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. 2023 Jan 4;14(2):226–234. doi: 10.1021/acschemneuro.2c00513

Angiopep-2, an MRI Biomarker, Dynamically Monitors Amyloid Deposition in Early Alzheimer’s Disease

Liang Xu †,‡, Lingfeng Lai †, Yaqi Wen †, Jia Lin §, Beibei Chen †, Yazhi Zhong †, Yan Cheng †, XiaoLei Zhang †,∥, Jitian Guan †,∥, David J Mikulis ⊥, Yan Lin †,∥, Gen Yan #,*, Renhua Wu †,∥,*
PMCID: PMC9854622  PMID: 36599050

Abstract

graphic file with name cn2c00513_0009.jpg

The reliable and dynamic detection of amyloid β-protein (Aβ) deposition using imaging technology is necessary for preclinical Alzheimer’s disease (AD), which may significantly improve prognosis. The present study aimed to evaluate the feasibility of applying angiopep-2 (ANG), a chemical exchange saturation transfer-magnetic resonance imaging (CEST-MRI) biomarker, for monitoring Aβ deposition in vivo. ANG exerted a good chemical exchange saturation transfer (CEST) effect and displayed a moderate binding affinity to Aβ1–42 in vitro. Six-month-old mice with AD injected with ANG exhibited a significantly enhanced CEST effect than controls in vivo; this effect gradually became more apparent at 8, 10, and 12 months. Spatial learning impairment caused by abundant Aβ deposition (representing mild cognitive impairment in AD patients) develops at 12 months in APPswe/PSEN1dE9 (line 85) AD mice. To conclude, the CEST of ANG could display very earlier age-related Aβ pathological progress in mice with AD, consistent with immunohistochemistry. ANG has extraordinary potential for clinical transformation as an imaging biomarker to diagnose early AD and track its progress dynamically and nonradiationally.

Keywords: Alzheimer’s disease, amyloid-β, magnetic resonance imaging, chemical exchange saturation transfer, Angiopep-2

Introduction

The treatment of Alzheimer’s disease (AD) progress was difficult in the past decade. Previous several Phase 3 clinical trials of amyloid-β (Aβ) antibody agents have failed owing to the irreversibility of the changes in the brain structure in patients with late-onset Alzheimer’s disease (LOAD).1 Thus, the current consensus focuses on diagnosing and treating AD preclinically, such as subjective cognitive decline (SCD); earlier the diagnosis, better the prognosis.2 Diagnostic tools of AD have noticeably improved in the past decades and are classified as neuroimaging and omics technologies.3 Neuroimaging tools include magnetic resonance imaging (MRI), metabolic changes detected by positron emission tomography (PET), and amyloid imaging. The majority of MRI technologies are based on structural MRI (sMRI), such as voxel-based morphometry4 or machine learning,5,6 which can visualize AD-affected brain structures at routine MRI sequences. However, these tools display insufficient sensitivity to early AD. This is because the brain structure changes are apparent and progress faster in symptomatic AD than that in preclinical AD.2 Moreover, MRI for AD diagnosis includes diffusion tension imaging (DTI),7 arterial spin labeling (ASL),8 blood oxygen level dependence (BOLD),9 proton magnetic resonance spectroscopy,10 and chemical exchange saturation transfer (CEST).11 Nonetheless, several research studies lack specificity. For example, the application of glucose-CEST MRI to patients with AD leads to an apparent reduction of 2-deoxy-d-glucose (FDG) uptake in different brain regions.12 The glucose-CEST MRI imaging technique can detect the metabolism of various brain diseases. However, epilepsy13 and insulin resistance14 also demonstrate an apparent reduction of FDG uptake, thus necessitating further research. Other neuroimaging technologies are based on radionuclide, which has adequate specificity. However, it is unsuitable for dynamically monitoring AD progress resulting from harmful radiations. Besides, it faces difficulty in ethics audits due to the “do no harm” principle. This warrants the identification of a specific agent for MRI to diagnose AD or monitor its progress preclinically, which can synergize with omics technologies. Aβ deposition is an early onset event and progresses most rapidly at the pathophysiological events of early AD, thus indicating Aβ as the most appropriate target for preclinically monitoring AD.2

CEST is a novel MR technology with the potential to provide target molecular information for AD diagnosis.15 It involves the application of a radio frequency (RF) pulse at the MR frequency of a proton (the nucleus of a hydrogen atom) on an exogenous or endogenous agent that can exchange with the protons on water molecules. Eventually, this results in the partial loss of the net water signal, as detected by MRI.16,17 CEST-MRI methods, such as glutamate CEST,18 glucose-CEST,12 protein CEST,19 and amide CEST,20 have exhibited promising results for assessing AD. However, it is difficult to obtain their specificity to proteins or metabolites serving as a biomarker for AD (for example, Aβ or Tau). Recent studies of CEST-MRI tend to develop a novel exogenous molecular agent. This can be attributed to the advantage of exogenous CEST agents of excluding other CEST effects by acquiring CEST-MRI images before and after the administration of an exogenous agent.21

Anigiopep-2(ANG) is an artificial peptide comprising 19 amino acids. It can penetrate the blood–brain barrier (BBB) via the mediation of the low-density lipoprotein receptor-related protein (LRP-1).22 Previous studies have used the penetrating ability of Angiopep-2 for drug delivery in brain tumors.23−25 Moreover, ANG-2 displays the binding ability of FITC-ANG to senile plaques (SPs) in APP/PS1 transgenic mouse brain sections by co-staining technology.26 Interestingly, ANG has −NH and −OH proton exchangeable sites. Thus, we hypothesized that ANG could be a specific agent for CEST-MRI to diagnose AD or monitor its progress preclinically.

Results

In Vitro Imaging of ANG

The ANG solution exerted a good CEST effect (Figure 1). Phantoms containing ANG solution revealed both concentrations and saturation power-dependent CEST contrast at 3.2 ppm. The magnetization transfer ratio with asymmetric analysis (MTRasym) of different concentrations linearly increased from 3.4 to 20% (pH = 7.0, saturation power = 4 μT, saturation duration = 5 s) (Figure 1A,B). To optimize the scan parameters of ANG, we used different saturation power (2.0, 2.5, 3.0, and 3.5 μT) and saturation durations (2, 3, 4, and 5 s). The MTRasym linearly increased with the saturation power (Figure 1C). However, considering the B1 limit of the surface coil (B1 should be <3.8 μT to protect the coil) and the safety of mice, the saturation power should be 3.0 μT. The saturation duration did not severely impact the MTRasym of ANG (Figure 1D); thus, the saturation duration would be 4 s.

Figure 1.

Figure 1

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CEST images and Z-spectra with different scanning parameters of ANG phantoms. The Z-spectra and CEST images of ANG with different concentrations (mM) reveal that the CEST effects were at approximately 3.2 ppm and linearly increased with the concentration (A, B). The Z-spectra of ANG with different saturation power (μT) reveal an increase in the CEST effect with the saturation power (C). The Z-spectra of ANG with different saturation duration(s) reveal an increase in the CEST effect with the saturation duration (D). CEST, chemical exchange saturation transfer; ANG, angiopep-2.

In Vitro Fluorescence Binding Assay

We used a fluorescence spectrophotometer to evaluate the interactions between FITC-ANG and aggregated Aβ1–42, Aβ1–40, and bovine serum albumin (BSA) peptides in solution. Upon mixture, the fluorescence intensity of FITC-ANG increased with the concentration of Aβ1–42 aggregates (Figure 2A). We calculated increases in the fluorescence intensity as Δfluorescence, which exhibited a linear correlation. It indicated that ANG displayed a binding affinity to Aβ1–42. We assessed this binding affinity by adding 5 μmol/L of Aβ1–42 to different concentrations of FITC-ANG. The Kd value of FITC-ANG to Aβ1–42 was 18.63 μmol/L (Figure 2B). The Kd value suggested that ANG has a moderate binding affinity to Aβ1–42 in vitro.

Figure 2.

Figure 2

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Fluorescence binding assays to probe the combination between dilute FITC-modified ANG and Aβ1–42, Aβ1–40, BSA. ANG displays moderate binding affinity to Aβ1–42 (A), and the Kd value is 18.63 μM (B). In contrast, there were no significant interactions between ANG and Aβ1–40 or BSA. FITC, fluorescein isothiocyanate; Aβ, amyloid beta; and ANG, angiopep-2.

In comparison, the fluorescence intensity of Aβ1–40 aggregates increased with the concentration, and the change was minimal (Figure 2C). It was not easy to establish a linear relationship. There was no significant change between FITC-ANG and BSA, indicating few nonspecific interactions (Figure 2D). It indicated that FITC-Angiopep-2 mainly binds to Aβ1–42 and has a weak binding ability to Aβ1–40.

In Vivo Imaging

Figure S1 depicts representative T2-weighted axial images in mice with AD. T2-weighted imaging was used to segment the cortex and hippocampus. We measured and analyzed the CEST ratio (CESTR) effect of ROIs following the injection of 1× phosphate-buffered saline (PBS) in the tail vein of 8- and 10-month-old APP/PS1 mice and age-matched C57 mice (per group n = 8) at different time points (30, 60, and 90 min), similar to mice that were treated with ANG.

First, we obtained the CEST images of 10-month-old mice with AD and C57 mice following different injecting times of ANG (Figure S2). The CESTR of the bilateral cortex or hippocampus substantially increased following ANG injection and subsequently descended. The maximum CESTR appeared at 60 min. Thus, the following CEST images would display the 60 min post-injecting CEST images.

Second, we obtained the CEST images of differently aged mice with AD and C57 mice following ANG injection (Figure 3). Six-month-old mice with AD showed enhanced CESTR following the injection compared with age-matched C57 mice. The same finding was observed and became increasingly apparent in 8-, 10-, and 12-month-old AD mice, respectively, following ANG injection.

Figure 3.

Figure 3

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CEST images of 6-, 8-, 10-, and 12-month-old mice with AD and C57 mice following 60 min of injecting ANG. The top row (A–D) represents the CEST images of C57 (control groups) mice. The bottom row (E–H) represents the CEST images of mice with AD. The value of the color bar represents the percentage. CEST, chemical exchange saturation transfer; AD, Alzheimer’s disease; and ANG, angiopep-2.

Third, following the MRI experiments, we assessed the Aβ depositions in mice brains by immunohistochemistry paraffin (IHC-P). Mice with AD revealed that Aβ depositions were predominantly distributed in the bilateral cortex and hippocampus and increased with age (Figure 4). By contrast, there were no obvious Aβ depositions in 6- to 10-month-old C57 mice. Nonetheless, we observed few Aβ depositions in 12-month-old C57 mice (Figure S3). We hypothesized that few Aβ depositions were attributed to aging. Figures 3 and 4 demonstrated that ANG could dynamically monitor Aβ depositions, consistent with IHC-P.

Figure 4.

Figure 4

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Ex vivo immunohistochemistry imaging of Aβ in mice with AD. Aβ antibody staining confirms the presence of Aβ deposits in APP/PS1 transgenic mice (A–D). The bilateral cortex and hippocampus are shown (A, red dotted line). The black box and arrows represent the local enlarged ROIs. Aβ deposits are predominantly distributed in the bilateral cortex and hippocampus. ROIs, regions of interest; AD, Alzheimer’s disease; and Aβ, amyloid beta.

We performed two-way analysis of variance (ANOVA) to compare the CESTR of different brain segments, followed by Tukey’s multiple comparisons test (Figures 5, S4). The CESTR was substantially enhanced in the bilateral cortex (6-month-old: 3.67 ± 0.88; 8-month-old: 6.50 ± 1.38; 10-month-old: 10.48 ± 1.38; and 12-month-old: 15.38 ± 1.55) following ANG injection in mice with AD. Compared to other groups (all pre-injection, PBS-injected mice, and ANG-injected C57 mice), AD mice groups injected with ANG showed significant differences in CESTR (Tukey’s multiple comparisons test, all p values <0.001). Moreover, the comparisons within the AD mice groups at different months revealed significant differences following ANG injection. The comparison of the bilateral hippocampus in AD mice following ANG injection was similar to the bilateral cortex (6-month-old: 3.30 ± 0.67; 8-month-old: 6.01 ± 1.21; 10-month-old: 7.40 ± 1.04; 12-month-old: 10.57 ± 1.45; Tukey’s multiple comparisons test, all p value <0.001). In summary, the mean CESTR of the ROIs in the cortex or hippocampus could provide a reliable effect for dynamically monitoring early AD progress.

Figure 5.

Figure 5

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Statistical analysis of mice with AD and different ROI mean CESTR following 60 min of injecting ANG or PBS. *Significant differences (P < 0.05); **Significant differences (P < 0.01); ***Significant differences (P < 0.001). CESTR, chemical exchange saturation transfer ratio; AD, Alzheimer’s disease; ROI, region of interest; PBS, phosphate-buffered saline; and ANG, angiopep-2.

There were significant differences in the bilateral hippocampus CESTR of 12-month-old ANG-injected C57 mice compared to the control groups. For example, the pre-injection CESTR of 12-month-old C57 mice was 1.20 ± 0.38, while the post-injection CESTR was 2.08 ± 0.41 (p = 0.0079). We observed some overlap between the two groups. Thus, these comparisons had no clinical significance (Figure S4). We hypothesized that the metabolism and clearance of the glymphatic system are impaired with age. Nonetheless, ANG displayed a low affinity to the normal brain parenchyma.

Moreover, there were no significant differences between the pre- and post-injection of PBS mice groups (bilateral cortex: interaction, F7,112 = 1.226, and p = 0.2945, two-sided; bilateral hippocampus: interaction, F7,112 = 2.051, and p = 0.8474, two-sided). Figure S5 depicts representative CEST images following PBS injection. Thus, PBS injection did not cause CESTR changes (Figures 5 and S5).

To further evaluate the traceability of ANG, we calculated the Aβ load by CEST images and IHC-P methods. During the post-processing of CEST images, we counted the bilateral hippocampus or cortex pixels with values >10%, then divided by the entire hippocampus or cortex pixels, termed the CEST-positive Aβ area. Following the MRI experiments, we obtained the IHC-P. ImageJ was used to segment the bilateral cortex or hippocampus and identify the ROIs’ Aβ deposit area. Then, the Aβ deposit areas were divided by the whole hippocampus or the cortex area named the IHC-P positive Aβ area. These data were obtained by linear regression (Figure 6). The CEST-positive Aβ area was correlated with the IHC-P-positive Aβ area and displayed certain amplification.

Figure 6.

Figure 6

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CEST and immunohistochemistry assessing the Aβ load. When post-processing the CEST images, pixels of the bilateral cortex or hippocampus with values greater than 10% would count, then divided by the whole hippocampus or cortex pixels named the CEST-positive Aβ area. Subsequently, the IHC-P was obtained following the MRI experiments. We used ImageJ to segment the bilateral cortex or hippocampus and to identify the Aβ deposit area of the ROIs, then divide by the whole hippocampus or cortex area named the IHC-P positive Aβ area.

Taken together, the CEST of ANG could dynamically monitor age-related Aβ pathological progress, consistent with immunohistochemistry.

Morris Maze Test

Before the MRI experiment, differently aged mice with AD underwent the Morris Maze Test on 6 consecutive days (four trials per day) to evaluate their spatial memory, which depends on the hippocampal function. Spatial acquisition was performed during the first 5 days, followed by a probe trial on day 6.27 The mean latency per day, time percent during platform quadrant, and times across the platform performances measures in mice are most sensitive and related to deficits in mild cognitive impairment (MCI)-AD.28 The test revealed an impairment in spatial acquisition in 12-month-old mice with AD (Figure 7A). Likewise, these mice could not navigate a direct path to the hidden platform (Figure 7B,C). Therefore, spatial learning impairment (representing MCI in patients with AD) developed at 12 months in mice with AD, consistent with previous studies.28,29

Figure 7.

Figure 7

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Neurobehavioral manifestation of mice with AD. Before the MRI experiment, mice with AD and different ages have been obtained for the Morris Maze Test to evaluate their spatial memory, which depends on hippocampal function. Spatial acquisition has been performed during the first 5 days. The mice must learn to use distal cues for navigating a direct path to the hidden platform; greater the time spent, worse the spatial learning ability (A). The probe trial has been conducted on day 6. The mice are required to navigate a direct path to the hidden platform, depending on their spatial memory. Lesser the time spent during the platform quadrant or fewer times across the platform, worse the spatial memory.

Discussion

Recent omic technological studies on AD have made significant advancements in clinical trials. These studies have the advantage of being noninvasive owing to easy accessibility. Contrarily, most clinical AD studies have been conducted using PET, which increases the difficulty of trials because of the radiation, security, and cost. Likewise, other neuroimaging technologies (sMRI, DTI, ASL, and BOLD) cannot meet the need for both specificity and sensibility. The present study assessed an exogenous biomarker, ANG combined with Aβ, and CSET-MRI manifestation both in vitro and in vivo. The major strength of this study was that we first dynamically monitored the beginning of Aβ deposition to spatial learning impairment caused by abundant Aβ deposition using 7T MRI. The CEST-MRI of ANG could meet the need for both specificity and sensitivity. Collectively, we detected Aβ deposition in vivo in as early as 6-month-old mice (consistent with IHC-P) and at the beginning of AD progress in APP/PS1 85Dbo/Mmjax transgenic mice. Meanwhile, these transgenic-type mice with AD developed spatial learning impairment (representing MCI for patients with AD) at 12 months. Taken together, this novel peptide-based probe achieved the dynamic monitoring of preclinical AD progress in APP/PS1 transgenic mice and displayed the potential for clinical transformation.

It is essential to associate the changes in CEST-MRI signals with the mechanisms underlying AD. LRP-1 is expressed in diverse cells, including neurons, astrocytes, and vasculatures in the brain, and its levels are dependent on the cell types in AD.30,31 LRP-1 levels are decreased in neurons but increased in vasculatures or astrocytes proximate to amyloid plaques in AD brains.32 Moreover, our fluorescence binding assays indicated that ANG displayed high binding affinity to Aβ. Zhou and co-workers conducted near-infrared imaging in vivo and revealed that ANG demonstrated high binding affinity to Aβ.26 These findings establish the correlation between CEST-MRI signal changes with the underlying mechanisms of AD, besides providing evidence for the specificity to Aβ. Upon comparing our results to non-neuroimaging studies (near-infrared imaging), our findings provided a potential technology for basic to clinical AD research using the CEST-MRI.

As Figure 2 shows, ANG has a moderate binding affinity to Aβ1–42. This may lead to the failure of ANG CEST-MRI imaging due to unknown competitive antagonists impacting. Thus, the binding affinity should be improved in the following study.

The routine CEST-MRI includes a long RF saturation pulse, followed by a fast image readout. We often used echo planar imaging (EPI)-based fast image readout in CEST. The increased sensitivity to off-resonance effects in EPI can be attributed to the absence of RF refocusing pulses, during which the spinning protons accumulate a phase error. This in turn causes positioning errors in the phase-encoding direction, thus resulting in significant artifacts.33 Moreover, EPI requires special hardware requirements because the entire k space can be traversed in a single RF excitation by using a rapidly oscillating frequency-encoding gradient. It would be limited for clinical translation. Consequently, we applied spoiled gradient echo (GRE) readout using centric spiral reordering, consistent with recent studies.34−36 The aforementioned method can provide an alternative, straightforward, and effective way to obtain CEST images on clinical MR scanners, without hardware or software modifications.

This study had several limitations. First, the GRE readout sequences took 13 min to acquire the CEST images and required high magnetic field homogeneity. Thus, the entire process would take 1/2 h. This necessitates further research to simplify the CEST readout sequence and improve the magnetic field homogeneity for shortening the scanning time without losing the signal-to-noise ratio. Second, the deposition rate of Aβ in patients with AD presented as a logistic curve, whereas that in transgenic mice with AD was linear. Hence, APP/PS1 mice achieved an approximation of the pathogenesis of AD. Future studies should focus on the development of novel and more suitable transgenic APP/PS1 mice.

Conclusions

In summary, we assessed the CEST-MRI manifestation of ANG at 7T MRI both in vitro and in vivo. The CEST-MRI of ANG displayed good specificity and sensibility. ANG could pass through the BBB, thus displaying high binding affinity to Aβ. This helped us distinguish Aβ deposition in 6-month-old mice with AD (no spatial learning impairment) from the control groups and differently aged mice with AD. It was much earlier than spatial learning impairment (representing MCI for AD patients) developed at 12-month-old. Therefore, the CEST-MRI of ANG displayed extraordinary potential for clinical transformation as an imaging biomarker to diagnose early AD, besides dynamically and nonradiationally tracking its progress.

Material and Methods

Phantom and Animal Preparation

Amyloid 1–42 (Aβ1–42) and ANG were purchased from Strong Yew Biological Co. Ltd. (Shanghai, China). ANG was synthesized according to the solid-phase synthesis method, and the peptide sequence of ANG was as follows: TFFYGGSRGKRNNFKTEEY.22 We prepared angiopep-2 solutions with different concentrations. It was dissolved in 1 × phosphate buffer (pH adjusted to 7.0) at 37 °C for in vitro and in vivo experiments for phantoms with different concentrations.

All animal experiments were performed according to the National Institutes of Health guidelines and were approved by the Ethics Committee of Shantou University Medical College. We purchased B6C3-Tg (APPswe, PSEN1dE9) 85Dbo/Mmjax AD model mice (APP/PS1, available at https://www.alzforum.org/research-models/appswepsen1de9-line-85) and age-matched C57BL/6J mice from the Guangdong Medical Laboratory Animal Center. Furthermore, these animals were anesthetized with 4% isoflurane in a mixture of O2 and air gases and kept warm. The isoflurane level was reduced to 1.4 to 1.5% during the MRI experiments. We continuously monitored the body temperature (maintained at 37 °C) and respiration using an MRI-compatible small animal monitoring system (SAII Technologies, USA).

MRI Experiments

All phantoms and animal experiments were performed on a 7.0 T horizontal bore small animal MRI scanner (Agilent Technologies, Santa Clara, CA, USA), with a horizontal bore size of 16 cm, a standard q72 volume coil (Agilent Technologies, Santa Clara, CA, USA), and surface coil (Timemedical Technologies, China) for transmission and reception.

First, we acquired T2-weighted images using a rapid acquisition with a relaxation enhancement (RARE) sequence (repetition time, TR/time to echo, TE = 2500/52 ms, NA = 2, RARE factor = 8, and matrix = 256 × 256). The main magnetic field (B0) was shimmed, and the radiofrequency (RF) field (B1) was calibrated prior to the experiment. To obtain the CEST imaging and Z-spectra, we used a GRE sequence (TR/TE = 6.4/3.1 ms, matrix = 64 × 64, field of view = 20 mm × 20 mm, slice thickness = 1.5 mm, and flip angle = 30°) with saturation preparation (100 Gaussian pulses, peak B1 = 3.0 μT, pulse duration = 50 ms, duty cycle = 50%, and total saturation time = 10 s) at 122 equally distributed offsets from −6 to 6 ppm. Moreover, we measured the B0 and B1 fields.

In Vitro Imaging of ANG

To optimize the Z-spectrum, we prepared CEST imaging parameters of ANG at 5, 10, 15, and 20 mM concentrations in nuclear magnetic resonance tubes. Subsequently, they were scanned using a 7.0 T horizontal bore small animal MRI scanner with different B1 (saturation power) ranging from 2.0 μT to 3.0 μT (step size: 0.5 μT), and the duration of saturation was 4 s. Moreover, we obtained different durations of saturation from 2 to 5 s (step size: 1 s) with 3.0 μT B1.

We performed fluorescence binding assays to probe into the combination between ANG and Aβ1–42. The fluorescein isothiocyanate (FITC)-modified ANG (final concentration 1 μM) was diluted with the Aβ1–42 protein at different concentrations (0, 0.001, 0.01, 0.1, and 1 μmol/L) and mixed with 5 μmol/L bovine serum albumin (BSA, 45 μg/mL). Consequently, the solution was incubated at 37 °C for 30 min. Following incubation, we collected fluorescence emission spectra in the emission wavelength range of 48 to 610 nm, which used a fluorescence spectrophotometer to record and plot the emission spectrum. The GraphPad Prism 8.0 (GraphPad Software, Inc., La Jolla, CA, USA) was used to generate the saturation curves and Kd values.

In Vivo Imaging in APP/PS1 and C57 Mice

We scanned 6-, 8-, 10-, and 12-month-old double transgenic APP/PS1 mice and age-matched C57 mice (per month n = 8, per group 3 males and 5 females, total n = 64) before any injection at the baseline. First, 0.3 mL 1× PBS was injected into the lateral tail vein of the mice. Subsequently, they were scanned at 30, 60, and 90 min post-injection. Following 24 h, we injected 0.3 mL of ANG (0.8 mg/g) and performed a similar step as 1× PBS.

Spatial Memory Test: Morris Maze Test

We evaluated the brain function on 6 consecutive days using the Morris Maze test (four trials per day). The maze consisted of a circular pool (60 cm in height and 120 cm in diameter), which consisted of a 6-cm-diameter platform at the center of the northwest quadrant, 1 cm below the surface of the water. We maintained the water temperature between 22 and 23 °C throughout training and the test. The mice underwent four trials (up to 60 s) per day from each of the four start locations (north, south, west, or east). They were allowed to rest on the platform for 20 s between the trials and subsequently placed in a holding cage for 5 min between the two blocks. The swim paths were recorded by an overhead video camera and tracked by an automated software.

Ex Vivo Analysis

To measure Aβ deposition in the brain, the mice were sacrificed by decapitation following the experiments. Consequently, we removed the brains after perfusion. The brains were fixed overnight in 4% paraformaldehyde and immersed in 20% sucrose for 24 h. The brain slides were sectioned into 25-μm-thick coronal sections. We performed immunohistochemical staining using an LSAB+ System-HPR kit (Dako, Den-mark) following the manufacturer’s protocols. We diluted the anti-Aβ primary antibody (Rabbit, monoclonal, ab201060; Abcam, Shanghai, China) at 1:200. Furthermore, the HRP-conjugated secondary antibody was used following the manufacturer’s instructions.

Image Processing and Statistics

All CEST imaging data were processed and analyzed in MATLAB (MathWorks, R2021a). The regions of interest (ROIs) were manually plotted on a T2-weighted image to segment the cortex and hippocampus.37 We corrected the ±3.2 ppm CEST imaging data by the B0 map, based on water saturation shift referencing.38 The B1 field was corrected by the B1 maps generated from a similar brain slice. We calculated the CEST effect as the difference between the ANG proton frequency from the magnetization and the corresponding reference frequency, symmetrically, at the opposite side of the water resonance. The CESTR signal of ANG was calculated using the following equation:

graphic file with name cn2c00513_m001.jpg

where M±3.2ppm represents the images obtained at ±3.2 ppm from the water resonance.

All statistical analyses and graphics production were performed using GraphPad Prism (version 8.0.4 for Windows, GraphPad Software, San Diego, California, USA). The Shapiro–Wilk test confirmed data normality, whereas the Bartlett test confirmed the homogeneity variance of data. We performed the two-way ANOVA to compare the CESTR of different brain segments, followed by Tukey’s multiple comparisons test. Adjusted p-values ≤0.05 were considered statistically significant. All data are expressed as mean ± standard deviation.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acschemneuro.2c00513.

  • Cortex and hippocampus area of AD mice at representative T2-weighted axial imaging; CEST images of 10-month-old mice with AD and C57 mice following different times of injecting ANG; ex vivo immunohistochemistry imaging of Aβ in C57 mice; statistical analysis of the mean CESTR in different ROIs of C57 mice following 60 min of injecting ANG or PBS; and CEST images of 10-month-old mice with AD and C57 mice following different times of injecting PBS (PDF)

Author Contributions

L.X., G.Y., and R.W. designed, executed, and supervised the study. L.L. and B.C. performed the fluorescence binding assays. Y.Z. and Y.W. assisted in the animal experiment and provided valuable insights. J.L. assisted with the data analysis and provided valuable insights. X.Z. performed the CEST data analysis and established the CEST imaging. J.G., Y.L., and D.J.M. advised the study. G.Y. and R.W. discussed the results, reviewed the manuscript, and provided funding for the study. All authors have read and approved the final version.

This work was supported by grants from the National Science Foundation of China (grant nos. 31870981 and 82020108016), the 2020 LKSF Cross-Disciplinary Research (grant nos. 2020LKSFBME06), the 2020 Li Ka Shing Foundation Cross-Disciplinary Research (grant nos. 2020LKSFG05D), and the Grant for 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.

Supplementary Material

cn2c00513_si_001.pdf (659.2KB, pdf)

References

  1. Walsh D. M.; Selkoe D. J. Amyloid beta-protein and beyond: the path forward in Alzheimer’s disease. Curr. Opin. Neurobiol. 2020, 61, 116–124. 10.1016/j.conb.2020.02.003. [DOI] [PubMed] [Google Scholar]
  2. Long J. M.; Holtzman D. M. Alzheimer Disease: An Update on Pathobiology and Treatment Strategies. Cell 2019, 179, 312–339. 10.1016/j.cell.2019.09.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Auso E.; Gomez-Vicente V.; Esquiva G. Biomarkers for Alzheimer’s Disease Early Diagnosis. J. Pers. Med. 2020, 10, 114. 10.3390/jpm10030114. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Matsuda H. MRI morphometry in Alzheimer’s disease. Ageing Res. Rev. 2016, 30, 17–24. 10.1016/j.arr.2016.01.003. [DOI] [PubMed] [Google Scholar]
  5. Andrade Cruz I.; Chuenchart W.; Long F.; Surendra K. C.; Renata Santos Andrade L.; Bilal M.; Liu H.; Tavares Figueiredo R.; Khanal S. K.; Fernando Romanholo Ferreira L. Application of machine learning in anaerobic digestion: Perspectives and challenges. Bioresour. Technol. 2022, 345, 126433 10.1016/j.biortech.2021.126433. [DOI] [PubMed] [Google Scholar]
  6. Beheshti I.; Mishra S.; Sone D.; Khanna P.; Matsuda H. T1-weighted MRI-driven Brain Age Estimation in Alzheimer’s Disease and Parkinson’s Disease. Aging Dis. 2020, 11, 618–628. 10.14336/AD.2019.0617. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Mayo C. D.; Garcia-Barrera M. A.; Mazerolle E. L.; Ritchie L. J.; Fisk J. D.; Gawryluk J. R.; Relationship Between DTI Metrics and Cognitive Function in Alzheimer’s Disease. Front. Aging Neurosci. 2019, 10, 436. 10.3389/fnagi.2018.00436. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Zhang N.; Gordon M. L.; Goldberg T. E. Cerebral blood flow measured by arterial spin labeling MRI at resting state in normal aging and Alzheimer’s disease. Neurosci. Biobehav. Rev. 2017, 72, 168–175. 10.1016/j.neubiorev.2016.11.023. [DOI] [PubMed] [Google Scholar]
  9. Ren P.; Ma M.; Xie G.; Wu Z.; Wu D.; Altered complexity of resting-state BOLD activity in Alzheimer’s disease-related neurodegeneration: a multiscale entropy analysis. Aging (Albany N. Y.) 2020, 12, 13571–13582. 10.18632/aging.103463. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Mitolo M.; Stanzani-Maserati M.; Capellari S.; Testa C.; Rucci P.; Poda R.; Oppi F.; Gallassi R.; Sambati L.; Rizzo G.; Parchi P.; Evangelisti S.; Talozzi L.; Tonon C.; Lodi R.; Liguori R. Predicting conversion from mild cognitive impairment to Alzheimer’s disease using brain (1)H-MRS and volumetric changes: A two- year retrospective follow-up study. Neuroimage Clin. 2019, 23, 101843 10.1016/j.nicl.2019.101843. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Chen L.; Wei Z.; Chan K. W. Y.; Cai S.; Liu G.; Lu H.; Wong P. C.; van Zijl P. C. M.; Li T.; Xu J. Protein aggregation linked to Alzheimer’s disease revealed by saturation transfer MRI. Neuroimage 2019, 188, 380–390. 10.1016/j.neuroimage.2018.12.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Tolomeo D.; Micotti E.; Serra S. C.; Chappell M.; Snellman A.; Forloni G. Chemical exchange saturation transfer MRI shows low cerebral 2-deoxy-D-glucose uptake in a model of Alzheimer’s Disease. Sci. Rep. 2018, 8, 9576. 10.1038/s41598-018-27839-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Ponisio M. R.; Zempel J. M.; Day B. K.; Eisenman L. N.; Miller-Thomas M. M.; Smyth M. D.; Hogan R. E. The Role of SPECT and PET in Epilepsy. AJR Am. J. Roentgenol. 2021, 216, 759–768. 10.2214/AJR.20.23336. [DOI] [PubMed] [Google Scholar]
  14. Chen Y.; Qiu C.; Yu W.; Shao X.; Zhou M.; Wang Y.; Shao X. The relationship between brain glucose metabolism and insulin resistance in subjects with normal cognition - a study based on 18F-FDG PET. Nucl. Med. Commun. 2022, 43, 275–283. 10.1097/MNM.0000000000001511. [DOI] [PubMed] [Google Scholar]
  15. Wang R.; Wang C.; Dai Z.; Chen Y.; Shen Z.; Xiao G.; Chen Y.; Zhou J. N.; Zhuang Z.; Wu R. An Amyloid-beta Targeting Chemical Exchange Saturation Transfer Probe for In Vivo Detection of Alzheimer’s Disease. ACS Chem. Neurosci. 2019, 10, 3859–3867. 10.1021/acschemneuro.9b00334. [DOI] [PubMed] [Google Scholar]
  16. Chen P.; Shen Z.; Wang Q.; Zhang B.; Zhuang Z.; Lin J.; Shen Y.; Chen Y.; Dai Z.; Wu R. Reduced Cerebral Glucose Uptake in an Alzheimer’s Rat Model With Glucose-Weighted Chemical Exchange Saturation Transfer Imaging. Front. Aging Neurosci. 2021, 13, 618690 10.3389/fnagi.2021.618690. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Tang Y.; Xiao G.; Shen Z.; Zhuang C.; Xie Y.; Zhang X.; Yang Z.; Guan J.; Shen Y.; Chen Y.; et al. Noninvasive Detection of Extracellular pH in Human Benign and Malignant Liver Tumors Using CEST MRI. Front. Oncol. 2020, 10, 578985 10.3389/fonc.2020.578985. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Crescenzi R.; DeBrosse C.; Nanga R. P.; Byrne M. D.; Krishnamoorthy G.; D’Aquilla K.; Nath H.; Morales K. H.; Iba M.; Hariharan H.; Lee V. M. Y.; Detre J. A.; Reddy R. Longitudinal imaging reveals subhippocampal dynamics in glutamate levels associated with histopathologic events in a mouse model of tauopathy and healthy mice. Hippocampus 2017, 27, 285–302. 10.1002/hipo.22693. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Wang R.; Chen P.; Shen Z.; Lin G.; Xiao G.; Dai Z.; Zhang B.; Chen Y.; Lai L.; Zong X.; et al. Brain Amide Proton Transfer Imaging of Rat With Alzheimer’s Disease Using Saturation With Frequency Alternating RF Irradiation Method. Front. Aging Neurosci. 2019, 11, 217. 10.3389/fnagi.2019.00217. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Wang R.; Li S. Y.; Chen M.; Zhou J. Y.; Peng D. T.; Zhang C.; Dai Y. M. Amide proton transfer magnetic resonance imaging of Alzheimer’s disease at 3.0 Tesla: a preliminary study. Chin. Med. J. (Engl.) 2015, 128, 615–619. 10.4103/0366-6999.151658. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Jones K. M.; Pollard A. C.; Pagel M. D. Clinical applications of chemical exchange saturation transfer (CEST) MRI. J. Magn. Reson. Imaging 2018, 47, 11–27. 10.1002/jmri.25838. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Demeule M.; Regina A.; Che C.; Poirier J.; Nguyen T.; Gabathuler R.; Castaigne J. P.; Beliveau R. Identification and design of peptides as a new drug delivery system for the brain. J. Pharmacol. Exp. Ther. 2008, 324, 1064–1072. 10.1124/jpet.107.131318. [DOI] [PubMed] [Google Scholar]
  23. Qu F.; Wang P.; Zhang K.; Shi Y.; Li Y.; Li C.; Lu J.; Liu Q.; Wang X. Manipulation of Mitophagy by ″All-in-One″ nanosensitizer augments sonodynamic glioma therapy. Autophagy 2020, 16, 1413–1435. 10.1080/15548627.2019.1687210. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Wang X.; Xiong Z.; Liu Z.; Huang X.; Jiang X. Author Correction: Angiopep-2/IP10-EGFRvIIIscFv modified nanoparticles and CTL synergistically inhibit malignant glioblastoma. Sci. Rep. 2019, 9, 4650. 10.1038/s41598-019-40291-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Kafa H.; Wang J. T.; Al-Jamal K. T. Current Perspective of Carbon Nanotubes Application in Neurology. Int. Rev. Neurobiol. 2016, 130, 229–263. 10.1016/bs.irn.2016.07.001. [DOI] [PubMed] [Google Scholar]
  26. Wang C.-W.; Nan D.-D.; Wang X.-M.; Ke Z.-J.; Chen G.-J.; Zhou J.-N. A peptide-based near-infrared fluorescence probe for dynamic monitoring senile plaques in Alzheimer’s disease mouse model. Sci. Bull. 2017, 62, 1593–1601. 10.1016/j.scib.2017.11.005. [DOI] [PubMed] [Google Scholar]
  27. Vorhees C. V.; Williams M. T. Morris water maze: procedures for assessing spatial and related forms of learning and memory. Nat. Protoc. 2006, 1, 848–858. 10.1038/nprot.2006.116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Possin K. L.; Sanchez P. E.; Anderson-Bergman C.; Fernandez R.; Kerchner G. A.; Johnson E. T.; Davis A.; Lo I.; Bott N. T.; Kiely T.; Fenesy M. C.; Miller B. L.; Kramer J. H.; Finkbeiner S. Cross-species translation of the Morris maze for Alzheimer’s disease. J. Clin. Invest. 2016, 126, 779–783. 10.1172/JCI78464. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Lalonde R.; Kim H. D.; Maxwell J. A.; Fukuchi K. Exploratory activity and spatial learning in 12-month-old APP(695)SWE/co+PS1/DeltaE9 mice with amyloid plaques. Neurosci. Lett. 2005, 390, 87–92. 10.1016/j.neulet.2005.08.028. [DOI] [PubMed] [Google Scholar]
  30. Ruzali W. A. W.; Kehoe P. G.; Love S. LRP1 expression in cerebral cortex, choroid plexus and meningeal blood vessels: relationship to cerebral amyloid angiopathy and APOE status. Neurosci. Lett. 2012, 525, 123–128. 10.1016/j.neulet.2012.07.065. [DOI] [PubMed] [Google Scholar]
  31. Yamanaka Y.; Faghihi M. A.; Magistri M.; Alvarez-Garcia O.; Lotz M.; Wahlestedt C. Antisense RNA controls LRP1 Sense transcript expression through interaction with a chromatin-associated protein, HMGB2. Cell Rep. 2015, 11, 967–976. 10.1016/j.celrep.2015.04.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Zhang H.; Chen W.; Tan Z.; Zhang L.; Dong Z.; Cui W.; Zhao K.; Wang H.; Jing H.; Cao R.; Kim C.; Safar J. G.; Xiong W. C.; Mei L. A Role of Low-Density Lipoprotein Receptor-Related Protein 4 (LRP4) in Astrocytic Abeta Clearance. J. Neurosci. 2020, 40, 5347–5361. 10.1523/JNEUROSCI.0250-20.2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Poustchi-Amin M.; Mirowitz S. A.; Brown J. J.; McKinstry R. C.; Li T. Principles and applications of echo-planar imaging: a review for the general radiologist. Radiographics 2001, 21, 767–779. 10.1148/radiographics.21.3.g01ma23767. [DOI] [PubMed] [Google Scholar]
  34. Dai Z.; Ji J.; Xiao G.; Yan G.; Li S.; Zhang G.; Lin Y.; Shen Z.; Wu R. Magnetization transfer prepared gradient echo MRI for CEST imaging. PLoS One 2014, 9, e112219 10.1371/journal.pone.0112219. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Liu R.; Zhang H.; Qian Y.; Hsu Y. C.; Fu C.; Sun Y.; Wu D.; Zhang Y. Frequency-stabilized chemical exchange saturation transfer imaging with real-time free-induction-decay readout. Magn. Reson. Med. 2021, 85, 1322–1334. 10.1002/mrm.28513. [DOI] [PubMed] [Google Scholar]
  36. Deshmane A.; Zaiss M.; Lindig T.; Herz K.; Schuppert M.; Gandhi C.; Bender B.; Ernemann U.; Scheffler K. 3D gradient echo snapshot CEST MRI with low power saturation for human studies at 3T. Magn. Reson. Med. 2019, 81, 2412–2423. 10.1002/mrm.27569. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Jia Y.; Wang C.; Zheng J.; Lin G.; Ni D.; Shen Z.; Huang B.; Li Y.; Guan J.; Hong W.; et al. Novel nanomedicine with a chemical-exchange saturation transfer effect for breast cancer treatment in vivo. J. Nanobiotechnology 2019, 17, 123. 10.1186/s12951-019-0557-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Kim M.; Gillen J.; Landman B. A.; Zhou J.; van Zijl P. C. M. Water saturation shift referencing (WASSR) for chemical exchange saturation transfer (CEST) experiments. Magn. Reson. Med. 2009, 61, 1441–1450. 10.1002/mrm.21873. [DOI] [PMC free article] [PubMed] [Google Scholar]

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