Amyloid-ID: photocatalytic profiling of amyloid deposits in Alzheimer’s disease tissue

Domain: physics
Published: 2026-01-09
Editorial outcome: major_revision

Abstract
Deposition of amyloid proteins and their associated interactome is a hallmark of Alzheimer’s disease (AD) and other amyloidosis diseases, with their composition implying disease etiology. However, precise in-situ micro-dissection of amyloid deposits in AD brain tissue remains a challenge. In this work, we first divert the excited state energy of Thioflavin T from singlet fluorescence to triplet photocatalytic amyloid protein labeling through molecular engineering, while maintain its pan-amyloid binding affinity and selectivity. We further demonstrate that the amyloid labeling is catalyzed via type-I radical-based photosensitization with diverse residue modification sites. In female AD mouse brain tissue without homogenization, Amyloid-ID in-situ captures and profiles amyloid deposits, reliably reporting the often-lost tau biomarker. Finally, we provide comparative amyloidomics resources across 3 commonly used AD mouse models, revealing conjunct mitochondrial entangling pattern within amyloid deposits. Overall, we report a photocatalytic proteomics strategy (namely Amyloid-ID) to profile amyloid deposits directly from AD brain tissue.

Introduction
Neurodegenerative disorders (NDs), including Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), and amyotrophic lateral sclerosis (ALS), manifest diversely in clinics1. Despite various symptoms, NDs share conjunct molecular mechanisms and disease pathology2. While neuro-inflammation, mitochondrial damage, oxidative stress, and gut-brain axis metabolism hypotheses emerge, the amyloid hypothesis still dominates due to the mounting pathological evidence of amyloid deposits3–5. As a disease hallmark, pathogenic amyloid proteins, such as beta amyloid (Aβ)6,7, microtubule-associated protein tau (MAPT, tau)8, alpha-synuclein (α-syn)8, or TAR DNA-binding protein (TDP-43)8, co-aggregates with interacting proteins to form the amyloid deposits9. Over decades, efforts have been deployed for developing diagnostic tools and ameliorating therapeutics by targeting these amyloid deposits10–12.

Towards clinical diagnosis, much light has been shed on amyloid imaging reagents. Non-invasive positron emission tomography (PET) employs florbetapir F-18 as a radioactive probe to image Aβ plaques in AD patients’ brain13,14. Congo Red (CR) probe stands as a benchmark dye for general pathological diagnosis of pan-amyloid deposits in amyloidosis diseases’ tissue15. Fluorescent sensors, such as classical Thioflavin-T (ThT)16, curcumin derivatives17, BODIPY-derived probes18, and Aggregation-Induced Emission Luminogens (AIEgens)19,20, etc., have emerged as appealing alternatives to monitor fibrillation process in vitro and disease progression in animal models21. Despite their ability to precisely localize amyloid deposition site, these imaging reagents fall short in reporting on the highly heterogeneous protein composition of amyloid deposits.

Mass spectrometry (MS) based proteomics technologies can resolve the pathogenic amyloid proteins in neurodegenerative tissues22,23. Traditional methods enrich insoluble amyloid proteomes from homogenized AD brain tissues via centrifugation and label-free24 or TMT-labeled25 MS analysis. For spatially-resolved enrichment, laser capture microdissection (LCM) guided by the amyloid staining dye offered spatial resolution down to 5 μm range nearby amyloid deposits26,27. However, these two methods are either tissue-disruptive or high-end microscope-dependent. Proximity labeling technologies (PLTs) utilized engineered enzymes (e.g. peroxidases, biotin ligases, and miniSOG) or catalytic photosensitizers to label and profile targeted transcriptomes, sub-organellar proteomes, and protein interactomes28–34. Guided by either genetic tags or antibodies35, these PLTs have mapped neurodegenerative interactomes of TDP-4336, α-syn37, and phosphor-tau38. To the best of our knowledge, no small-molecule based PLTs39,40 have been reported to profile amyloid deposits in amyloidosis disease tissue using an amyloid-targeting photocatalyst, which offers improved amyloidome coverage, spatial resolution, and tissue compatibility for omics analysis.

Here, we develop a small molecule based photochemical proteomics method (namely Amyloid-ID) to profile the protein composition of amyloid deposits in AD mouse brain tissue based on the protein proximity labeling concept (Fig. 1a). This method is enabled by engineering a photosensitized Thioflavin T (ThT) derivative to photocatalytically label amyloid and adjacent interacting proteins. We show the optimal probe (P9) diverts its most singlet fluorescence to triplet photosensitized amyloid labeling using nucleophilic substrates, while preserving its pan-amyloid binding affinity and selectivity. Further tandem mass spectrometry (LC-MS/MS) analysis identifies broad chemoreactivity to amyloid protein residues. Photochemical characterizations reveal such labeling is mediated by type-I radical based reactive oxygen species (ROS) and ends up with diverse bioconjugation linkages. We first exemplify the Amyloid-ID technique to profile amyloid deposits, highlighting the technical advances in pinpointing the biomarker tau protein, frequently absent in prior proteomic analyses. Subsequent proteomic comparisons across three AD mouse models (3×Tg, rTg4510, and APP/PS1) reveal common mitochondrial entangling with amyloid deposits.

Results
MolecularengineeringofThTtowardsphotocatalyticproperties
Small molecules performing targeted amyloid proximity labeling require three functions: (1) pan-amyloid protein binding affinity; (2) triplet photosensitized ROS production; (3) high ROS yield to mediate nucleophilic substrate labeling. Inspired by Kanai’s work41,42 on amyloid-targeted photo-oxidizers, we aimed to develop probes with photocatalytic labeling function. We embarked on molecular engineering of Thioflavin T scaffold through substitution of heavy/hetero atoms (P1-P9), variation of charge (P1-P3)/D-A (P4-P9) effect, and conjugation extension (P7-P9, Fig. 1b). These molecular efforts intended to maximize the probe’s photosensitizing properties for protein labeling. In general, we observed opposite trend of Φ and Φ , indicating successful diversion of excited state energy from singlet fluorescence to triplet photosensitization (Fig. 1c, d, Supplementary Table 1). Notably, the optimal photosensitized ThT derivative (P9) exhibited a 62-fold increase in ROS efficiency compared to that of ThT (P0), accompanied by efficient fluorescence quenching (Fig. 1e).

To understand P9’s photo-properties (Fig. 1f), we performed time-dependent density functional theory (TD-DFT) analyses on ThT and P9’s singlet and triplet excited states. First, per perturbation theory, a smaller singlet-triplet energy gap (Δ ) and increased spin-orbit coupling (SOC, ξ) constants promoted a higher Φ rate43. Both ThT and P9 exhibited negligible Δ values (less than 0.37 eV, Singlet State 1-Triplet State 2 (S–T ) and S–T ) from basic energetic assessments (Fig. 1g). However, a substantial rise in SOC on S–T was observed in P9 (0.41 versus ThT’s 0.04), suggesting its enhanced potential for inter-system crossing (ISC) and downstream elevated ROS generation (Fig. 1g, Supplementary Fig. 1, Supplementary Table 2, 3). Additionally, the decrease in fluorescence lifetime from 1.05 ns for ThT to 0.25 ns for P9 also indicated the occurrence of inter-system crossing (Supplementary Fig. 2, Supplementary Table 4).

Further, we examined the photosensitization pathway by identifying the ROS types produced by P9. First, the T-S energy gap of P9 (1.36 eV) was lower than the energy required to excite 3O to 1O (1.61 eV), theoretically excluding the production of 1O (Supplementary Fig. 1). Second, via electron paramagnetic resonance (EPR), we exclusively observed type-I radical based ROS production, including superoxide anions and hydroxyl radicals (Fig. 1h), but not type-II singlet oxygen (Fig. 1i). These results were further confirmed by ROS indicators (Fig. 1j, k, and I, Supplementary Fig. 3, Supplementary Fig. 4). Overall, P9 photocatalyzed ROS production via type-I photosensitization pathway. We next examined whether all ThT-based probes generate radical ROS. We found that conjugation extension from P6 to P9 only enhanced ROS production without altering ROS species (Supplementary Fig. 5). Additionally, P9 of high water solubility tolerated wide range of concentration up to 30 µM without altering ROS species (Supplementary Fig. 6).

Photosensitized ThT retained pan-amyloid high binding affinity
We next assessed whether these derivatives retained ThT’s amyloid binding affinity after structural modulations. Using tau-K18 fibrils, we measured the probes’ binding efficiency via absorption partition into the insoluble fraction after centrifugation (Fig. 2a). Through this facile assay, we observed that conjugation extension (P7-P9) improved probes’ retention in insoluble fibrils (Fig. 2b), due to enhanced hydrophobic effect with the cross-β-sheet structures of amyloid fibrils (Supplementary Fig. 7). P9 of the highest ROS efficiency (Fig. 1c) and strong amyloid binding (Fig. 2b) showed almost identical kinetic profiles with ThT in tracking tau-K18 fibrillation, indicating its retained binding mechanism (Fig. 2c). This notion was also echoed by their similar binding sites predicted by molecular docking of P9 in amyloid tau (PDB: 7SP1), Aβ (PDB: 8OVM), and α-syn (PDB: 6A6B) (Fig. 2d, e, Supplementary Fig. 8).

P9 exhibited pan-amyloid binding, as evidenced by its successful monitoring of fibrillation of different proteins (tau, Aβ, transthyretin (TTR), & α-syn) using its residual fluorescence signal (Fig. 2f, Supplementary Fig. 9). Its dissociation constants (K ) for tau-K18, Aβ , TTR-WT, and α-syn were sub-micromolar at 0.44 μM, 0.26 μM, 1.10 μM, and 1.55 μM, respectively (Fig. 2g, Supplementary Fig. 9). P9 also showed strong retention (>80%) after multiple PBS washes, crucial for tissue staining applications (Fig. 2h, i). Overall, despite extensive scaffold modifications on ThT for photosensitization purpose, P9 retained its inherent high binding affinity towards pan-amyloid fibrils.

Photocatalytic labeling of amyloid fibrils by P9
Combining efficient photosensitized ROS production (Fig. 1) and strong binding affinity towards amyloid fibrils (Fig. 2), we anticipated P9 can perform selective photocatalytic labeling of amyloid fibrils. To this end, we assessed amyloid protein labeling catalyzed by P9 using a standard substrate, e.g. propargylamine (PA), commonly used in ROS mediated photocatalytic protein labeling44. Quantification of labeling efficiency on dot blot assay was achieved after clicking to a fluorescent TMR-azide reporter (Fig. 3a). First, we observed successful tau-K18 labeling catalyzed by P9 under white light (Fig. 3b, Supplementary Fig. 10). Among all probes, we confirmed that P9 emerged as the best probe for amyloid fibrils labeling (Supplementary Fig. 11). To exclude the possible photo-damage to amyloid fibrils by P9, we compared the morphology of tau fibrils before and after P9 photo-illumination and observed retained fibrillar feature (Supplementary Fig. 12). Notably, P9 also exhibited labeling selectivity to amyloid fibrils over the monomeric counterparts, with improved signal-to-noise ratios over time (Fig. 3c, Supplementary Fig. 13). However, P9 exhibited moderate efficiency in labeling oligomers, a conformation known to be more toxic (Supplementary Fig. 14). Mirroring the general binding to pan-amyloids (Fig. 2f, Supplementary Fig. 9), P9 photo-catalyzed proximity labeling of various types of amyloid fibrils, including Aβ , tau-K18-WT, tau-K18-P301L, α-syn, TTR-WT, TTR-S77Y, lysozyme (egg white), and insulin (Fig. 3d). Due to its pan-amyloid binding properties, the P9 probe exhibited limited selectivity in distinguishing between different tau and Aβ isoforms (Supplementary Fig. 15).

We next optimized labeling efficiency by exploring various labeling substrates and illumination wavelengths. Substrate 2 (S2, propargyl-PEG1-amine) emerged as the most suitable substrate for protein labeling in gel analysis (Fig. 3e). It is worth noting that labeling using substrate 3 (S3, propargyl-PEG1-thiol) appeared to be more effective, as confirmed by dot blot assays (Supplementary Fig. 16). However, the efficiency of labeling was notably reduced when samples were boiled in reducing loading buffer for gel analysis, indicating the S-based labeling linkage was labile in reducing environment (Fig. 3e). Considering that the reducing reagent was necessary for downstream MS sample preparation, S2 was chosen as the labeling substrate for subsequent experiments. Optimization of illumination wavelengths revealed that broad-spectrum white light (400-800nm) provided maximal labeling efficiency compared to monochromatic sources (Supplementary Fig. 17). The labeling efficiency was further evaluated and optimized in coordination with the probe’s concentration and light intensity (Supplementary Fig. 18). To verify the long-term labeling capability of P9 under prolonged illumination, we further characterized its photostability profile over a 30-minute illumination period (Supplementary Fig. 19).

We further confirmed that P9-mediated labeling of amyloid fibrils indeed relies on type-I ROS. We employed scavengers specific to the type-I or type-II pathway to ablate the corresponding ROS intermediates, respectively. Consistent with our findings (Fig. 1), Vitamin C as a scavenger for superoxide anion and hydroxyl radical, notably decreased the efficiency of amyloid labeling (Fig. 3f, lane 2), whereas the impact of singlet oxygen quencher sodium azide was insignificant (Fig. 3f, lane 3)45. Notably, hydrogen peroxide, a typical component of type I ROS, appeared ineffective in labeling proteins (Fig. 3f, lane 4).

Investigation of the amyloid labeling mechanism
As evidenced above, the photocatalytic amyloid fibrils labeling by P9 was mediated by type-I radicals. Another two downstream mechanistic questions were where it labeled (i.e. chemoselectivity if any) and how it labeled (i.e. modification linkages) on amyloid proteins, which jointly outlined plausible labeling mechanisms (Fig. 4a). We used LC-MS/MS to identify modification sites and products on tau-K18 and Aβ (Fig. 4b, c). A variety of amino acid residues were found to be modified (Fig. 4b–d, H, Y, C, F, and M). Notably, histidine labeling exhibited the highest frequency, well aligned with miniSOG photocatalytic labeling scenario (Fig. 4d)46, though miniSOG underwent type II ROS pathway. A two-step mechanism can be proposed for histidine modification based on LC-MS/MS results: (1) oxidation of H residue and (2) nucleophilic substrate modifications (Fig. 4e and Supplementary Fig. 11). Similar two-step labeling processes were also evidenced in cysteine, tyrosine, phenylalanine, and methionine (Supplementary Fig. 20–25) with additionally identified oxidized intermediates ready for bioconjugation by other potential nucleophilic substrates (Supplementary Fig. 26, 27). Such broad reactivity to different residues, in principle, may contribute to improve the labeling and proteome enrichment efficiencies for downstream proteomic analysis. Collectively, amyloid-ID probe photocatalyzed type I ROS to label amyloid proteins with broad residue coverage.

Profiling amyloid deposits in Alzheimer’s disease tissue
Alzheimer’s disease (AD) is the most common amyloidosis disease47,48 (Fig. 5a). We first exemplified Amyloid-ID (based on P9) to profile amyloid deposits in AD mouse models in basic medical application scenario. In 3×Tg AD model mice (APP Swedish, PSEN1 M146V, and MAPT P301L), ThS staining, immunofluorescence, and Congo Red polarization imaging together confirmed P9 stained the amyloid regions with Aβ amyloid and tau fibrils (Fig. 5b, Supplementary Fig. 28). The selectivity of P9 towards amyloid deposits in tissue was further consolidated by excluding the potential off-target staining of nucleus, lipofuscin, lipid materials, and ordered structured collagen fibers (Supplementary Fig. 29, 30). Finally, photocatalytic labeling was spatially-confined to amyloid regions as evidenced by fluorescence co-localization of P9 (red) and FAM-N (green) after clicking to alkyne handles of substrate molecules (Supplementary Fig. 31a). The labeling effect was correlated to the probe’s concentration (Supplementary Fig. 31b).

Next, to enrich amyloid proteome from AD brain tissues, we optimized the previous two-step click chemistry protocol (1st conjugation to propargylamine and 2nd click to azide enrichment handle) into one-step by amino-PEG2-biotin for direct protein enrichment purpose (Supplementary Fig. 31c). After enrichment, LC-MS/MS analysis differentially profiled 357 proteins with over 200 spectral counts in the AD samples (Fig. 5c and Supplementary Data 1). Among them, 59 (17%) were classified as neurodegeneration-related proteins according to KEGG analysis (Fig. 5c and Supplementary Data 1). Notably, 8 of the top 10 proteins were associated with neurodegenerative diseases (Fig. 5c and Supplementary Data 1). Metabolic proteins that play a key role in Alzheimer’s disease development, on the other hand, were found to comprise 76 out of 357 (21%) deposited proteins (Fig. 5d). By immunofluorescence validations of protein hits identified above, we showcased that the microtubule-associated protein 1A (Map1a, an early target of soluble Aβ-peptide), pyruvate kinase M (Pkm, implicated in Aβ production and exacerbation of cognitive impairment), and heat shock protein A8 (Hspa8, a potential molecular prognostic marker within the HSP70 family for Alzheimer’s disease), all co-localized well with amyloid fibrils stained by ThS (Fig. 5e and Supplementary Fig. 32).

In addition, we compared amyloid deposit proteomic datasets obtained by our Amyloid-ID and three previous works using label-free based MS24, TMT labeling based MS25, and laser capture microdissection (LCM)26 based MS analysis. Despite slight variations in mouse models across the studies, 79.6% (284) of the proteins were shared by these 4 datasets (Fig. 5f), indicating the reliability of this method. Out of all 357 hits, only 3 proteins (0.01%) were exclusively identified by our Amyloid-ID. Intriguingly, one out of these 3 proteins was MAPT (tau) causing tauopathies (Fig. 5f and Supplementary Table 5), which were absent in some previous datasets. The presence of MAPT protein (tau) in amyloid deposits was validated by immunofluorescence co-localization (Fig. 5g). Moreover, the microtubule actin crosslinking factor 1 (Macf1, critical for neuron-migration, neurite formation, synaptic function, and hippocampus-dependent learning and memory) exclusively identified by our method was also validated in amyloid deposits (Fig. 5g). The loss of tau fibrils in previous AD datasets may be attributed to the fact that fibrils tend to be incompatible to multi-step centrifugation-based protocol, highlighting the technical advance of in-situ labeling developed herein.

Proteomics comparison of amyloid deposits across AD mouse models
Finally, Amyloid-ID was used to compare the amyloidomic profiles across three AD mouse models (3×Tg, rTg4510, and APP/PS1) (Fig. 6a). Despite slight morphological differences (Fig. 6b), we found the proteomic uniformity of amyloid deposits across three models, with 410 up-regulated proteins shared in common, accounting for 80.1%, 59.2%, and 61.0% of all identified proteins in each group, respectively (Fig. 6c). Notably, we observed a high degree of similarity in AD-related proteins as categorized by KEGG, as depicted in their volcano plots (marked in colors, Fig. 6d–f). More than half of these proteins were mitochondria-localized (highlighted in red, Figs. 6d–f). Gene Ontology (GO) analysis further revealed that a substantial proportion of these shared proteins were functionally related to mitochondria (90 proteins, Fig. 6g, green bars), which was consistent with the KEGG analysis showing a strong association with metabolic pathways (Fig. 6g, blue bars). Mitochondrial proteins were also prominent in the metabolic interaction map (Fig. 6h, 16 of 53 proteins). Furthermore, the deposition of mt-Nd2, an important subunit of Mitochondrial Complex I, was validated by immunofluorescence co-localization across the three mouse models (Fig. 6i–k). This result highlighted interactions between amyloid proteins and mitochondrial proteome, which was also hinted in literatures49. Literatures describe that Alzheimer’s disease (AD) progression exhibits a strong correlation between clinical symptom severity and cerebral hypometabolism, primarily mediated through mitochondrial dysfunction50,51. As the brain’s predominant glucose oxidation pathway, the mitochondrial tricarboxylic acid (TCA) cycle serves as the central hub for bioenergetic flux, with its impairment directly contributing to the observed metabolic deficits in AD pathogenesis52. Together, despite their distinct AD manifestations, our proteomics results revealed similarity in amyloid pathological deposition shared by three different AD mouse models, including their protein types, functional clusters, and metabolic pathways.

Methods
Ethics statement
All animal experiments were performed in compliance with the institutional guidelines for the care and use of laboratory animals and were approved by the Animal Ethics Committee of Dalian Medical University (Approval No. AEE24084). All mouse models were commercially sourced with validated phenotypes.
The use of human samples (including cardiac valves, liver, and pancreatic tissues) was approved by the same committee (Approval Nos. PJ-KS-KY-2025-128, KY2024-241-02, and KY2024-437-01, respectively). These samples were obtained from patients with clinically confirmed diagnoses at the affiliated hospitals of Dalian Medical University. All samples were fully de-identified, and no demographic or sex/gender information was collected or considered in the study design. Written informed consent was obtained from all participants prior to sample collection, explicitly authorizing the use of their tissues for research purposes.

Statistics and reproducibility
This study was designed to evaluate the reliability of Amyloid-ID for binding, labeling, and analyzing amyloid components. No statistical method was used to predetermine sample size, with three independent replicates performed for each experiment based on common practice in methodological validation studies. No data were excluded from the analyses. Sample allocation was randomized in all experiments, though investigators were not blinded to group allocation during experiments and outcome assessment. Data are presented as mean ± SD. All findings were successfully reproduced across three independent biological replicates.

ROS quantum yield definition
The overall reactive oxygen species (ROS) production from probes under the illumination of white light illumination was determined using the 2,7-dichlorodihydrofluorescein (DCFH) assay. To the best of our knowledge, existing methods for measuring ROS quantum yield have focused on detecting singlet oxygen (1O ) quantum yield, with no method yet reported for measuring type I ROS quantum yield. In this study, we introduce a method for determining type I ROS quantum yield, as defined by the following equation:

Φ = K / A (1)

Where K was the oxidation rate constant of DCFH by the ROS produced by probes versus light illumination. A was the absorption ability of probes.

Labeling of amyloid fibrils for gel analysis and dot blot experiments
Amyloid proteins (2.0 mg·mL−1) and probes (20.0 μM) were incubated at room temperature for 30 min before the addition of 10.0 mM labeling substrate (final concentration). The resulting reaction mixture was exposed to white light (25.0 mW·cm−2) and precipitated with acetone at −20°C for 4 h. The resultant precipitated protein was redissolved in 10% SDS and then reacted with a freshly prepared click solution comprising 1.7 mM tert-Butyl 2,2,2-trichloroacetimidate (TBTA), 50.0 mM CuSO , 50.0 mM Tris (2-carboxyethyl) phosphine (TCEP), and 50.0 mM tetramethylrhodamine azide (TMR-N ) in the dark for 1 h. Acetone precipitation was repeated to removed unlabeled TMR fluorophore, and the obtained aggregated protein precipitate was subsequently dissolved in 10% SDS for further analysis using Dot blot assay and SDS-PAGE electrophoresis. The labeling outcomes were observed using the VISQUE InVivoSmart-LF bio-imaging system for dot blot experiments and the Tanon-2500BR system for SDS-PAGE imaging, respectively.

Labeled protein preparation for LC-MS/MS analysis
For the identification of oxidation sites in model proteins tau-K18-WT and Aβ , the amyloid proteins (2.0 mg·mL−1) and P9 probe (20.0 µM) were incubated at room temperature for 30 min. The resulting mixture was exposed to white light (25 mW·cm−2) for 1 h. Subsequently, the oxidized proteins were precipitated with acetone and then redissolved in 6.0 M urea.
For the identification of labeling sites in model proteins tau-K18-WT and Aβ , the amyloid proteins (2.0 mg·mL−1) and P9 probe (20.0 µM) were incubated at room temperature for 30 min. Then, 10.0 mM substrate probe was added to the reaction system before white light illumination (25 mW·cm−2) for 1 h. Subsequently, the labeled proteins were precipitated with acetone and then redissolved in 6.0 M urea.
The proteins were reduced using 5.0 mM DTT for 1 h and alkylated with 12.5 mM IAA for 20 min at room temperature in dark. The sample was diluted to a 1.0 M urea solution with 50.0 mM ammonium bicarbonate (ABC) before digestion with trypsin at an enzyme-to-protein ratio of 1:20 (w/w) at 37°C for 14 h. Subsequently, the resulting peptides were desalted using homemade C18 tips (5 μm, 150 A, Venusil XBP). To activate the C18 tips, solvent B (80% acetonitrile/H O, 0.1% Formic acid (FA), v/v) was added three times and centrifuged at 500 g for 5 min. After activation, equilibration with solvent A (0.1% FA/H O, v/v) was performed three times by centrifugation (500 × g, 5 min). The peptides were loaded onto the C18 tips, washed three times with solvent A by centrifugation (500 × g, 5 min) for desalting. Subsequently, the peptides were eluted using solvent B twice by centrifugation (300 × g, 10 min). The resulting peptides were then lyophilized and subjected to nano-LC-MS/MS analysis. All samples were processed in triplicate for data consistency.

Ex vivo fluorescence imaging on AD mouse model brain sections
The AD mouse models employed (3xTg (APPswe, PS1M146V, TauP301L), rTg4510 (Tau P301L), and APP/PS1 (APPswe, PS1M146V)) were all female at 12 months to ensure positive amyloid depositions. Age- and sex-matched wild-type (WT) mice (C57BL/6 strain) were used as the negative control in this work. Although the background mouse strain (C57BL/6) used herein is common in AD field, the choice of control group may impact the phenotype of AD models in term of their vulnerability to hAPP/Aβ effects, inflammation, neurodegeneration, and learning/memory abilities. In particular, one should notice that the proteomics discoveries based on background normalization will be strongly impacted by the choice of background strain. Therefore, the robustness of proteomics findings in this work is limited by the lack of multiple genetic backgrounds. One should seriously consider the effects of genetic manipulation of the disease model mice when using this method for AD biomarker discoveries.
The animals’ processing protocol was followed to previously reported methods with modifications58. Briefly, the neurodegenerative disease mouse models were humanely euthanized using CO . Subsequently, fresh mouse hippocampus tissue was fixed in 4% paraformaldehyde solution at 4°C for 24 h, followed by isolation of the hippocampus and rapid freezing in liquid nitrogen. The tissues were then macroscopically sectioned into 6.0 μm sections and stored at −20°C.
For ThS or probe staining: brain sections were directly immersed in 2% ThS or/and 20.0 μM P9 for 1 h, followed by three washes with PBS (5 min each). The sections were then sealed with an anti-fade medium and covered with glass slides before fluorescence imaging59.
For immunofluorescence imaging: brain sections underwent heat-mediated antigen retrieval using a sodium citrate buffer (10.0 mM, pH=6.0) for 20 min, and were subsequently blocked with 5% BSA for 30 min at room temperature. The sections were then incubated with primary antibodies overnight at 4°C, followed by 5 min PBS washing and incubation with corresponding secondary antibodies conjugated with Alexa 488 or 594 at room temperature for 2 h. Finally, the sections were sealed with an anti-fade medium and covered with glass slides prior to fluorescence imaging. Detailed antibody information was listed in Supplementary Table 7.
Fluorescence images were collected using Olympus® FV1000 MPE two photon laser scan confocal microscope. Excitation wavelength for blue channel: 405 nm; Green channel: 488 nm; Red channel: 543 nm.

Ex vivo proximity labeling imaging on tissue sections
Frozen sections were gradiently rewarmed to room temperature (4°C for 30 min and ambient temperature for 30 min). Three PBS washes were then conducted to remove OCT. For staining, a 20 µM P9 probe solution was applied to the sections for 30 min, followed by three washes with PBS, each lasting 5 min. Next, 10.0 mM propynylamine was added to the tissue, and the sections were washed once with PBS for 5 min. Residual liquid on the sections was carefully removed using lint-free paper. Click chemistry was performed by applying freshly prepared click reation reagent (1.0 mM tris(benzyltriazolylmethyl) amine, 10.0 mM CuSO , 10.0 mM tris(2-carboxyethyl) phosphine, and 0.25 mM FITC-azide) to the sections. The reaction was incubated in the dark at room temperature for 1 h. After the reation, the sections were washed three times with PBS to remove unbound reagents. Finally, the stained sections were imaged using fluorescence microscopes.

Ex vivo proximity labeling on tissue sections for MS analysis
Pretreatment protocols for AD brain frozen sections were the same as imaging sections. Next, tissue sections underwent antigen retrieval using a sodium citrate buffer (10.0 mM, pH=6.0) for 20 min. Subsequently, the sections were blocked with an endogenous biotin blocking solution for 30 min and stained with P9 (20.0 μM) for an additional 30 min. After three washes with PBS, the sections were treated with Biotin-PEG2-Amine (10.0 mM, CAS: 138529-46-1) for 10 min in the dark, followed by illumination under white light for 10 min. The illuminated sections were promptly washed with PBS and lysed with 6.0 M guanidine hydrochloride solution containing 1% protease inhibitor and 1% protein phosphatase inhibitor. Next, the 6.0 M guanidine hydrochloride buffer containing labeled proteins was diluted to 1.0 M with 50.0 mM NH HCO . The sample was then incubated with streptavidin agarose resin (17511301, Cytiva) at room temperature for 3 h with gentle rotation. The beads were washed twice with 6.0 M guanidine hydrochloride and 2.0 M NaCl solution and resuspended in 50 mM ABC. Subsequently, 10.0 mM dithiothreitol was added and incubated at room temperature for 1 h. Following this, a 20.0 mM iodoacetamide aqueous solution was added and incubated at room temperature for 30 min in the dark. After centrifugation, the beads were washed twice with NH HCO solution (50.0 mM) and then treated with trypsin at 37°C for 12 h at an enzyme to protein ratio of 1:30 (w/w). The released peptides in the supernatant were collected by centrifugation. Peptides were desalted and fractionated using homemade C18 tips (5 μm, 100 Å; Durashell). The C18 tips were activated, equilibrated, and then loaded with peptides, followed by washing twice with solvent A (H O with added ammonia until pH=10.0) for desalting. Elution was carried out using a mixture of solvent A and solvent B (acetonitrile with the same volume of ammonia added as in solvent A), resulting in 9 eluates (6%, 9%, 12%, 15%, 18%, 21%, 25%, 30%, and 80% B). Fractions were combined as follows: fractions 1 were obtained by combining 6% and 25%, fractions 2 by combining 9% and 30%, fractions 3 by combining 12% and 80%, and fractions 4, 5, and 6 by using 15%, 18%, and 21%, respectively. Following the same steps for desalting with homemade C18 tips, the peptides were then lyophilized and subjected to nano-LC-MS/MS analysis. Three biological replicates were conducted on brain sections from both non-neurodegenerative and neurodegenerative mouse models.

Mass spectrometry analysis
The dried tryptic digestion samples were initially re-dissolved in a solution containing 0.1% formic acid (FA) and subsequently analyzed using an Easy-nanoLC 1200 system coupled with an Orbitrap Fusion Lumos mass spectrometer (Thermo Fisher Scientific). For the mass spectrometry (MS) analysis, two mobile phases were used: mobile phase A, which consisted of 0.1% FA in HPLC-grade water, and mobile phase B, which was composed of acetonitrile with 20% water and 0.1% FA.
Peptide separation was performed using nano-RPLC (reversed-phase liquid chromatography) on a C18 capillary column (150 μm i.d. × 300 mm) packed with C18 silica particles (1.9 μm, 120 Å) from Dr. Maisch GmbH, Beim Brueckle, Germany. The column was maintained at 55°C, and the flow rate was set to 600 nL/min. The following gradient was applied for separation: starting from 3% B (mobile phase B) and increasing to 14% B over 30 minutes, followed by a gradient from 14% to 36% B over the next 65 min, then an increase from 36% to 46% B over 20 min, followed by a rapid increase to 95% B in 1 min, and finally maintaining 95% B for 4 min. The mass spectrometer was operated in positive ion mode.
DDA Mode: MS1 scans were performed with a resolution of 60,000 (at 200 m/z) covering the range from 350 to 1500 m/z. MS2 scans were conducted with a resolution of 15,000, with the first mass set at 110 m/z. The maximum injection times for MS1 and MS2 were 50 ms and 30 ms, respectively. During each full MS scan, the most intense ions with charge states ranging from 2 to 7 were selected for monomer fragmentation. The selection process was carried out with a cycle time of 1 second, using an isolation window of 1.6 m/z. Fragmentation of precursor ions was performed using data-dependent HCD (higher-energy collisional dissociation) mode with a normalized collision energy set at 30%.
DIA Mode: MS data acquisition in DIA mode was performed on the Orbitrap Fusion Lumos Tribrid using 28 variable windows covering a mass range of 350-1350 m/z. The resolution was set at 60,000 for MS1 and 30,000 for MS2. The AGC target was set to 4e5 for MS1 and 5e5 for MS2, with maximum injection times of 100 ms for MS1 and 50 ms for MS2. The HCD collision energy was maintained at 30%.

Mass data analysis
Spectral Library Generation in FragPipe was used the FragPipe computational platform (version 15) with MSFragger (version 3.2), Philosopher (version 3.4.13), and EasyPQP (version 0.1.9) to build spectral libraries. Peptide identification from tandem mass spectra (MS/MS) was performed using the MSFragger search engine, with DDA raw files as input. The protein sequence database used was Mus musculus (20422 entries, reviewed). For the MSFragger analysis, both precursor and fragment mass tolerances were set to 20 ppm. Up to two trypsin cleavages were allowed. Peptide length was set between 7 and 50 amino acids, and peptide mass was set between 500 and 5000 Da. Oxidation of methionine and acetylation of protein N-termini were set as variable modifications, while carbamidomethylation of cysteine was set as a fixed modification. The maximum number of variable modifications per peptide was set to 3.
DIA Data Analysis in DIA-NN For the analysis of DIA data, the search parameters in DIA-NN (version 1.8.1) were set as follows: precursor FDR 1%; mass accuracy at MS1 and MS2 were set to 5 ppm and 15 ppm, respectively; scan window was set to 0; isotopologues and match-between-runs (MBR) were enabled; protein inference was performed at the gene level; heuristic protein inference was enabled; quantification strategy was set to Robust LC (high precision); the neural network classifier was used in double-pass mode; and cross-run normalization was turned off. The universal library was used, and protein re-annotation was performed. In the library-based mode, the spectral library file generated from FragPipe was used. The main search settings were as follows: Trypsin/P with a maximum of 3 missed cleavages; protein N-terminal methionine excision enabled; oxidation of methionine and acetylation of protein N-termini were set as variable modifications; carbamidomethylation of cysteine was set as a fixed modification; peptide length was between 7 and 30 amino acids; precursor charge ranged from 1 to 4; precursor m/z ranged from 350 to 1350; fragment m/z ranged from 200 to 1800. The search results were further filtered with a q-value < 0.01 for protein groups at the library level.
The mass spectrometry data and search results have been deposited in the ProteomeXchange Consortium repository as an open-access dataset. The data can be accessed through the following link: https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD058639 (dataset identifier: PXD058639).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript by Feng et al. presents a photocatalytic proteomics strategy (Amyloid-ID) for in situ profiling of amyloid deposits using a rationally engineered Thioflavin T derivative (P9). Previously, such proximity labeling approach was only achieved by conventional antibody mediated labeling techniques (e.g. HRP). The molecular design in this work transforms a classical amyloid dye ThT into a functional photocatalytic probe, while preserving its pan-amyloid binding property. The authors demonstrate compelling applications in Alzheimer's disease mouse models, with detailed mechanistic characterizations to reveal type-I ROS-mediated labeling pathway. This is different from other labeling techniques, like APEX, miniSOG and HRP, in terms of their reactive intermediate and bioconjugation styles. Expansion of labeling chemo-reactivities may benefit labeling and enrichment efficiencies, as well as proteomics coverage.\nTechnically, this methodology addresses the limitations in current amyloid proteomics without tissue homogenization or involvement of high-end laser based micro-dissection instrument. While the work is appealing in terms of its chemical novelty, technical advances, and some biological insights for amyloid research, several key issues should be addressed to strengthen its impact before being considered to be published in Nature communications.\nMajor Concerns:\n1.Photo-damage concern on amyloid interactome upon long-term illumination. The authors have done extensive work to characterize P9's photochemical properties and amyloid-binding affinity. However, experimental validation is needed to confirm that P9 does not induce significant photo-damage to amyloid fibrils during labeling, which could alter their native interactome. Controls such as TEM imaging of fibrils pre- and post-labeling would strengthen the claims.\n2.Labeling selectivity concern of ThT towards amyloid deposit region. Small molecule-based labeling methods are widely favored due to their ease of use; however, their reliance on non-covalent interactions may raise concerns about off-target binding. For Amyloid-ID reported in this work, whether the ThT-based probe, P9, suffered similar problems on staining nucleus like ThT? Given that Alzheimer’s disease is age-associated, the presence of lipofuscin, a known age-related pigment composed of aggregated proteins, lipids, and other components, may also pose potential interference in amyloid-specific labeling. More experimental evidence should be provided to ensure spatial selectivity of ThT based probe.\n3.General applicability of this approach in other types of amyloidosis tissues. A key strength of this work lies in the development of a novel methodology for profiling the composition of amyloid deposits, which was effectively demonstrated in Alzheimer’s disease (AD) mouse brain sections. To further enhance the impact and generalizability of the Amyloid-ID technique, the authors are encouraged to consider validating this method across additional disease models involving amyloidosis, such as Parkinson’s disease (PD), transthyretin (TTR) amyloidosis, or, where feasible, in human tissue samples. Furthermore, the current study utilizes only frozen brain sections. Given the authors’ stated intention for potential clinical diagnostic applications, it would be highly valuable to discuss the compatibility of Amyloid-ID with formalin-fixed paraffin-embedded (FFPE) tissue samples, which are important for translational adoption.\n4.While the authors emphasize the antibody-free and gene-editing-free nature of Amyloid-ID, a more direct comparison with established methods (e.g., APEX, miniSOG) is lacking. A comparative table or dedicated discussion section summarizing the strengths and limitations of Amyloid-ID versus other proximity labeling platforms (in terms of labeling intermediates, labeling residues, ease of use, etc.) would be highly valuable for readers (refer to James Wells’latest Chemical Reviews).\n5.Amyloid-ID achieves amyloid labeling through type-I ROS generation, a relatively underexplored mechanism in proteins labeling. In contrast, previous ThT-based photosensitizers, such as those reported by Kanai’s group, primarily function via singlet oxygen (type-II) pathways. It would be informative for the authors to elaborate on whether the scaffold modifications, particularly the conjugation extension in P9, shifted the photosensitization mechanism from type-II to type-I. Additionally, while the manuscript claims that P9 retains similar binding characteristics to ThT, it also reports a lower dissociation constant (Kd), indicating enhanced binding affinity. The authors are encouraged to provide further structural insights or molecular docking analysis to clarify how these chemical modifications contribute to improved amyloid affinity and selectivity.\nMinor Technical Issues:\n1.Define abbreviations (e.g., TMT, AIEgens, S1-T3) upon first use for general readership clarity.\n2.Does P9 itself undergo photobleaching or degradation during illumination? Quantifying probe stability under labeling conditions would ensure reproducibility.\n3.Illustrate the detection mechanism of ROS-sensitive indicators, such as the reaction between hydroxyl radicals and HPF.\n4.The labeling efficiency for different amyloid subtypes (e.g., oligomers vs. fibrils) is not addressed. Given the pathological significance of oligomers, this could be explored.\n5.In legends of figure 3e, give full names of labeling substrates.\n6.Whether the light wavelength influences the labeling efficiency?\n7.Provide the exact number of biological and technical replicates for each experiment.\n8.Provide more details about the mouse models used (ages, genders, etc.) in this work for proper interpretation.\n9.Provide more details about the immunofluorescence validation methods (antibodies used, dilution factors, etc.).\n10.The identification of mitochondrial proteins within amyloid deposits is intriguing. The authors are encouraged to further contextualize this finding within the framework of established Alzheimer's disease (AD) pathology. Incorporating discussion and comparison with previously reported literatures would enrich the biological relevance and interpretation of the results.", "reviewer_2": "This is a proof-of-concept paper that explores a method for identifying amyloid interactome using a small molecule photocatalyst PC9. The catalytic oxidation produced type-I radicals, such as hydroxy radicals. The generated active oxygen species oxidized amino acid residues of His, Met, and Phe, among others, which subsequently reacted with alkyne or biotin tags. The MS analysis of the tagged proteins identified that mitochondrial proteins interact with amyloids. These results validated that the developed method can clarify the interactome of various amyloids. Although the identified proteins and biological results are not novel, the method is new. Therefore, this reviewer supports publication in Nat Commun. However, the following points need to be addressed before complete acceptance.\n1. The concentration of PC9 used is relatively high (20 µM). How did the results compare when using a more diluted catalyst? I am concerned that the high concentration induced catalyst stacking, leading to the activation of the type-I pathway. The authors should measure UV-VIS to determine if any stacked dimers are present.\n2. It is unclear what conditions the authors used as negative controls. Is the “normal brain” shown in Fig. 5a derived from normal mice? If so, how can the authors negate the effects of genetic manipulation of the disease model mice?\n3. The authors must assess the potential for amyloid selectivity, which could lead to complications due to false-positive proteins.\n4. Regarding AD mouse models, their ages should be noted in the figure legend or methods section. Is there a relationship between the age of mice (which corresponds to the maturity of amyloid fibrils) and the interactome profile?\n5. As an advantage of this method compared to previous methods, the authors identified tau as an interacting protein with Abeta. However, tau itself is amyloidogenic. Is it possible that the identified tau is a result of partial aggregation, not by interacting with Abeta amyloids?\n6. Can the authors discuss the effective distance between amyloids and detected proteins, especially in comparison to previous methods like APEX, miniSOG, Turbo ID, or carbene-mediated techniques?", "reviewer_3": "Yu Liu et al. investigated a novel strategy to dissect amyloid related proteins from Alzheimer’s disease (AD) brains entitled, “Amyloid-ID: photocatalytic profiling of amyloid deposits in Alzheimer’s disease tissue”. In this study, they developed a small molecule based photochemical proteomics method, namely Amyloid-ID, to profile the protein composition of amyloid deposits in three different AD animal model and highlighted the biomarker tau protein, frequently absent in prior proteomic analyses. The most important message of the current strategy was to show clear evidence of MAPT association with A  in three different AD model mouse, so far lost in the previous reports. Furthermore, they showed mitochondrion-amyloid beta deposits interactions with their original strategy. In conclusion, the paper is recommended to publish in Nature Communications, and there are some major issues to be discussed before fully accepted.\n1. Amyloid beta (A ) proteoform is an important determinant of single plaque polymorphism (see : Koutarapu, S., Ge, J., Dulewicz, M. et al. Chemical imaging delineates Aβ plaque polymorphism across the Alzheimer’s disease spectrum. Nat Commun 16, 3889 (2025). https://doi.org/10.1038/s41467-025-59085-7). In the submitted text, explanations about the rationale choosing A 1-40 for the bait sequence of senile plaque. By cryogenic electron microscopy (cryo-EM), it is evident that A 1-40 and A 1-42 generate completely different structure, and their distribution are quite different in human autopsied brains. If the authors have any data which discriminate between A 1-40 and A 1-42, it appeals many researchers of the field.\n2. The same issues with Tau-K18 and other MAPT isoforms.\n3. In applying Amyloid-ID to decipher molecular mechanism of systemic amyloidosis, what is the advantage in comparison with conventional immunohistochemistry. In AD case, they showed a novel finding of mitochondrion-amyloid beta deposits interactions, however, are there any evidence that current strategy is superior to prior methods in elucidating molecular mechanisms not for diagnosis or typing.\n4. How about considering advantage and disadvantage or limitation of Amyloid-ID application for human samples. Human post-mortem brain tissue-samples are heterogenous in pathological features as well as quality in a various clinical setting."}, "author_response": "Point by Point Response to Reviewer\nWe kindly thank all reviewers for carefully evaluating our manuscript and offering valuable\nfeedback. In this point-by-point letter, we elucidate how we incorporated all the constructive\nsuggestions and critiques from reviewers. To facilitate the verification of the changes made in\nresponse to these insightful comments, we have reproduced them verbatim below in italics. Our\nresponses to each comment are directly indicated below the comment in blue font. All revised\npassages in the manuscript that address these comments are highlighted in yellow background.\nWe hope that our revisions have satisfactorily addressed all the reviewers' valuable feedback.\nReviewer 1:\n[... Author responses to Reviewer 1 ...]\nReviewer 2:\n[... Author responses to Reviewer 2 ...]\nReviewer 3:\n[... Author responses to Reviewer 3 ...]"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors addressed my concerns. I support publication of this work in NC.", "reviewer_2": "The authors addressed all the concerns this reviewer raised to the initial submission. The revised manuscript is now acceptable.\nOne minor comment is that the product structure shown in Fig. 4a above \"amyloid labeling sites & mechanisms?\" is wrong (no O-NH bond in the actual products).", "reviewer_4": "Follow up on the concerns raised by Reviewer #3\nYu Liu et. al have provided detailed responses to all concerns raised by the reviewer, however there are a few points that would further strengthen the work (please see below).\nConcern #1 and 2.\nThank you for providing additional information about the affinity of the probe to different types of protein aggregates. Considering frequently co-occurring protein aggregates in human AD tissue (codeposition of tau and amyloid beta), is there a risk of simultaneous labeling and examination of very different protein deposits, obscuring interpretation of the results? Considering limited specificity of the probe, existing immunolabeling approaches offer a significant advantage compared to the method described in the manuscript.\nConcern #3. In applying Amyloid-ID to decipher molecular mechanism of systemic amyloidosis, what is the advantage in comparison with conventional immunohistochemistry. In AD case, they showed a novel finding of mitochondrion-amyloid beta deposits interactions, however, are there any evidence that current strategy is superior to prior methods in elucidating molecular mechanisms not for diagnosis or typing.\nThank you for addressing this comment. Fibrinogen aggregates are not always amyloid in nature: they can form clot-like structures, which ThT also binds to, further raising concerns about the specificity of P9 - this piece of evidence is missing a datapoint on fluorescence intensity of both probes with non-aggregated protein (panel g).\nConcern #4. How about considering advantage and disadvantage or limitation of Amyloid-ID application for human samples. Human post-mortem brain tissue-samples are heterogenous in pathological features as well as quality in a various clinical setting.\nThank you for providing a different application case for the probe. While these results indeed show promise, the authors should indicate whether the probe binds to non-amyloid ordered structured present in human samples, given their heterogeneity. This includes collagen fibers."}, "author_response": "Point by Point Response to Reviewer\nWe kindly thank all reviewers for carefully evaluating our manuscript and offering valuable\nfeedback. In this point-by-point letter, we elucidate how we incorporated all the constructive\nsuggestions and critiques from reviewers. To facilitate the verification of the changes made in\nresponse to these insightful comments, we have reproduced them verbatim below in italics. Our\nresponses to each comment are directly indicated below the comment in blue font. All revised\npassages in the manuscript that address these comments are highlighted in yellow background.\nWe hope that our revisions have satisfactorily addressed all the reviewers' valuable feedback.\nReviewer 1:\nThe authors addressed my concerns. I support publication of this work in NC.\nWe thank the reviewer for careful evaluation of our manuscript and support for its\npublication.\nReviewer 2:\nThe authors addressed all the concerns this reviewer raised to the initial submission. The revised\nmanuscript is now acceptable.\nWe thank the reviewer for favorable comments and support for the publication of our work.\nConcern # 1. One minor comment is that the product structure shown in Fig. 4a above \"amyloid\nlabeling sites & mechanisms?\" is wrong (no O-NH bond in the actual products).\nWe thank the reviewer for identifying the inaccurate schematic diagram in Fig. 4a. This has\nbeen corrected in the revised manuscript by removing the O-NH bond, as shown below.\nFig. 4 | Mechanism of amyloid labeling photocatalyzed by P9. a How did P9 photocatalytic\nlabel amyloid proteins?\nReviewer 3&4:\n[... Author responses to Reviewer 4 ...]"}, {"round": 3, "reviewer_comments": {"reviewer_4": "Thank you for your thoughtful responses to the comments. All issues have been addressed appropriately and the manuscript is now suitable for publication."}, "author_response": ""}]

Decision letter