mechanism that pH modulates the proton exchange rate,26 in which the hydroxide ions stimulate the dissociation of the exchangeable protons from metabolites to form water, thus promoting their exchange with water protons. Notably, this experiment cannot completely simulate the dynamic redox cycle in living cells, in which GSH is oxidized to GSSG by H2O2 under the catalysis of glutathione peroxidase, and GSSG is reduced to GSH by NADPH under the catalysis of glutathione reductase.10 However, this result indicates that the GSH-thiol-weighted VDMP-CEST effect is sensitive to changes in the ROS content, which provides an opportunity for GSH-thiol-weighted imaging to detect brain homeostasis disturbance.

Our in vivo imaging demonstrated that the GSH-thiol-weighted VDMP-CEST signal decreased with the mixing time in normal rat brains. This result indicates that signal changes in rat brains originate from fast-exchanging protons but not from slow-exchanging protons such as NOE, which exhibit a slow buildup of the CEST effect with mixing time ranging from 40 to 80 ms.23 In this result, the GSH-thiol-weighted VDMP-CEST signal had regional heterogeneity throughout the brain. Excitingly, this signal showed a significant positive correlation with the DCF fluorescence intensity, which represents the ROS content in in vitro detection. The regional heterogeneity of ROS may be explained by the difference in ROS production, lipid peroxidation, and different GSH levels across brain regions.27 This result indicates that the GSH-thiol-weighted VDMP-CEST signal has the ability to reflect levels of ROS in vivo. The cortex and the hippocampus demonstrated a lower GSH-thiol-weighted VDMP-CEST signal than the thalamus in normal rat brains, reflecting the higher antioxidant activity in these brain regions, which results in lower ROS production. This phenomenon is supported by the fact that transcription of antioxidant genes is induced by neuronal activity,28 which is more intensive in the cortex and the hippocampus. Interestingly, the GSH-thiol effect in the lateral thalamus was significantly higher compared to that in the medial thalamus, indicating the ability of GSH-thiol-weighted VDMP-CEST to demonstrate subtle differences in the ROS content among various brain nuclei.

In this study, we obtained oxidative stress models through sleep deprivation, which causes an increase in ROS in the brain.29 It was demonstrated that the GSH-thiol-weighted VDMP-CEST signal significantly increased after sleep deprivation, revealing the potential of GSH-thiol-weighted VDMP-CEST imaging in reflecting changes in ROS in vivo. This alteration may mainly be caused by the acceleration of the GSH-thiol proton exchange rate. According to the literature, sleep deprivation causes GSH reduction.30 However, since GSH is an antioxidant varying at a level of nanomolar to micromolar in the brain,24 the alteration of GSH concentration is insufficient to cause significant changes in CEST imaging. In contrast, the GSH decrease causes the production and accumulation of ROS,31 which may accelerate the GSH-thiol proton exchange rate, leading to an increase in the GSH-thiol signal. Although sleep deprivation causes comprehensive changes in brain metabolites such as glutamate and creatine, the frequency offset of the GSH-thiol signal (−2.5 ppm) is far shifted from most endogenous metabolites (0–4 ppm). Furthermore, the exchange rate of protons can be sensitively influenced by the pH values;26,32 however, there is still no clear evidence that sleep deprivation changes pH value in the brain. Notably, a previous study reported that the APT-weighted imaging can also detect increased ROS levels due to the accelerated protein-amide proton exchange rate.25 However, APT-weighted signal is associated with brain proteostasis including protein concentration, conformation, and aggregation state in vivo.33 These factors may weaken the sensitivity of APT-weighted imaging to changes in ROS. We believe that GSH-thiol-weighted signal is more specific for characterizing changes in ROS than the APT-weighted signal.

The development of a new technique for imaging GSH-thiol could revolutionize the way we monitor brain health and diseases associated with ROS. The ability to detect subtle changes in ROS could lead to a more accurate diagnosis and a better understanding of the underlying biological processes under various brain conditions. However, this study has a few limitations. Precise quantification of alterations in the ROS content remains an unsolved challenge. Although we applied optimized scanning parameters and a three-offset measurement approach to minimize the influence of the NOE, DS, and MTC effects, GSH-thiol-weighted VDMP-CEST imaging may still be subject to tiny contamination from these potential factors. Furthermore, more systematic experiments are needed to investigate whether this signal increases under other conditions of oxidative stress, in addition to sleep deprivation.

Conclusions

In this study, we demonstrate that GSH-thiol-weighted imaging via VDMP-CEST can serve as a new method for detecting changes in ROS in the brain.

Acknowledgments

We thank Kai Huang for his support in the experiments.

Author Contributions

Z.Z. was responsible for the conception, MRI experiment, data analysis, and manuscript preparation. Y.C. conducted MRI sequence encoding. S.X. and J.L. performed the detection of the ROS level in vitro. X.Z., Y.S., and G.X. performed MATLAB programming and data processing. J.G. and Y.L. reviewed the manuscript. R.W. supervised the study. All authors read and approved the final manuscript.

This work was supported by grants from the National Natural Science Foundation of China (grant nos. 82471974, 82020108016, and 31870981), the 2020 Li Ka Shing Foundation Cross-Disciplinary Research (grant nos. 2020LKSFG06C and 2020LKSFG05D), and the Key Disciplinary Project of Clinical Medicine under the Guangdong High-Level University Development Program (grant no. 002–18120302).

The authors declare no competing financial interest.

References

  1. Sies H.; Jones D. P. Reactive oxygen species (ROS) as pleiotropic physiological signalling agents. Nat. Rev. Mol. Cell Biol. 2020, 21 (7), 363–383. 10.1038/s41580-020-0230-3. [DOI] [PubMed] [Google Scholar]
  2. Dan Dunn J.; Alvarez L. A.; Zhang X.; Soldati T. Reactive oxygen species and mitochondria: A nexus of cellular homeostasis. Redox Biol. 2015, 6, 472–485. 10.1016/j.redox.2015.09.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Sun L.; Wang X.; Saredy J.; et al. Innate-adaptive immunity interplay and redox regulation in immune response. Redox Biol. 2020, 37, 101759 10.1016/j.redox.2020.101759. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Wilson C.; Muñoz-Palma E.; González-Billault C. From birth to death: A role for reactive oxygen species in neuronal development. Semin. Cell Dev. Biol. 2018, 80, 43–49. 10.1016/j.semcdb.2017.09.012. [DOI] [PubMed] [Google Scholar]
  5. Wu Q.; Zou C. Microglial Dysfunction in Neurodegenerative Diseases via RIPK1 and ROS. Antioxidants 2022, 11 (11), 2201 10.3390/antiox11112201. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Aranda A.; Sequedo L.; Tolosa L.; et al. Dichloro-dihydro-fluorescein diacetate (DCFH-DA) assay: a quantitative method for oxidative stress assessment of nanoparticle-treated cells. Toxicol. In Vitro 2013, 27 (2), 954–963. 10.1016/j.tiv.2013.01.016. [DOI] [PubMed] [Google Scholar]
  7. Martínez S. R.; Durantini A. M. Revealing ROS Production by Antibiotics and Photosensitizers in Biofilms: A Fluorescence Microscopy Approach. Methods Mol. Biol. 2021, 2202, 125–135. 10.1007/978-1-0716-0896-8_11. [DOI] [PubMed] [Google Scholar]
  8. Dikalov S. I.; Polienko Y. F.; Kirilyuk I. Electron Paramagnetic Resonance Measurements of Reactive Oxygen Species by Cyclic Hydroxylamine Spin Probes. Antioxid. Redox Signaling 2018, 28 (15), 1433–1443. 10.1089/ars.2017.7396. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Chen J.; Yadav N. N.; Stait-Gardner T.; et al. Thiol-water proton exchange of glutathione, cysteine, and N-acetylcysteine: Implications for CEST MRI. NMR Biomed. 2020, 33 (1), e4188 10.1002/nbm.4188. [DOI] [PubMed] [Google Scholar]
  10. Lv H.; Zhen C.; Liu J.; et al. Unraveling the Potential Role of Glutathione in Multiple Forms of Cell Death in Cancer Therapy. Oxid. Med. Cell Longevity 2019, 2019, 3150145 10.1155/2019/3150145. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Wu B.; Warnock G.; Zaiss M.; et al. An overview of CEST MRI for non-MR physicists. EJNMMI Phys. 2016, 3 (1), 19 10.1186/s40658-016-0155-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Liu T.; Sun L.; Zhang Y.; et al. Imbalanced GSH/ROS and sequential cell death. J. Biochem. Mol. Toxicol. 2022, 36 (1), e22942 10.1002/jbt.22942. [DOI] [PubMed] [Google Scholar]
  13. Lu D.; Ji Y.; Sundaram P.; et al. Alkaline brain pH shift in rodent lithium-pilocarpine model of epilepsy with chronic seizures. Brain Res. 2021, 1758, 147345 10.1016/j.brainres.2021.147345. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Mandal P. K.; Dwivedi D.; Shukla D.; et al. Interplay Between Hippocampal Glutathione Depletion and pH Increment in Alzheimer’s Disease. J. Alzheimer’s Dis. 2022, 88 (1), 1–6. 10.3233/JAD-215729. [DOI] [PubMed] [Google Scholar]
  15. Chen L.; Xu X.; Zeng H.; et al. Separating fast and slow exchange transfer and magnetization transfer using off-resonance variable-delay multiple-pulse (VDMP) MRI. Magn. Reson. Med. 2018, 80 (4), 1568–1576. 10.1002/mrm.27111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Xu X.; Xu J.; Chan K. W. Y.; et al. GlucoCEST imaging with on-resonance variable delay multiple pulse (onVDMP) MRI. Magn. Reson. Med. 2019, 81 (1), 47–56. 10.1002/mrm.27364. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Xu J.; Chan K. W.; Xu X.; et al. On-resonance variable delay multipulse scheme for imaging of fast-exchanging protons and semisolid macromolecules. Magn. Reson. Med. 2017, 77, 730–739. 10.1002/mrm.26165. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Wu R.; Chen Y.; Zhao Z.. et al. Method and system for detecting GSH-thiol under weakly alkaline conditions based on VDMP-CEST. CN Patent, CN117309924B, 2024.
  19. Jin T.; Wang P.; Zong X.; Kim S. MR imaging of the amide-proton transfer effect and the pH-insensitive nuclear overhauser effect at 9.4 T. Magn. Reson. Med. 2013, 69 (3), 760–770. 10.1002/mrm.24315. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Schmitz-Abecassis B.; Vinogradov E.; Wijnen J. P.; et al. The use of variable delay multipulse chemical exchange saturation transfer for separately assessing different CEST pools in the human brain at 7T. Magn. Reson. Med. 2022, 87 (2), 872–883. 10.1002/mrm.29005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Zaiss M.; Jin T.; Kim S. G.; Gochberg D. F. Theory of chemical exchange saturation transfer MRI in the context of different magnetic fields. NMR Biomed. 2022, 35 (11), e4789 10.1002/nbm.4789. [DOI] [PubMed] [Google Scholar]
  22. Shen Y.; Xiao G.; Shen Z.; et al. Imaging of nuclear Overhauser enhancement at 7 and 3 T. NMR Biomed. 2017, 30 (9), e3735 10.1002/nbm.3735. [DOI] [PubMed] [Google Scholar]
  23. Xu X.; Yadav N. N.; Zeng H.; et al. Magnetization Transfer Contrast–Suppressed Imaging of Amide Proton Transfer and Relayed Nuclear Overhauser Enhancement Chemical Exchange Saturation Transfer Effects in the Human Brain at 7T. Magn. Reson. Med. 2016, 75 (1), 88–96. 10.1002/mrm.25990. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Bottino F.; Lucignani M.; Napolitano A.; et al. In Vivo Brain GSH: MRS Methods and Clinical Applications. Antioxidants 2021, 10 (9), 1407 10.3390/antiox10091407. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Tain R. W.; Scotti A. M.; Cai K. Improving the detection specificity of endogenous MRI for reactive oxygen species (ROS). J. Magn. Reson. Imaging 2019, 50 (2), 583–591. 10.1002/jmri.26629. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Sun P. Z.; Xiao G.; Zhou I. Y.; Guo Y.; Wu R. A method for accurate pH mapping with chemical exchange saturation transfer (CEST) MRI. Contrast Media Mol. Imaging 2016, 11 (3), 195–202. 10.1002/cmmi.1680. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Vinokurov A. Y.; Stelmashuk O. A.; Ukolova P. A.; et al. Brain region specificity in reactive oxygen species production and maintenance of redox balance. Free Radical Biol. Med. 2021, 174, 195–201. 10.1016/j.freeradbiomed.2021.08.014. [DOI] [PubMed] [Google Scholar]
  28. Qiu J.; Dando O.; Febery J. A.; et al. Neuronal Activity and Its Role in Controlling Antioxidant Genes. Int. J. Mol. Sci. 2020, 21 (6), 1933 10.3390/ijms21061933. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Atrooz F.; Salim S. Sleep deprivation, oxidative stress and inflammation. Adv. Protein Chem. Struct. Biol. 2020, 119, 309–336. 10.1016/bs.apcsb.2019.03.001. [DOI] [PubMed] [Google Scholar]
  30. Mathangi D. C.; Shyamala R.; Subhashini A. S. Effect of REM sleep deprivation on the antioxidant status in the brain of Wistar rats. Ann. Neurosci. 2012, 19 (4), 161–164. 10.5214/ans.0972.7531.190405. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Ross E. K.; Ross E. K.; Gray J. J.; Gray J. J.; Winter A. N.; Winter A. N. Immunocal and preservation of glutathione as a novel neuroprotective strategy for degenerative disorders of the nervous system. Recent Pat. CNS Drug Discovery 2012, 7 (3), 230–235. 10.2174/157488912803252014. [DOI] [PubMed] [Google Scholar]
  32. Igarashi T.; Kim H.; Sun P. Z. Detection of tissue pH with quantitative chemical exchange saturation transfer magnetic resonance imaging. NMR Biomed. 2022, e4711 10.1002/nbm.4711. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Zhao Z.; Zhang X.; Zhang X.; et al. Amide proton transfer-weighted imaging detects hippocampal proteostasis disturbance induced by sleep deprivation at 7.0 T MRI. ACS Chem. Neurosci. 2022, 13 (24), 3597–3607. 10.1021/acschemneuro.2c00494. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from ACS Omega are provided here courtesy of American Chemical Society

RESOURCES