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. 2022 Dec 5;13(24):3597–3607. doi: 10.1021/acschemneuro.2c00494

Amide Proton Transfer-Weighted Imaging Detects Hippocampal Proteostasis Disturbance Induced by Sleep Deprivation at 7.0 T MRI

Zhihong Zhao †, Xiaojun Zhang ‡, Xiaolei Zhang †, Yan Cheng †, Lihua Chen †, Zhiwei Shen †, Beibei Chen †, Hongzhi Wang §, Yue Chen †, Wentao Xuan †, Zerui Zhuang †, Xinhui Zheng †, Yiqun Geng ∥, Geng Dong ⊥, Jitian Guan †, Yan Lin †, Renhua Wu †,*
PMCID: PMC9785040  PMID: 36469930

Abstract

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Sleep deprivation leads to hippocampal injury. Proteostasis disturbance is an important mechanism linking sleep deprivation and hippocampal injury. However, identifying noninvasive imaging biomarkers for hippocampal proteostasis disturbance remains challenging. Amide proton transfer-weighted (APTw) imaging is a chemical exchange saturation transfer technique based on the amide protons in proteins and peptides. We aimed to explore the ability of APTw imaging in detecting sleep deprivation-induced hippocampal proteostasis disturbance and its biological significance, as well as its biological basis. In vitro, the feasibility of APTw imaging in detecting changes of the protein state was evaluated, demonstrating that APTw imaging can detect alterations in the protein concentration, conformation, and aggregation state. In vivo, the hippocampal APTw signal declined with increased sleep deprivation time and was significantly lower in sleep-deprived rats than that in normal rats. This signal was positively correlated with the number of surviving neurons counted in Nissl staining and negatively correlated with the expression of glucose-regulated protein 78 evaluated in immunohistochemistry. Differentially expressed proteins in proteostasis network pathways were identified in the hippocampi of normal rats and sleep-deprived rats via mass spectrometry proteomics analysis, providing the biological basis for the change of the hippocampal APTw signal in sleep-deprived rats. These findings demonstrate that APTw imaging can detect hippocampal proteostasis disturbance induced by sleep deprivation and reflect the extent of neuronal injury and endoplasmic reticulum stress.

Keywords: APT-weighted, hippocampus, sleep deprivation, proteostasis disturbance

Introduction

Sleep loss is a public health problem that has a profound impact on human beings. Sleep deprivation leads to hippocampal injury,1 which is manifested by mood and memory impairments.2 This process may be associated with several underlying mechanisms including altered energy metabolism,3 neuroinflammation,4 impaired cAMP and mTOR signaling pathways,5 disturbed calcium signaling,6 iron accumulation, and oxidative stress.7 Recent studies showed that one night of sleep deprivation increases Aβ burden in the human hippocampus.8 In wild-type mice, chronic sleep deprivation increases Aβ42 and hyperphosphorylated tau in the hippocampal CA1 region.9 Moreover, sleep deprivation impairs protein synthesis by attenuating phosphorylation of 4E-binding protein 2 mediated by rapamycin complex 1 in the hippocampus10 and leads to excessive autophagy.11 These findings suggest that proteostasis disturbance is an important mechanism linking sleep deprivation and hippocampal injury. However, identifying noninvasive imaging biomarkers for hippocampal proteostasis disturbance remains a challenge.

Amide proton transfer-weighted (APTw) imaging is a magnetic resonance imaging (MRI)-chemical exchange saturation transfer (CEST) technique that generates image contrast at a molecular level without injecting exogenous agents.12,13 In this technique, amide protons in proteins and peptides are saturated by radiofrequency irradiation and then are exchanged with water protons. The process of saturation transfer from amide protons to water protons could be used to acquire information from proteins and peptides.14 The APTw effect is usually assessed by the magnetization transfer ratio asymmetry (MTRasym) analysis by acquiring a Z-spectrum, in which the effect at 3.5 ppm in the downfield region is mainly attributed to the exchangeable amide protons in proteins and peptides, while the effect at −3.5 ppm is due to the relayed nuclear Overhauser enhancement (NOE) effect from the aliphatic protons.12 This method is the most common way to evaluate the APTw effect due to its efficiency in eliminating the influence of semisolid magnetization transfer and direct water saturation contrast.15

Although the APTw effect depends not only on the content of exchangeable amide protons in proteins and peptides, but also on other factors that affect the chemical exchange rate, such as the pH value16 and reactive oxygen species (ROS),17 the alterations of proteins are demonstrated to be the main contributor to the change of the APTw signal under diseases associated with protein alterations.18,19 These findings suggest that APTw imaging has the potential to become a noninvasive tool for monitoring the pathological changes of proteins in vivo. So far, APTw imaging has been successfully applied in assessing brain tumors,20,21 acute stroke,22,23 and neurodegenerative diseases;19,24 however, it has not been used to visualize the negative impact of sleep deprivation on the brain. In this study, we explored the ability of APTw imaging in detecting sleep deprivation-induced hippocampal proteostasis disturbance and its biological significance, as well as its biological basis.

Results and Discussion

In Vitro MRI Experiments

As shown in the Z-spectra (Figure 1A) and APTw images (Figure 1B) of egg white solution with different saturation powers at the saturation time of 2 s, the APTw effect at 3.5 ppm was obvious and increased with the saturation power. The MTRasym values measured at the saturation power of 0.5–3.5 μT were 1.06, 9.19, 10.73, 16.75, 21.03, 23.42, and 24.13%, respectively. As shown in the Z-spectra (Figure 1C) and APTw images (Figure 1D) of egg white solution with different saturation times at the saturation power of 2.0 μT, the APTw effect at 3.5 ppm increased with the saturation time. The MTRasym values measured at the saturation time of 1–5 s were 13.24, 16.83, 20.31, 20.99, and 21.63%, respectively.

Figure 1.

Figure 1

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Z-spectra and APTw images of egg white solution with different saturation powers of 0.5–3.5 μT (A,B) and different saturation times of 1–5 s (C,D).

This result indicates the dependence of the APTw effect on the saturation power and saturation time. An increased APTw signal at a higher saturation power may be due to the faster exchanging rate of amide protons in proteins.15 We selected 2.0 μT as the saturation power in detecting hippocampal proteostasis disturbance, considering the special absorption rate safety limit of animals.25 We observed that when 2.0 μT was used as the saturation power, even if a short saturation time is used, an ideal APTw signal can be obtained. Although the MTRasym at 3.5 ppm is partly attributed to the NOE effect at −3.5 ppm from aliphatic protons in mobile macromolecules, the APT effect from the amide protons in proteins is the major contributor to the MTRasym when a relatively higher saturation power is used.15

The Z-spectra, MTRasym curves, and APTw images (Figure 2A) showed that the APTw signal decreased with a reduction in the protein concentration. The MTRasym values of egg white solution at concentrations of 100, 50, 25, and 12.5% were 16.80, 10.75, 4.69, and 2.47%, respectively. The Z-spectra, MTRasym curves, and APTw images (Figure 2B) showed that the APTw signal increased when the protein conformation was changed by urea. The egg white and urea mixture had a higher MTRasym value (30.08%) than the egg white and water mixture (9.54%) or the 100% egg white solution (26.33%), whereas the water and urea mixture had the lowest MTRasym value (4.27%). The Z-spectra, MTRasym curves, and APTw images (Figure 2C) showed that the APTw signal decreased with heating-induced protein aggregation. The MTRasym value of the heated egg white solution (22.52%) was lower than that of the unheated egg white solution (25.95%).

Figure 2.

Figure 2

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Z-spectra, MTRasym curves, and APTw images of the egg white solution with different concentrations (A), conformations (B), and aggregation states (C); 100, 50, 25 and 12.5% represent different concentrations. E + U refers to a mixture of egg white and urea; E + W refers to a mixture of egg white and water; W + U refers to a mixture of water and urea; and E refers to an egg white solution (100%). Heated and unheated egg white solutions represent proteins in different aggregation states.

According to the principle of APTw imaging,15 the decreased signal of the diluted egg white solution may be due to the reduction of amide proton concentration, the increased signal of the egg white and urea mixture may be attributed to the protein unfolding process, which induced the exposure of more exchangeable amide protons,26 and the decreased signal of the heated egg white solution may be explained by the reduced amide proton exchange rate, which is due to the formation of intermolecular hydrogen bonds27 and the loss of molecular mobility.28 Although the in vitro experiments cannot simulate the state of proteostasis disturbance in vivo, these results demonstrate that APTw imaging is sensitive to alterations in the protein state.

In Vivo MRI Experiments

Our longitudinal study showed that the APTw signal did not significantly change during normal sleep (NS) but declined with increased sleep deprivation time (Figure 3A). In the rats with NS, the MTRasym values measured on day 1, day 3, and day 5 were 6.495 ± 0.447%, 5.915 ± 1.027% (P = 0.4282), and 6.173 ± 1.017% (P = 0.7525) in the whole brain, 7.203 ± 0.375%, 6.692 ± 0.762% (P = 0.4585), and 6.797 ± 1.111% (P = 0.5990) in the hippocampus, 6.738 ± 0.518%, 6.342 ± 0.833% (P = 0.5860), and 6.475 ± 0.904% (P = 0.7813) in the cortex, and 5.868 ± 0.431%, 5.682 ± 0.822% (P = 0.8927), and 5.475 ± 1.062% (P = 0.6222) in the thalamus (Figure 3C). In the rats with sleep deprivation, the MTRasym values measured on day 1, day 3, and day 5 were 6.410 ± 0.372%, 5.702 ± 0.427% (P = 0.0703), and 4.645 ± 0.751% (P < 0.0001) in the whole brain, 7.087 ± 0.498%, 6.355 ± 0.428% (P = 0.0227), and 5.433 ± 0.405% (P < 0.0001) in the hippocampus, 6.683 ± 0.498%, 5.660 ± 0.611% (P = 0.0038), and 4.892 ± 0.242% (P < 0.0001) in the cortex, and 5.600 ± 0.716%, 4.658 ± 0.784% (P = 0.1201), and 3.845 ± 0.971% (P = 0.0044) in the thalamus, respectively (Figure 3D). Our horizontal study showed that the hippocampal APTw signal was significantly lower in the 2 days of sleep deprivation (SD2d) and 4 days of sleep deprivation (SD4d) groups than that in the NS group, while there was no significant difference in the hippocampal volume in the SD2d and SD4d groups compared with that in the NS group (Figure 4A). The MTRasym values of the NS, SD2d, and SD4d groups were 6.997 ± 0.595%, 6.268 ± 0.202% (P = 0.0083), and 4.938 ± 0.180% (P < 0.0001), respectively (Figure 4B). The hippocampal volume of the NS, SD2d, and SD4d groups were 152.0 ± 5.367 mm3, 152.8 ± 5.307 mm3 (P = 0.9434), and 151.7 ± 4.590 mm3 (P = 0.9907), respectively (Figure 4C).

Figure 3.

Figure 3

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APTw images of rat brains on day 1, day 3, and day 5 during normal sleep (NS) and sleep deprivation (SD) (A); regions of interests in rat brains (B); quantitative analysis of the MTRasym value at different time points in different regions of interest during NS (C) and SD (D) One-way ANOVA and Dunnett’s multiple comparisons test (n = 6 rats/group), *P < 0.05, **P < 0.01, ****P < 0.0001, and ns: no significant difference.

Figure 4.

Figure 4

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APT-weighted images and T2-weighted images of the normal sleep (NS), 2 days of sleep deprivation (SD2d), and 4 days of sleep deprivation (SD4d) groups (A); quantitative analysis of the hippocampal MTRasym value (B) and hippocampal volume (C). One-way ANOVA and Dunnett’s multiple comparisons test (n = 6 rats/group), **P < 0.01, ****P < 0.0001, and ns: no significant difference.

We observed the heterogeneity of the APTw signal in each brain region, no matter during NS or sleep deprivation, which may be caused by the inhomogeneity of the B1 radiofrequency field. There was no significant change in the APTw signal in each brain region during NS, implicating that the experimental condition and physiological condition were stable. By contrast, the APTw signal declined with increased sleep deprivation time in each brain region, indicating that the brain is easily disturbed by sleep deprivation. Interestingly, we observed that the change of the APTw signal in the cortex was more significant than that in the hippocampus at day 3. This result is consistent with a recent study, which showed that sleep deprivation-induced ribosome-associated transcripts change in a more pronounced manner in the cortex than in the hippocampus.29 In this study, a significant difference in the hippocampal APTw signal was observed in sleep-deprived rats than that in normal rats, but no significant difference was observed in the hippocampal volume. This finding indicates that the APTw signal can reflect alterations in the hippocampus more sensitively than the hippocampal volume, which was reported to reduce after 5 weeks of sleep deprivation.30 Notably, a hyperintensity was observed in the cerebral ventricle, which may be due to the artifacts generated by the cerebrospinal fluid flow. This sign reminds us that we should avoid including the cerebral ventricle when analyzing the APTw signal in the hippocampus. Interestingly, although sleep deprivation causes oxidative stress in the brain tissue, increasing ROS,31 which can cause an increase of the APTw signal in several hours,17 we did not observe an increase in the hippocampal APTw signal in sleep-deprived rats compared with that in normal rats. One possible explanation is that the effect of proteostasis disturbance supersedes ROS on the APTw signal when the oxidative stress persists. Besides proteins and peptides, several factors may contribute to the hippocampal APTw signal. First, the pH value affects the exchange rate of amide protons,32 causing the change of the APTw signal. Fortunately, a previous study showed that no significant changes in the pH value were found in the brain during sleep deprivation.33 Second, it is worth noting that glutathione (GSH), a low-molecular-weight polypeptide with amine and thiol groups, which is abundant in cells, may be another contributor to the APTw signal. Nevertheless, we observed that the CEST effect of GSH amine and thiol only occurred under acidic conditions and was invisible under the physiological condition (Figure S1A). This phenomenon is owing to the fast exchange rate of GSH amine and thiol, which makes the GSH resonance poorly resolved from the water resonance.34 Accordingly, under the physiological condition in sleep deprivation,33 the contribution of GSH to the hippocampal APTw signal can be omitted. Third, the amine in glutamate may also affect the APTw signal. We found that the CEST effect of glutamate was very slight under the physiological condition (Figure S1B). Therefore, we attribute the change of the hippocampal APTw signal during sleep deprivation to proteostasis disturbance.

Nissl Staining

Nissl staining showed that damaged neurons with condensed nuclei appeared in the left and right hippocampal CA1, CA3, and dentate gyrus (DG) regions in the SD2d and SD4d groups (Figure 5A). Compared with that of the NS group, the number of surviving neurons was lower in the hippocampus of the SD2d (CA1: P = 0.0124; CA3: P = 0.0020; and DG: P = 0.0211) and SD4d groups (CA1: P < 0.0001; CA3: P < 0.0001; and DG: P < 0.0001) (Figure 5B). The average number of surviving neurons in CA1, CA3, and DG was positively correlated with the MTRasym value (r = 0.7936, P < 0.0001) (Figure 5C).

Figure 5.

Figure 5

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Nissl staining of the left and right hippocampal CA1, CA3, and DG regions in the normal sleep (NS), 2 days of sleep deprivation (SD2d), and 4 days of sleep deprivation (SD4d) groups (×400 magnification) (A); quantitative analysis of surviving neurons in CA1, CA3, and DG regions (B); and Spearman’s rank correlation between the average number of surviving neurons in CA1, CA3, and DG regions and the MTRasym value (C). Red arrows indicate the damaged neurons with condensed nuclei. One-way ANOVA and Dunnett’s multiple comparisons test (n = 6 rats/group), *P < 0.05, **P < 0.01, and ****P < 0.0001. Spearman correlation coefficient (r), P-value (P).

Neuronal injury is a result of proteostasis disturbance.35 We observed a decrease of surviving neurons in the hippocampus of sleep-deprived rats. This may be attributed to the effect of sleep deprivation on neuronal autophagy, apoptosis, and neurogenesis in the hippocampus.36,37 This result explains the phenomenon that hippocampal–memory association and neurobehavioral deficits are difficult to reverse after total sleep deprivation.38,39 The correlation between the APTw signal and the average number of surviving neurons in CA1, CA3, and DG regions suggests that APTw imaging can reflect the extent of neuronal injury in the hippocampus caused by sleep deprivation.

Immunohistochemical Analysis

Our immunohistochemical analysis showed that the mean optical density of glucose-regulated protein 78 (GRP78) was higher in the SD2d (CA1: P = 0.0002; CA3: P = 0.0023; and DG: P = 0.0086) and SD4d groups (CA1: P < 0.0001; CA3: P < 0.0001; and DG: P = 0.0009) than in the NS group (Figure 6A,B). The mean optical density of GRP78 (the average in CA1, CA3, and DG regions) was negatively correlated with the MTRasym value (r = −0.6058, P = 0.0077) (Figure 6C).

Figure 6.

Figure 6

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Immunohistochemistry of GRP78 in the left and right hippocampal CA1, CA3, and DG regions in the normal sleep (NS), 2 days of sleep deprivation (SD2d), and 4 days of sleep deprivation (SD4d) groups (×200 magnification) (A); quantitative analysis of GRP78 expression in CA1, CA3, and DG regions (B); and Spearman’s rank correlation between the mean optical density of GRP78 expression and the MTRasym value (C). One-way ANOVA and Dunnett’s multiple comparisons test (n = 6 rats/group), **P < 0.01, ***P < 0.001, and ****P < 0.0001. Spearman correlation coefficient (r), P-value (P).

Proteostasis disturbance induces endoplasmic reticulum (ER) stress.40 Excessive ER stress leads to neuronal death.41 As a major regulator in proteostasis disturbance signaling pathways, GRP78 plays a key role in ER stress.42 Recent study showed that supplementing the level of GRP78 can reduce ER stress and improve cognition in aging mice.43 These findings highlight the relationship between GRP78 and ER stress. In this study, the increased GRP78 suggests the activation of ER stress in sleep-deprived rats. The negative correlation between the APTw signal and the expression level of GRP78 indicates that APTw imaging can reflect the extent of ER stress in the hippocampus caused by sleep deprivation. Moreover, this result also suggests that the decrease of the hippocampal APTw signal in sleep-deprived rats may be relative to ER stress, which enhances the capacity of protein degradation and blocks the global protein synthesis.44

Mass Spectrometry Proteomics Analysis

Mass spectrometry (MS) proteomics analysis identified 7088 proteins in the hippocampi of sleep-deprived rats and NS rats. In total, 743 differentially expressed proteins were screened, including 329 upregulated and 414 downregulated proteins (P < 0.05, fold-change ≥ 1.5). 124 differentially expressed proteins in the proteostasis network, which are involved in transcription and translation, chaperones, the autophagy and cellular protein catabolic process, and vesicle-mediated transport, were screened. Downregulated proteins were dominant in the protein synthesis pathway (downregulated: n = 33 and upregulated: n = 13) (Figure 7A). The upregulated and downregulated proteins were nearly balanced in chaperones (downregulated: n = 9 and upregulated: n = 11) (Figure 7B), the autophagy and cellular protein catabolic process (downregulated: n = 12 and upregulated: n = 10) (Figure 7C), and vesicle-mediated transport (downregulated: n = 21 and upregulated: n = 19) (Figure 7D). Moreover, multiple interactions and connections were observed among the proteostasis network components, with proteins including Lrpap1, Becn1, Trappc8, and Tceb1 involved in more than one pathway (Figure 7E).

Figure 7.

Figure 7

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Relative quantitative abundance of differentially expressed proteins in transcription and translation (A), chaperones (B), the autophagy and cellular protein catabolic process (C), and vesicle-mediated transport (D); protein–protein interaction of differentially expressed proteostasis network components (E). In (E), the nodes represent the differentially expressed proteins and the edges represent the protein–protein associations. n = 3 rats/group, technical replicates = 2.

These changes may have a compound effect on the protein concentration, conformation, and aggregation state in the hippocampus, thus providing an opportunity to detect proteostasis disturbance by APTw imaging. Notably, the downregulated proteins were dominant in the protein synthesis pathway. This result coincides with the genomic analysis of sleep-deprived mice,45 indicating that protein synthesis was attenuated in sleep-deprived brains. This alteration leads to the reduction of protein concentration, causing the decrease of the hippocampal APTw signal. The upregulated and downregulated proteins were nearly balanced in the folding, degradation, and transport pathways, indicating that these pathways were disturbed by sleep deprivation but that there was still some compensatory regulation. Although the outcome of the regulation is unclear, failure in either pathway will lead to the accumulation and aggregation of misfolded proteins,46,47 contributing to the decrease of the APTw signal. Multiple interactions and connections among proteostasis network components demonstrate that sleep deprivation-induced hippocampal proteostasis disturbance is a synergistic process of multiple pathways.

Limitations

Due to the lack of electroencephalogram recordings, a few limitations merit attention. First, we cannot exclude the possibility of microsleep during sleep deprivation, which may lead to underestimating the effect of sleep deprivation. Second, we cannot evaluate the change of electroencephalography activities in sleep deprivation, which prevents us from studying the association between the changes in the APTw signal and the alterations in sleep architectures. Future studies require APTw imaging in combination with electroencephalogram recording.

Conclusions

This study demonstrates that the APTw signal can be used as a noninvasive imaging biomarker for hippocampal proteostasis disturbance induced by sleep deprivation. This signal reflects the extent of neuronal injury and ER stress. As a new application of APTw imaging, this study achieves the visualization of the sleep deprivation-induced negative effect without injecting exogenous agents and provides new insights for studying the mechanism of hippocampal injury.

Methods

Study Design

MRI experiments included in vitro and in vivo experiments. In vitro experiments were designed to evaluate the dependance of APTw signal on the saturation parameters and the protein state. In vivo experiments included longitudinal and horizontal experiments. The longitudinal experiment contained 12 rats randomized to two groups: six rats underwent MRI scanning at day 1, day 3, and day 5 during NS and another six rats underwent MRI scanning at day 1 (before sleep deprivation), day 3 (after 2 days of sleep deprivation), and day 5 (after 4 days of sleep deprivation) during sleep deprivation. The horizontal experiment contained 18 rats randomized to the NS, SD2d, and SD4d groups (n = 6 rats/group); each rat underwent MRI scanning at the end time point of NS or sleep deprivation. After MRI scanning in the horizontal experiment, the rats were perfused via the ascending aorta with normal saline (100 mL) and 4% paraformaldehyde (100 mL); the brain was harvested, stored in 4% paraformaldehyde for 24 h, and embedded in paraffin for Nissl staining and immunohistochemistry analysis. Hippocampi of another six rats including the NS and SD4d groups (n = 3 rats/group) were harvested for MS proteomic analysis.

Phantom Preparation

Egg white (100%) solution was prepared and diluted with water to concentrations of 50, 25, and 12.5% at 37 °C. Urea, a recognized denaturant, was used to change the protein conformation. A mixture of egg white (100%) and urea (8 M), a mixture of egg white (100%) and water, and a mixture of water and urea (8 M) were prepared at a volume ratio of 1:3 at 37 °C. Egg white (100%) solution was heated in a 62 °C water bath for 10 min or maintained at 37 °C. All solutions were loaded in 2 mL test tubes for MRI scanning.

Animal Preparation

All animal protocols were reviewed and approved by the Ethics Committee of Shantou University Medical College and complied with the National Research Council (US) Committee’s Guide for the Care and Use of Laboratory Animals (Washington: National Academies Press; 2011). Adult female Sprague-Dawley rats, weighing 220–240 g, were placed in cages in the institutional animal room with a 12:12 light–dark cycle (lights on: 08:00 h to 20:00 h) at 25 °C. The animals were provided with food pellets and water ad libitum and habituated to experimental conditions to avoid other stresses. To obtain sleep-deprived models, the rats were placed in an automated sleep deprivation apparatus (XR-XS108, Shanghai Xinyuan Information Technology Co., Ltd.), in which a metal rod was set to rotate at a speed of 5 rpm with an interval time of 1 min. Food and water remained available during sleep deprivation. Before the MRI experiment, anesthesia was induced with a mixture of 4.0% isoflurane, O2, and air and maintained with a mixture of 3.0% isoflurane, O2, and air.

MRI Acquisitions

All MRI experiments were performed using a 7.0 T animal MRI system (Agilent Technologies, CA, USA) with a standard brain coil for signal transmitting and receiving. T2-weighted imaging was performed 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 × 35 mm, and data matrix = 128 × 128. The frequency and power of the B1 radiofrequency field were calibrated, and the B0 main magnetic field was shimmed. An echo planar sequence with a continuous-wave pre-saturating pulse was used to perform APTw imaging with the parameters as follows: repetition time = 6000 ms, echo time = 26.26 ms, slice thickness = 2.00 mm, slices = 1, gap = 0.00 mm, field of view = 35 × 35 mm, and data matrix = 64 × 64. The saturation frequency ranged from −6 to 6 ppm at increments of 0.1 ppm. APTw imaging of egg white (100%) solution was performed with a saturation power of 0.5–3.5 μT (saturation time = 2 s) and a saturation time of 1–5 s (saturation power = 2 μT) to explore the dependence of the APTw signal on the saturation parameters. All subsequent imaging was performed with a saturation power of 2 μT and a saturation time of 2 s.

Data Processing

The MRI data were processed using MATLAB R2018a (MathWorks, Natick MA, USA). The APTw effect was assessed and imaged via MTRasym at a frequency offset of 3.5 ppm in the Z-spectra. The signal intensity with saturation (Ssat) was normalized by the signal intensity without saturation (S0) using water as a zero-frequency reference. The MTRasym (3.5 ppm) value was calculated using the following equation6

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The hippocampal volume was calculated from T2-weighted images using the equation

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Nissl Staining

For Nissl staining, the brains were sliced at a thickness of 3–4 μm, deparaffinized in xylene, stained with cresyl violet, and photographed under an Axio Imager A2 light microscope (Carl Zeiss, Germany). Cells with round and palely stained nuclei were regarded as surviving neurons, whereas cells with condensed nuclei were regarded as damaged neurons.48 The number of surviving neurons (number/field) in the left and right hippocampal CA1, CA3, and DG regions were counted and averaged. The average number of surviving neurons in CA1, CA3, and DG was calculated, and the correlation with the MTRasym value was analyzed .

Immunohistochemistry of GRP78

For immunochemical analysis, the brains were sliced at a thickness of 3–4 μm and subjected to microwave antigen repair with ethylenediaminetetraacetic acid (pH 9.0) and depletion of endogenous peroxidase activity with 3% hydrogen peroxide. The sections were preincubated with bovine serum albumin (3%) for 30 min and incubated with the GRP78 antibody (Abcam, ab108613, 1:400) at 4 °C overnight, followed by incubation with the horseradish peroxidase-coupled IgG antibody for 50 min and 3,3′-diaminobenzidine in the dark for 5 min. After counterstaining with hematoxylin, dehydrating, and sealing with neutral gum, the sections were photographed under an Axio Imager A2 light microscope (Carl Zeiss, Germany). Brown staining of the neuronal cytoplasm represented positive expression. The mean optical density of GRP78 expression in the left and right hippocampal CA1, CA3, and DG regions was calculated using Image-Pro plus 6.0 and averaged. The average mean optical density of GRP78 expression in CA1, CA3, and DG was calculated, and the correlation with the MTRasym value was analyzed.

Mass Spectrometry Proteomics Analysis

Protein Extraction and Digestion

The hippocampal tissue was rinsed with phosphate buffer solution and homogenized in lysis buffer containing 8 M urea, 2 M thiourea, 4% 3-cholamidopropyl dimethylammonio 1-propanesulfonate, 100 mM ammonium acetate, and a protease inhibitor (Thermo Scientific, USA). The homogenate was quantified using an bicinchoninic acid assay; and 1 mg of protein was prepared for reduction and alkylation. After incubating with 10 mM dithiothreitol at 37 °C for 1 h, the homogenate was incubated with 20 mM iodoacetamide at 25 °C in the dark for 40 min, precipitated with 25% trichloroacetic acid, and rinsed twice with pre-chilled (−20 °C) acetone. The precipitate was centrifuged and resuspended in 100 mM ammonium acetate solution. Protein digestion was performed at 37 °C overnight using trypsin (Promega, USA) at a mass ratio of 50:1 protein to trypsin. For sufficient proteolysis, second digestion at a mass ratio of 100:1 protein to trypsin was performed at 37 °C for 6 h. After terminating enzymatic digestion with 10% trifluoroacetic acid, the sample was desalted using a C18 solid-phase extraction column and dried via vacuum centrifugation for further processing.

Peptide Separation

The samples were eluted in 50 μL of 80% acetonitrile (ACN) containing 0.1% formic acid (FA) and quantified using NanoDrop One (Thermo Scientific, USA), and 500 μg of peptides was loaded onto an anion-exchange PolyWAX LP column (4.6 × 100 mm, 5 μm, 300 Å, PolyLC, USA) in the Dionex Ultimate 3000 UPLC system (Thermo Scientific, USA). The mobile phases consisted of solvent A (90% ACN, 0.1% FA) and solvent B (30% ACN, 0.1% FA). The peptides were separated into 60 fractions with a 60 min gradient (0–35% solvent B for 40 min, 35–50% solvent B for 10 min, 50–100% solvent B for 5 min, and 100% solvent B for 5 min) at a flow rate of 1 mL/min and then was pooled into 12 fractions and lyophilized via vacuum centrifugation.

Liquid Chromatography–Tandem Mass Spectrometry Analysis

Liquid chromatography tandem mass spectrometry (LC–MS/MS) analysis was performed using an Orbitrap Elite mass spectrometer (Thermo Scientific, USA) coupled with an EASY-nLC 1000 nanoflow LC system (Thermo Scientific, USA). The mobile phases consisted of solvent A (0.1% FA in water) and solvent B (0.1% FA in 90% ACN). The peptides were re-dissolved in 0.1% FA, loaded onto an Acclaim PepMap 100 C18 Trap column (200 μm × 2 cm, 5 μm particle size, 100 Å, Thermo Scientific, USA), and then separated on a New Objective PicoChip column (75 μm × 10.5 cm) with a 120 min gradient (5–35% solvent B for 90 min, 35–50% solvent B for 15 min, 50–90% solvent B for 5 min, and 100% solvent B for 10 min) at a flow rate of 300 nL/min. Data acquisition was performed in a positive mode, alternating between MS full-scan and MS/MS scans (the spray voltage: 2.0 kV, the temperature of the ion transfer capillary: 280 °C, and dynamic exclusion time: 30 s). MS spectra were acquired with one full scan (range: 300–1800 m/z, resolution: 120,000 at 200 m/z, and automatic gain control: 1 × 106), followed by MS/MS scans (resolution: 60,000, automatic gain control: 2×105, maximum injection time: 200 ms, higher energy collision dissociation: 35%, and dynamic exclusion time: 30 s).

Database Search

The acquired data were searched based on the reviewed Rattus norvegicus database (SWISS-PROT, 8,131 entries, released in June 2020) using MaxQuant (version 1.6.8.0). Label-free quantitation was conducted to quantify the precursor ion peak area, which was linearly proportional to protein abundance. The false discovery rate for peptide spectrum matching and protein and modification site identification was set at 1%.

Bioinformatics Analysis

Differentially expressed protein screening was performed using an R-package (MSstats_v3.16.2).49 Differentially expressed proteins in the proteostasis network, including transcription and translation, chaperones, the autophagy and cellular protein catabolic process, and vesicle-mediated transport, were identified using Panther (pantherdb.org). Protein–protein interactions of differentially expressed proteins in the proteostasis network were analyzed using STRING (version 11.5) (https://string-db.org).

Statistical Analysis

GraphPad Prism (version 8.3.0) (GraphPad Inc., San Diego, California USA) was used for statistical analysis. Data were presented as mean ± standard deviation. Comparisons among groups were performed using a one-way analysis of variance (ANOVA), followed by Dunnett’s multiple comparisons test. Correlation analyses were performed using Spearman’s rank correlation coefficient. Statistical significance was set at P < 0.05 (two-tailed).

Acknowledgments

We thank Kai Huang and Haihua Huang for their support in the experiments.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acschemneuro.2c00494.

  • The Z-spectra of GSH and glutamate with a concentration of 20 mM under different pH value conditions (PDF)

Author Contributions

Z.Z. and X.Z. contributed to the study equally. Z.Z. was responsible for the conception of the study, data acquisition, data analysis, and manuscript preparation; X.Z. performed MS proteomics analysis, bioinformatics analysis, and manuscript preparation; X.Z., L.C., and Z.S. performed MATLAB programming and data processing; Y.C., B.C., and H.W. performed pathological experiments; Y.C., W.X., Z.Z., and X.Z. assisted in MRI experiments; Y.G., G.D., J.G., and Y.L. revised the manuscript; and 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. 31870981, 82020108016, and 82071973), the 2020 Li Ka Shing Foundation Cross-Disciplinary Research (grant nos. 2020LKSFG06C and 2020LKSFG05D), the Key Disciplinary Project of Clinical Medicine under the Guangdong High-Level University Development Program (grant no. 002-18120302), and the Natural Science Foundation of Guangdong Province (2020A1515011022).

The authors declare no competing financial interest.

Supplementary Material

cn2c00494_si_001.pdf (125.8KB, pdf)

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Supplementary Materials

cn2c00494_si_001.pdf (125.8KB, pdf)

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