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. 2026 Jan 16;6(2):1129–1140. doi: 10.1021/jacsau.5c01509

Dexter Energy Transfer Photocatalytic Microdissection of Amyloid Plaques in Alzheimer’s Disease

Rui Sun 1,5, Jintai Deng 1,4, Di Shen 1, Jing Yan 1,4, Yanan Huang 2,3, Huan Feng 2,3, Man Li 4, Yusong Ge 4, Xin Zhang 2,3,*, Yu Liu 1,*
PMCID: PMC12933367  PMID: 41755843

Abstract

Amyloid plaque is a pathological hallmark in Alzheimer’s disease (AD) brain with its composition associated with disease etiology. Here, we report a photocatalyst, namely, AmyCAT, that selectively binds to amyloid fibrils in AD brain tissues and undergoes Dexter energy transfer (DET) to catalyze carbene production for amyloid proximity labeling. First, we designed the AmyCAT photocatalyst to universally bind different types of amyloids. Second, we energetically match the DET effect in a 12 × 4 photocatalyst–substrate array to activate the diazo substrate into reactive carbene species by visible green light (530–545 nm). Further, we demonstrate that the optimal AmyCAT probe catalyzes pan-amyloid protein labeling with a 30-fold selectivity over folded counterparts. Mechanistic studies confirm that the photocatalytic labeling is indeed via the DET process, which tailors the proximity effect for spatially confined amyloid labeling. Finally, we employ the AmyCAT photocatalyst to label, enrich, and profile amyloid plaques in AD brain tissues, identifying key proteins and pathways associated with AD pathological deposition and etiology. The small-molecule-based strategy reported herein dispenses with the antibody, genetic modification, or microscopes to dissect and profile amyloid deposits from AD tissues.

Keywords: protein labeling, amyloid, carbene, Dexter energy transfer, photocatalysis, chemoproteomics

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Introduction

The deposition of amyloid plaques in the parenchymal and cortical regions of the brain is a pathological hallmark of Alzheimer’s disease (AD). , Much light has been shed on imaging-driven modalities to visualize amyloid deposits in vitro and in vivo for clinical diagnosis, such as positron emission tomography (PET), single-photon emission computed tomography (SPECT), and magnetic resonance imaging (MRI). Besides classical thioflavin T (ThT) − and Congo red stains for clinical immunohistology, a gallery of small-molecule fluorescent probes has been employed to detect amyloid deposits. The chemical scaffolds span across curcumin, , BODIPY, − AIEgens, − metal complexes, − nanoparticles, − etc. ,− In addition to singlet imaging applications, some of these probes, such as ThT-ROS, leuco ethyl violet, and FP chromophore analogues, , also exhibited triplet excited-state properties to catalyze reactive oxygen species (ROS) production. They have been shown useful to attenuate protein fibrillation or break down the preformed amyloid deposits ,, through photo-oxygenation.

However, what is the composition of amyloid deposits that are associated with AD etiology? While the above-mentioned imaging probes have been reported for amyloid visualization purposes, limited chemical tools are available to in situ label, capture, and profile the amyloid deposits in AD brain tissues. Conventionally, techniques such as direct homogenization, two-dimensional gel electrophoresis, − and laser capture microdissection (LCM) − have been widely used to prepare samples for AD proteomic analysis. Though highly accessible, direct homogenization and two-dimensional gel separation impair sample integrity and spatial resolution. Highly dependent on high-end, expensive microscopic instruments, LCM technology is limited by its μm-scale spatial resolution for tissue microdissection, falling short in precisely capturing amyloid aggregates at nanoscale sizes and instrumental accessibility.

The concept of protein proximity labeling (PPL) and related technologies show advantages for in situ dissection of protein, RNA, and their transient interactions with improved spatial resolution. Ting et al. pioneered to develop enzyme-catalyzed PPL methods using peroxidases (HRP or APEX) , and biotin ligases (BioID or TurboID). − Meanwhile, photocatalysts (Ir/Ru complexes, − DBF, , eosin Y, etc.) and photocatalytic enzymes (miniSOG , ) have been applied for catalyzing bioconjugation in close proximity via reactive chemical intermediates (e.g., 1O2, ,,− quinone methide, , and phenol radicals, − ). Although previous studies have reported antibody-mediated − and fusion protein-mediated − PPL methods for profiling amyloid deposits, their relatively long-lived reactive intermediates (1O2 and phenol radicals) often lead to an expanded labeling radius and selectivity concern. Recent progresses in targeted photocatalysts allowed for in situ generation of highly reactive carbenes − and nitrenes − via a Dexter energy transfer (DET) mechanism. In particular, DET-mediated labeling operates within the smallest known labeling radius, under 100 nm, thereby ensuring enhanced spatial resolution in labeling and accuracy in downstream proteomic analysis. ,

Here, we designed a set of Dexter energy transfer (DET) photocatalysts, namely, AmyCAT, to label amyloid plaques in AD brain tissues and identify their protein components (Figure ). We designed the AmyCAT probes from the fluorescent protein (FP) chromophore scaffold that intrinsically binds to aggregated proteins and bears tunable excited-state functions. First, we showed that the A2B1 probe outperformed other probes in amyloid binding affinity. Next, we matched the triplet excited-state energy (E T1) transferred from A2B1 to 4 substrates (S1–S4), which pinpointed diazo (S3) as the candidate DET acceptor. Further mechanistic studies confirmed the DET effect between A2B1 and S3 photocatalysis with a stringent spatial confinement. Meanwhile, out of the 12 × 4 photocatalysts (AxBy)–substrate (S1–S4) array, the A2B1/S3 photocatalyst/substrate pair excelled in labeling amyloid proteins with up to 34-fold selectivity over folded ones. Finally, we applied the AmyCAT (A2B1) probe to selective labeling, spatial enrichment, and proteomics profiling of amyloid plaques in AD model mouse brain tissues. LC-MS/MS reliably identified AD biomarkers (APP, APOE, and GFAP), highlighting key proteins and pathways associated with AD pathological deposition, well-aligned with the literature-reported disease etiology. Unlike other proximity labeling technologies (HRP, BioID, APEX, miniSOG, etc.), small-molecule-based AmyCAT eliminates the need for antibody guidance and genetic modifications to achieve spatially resolved labeling.

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Working principle of AmyCAT. The AmyCAT photocatalyst is designed to selectively bind amyloid proteins and undergo Dexter energy transfer (DET) to catalyze protein proximity labeling of amyloid plaques in Alzheimer’s disease (AD) brain tissues. Upon intercalating into rigid amyloid and inhibiting bond rotations, DET photocatalysis is activated to convert the diazo substrate into a reactive carbene intermediate. The hyperactive and short-lived carbene is confined to label amyloid and interacting proteins in proximity to the photocatalyst molecule but rapidly quenched for the far-end noninteracting proteome. The DET-based AmyCAT photocatalyst allows for in situ proteomics profiling and identification of the composition of amyloid deposits with spatial resolution.

Results and Discussion

Design of AmyCAT to Catalyze Amyloid Proximity Labeling

As proposed, an ideal AmyCAT method could be selected from two aspects (Figure a): (1) binding affinity to amyloid proteins; (2) matched excited-state energy for the DET process between the photocatalyst and substrate. Toward these functional prerequisites, we set off rational design of the AmyCAT probe from a fluorescent protein (FP) chromophore. Previous studies have shown that FP chromophore analogues exhibited inherent binding affinity to amorphous aggregated proteins to image intracellular aggresomes in stress cells. , In addition to singlet fluorescence imaging applications, this scaffold is of tunable excited-state properties and can be easily engineered to acquire triplet state functions, such as reactive oxygen species (ROS) production, protein photo-oxidation, and photo-cross-linking applications. , Therefore, FP chromophore analogues may be transformed into a photocatalyst to catalyze carbene production via DET for amyloid proximity labeling as long as their triplet state energy matches that of the substrate for DET photocatalysis.

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Rational design of AmyCAT to catalyze amyloid proximity labeling. (a) Scheme to illustrate the selection criteria of the photocatalyst/substrate pair. (b) Structures of photocatalysts designed from the FP chromophore and potential substrates to generate carbene and nitrene for protein labeling. (c) FP chromophores intrinsically bound and partitioned into insoluble protein aggregates. [Probe]: 20 μM, [amorphous aggregated DHFR]: 100 μM, and [amyloid aggregated TTR]: 100 μM. (d) Dissociation constants (K d) of FP chromophores and amyloid Aβ simulated by molecular docking. The molecular structures were optimized by Gaussian 09, and the docking results were simulated by AutoDockTools 1.5.7. (e) Simulated binding mode between A2B1 and Aβ amyloid (PDB code 8OVM). (f) Schematic diagram to illustrate the DET process from both the electron pathway and energy pathway. As shown in the electron pathway, the Dexter energy transfer (DET) mechanism operates through concerted two-electron exchange, represented by the state transition, from [photocatalyst (triplet excited state)|substrate (ground state)] to [photocatalyst (ground state)|substrate (triplet excited state)]. Therefore, as shown in the energy pathway, thermodynamically, this process is favorable when the E T1 of the substrate is lower than that of the photocatalyst. After calculating the E T1 of A2B1 and four candidate substrates, only S3 only exhibited a slightly lower E T1 than that of A2B1. E T1 = triplet excited energy. (g) Mismatched spectra between S3 absorption (blue lines) and A2B1 emission (red line). [A2B1]: 50 μM; solvent: DMSO. (h) Fluorescence intensity of A2B1 gradually decreased upon increasing the S3 concentration. [A2B1]: 50 μM; solvent: DMSO.

To match the triplet excited-state energy (E T1) between the synthetic FP chromophores and the substrates, we constructed a 12 × 4 photocatalyst–substrate array (Figure b). Twelve FP chromophore analogues (AxBy) were synthesized to modulate the excited-state energy for DET photocatalysis while retaining their binding affinity to pan-amyloids. We designed these photocatalysts with consideration of electronic effects, heavy-atom effects, and asymmetric-ring swapping for push–pull directionality effects. Meanwhile, we also prepared 4 caged substrates (S1–S4) with potential carbene/nitrene generation upon DET. The resulting FP chromophores showed absorption wavelengths ranging from 420 to 550 nm and emission wavelengths ranging from 560 to 670 nm with fluorescence quantum yields ranging from 0.01 to 0.24 (Figure S1b), indicating successful fine-tuning of their excited-state energy levels for potential DET activation of substrates.

After constructing the 12 × 4 photocatalyst–substrate array, we first evaluated the binding affinity of the 12 designed photocatalysts (AxBy) toward aggregated proteins (Figure a–d). Recent studies have shown that the amyloid plaque microenvironment incorporates a wide range of coaggregated protein species including both amyloid and amorphous aggregates, forming amyloidome. Therefore, to cover the entire amyloidome, we evaluated the catalysts’ affinity to both amyloid and amorphous aggregates. Fractionation experiments revealed that nearly all probes preferentially partitioned into the insoluble aggregated fraction, confirming their intrinsic affinity for both amyloid and amorphous species (Figure c). We then simulated the dissociation constants (K d) of the fluorophore-based chromophores to amyloid aggregates using AutoDock (Figure d andFigure S2). The strongest binder, A2B1 (K d = 4.78 μM), exhibited binding affinity comparable to that of the standard amyloid dye thioflavin T (ThT) (Figure e). Molecular docking (Figure S2) and competitive binding assays (Figure S3) further indicated that A2B1 and ThT share an identical binding site. The high affinity of A2B1 for amyloid Aβ was primarily driven by π–π interactions, hydrophobic effects, and hydrogen bonding (Figure e). Additionally, leveraging the fluorogenic property of A2B1 (Figure S4), we experimentally determined its affinity for amorphous aggregates to be 4.83 μM. Such general affinity for both amyloid and amorphous aggregates rendered A2B1 suited to profile amyloidome entangled with both amyloid core proteins and the amorphous coaggregated proteome. Therefore, A2B1 was downselected for lateral evaluations.

We further calculated the excited-state energies of A2B1 and the candidate substrates to examine their energetic overlap for the potential DET effect (Figure f). Considering that the DET mechanism operates through concerted two-electron exchange, represented by the state transition from [photocatalyst (triplet excited state)|substrate (ground state)] to [photocatalyst (ground state)|substrate (triplet excited state)]. Therefore, thermodynamically, this process is favorable when the E T1 of the substrate is lower than that of the photocatalyst. Among the 4 candidate substrates, only S3 exhibited slightly lower triplet state E T1 than that of A2B1 (1.122 eV vs 1.262 eV), which was facilitated by the DET process between them. Notably, the singlet state E S1 of S3 was also lower than that of A2B1 (1.659 vs 2.049 eV), which may lead to Förster resonance energy transfer (FRET) quenching. Fortunately, there is no obvious spectral overlap between S3 absorption and A2B1 emission (Figure g), forbidding such FRET quenching. Finally, even without the FRET quenching possibility, we still observed a gradual decrease in A2B1 fluorescence intensity upon adding S3. The decrease in singlet state fluorescence of A2B1 can be ascribed to intersystem crossing (ISC) upon DET from A2B1 to S3 in a highly concentrated mixture (Figure h). Together, A2B1–S3 was eventually picked as the optimal photocatalyst–substrate pair for lateral evaluations of amyloid proximity labeling via the DET mechanism.

DET Activation of Diazo into Carbene

In this section, we show the mechanistic basis for spatially confined proximity labeling and amyloid selectivity. First, DET-based photocatalysis of A2B1/S3 was activated within stringent spatial confinement. Second, such photocatalysis was further enhanced by restricted intramolecular bond rotation in a high-viscosity environment.

Two mechanistic pathways for S3 may be undergone to activate the diazo substrate and generate carbene (Figure a, pathway I direct photolysis and pathway II DET-mediated photocatalysis). First, NMR experiments by tracking the chemical shift of the ester methyl group (Ha) showed that direct photolysis of S3 into carbenes without photocatalyst A2B1 can be fully converted using high-energy blue LED light (440–470 nm, Figure b andFigure S4, second lane in blue). However, direct photolysis was energetically unfeasible when switched to low-energy green LED light (530–545 nm, Figure b, third lane in green). After 5 h of prolonged irradiation in deuterated methanol (CD3OD), no obvious converted product was observed w./wo. A2B1 (Figure b). Even at a nearly saturated concentration, we still detected no obvious insertion product (Figure S6).

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When A2B1 and S3 diffuse freely in solution, S3 cannot be catalyzed to carbene by A2B1. (a) Schematic illustration of direct photolysis or DET photocatalysis to activate diazo into carbene. (b) 1H NMR spectra of S3 before and after photoactivation in CD3OD at given conditions. The chemical shift of the ester methyl group (Ha and Hb) was monitored for product conversion. Illumination time: 5 h; illumination intensity: 6 mW cm–2; S3: 10 mg, 72 mM, 1 equiv; A2B1: 14 mM, 0.2 equiv; CD3OD: 600 μL. Detailed analysis of the 1H spectra is shown in Figure S5.

Next, we employed a simple collision theory to elucidate the spatially confined photocatalytic process (Figure a). In contrast to most metal complexes, the organic photocatalyst A2B1 exhibits a substantially shorter triplet excited-state lifetime. This originates from the lack of heavy atoms, which are essential for inducing strong spin–orbit coupling to circumvent the spin-forbidden singlet-to-triplet transition. Consequently, during photocatalysis, A2B1 possesses a significantly narrower temporal window for effective collision with S3 compared with metal-complex-based catalysts. Assuming that the diffusion rate of S3 remains constant, A2B1 would thus display a smaller catalytic radius within a given time frame. Consistent with this theory, no significant substrate conversion was detected when A2B1 and S3 were allowed to diffuse freely in solution. To restrain the relative motion and spatial distance between A2B1 and S3, we synthesized a covalently tethered conjugate, A2B1–S3, to confine them in close proximity, thereby ensuring the spatial requirements for efficient DET (Figure a). In contrast to freely diffusing A2B1 and S3, we successfully obtained the insertion product of the A2B1–S3 conjugate (Figure b), indicating that the photocatalysis via DET can be activated by confining spatial distance.

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Photocatalysis of A2B1/S3 is promoted by both spatial confinement and the restriction of intramolecular rotation (RIR). (a) Photocatalysis via DET is triggered by covalently tethering in the A2B1–S3 photocatalyst–substrate conjugate. (b) 1H NMR spectra of A2B1–S3 with/without 5 h of green LED illumination in methanol. The chemical shift of the ester methyl group (Ha and Hb) was monitored to track the product formation. Illumination time: 5 h; illumination intensity: 6 mW cm–2. Detailed analysis of the 1H spectra is shown in Figure S7. (c) Restriction of intramolecular rotation (RIR) in a viscous solvent improved the efficiency of the DET process in A2B1–S3. (d) A2B1–S3 conversion was monitored by HPLC. Samples were dissolved in fluid MeOH/DMSO (3:7) or viscous glycerol/DMSO (3:7). Illumination intensity: 6 mW cm–2. [A2B1–S3]: 1 mg mL–1. Due to the poor solubility of A2B1–S3, the maximum volume fraction of glycerol can reach only reach 30%. Detailed analysis of the HPLC results is shown in Figure S8. (e) A2B1–S3 conversion rate calculated from HPLC. (f) Kinetic fittings of the conversion rate of A2B1–S3 in nonviscous MeOH/DMSO (3:7) or viscous glycerol/DMSO (3:7).

On the other hand, A2B1 is a classical molecular rotor that tends to dissipate excited-state energy via intramolecular rotation in low-viscosity solutions. − Therefore, restricting intramolecular rotation (RIR) could potentially divert the excited-state energy of A2B1 toward the DET pathway and thereby improve the photocatalysis efficiency (Figure c). To test this hypothesis, we monitored the product conversion rate of A2B1–S3 using HPLC in fluid 30% methanol versus viscous 30% glycerol (Figure d and Figure S8). During 5 h of green LED light illumination, a significantly higher conversion rate was observed in viscous glycerol medium (Figure e) with a more than 3-fold increase in the reaction rate (0.067 h–1 in 30% methanol vs 0.213 h–1 in 30% glycerol, Figure f).

AmyCAT Photocatalyzes Selective Amyloid Proximity Labeling In Vitro

The “rigidity-activation” feature may help the AmyCAT photocatalyst to improve labeling selectivity toward amyloid proteins. Upon intercalating into the rigid microenvironment of amyloid, the intramolecular bond rotations of A2B1 are restricted and trigger its environment-conditioned DET photocatalytic labeling (Figure a).

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AmyCAT selectively photocatalyzed proximity labeling of aggregated proteins. (a) When A2B1 and S3 are adsorbed into porous aggregated proteins, the spatial distance between them becomes closer. The intramolecular rotation of A2B1 is also restricted by the rigid microenvironment in aggregates, triggering DET photocatalysis. (b) Scheme to quantify the photocatalytic labeling efficiency. [AmyCAT]: 10 μM; [substrate]: 50 μM; [aggregated DHFR]: 50 μM; illumination intensity: 6 mW cm–2; illumination time: 20 min. The labeling efficiency was quantified by ImageJ. (c) Quantified labeling efficiency of the 12 × 4 photocatalyst/substrate pairs toward aggregated DHFR. (d) Quantified labeling efficiency of A2B1/S3 toward aggregated protein upon illumination by light sources spanning different wavelengths. Error bars: standard error (n = 3). (e) AmyCAT exhibited selectivity for catalyzing labeling of aggregated proteins over folded ones upon green LED illumination. F = folded. A = aggregated. [DHFR]: 50 μM. [Halo]: 50 μM. [Tau]: 100 μM. [α-Syn]: 100 μM. Error bars: standard error (n = 3). (f) PPL mediated by DET provided a higher S/N ratio for aggregated DHFR labeling compared to that mediated by reactive oxygen species (ROS) using folded halo as a counterpart. [AmyCAT]: 10 μM; [aggregated DHFR]: 50 μM; [folded halo]: 50 μM; [S3]: 50 μM; [PA]: 10 mM. Error bars: standard error (n = 3).

First, we assessed its photocatalytic labeling on protein aggregates by using fluorescent SDS-PAGE (Figure b). Within the 12 × 4 photocatalyst–substrate array, the A2B1/S3 pair exhibited the highest labeling efficiency (Figure c and Figures S9 and S10), consistent with their optimal binding affinity and energetic alignment (Figure d,f). However, under white LED illumination substantial background labeling occurred due to direct photolysis of S3 caused by overlapping absorption spectra (Figure c and Figure S11), resulting in a low signal-to-noise (S/N) ratio (Figure d, 4-fold). To improve the S/N ratio, we screened various light sources. Although it reduced the maximum labeling efficiency, low-energy green light effectively minimized background labeling and achieved a higher signal-to-noise ratio (Figure d, up to 14-fold). Using 1H NMR spectroscopy, we also confirmed that AmyCAT catalyzes multiple types of modifications on various amino acid residue mimics, including C-termini, N-termini, C, E, D, K, S, T, and Y (Figures S12–S16).

Next, we demonstrated labeling selectivity and general applicability. Owing to its general binding affinity to various types of aggregated proteins (Figure c), A2B1 catalyzed pan-aggregate labeling, including both amorphous aggregates (DHFR and halo) and amyloids (α-synuclein and tau) (Figure e). Further, the AmyCAT photocatalytic system exhibited minimal off-target toward their folded counterparts, gifted by the spatially confined reactivity of short-lived carbene and the “rigidity-turn-on” property of A2B1. Further experiments confirmed the reliability and effectiveness of the AmyCAT probe in proximity labeling of protein aggregates (Figures S17–S24).

Finally, we showed that DET-based AmyCAT resulted in a higher S/N ratio in labeling selectivity compared to ROS-mediated proximity labeling. To this end, we mixed folded halo and aggregated DHFR to evaluate the labeling selectivity. During a 30 min illumination period, no discernible off-target labeling was observed on folded halo in the DET-based group, whereas significant off-target labeling occurred in the ROS-based group (Figure f). Extending the illumination period, the DET group retained a higher S/N ratio compared to the ROS group (Figure f, 34-fold vs 2.5-fold). These results revealed the ability of AmyCAT to confer stringent spatial selectivity for aggregated proteins, potentiating high-precision labeling and microdissection for tissue applications.

AmyCAT for Amyloid Plaque Labeling and Profiling in AD Brain Tissues

We next applied the AmyCAT photocatalyst to amyloid deposits in an AD neurodegenerative mouse model (APP/PS1) to demonstrate its in situ labeling and proteomic profiling functions (Figure a). First, we confirmed that A2B1 selectively stained amyloid deposits by colocalizing its red fluorescence with blue fluorescence arising from ThS (Figure b and Figure S25, Pearson correlation coefficient (PCC) = 0.90). Subsequently, we performed photocatalytic labeling of amyloid deposits using A2B1/S3 followed by click chemistry using coumarin-azide for visualization. The red fluorescence from A2B1 merged well with the labeled protein marked with coumarin-azide (Figure c and Figure S26, PCC = 0.86), confirming successful target-specific labeling.

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AmyCAT photocatalyzed labeling of amyloid deposits in Alzheimer’s disease tissues. (a) Schematic illustration of the photocatalytic labeling procedures using AD brain tissues. [AmyCAT]: 10 μM; [substrate]: 50 μM; illumination intensity: 6 mW cm–2; illumination time: 20 min. (b) Colocalization of fluorescence images of AD tissues stained by ThS (blue fluorescence) and A2B1 (red fluorescence). (c) Proximity labeling region (blue fluorescence upon clicking to coumarin-N3) colocalized well with A2B1 (red fluorescence). (d) Schematic of the photocatalytic mechanism of HRP, AggID, and AmyCAT techniques. (e) HRP, AggID, and AmyCAT labeled Aβ in vitro effectively. (f) HRP, AggID, and AmyCAT labeled amyloid plaques on AD brain tissue sections effectively with different colocalization. (g) Comparison of the labeling radius and precision by calculating the Pearson correlation coefficient for colocalizing ThS amyloid signal and TMR labeling signal. Error bars: standard error (n = 5). **P < 0.01; *P < 0.05.

Additionally, we compared the labeling precision of AD plaques among established methods (Figure d), including HRP mediated by the Aβ antibody and AggID for intracellular aggresomes with AmyCAT for amyloid plaques (this work). First, the labeling capacities of these three methods were validated using amyloid Aβ in vitro and amyloid plaques on brain sections. All three of these methods can label amyloid proteins effectively (Figure e,f). Second, we compared the relative labeling radius of these three methods by calculating the PCC between the ThS-stained amyloid region (cyan) and the labeling region (red). A higher colocalization rank between the ThS-stained region and the labeling region reflects a relatively smaller labeling radius and higher precision of the labeling method (Figure g). HRP and AmyCAT exhibited a distinguished higher PCC than AggID, revealing a smaller labeling radius mediated by the phenoxyl radical (HRP) and carbene (AmyCAT in this work) than that mediated by singlet oxygen (AggID). Further, although there was no significant difference in PCC between HRP and AmyCAT, AmyCAT exhibited a slightly higher averaged PCC than HRP (0.72 vs 0.69), consistent with a previous report. These results confirmed that AmyCAT was capable of precisely labeling amyloid deposits in AD brain tissue sections upon visible-light illumination.

Next, the labeled proteins from AD mouse hippocampus sections were clicked to biotin-PEG4-azide followed by enrichment using Streptavidin Mag Sepharose for standard proteomic sample preparation (Figure a). Upon LC-MS/MS analysis (fold of change >2, P-value <0.01), we identified 44 upregulated (enriched) proteins, including APP, APOE, and GFAP, which were classic biomarkers in AD (Figure b). Taking the advantages of the confined labeling of AmyCAT, we successfully captured the AD deposition biomarkers (APP, APOE, and GFAP) with a higher enrichment fold than conventional sample preparation methods (Figure c), including direct homogenization and LCM. Additionally, we also identified other AD-related proteins with functions of chaperone (e.g., CLU and HSPB1), immunity (e.g., TAPBP and C4B), inflammation (e.g., FGA and SERPINA3N), and structural filament (e.g., Vim and FLNC). These upregulated proteins associated with AD are listed in Table S1.

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AmyCAT photocatalyzed selective labeling of amyloid deposits in Alzheimer’s disease tissues for proteomic profiling application. (a) Schematic illustration of the photocatalytic labeling and proteomic profiling procedures using AD brain tissues. [AmyCAT]: 10 μM; [substrate]: 50 μM; illumination intensity: 6 mW cm–2; illumination time: 20 min. (b) Volcano plot of the significantly differentially upregulated/enriched proteins in the AD mouse brain. (c) Comparison of the enrichment fold among direct homogenization, laser capture microdissection, and this work. All data sets used APP/PS1 mice as the AD model. (d) Target validation by immunofluorescence colocalization imaging of identified AD-associated biomarkers (APP, APOE, GFAP, CLU, TAPBP, and FGA) stained by antibodies and amyloid deposits stained by ThS. (e) Gene ontology (GO) analysis of the biological process of the enriched proteins. (f) Key proteins identified by AmyCAT can be attributed to established pathways (chaperone, lipid metabolism, inflammation, immunity, etc.) related to AD etiology reported in the literature.

To validate these proteins found in amyloid deposits by AmyCAT, we cherry-picked 6 proteomic hits related to different pathogenic pathways from the aforementioned results and examined whether they were indeed in proximity with the amyloid deposits using immunohistochemistry. We showed that these identified proteins colocalized well with amyloid deposits stained by ThS (Figure d). Together, these key proteins echoed some of the hypotheses about the etiology of AD in the literature at the tissue level, including the amyloid cascade hypothesis and neuroinflammation hypothesis. Such amyloid plaque deposition information partially reflected the AD pathological mechanisms that echoed literature reports (Figure e,f).

Conclusions

In this work, we described a DET photocatalyst “AmyCAT” that enabled selective labeling and proteomic profiling of amyloid proteins in AD brain tissues. The key to successful development of such a probe was matching the triplet state energies between the amyloid-targeted photocatalysts and various activatable substrates. The labeling selectivity originated from its effective photocatalytic activation of the diazo substrate via the Dexter energy transfer (DET) mechanism, rendering short-lived carbene intermediates and labeling radius. We showed that such photocatalytic labeling was generally applicable to different types of protein aggregates with wide residue coverage. We exemplified its application in labeling and profiling amyloid deposits in AD mouse brain sections. We revealed both AD biomarkers and key proteins associated with AD etiology that supported the amyloid cascade and neuroinflammation hypotheses. Overall, as the first DET-mediated photocatalyst targeting amyloid proteins, the AmyCAT probe enabled clear-cut labeling in ultraproximity to amyloid deposits with stringent spatial resolution.

Supplementary Material

au5c01509_si_001.pdf (6MB, pdf)
au5c01509_si_002.zip (20.3MB, zip)
au5c01509_si_003.xlsx (402.1KB, xlsx)

Acknowledgments

This work was supported by the National Natural Science Foundation of China (22222410, 22374148, 22494700, 22494702, and 22477102), Dalian Science Foundation for Distinguished Young Scholars (2022RJ04), Distinguished Youth Fund Program of Liaoning Province (2024JH3/50100009), Dalian Science and Technology Innovation Fund (2023JJ12WZ037), International Partnership Program of Chinese Academy of Sciences for Future Network (028GJHZ2023079FN), Innovation Program of Science and Research from the DICP, CAS (DICP I202245), and Fundamental Scientific Research Project of Liaoning Provincial Department of Education (LJ212410161028). Support from the Zhejiang Provincial Key Laboratory Construction Project is acknowledged.

Glossary

Abbreviations

AD

Alzheimer’s disease

DET

Dexter energy transfer

FRET

Förster resonance energy transfer

PET

positron emission tomography

SPECT

single-photon emission computed tomography

MRI

magnetic resonance imaging

NMR

nuclear magnetic resonance

UV

ultraviolet

PPL

protein proximity labeling

ROS

reactive oxygen species

DFT

density functional theory

GA

genetic algorithm

K d

dissociation constant

ET1

triplet excited energy

ES1

singlet excited energy

OD600

absorption at a wavelength of 600 nm

S/N ratio

signal-to-noise ratio

PCC

Pearson correlation coefficient

ThT

thioflavin T

ThS

thioflavin S

AIEgens

aggregate-induced emission luminogens

DBF

dibromofluorescein

PA

propargylamine

IPTG

isopropyl β-d-thiogalactoside

DTT

dithiothreitol

FP

fluorescent protein

GFP

green fluorescent protein

RFP

red fluorescent protein

DHFR

dihydrofolate reductase

Proteomic raw data have been uploaded to the ProteomeXchange Consortium repository of open source: http://www.ebi.ac.uk/pride/archive/projects/PXD060112.

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/jacsau.5c01509.

  • Supporting figures and tables, experimental methods, and synthetic characterizations (PDF)

  • Calculation methods and raw data (ZIP)

  • Data for Figure b (XLSX)

R. Sun performed the experiments and wrote the manuscript draft. J. Deng performed the experiments. D. Shen contributed to organic synthesis. Y. Huang contributed to the visualization. J. Yan and H. Feng contributed to the proteomic sample preparation. M. Li and Y. Ge contributed to the tissue section preparation. X. Zhang and Y. Liu conceptualized this work and edited the manuscript. CRediT: Rui Sun data curation, formal analysis, investigation, methodology, validation, visualization, writing - original draft; Jintai Deng data curation, investigation; Di Shen resources; Jing Yan resources; Yanan Huang visualization; Huan Feng methodology; Man Li resources; Yusong Ge resources; Xin Zhang conceptualization, software, supervision, writing - review & editing; Yu Liu conceptualization, funding acquisition, project administration, resources, supervision, writing - review & editing.

The authors declare no competing financial interest.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

au5c01509_si_001.pdf (6MB, pdf)
au5c01509_si_002.zip (20.3MB, zip)
au5c01509_si_003.xlsx (402.1KB, xlsx)

Data Availability Statement

Proteomic raw data have been uploaded to the ProteomeXchange Consortium repository of open source: http://www.ebi.ac.uk/pride/archive/projects/PXD060112.

Articles from JACS Au are provided here courtesy of American Chemical Society

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