A versatile platform for sequential glyco-, phospho-, and proteomics with multi-PTMs integration

Domain: physics
Published: 2026-01-28
Editorial outcome: minor_revision

Abstract
Serial multi-omic analysis of proteome, phosphoproteome, and glycoproteome is pivotal for elucidating drug mechanisms, discovering biomarkers, and identifying therapeutic targets. However, simultaneous multi-level post-translational modifications (PTMs) analysis via parallel processing is hampered by laborious, time-consuming procedures and inconsistent reproducibility. We present an integrated Multi-level PTMs-Proteomic Enrichment platform (MuPPE), enabling sequential glycoproteome, phosphoproteome, and proteome analysis from single biological samples. It combines protein aggregation capture with on-bead digestion and tandem enrichment, achieving superior reproducibility (CV 12.3% vs 17.6% conventional methods) while reducing processing time by 87.5% (4 hours vs 32 hours). MuPPE also enhances coverage, identifying more serum glycopeptides and brain phosphopeptides than other platforms. Applied to aging mouse cohorts, the platform uncovers tissue-specific PTMs remodeling and brain barrier dysfunction. For arsenic mechanisms of action, MuPPE reveals drug-induced PTMs crosstalk between glycosylation and phosphorylation-driven pathway regulation. MuPPE offers a transformative tool for advancing multi-omics insights across precision medicine and disease research.

Introduction
With the achievement of the Human Genome Project and the Human Proteome Project, the exploration of physiology mechanism and disease biomarkers on the genetic and proteomic levels has become possible. Yet the correlation between the proteome and the genome is not always straightforward due to the complexity of biological systems. One of the most significant factors is the widespread cooccurrence of post-translational modifications (PTMs) of proteins, such as phosphorylation, glycosylation, and acetylation. While these PTMs cannot be directly predictable and controlled from the genome, they constantly modulate the dynamic changes and functional states of proteins in response to various cellular conditions and external stimuli, alter protein function and stability. Decrypting proteomic PTMs can not only characterize mechanisms of action (MoA) for drugs, generate drug-specific PTM signatures, but also reveal potential tumor-specific biomarkers or drug targets. Thus, elucidating underlying biological mechanisms by integrated multi-level PTMs proteomics holds great promise and provides a deeper insight of functional proteome. However, the diversity of PTMs typically increases the complexity of multi-level PTMs proteomic research and then proposes great challenges to sample preparation before Mass spectrometry (MS) analysis. Therefore, establishing an integrated and serial enrichment approach that enables synchronous analysis of multi-level PTMs is inevitable with the single aliquot of sample input. Besides, serial processing of the same proteomic sample in contrast to the parallel samples processing is expected to yield a stronger correlation between multi-level PTMs proteomic analysis. This would generate more comprehensive proteomic datasets with different PTMs layers, significantly promoting subsequent biological information analysis.

Recently, a series of integrated proteomic sample preparation methodologies, e.g., practical SISPROT, in-StageTip (iST), pressure cycling technology-assisted, and single-pot solid-phase-enhanced sample preparation (SP3) methods, have been developed. Although these exciting methodologies have achieved significant advancements, they are primarily tailored for the proteome, yet they do not adequately account for the complexity of multi-PTMs. Parallel methods facilitate simultaneous analysis of different PTMs, while sequential methods permit stepwise enrichment process. However, these approaches are not integrated, face challenges such as low throughput, sample loss during multiple-step processes and the reproducibility concerns. These limitations can significantly impact the comprehensiveness and reliability of PTMs-proteomics, potentially leading to compromised results. Steven A. Carr et al. presented the MONTE enrichment workflow, enabling effectively serial, deep-scale analysis of immunopeptidome, ubiquitylome, proteome, phosphoproteome, and acetylome from the same tissue sample and offering an extensive view of disease biology. While powerful, this workflow was tailored for tissue samples, not for biological liquid samples and does not introduce other crucial PTMs, such as glycosylation. Therefore, it is essential to develop a universal sample preparation workflow that enables comprehensive, simultaneous analysis of the proteome and multi-level PTMs from the same biological sample, even in minor sample volumes or low concentrations of proteins. Meanwhile, this type of workflow should not only ensure the consistency during sample analysis for both the proteome and multi-level PTMs but also enhance the utilization efficiency of precious biological samples.

To address this issue, an integrated multi-level proteomic platform, termed MuPPE (Multi-level PTM-Proteomic Enrichment), was introduced and exemplified with the profiling of serial glycoproteome and phosphoproteome and proteome. MuPPE can achieve serial sample preparation in one single tube and exhibit potentials for high-throughput sample preparation. And this platform supports customized combinations of various multi-level proteomics according to individual research requirements. MuPPE was systematically evaluated through leveraging different types of biological samples (cell lysates, body fluids and tissues) and also displays its advantages for the samples of small volume or in low protein concentration such as cerebrospinal fluid (CSF). And its versatility was also demonstrated through interpreting the mechanisms of aging in multiple samples from mice cohorts of different ages and investigating MoA of arsenic trioxide with the NB4 cell line at multi-level proteomics. This concise and integrated platform brings the convenience to proteomic sample preparation, improving efficiency in multi-level proteomic analysis.

Results
Development of MuPPE: an integrated platform for streamlined multi-level proteome and PTMs analysis

General PTMs-proteomics workflows rely on fragmented, multi-step processing for the analysis of the proteome, glycoproteome, and phosphoproteome. These workflows introduce challenges such as sample loss, variability, and inefficiency (Fig. 1a). To overcome these limitations, we have developed the MuPPE platform-a unified pipeline that integrates protein aggregation capture (PAC), on-beads digestion, and sequential PTMs enrichment. As a proof-of-principle, this platform enables the simultaneous interrogation of the proteome, glycoproteome, and phosphoproteome (Fig. 1b). The MuPPE workflow commences with protein denaturation, reduction, and alkylation, followed by the introduction of a modified and scalable PAC method for efficient protein isolation. Proteins aggregate on hydrophilic beads, and on-beads digestion is employed to minimize sample transfers. Glycopeptides are enriched directly from these microspheres, while the flow-through is reserved for phosphopeptides enrichment and global proteome analysis. This design simplifies the workflow to just two tubes, eliminating intermediate transfers and enabling the potential for high-throughput automation. Overall, the MuPPE platform simplifies the workflow (requiring only two tubes), eliminating the need for multiple sample transfers and enabling the simultaneous exploration of the proteome, glycoproteome, and phosphoproteome. The streamlined workflow also endows MuPPE with the potential for easy automation in high-throughput applications. Furthermore, this platform facilitates the tailored integration of diverse multi-level proteomic approaches, in alignment with individualized research requirements. Meanwhile, the incorporation of PAC significantly enhances the applicability of MuPPE for samples with low protein abundance or small volume. The application of this platform to different biological samples will underscore its robustness and efficiency, leading to a more holistic understanding of biological mechanisms.

In-solution digestion is widely employed to enzymatically cleave proteins into peptides, facilitating subsequent MS analysis. This process is conducted in a homogeneous liquid phase without the need for immobilized substrates, making it suitable for a broad range of protein samples. Contrastively, PAC is an advanced technique and leverages the inherent tendency of proteins to form aggregates under organic solvent conditions, which can then be captured and isolated from the sample. We systematically compared MuPPE with the gold-standard in-solution digestion method using human serum and mouse brain tissue samples. In terms of MS pattern compatibility and minimal bias, the base peak chromatograms (Fig. 1c) showed that the MS patterns generated by the two methods were nearly identical, confirming the compatibility of MuPPE with downstream MS analysis. For global human serum proteome analysis, MuPPE equipped with Click-Maltose (Click-Mal) hydrophilic beads identified 313 protein groups, while in-solution digestion method identified 298 protein groups, with 297 shared ones (Fig. 1d). The overlap of identified proteins and the correlation of protein identification coverage (R²=0.88, Fig. 1e) demonstrated that MuPPE does not introduce significant bias, validating its reliability. Coefficient of variation (CV%) analysis (Fig. 1f) revealed that MuPPE yields more precise quantifications (mean CV=12.3% vs. 17.6% for in-solution digestion), attributed to reduced sample handling. For glycoproteome/phosphoproteome analysis in human serum, MuPPE identified 2640 glycopeptides (compared to 1520 identified by in-solution digestion) and 137 glycoproteins (compared to 121 identified by in-solution digestion); in mouse brain, MuPPE identified 4144 phosphopeptides (compared to 3778 identified by in-solution digestion) and 1586 phosphoproteins (compared to 1445 identified by in-solution digestion, Fig. 1g). Notably, the enrichment workflow of MuPPE and the in-solution method were both performed with Click-Mal beads and IMAC-Ti4+ beads for glycopeptide and phosphopeptide enrichment. These results clearly demonstrate that MuPPE outperforms the conventional in-solution method in PTMs and proteome identification efficiency. Additionally, we compared the compatibility of the in-solution method and the MuPPE method with TMT labeling experiments, and confirmed that MuPPE is compatible with TMT-based multiplexed quantification (Supplementary Fig. 1).

MuPPE enables streamlined and robust glycoproteomic analysis across diverse biological matrices

Glycoproteomics links proteome and glycosylation analysis, and enables the identification of the individual molecular features of all glycoproteins in different biological samples. In such analysis, the proteome and glycoproteome are typically evaluated independently using independent biological samples. To facilitate sequential identification of the proteome and glycoproteome, we optimized a glycoproteomic workflow within MuPPE that allows for subsequent enrichment of glycopeptides. PTMs enrichment efficiency hinges critically on enzymatic digestion-a pivotal step bridging protein capture and PTMs interrogation. To optimize this, we systematically varied tryptic digestion durations (1–12 h) within the MuPPE workflow using human serum, comparing the performance via protein and glycopeptide identifications (Fig. 2a–c). The comparative analysis indicated that a digestion time of under 3 h is sufficient to achieve nearly consistent identification outcomes (Fig. 2a) and glycopeptides (Fig. 2b), with glycopeptides selectivity remaining stable across these time points (Fig. 2c). This underscore that short digestion windows (≤3 h) suffice to maximize proteomic coverage while streamlining workflow timelines, aligning with MuPPE’s efficiency-focused design. Then we evaluated the enrichment performance of various hydrophilic beads, including both commercially available (ZIC-HILIC, Amino-NH2 bonded, SAX beads) and home-made beads (Click-Mal, HBS, Click-Cys beads) for N-linked glycopeptide enrichment. Click-Mal beads, reported widely applicable in glycopeptides enrichment, demonstrated the relative superior enrichment performance based on the numbers of identified N-linked glycopeptides and glycoproteins indeed (Fig. 2d, e). Considering the potential effects of beads surface charge properties and hydrophilicity, we hypothesize that the superior performance of Click-Mal maybe associated with the relatively neutral surface charge and strong hydrophilicity of the beads. Consequently, Click-Mal beads were employed for glycopeptide capture in all subsequent experiments. We then conducted glycopeptides enrichment using Click-Mal beads and observed distinct glycosylation profiles across various biological samples for the following work. The robustness of MuPPE was tested by examining the intra-laboratory reproducibility of glycosylation results with mouse brain lysates when MuPPE was processed by different operators (Fig. 2f). Remarkably, different operators can achieve almost consistent identification results and selectivity.

The MuPPE also exhibits no bias towards glycan types and provides valuable insights into glycosylation patterns (Fig. 2g). Specifically, sialylation levels exceeded neutral glycosylation levels in human serum but opposite in mouse brain, whereas they were comparable in human CSF consistent with previous reports. Collectively, MuPPE can achieve a precise and robust qualification at the glycoproteomic level. In contrast to a protein-rich biofluid serum, the low protein concentrations of CSF (0.15–0.45 mg/mL) pose additional challenges for glycosylation identification.

CSF is a biological sample with low protein concentration, posing significant challenges for efficient glycoproteome identification due to limited protein availability. Applying the MuPPE platform, we successfully identified an average of 5200 glycopeptides from just 50 μg of CSF protein (equivalent to ~142 μL of CSF) (Fig. 2h). Additionally, a significant overlap (~77%) of identified N-linked glycoproteins in human CSF across triplicates underscores the repeatability of the MuPPE workflow (Fig. 2h, inset). These results illustrated the improved glycopeptide enrichment capacity and glycosylation identification efficiency in CSF samples compared to previous benchmarks (Supplementary Fig. 2). The CV distribution across different glycoproteins for N-linked glycan type quantification also demonstrated the robustness (Supplementary Fig. 3). The reliable quantification of various glycoproteins and their associated glycans further implies the effectiveness of MuPPE in glycosylation studies (Supplementary Fig. 3, inset). By comparing our identified N-glycosylation sites with UniProt annotations, we observed that 30.0% (MB), 12.4% (HS), and 9.8% (HCSF) of sites were not annotated in UniProt, expanding the annotated N-glycosylation site landscape (Fig. 2i). We attribute this enhanced performance to the optimized PAC step, where the addition of organic solvents promotes efficient protein aggregation and immobilization, thereby minimizing loss and significantly improving protein recovery prior to enzymatic digestion. Collectively, these results support the feasibility of incorporating glycosylation enrichment workflow into MuPPE.

MuPPE benchmarking: validating simultaneous profiling of the proteome, glycoproteome, and phosphoproteome

Building on our previous findings that highlight the advantages of enriching glycopeptides prior to phosphopeptides with hydrophilic beads and commercial IMAC-Ti4+ beads, respectively, we adopted this sequential order in all subsequent MuPPE enrichment. We also assessed the efficacy of integrating sequential phosphopeptides enrichment into the MuPPE workflow, utilizing mouse brain lysate as a model sample. The flow-through from the initial glycoproteome enrichment was reserved for subsequent phosphopeptides enrichment, as mentioned before. The comprehensive MuPPE workflow at three proteomic levels is represented in Fig. 3a. We investigated the effect of sample input amounts on identification at PTMs levels and a summary of the glycoproteome, and phosphoproteome results for different sample loading amounts was depicted (Fig. 3b). The identified numbers of glycopeptides, glycoproteins, phosphopeptides, and phosphoproteins showed a gradual increase as the sample amount increased from 5 to 75 μg; however, when the sample amount further increased from 75 to 125 μg, the identification numbers plateaued. Notably, even at 25 μg of MB lysate, the method achieved approximately 60% of the phosphopeptides, 66% of the phosphoproteins, 53% of the glycopeptides, and 57% of the glycoproteins identified at the 75 μg level.

To elucidate the distinct characteristics of various enrichment materials and the method’s robustness following the integration of phosphopeptide enrichment into MuPPE workflow, we conducted a comparative analysis of enrichment properties for commonly used beads in phosphoproteomics, as well as the robustness of MuPPE. The study involved comparing different enrichment materials, such as TiO2 and IMAC with distinct metal ions (Ti4+ and Fe3+, Supplementary Fig. 4). The findings revealed that the IMAC-Ti4+ method yielded the highest number of identified phosphoproteins, highlighting its superior enrichment efficacy. Additionally, the workflow’s robustness was demonstrated when executed by two operators using TiO2, ensuring reliable and consistent results. Besides the mouse brain, we also applied this MuPPE workflow to identify the glycoproteome, phosphoproteome, and proteome of the mouse liver, heart, and kidney, respectively. Notably, MuPPE demonstrated broad adaptability to diverse organ-derived samples, with each tissue type exhibiting distinct identification results across multiple proteomic levels (Supplementary Fig. 5).

We initially attempted to incorporate individual phosphopeptide enrichment materials from the protein aggregation in the workflow, but the outcomes were disappointing. The number of identified phosphopeptides remained low, and the selectivity for phosphopeptides was not satisfied. We infer that this phenomenon, which aligns with observations reported in other studies, demonstrated this protein aggregation instability, likely due to the charge properties of the phosphopeptide enrichment materials, adversely affected the subsequent enrichment results (Supplementary Fig. 6). Consequently, the optimal workflow is to utilize phosphopeptide enrichment materials exclusively during the phosphopeptide enrichment, rather than as the initial protein aggregation materials. Collectively, the MuPPE workflow facilitates multi-level proteomic analysis by enabling sequential analysis of the glycoproteome, followed by the phosphoproteome from just single biological sample, previously only feasible through parallel processing of multiple sample aliquots. In multi-PTMs research, workflow duration is a critical parameter that directly determines overall efficiency. MuPPE excelled in time efficiency (total workflow <4 h vs. more than 32 h for others, Fig. 3c) and sample transfers (2 transfers vs. 5–10 transfers for others), positioning it as a leading method. These comparisons fully demonstrate the efficiency and advantages of MuPPE in multi-levels proteomic research. A radar plot (Fig. 3d) benchmarked MuPPE against five state-of-the-art methods, evaluating parameters such as on-bead digestion, trace sample applicability, sample compatibility, time efficiency, and minimized transfers numbers.

We compared MuPPE with conventional in-solution digestion protocols at the proteome level (Fig. 1), demonstrating its superiority in precursor, peptide, and protein identifications, as well as quantitative precision in serum samples. However, these comparisons primarily addressed proteomic profiling rather than systematic multi-PTMs benchmarking against dedicated enrichment strategies. To fill this gap, we evaluated MuPPE alongside four representative studies that reported simultaneous enrichment and identification of glycosylation and phosphorylation, including one work that enabled broader multi-PTMs capture. We summarized and visualized the workflows of all platforms and presented a side-by-side schematic to highlight methodological distinctions (Supplementary Fig. 7). To ensure methodological comparability, we benchmarked MuPPE against the conventional in-solution approach and two previously reported strategies under strictly matched conditions, including identical sample input, mass spectrometric acquisition parameters, database search settings, and data processing workflows. MuPPE yielded a substantially higher number of glycopeptide-spectrum matches than all three reference methods (Fig. 3e). Notably, the number of identified proteins and phosphopeptides were comparable between MuPPE and the other methods, indicating that the integrated multi-omics enrichment design did not compromise proteome or phosphoproteome coverage (Fig. 3f, g). The primary advantage of MuPPE in glycoproteomics lies in its enhanced glycan coverage and structural diversity, as well as its superior capacity to capture a greater proportion of medium-length and structurally complex glycopeptides (Fig. 3h, i). Peptide length distribution analysis (Fig. 3j) revealed that MuPPE achieved a more uniform distribution of identified peptide lengths and a higher overall number of peptides compared to the other three methods. This reflects MuPPE’s superiority in capturing peptides across a broader size range, enabling more comprehensive proteomic coverage. Additionally, by comparing the overall composition of glycan units identified by the four methods (Fig. 3k), MuPPE enabled improved characterization of glycan structural features. These combined results highlight MuPPE’s enhanced capability to dissect the complexity of glycoproteomes at both peptide- and glycan-structural levels, providing deeper insights into glycosylation biology.

MuPPE enables simultaneous profiling of proteome, glycoproteome, and phosphoproteome in the aging mice cohort

Aging is a natural biological process characterized by the gradual decline in physiological functions and an increased susceptibility to various age-related diseases. Despite extensive research into the molecular mechanisms of aging based on genomics, transcriptomics, and proteomics, there remains a significant gap in our understanding of how PTMs, such as phosphorylation and glycosylation, concurrently influence cellular processes in aging. This gap hinders our comprehensive understanding of the molecular mechanism of aging. In studies of physiological aging, certain mammalian brain regions yield minute tissue mass and limited peptide amounts, posing practical challenges for conventional proteomics input and statistical stability. To address this gap, we utilized the established MuPPE platform to perform a comprehensive and integrated analysis of phosphoproteome, glycoproteome, and proteome across different life stages, hoping to uncover new insights into aging-related mechanisms. The choroid plexus (CP), responsible for CSF production and forming a selective barrier between the central nervous system and bloodstream, is affected by aging, which impact blood perfusion and microstructural integrity. Yet a systematic view of age-dependent PTM changes in the CP remains lacking, owing to the limited input and analytical challenges posed by this minute tissue when using conventional enrichment workflows. To address this, we used the MuPPE platform to profile mouse CP (MCP), CSF (MCSF), and serum (Mserum) from 2-, 12-, and 24-month (M)-old cohorts (14 females and 14 males per group), generating matched phosphoproteome, glycoproteome, and proteome datasets (Fig. 4a). In the MCP samples, a total of 3845 N-glycosites were identified, corresponding to 30,153 unique site-specific glycan structures derived from 1,096 glycoproteins (Fig. 4b). In the MCSF and Mserum samples, we identified 3934 and 5442 unique site-specific glycan structures, respectively, associated with 511 and 510 unique N-glycosites from 200 and 197 N-glycoproteins (Supplementary Fig. 8). Approximately 45% of the identified glycosites in MCP samples were previously unreported in the UniProt database. Despite the previously extensive glycoproteomic analysis in serum samples, we still identified 14.9% of the glycosites as new. These results further demonstrate that MuPPE platform significantly expand the known glycosylation repertoire, offering valuable new data to advance understanding in this field (Supplementary Fig. 9). Figure 4c shows the distribution of high-mannose, fucosylated, and sialylated glycans on identified glycosites, which aligns with the patterns observed in previous study. Supplementary Fig. 10 presents the percentages of proteins with identified single to multiple glycosites (left panel) and the distribution of glycans on these glycosites (right panel), emphasizing the macro- and micro-heterogeneity inherent in protein glycosylation. Notably, the levels of macro-heterogeneity in glycoproteins from MCSF and Mserum are roughly similar, though Mserum glycoproteins exhibit greater micro-heterogeneity, reflecting broader glycosylation diversity of different biological samples.

Moreover, the analysis of digalactosylation (the addition of two galactose residues) revealed distinct sex- and age-related differences (Fig. 4d). Galactosylation levels decreased with advancing age, with higher levels observed in females compared to males. Notably, females exhibited a pronounced decline in galactosylation during middle age, whereas males showed a more gradual reduction over time (Supplementary Fig. 11). We then performed differential expression (DE) analysis on the glycopeptides identified in any two groups of the three age groups. Supplementary Fig. 12 showed that after FDR correction, there was no significant difference in the expression of glycopeptides between the 24M and 12M groups, and the comparison between the other two groups showed that the upregulated glycopeptides mainly decorated with fucosylated and neutral unmodified complex/hybrid glycans. We assessed potential protein-level confounding by pairing each quantified PTM site with its parent protein and plotting site log2 fold-change versus protein log2 fold-change for each contrast (Supplementary Fig. 13). The changes in N-glycopeptide levels between groups were not significantly correlated with the corresponding unmodified protein levels (R2=0.21). As a rule, when PTM regulation was not coupled to protein abundance (i.e., regulated sites with parent proteins showing no detectable change), we performed downstream analyses on site-level intensities after standard sample-level normalization. Because dividing by protein restricts coverage to sites with complete protein co-quantification, protein-division was not used for the primary results.

Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that pathways such as extracellular matrix-receptor interaction, focal adhesion, and PI3K-AKT signaling were enriched among positively regulated glycoproteins. Conversely, the pathways related to autophagy and lysosomes were enriched among negatively regulated glycoproteins. (Supplementary Fig. 14). Multiple identical upregulated glycopeptides in both Mserum and MCSF is associated with the selective permeability of the blood-brain barrier allowing their bidirectional exchange between the two. Then an upset plot was generated to illustrate the distribution of upregulated glycopeptides across MCP, MCSF, and Mserum samples (Fig. 4e). Ighm represents the initial antibody response from B cells upon encountering the infection and four Ighm glycopeptides (Ighm-280-H2N5; Ighm-280-H6N3F1; Ighm-280-H4N4F1; Ighm-280-H3N3F1 and Ighm-280-H3N4F1) significantly upregulated across all three sample groups. Notably, they are characterized by the presence of bisecting GlcNAc in their core-fucosylated glycans. This finding suggests that the core-fucosylated glycans attached to immune-related proteins are pivotal in the context of aging, acting as potential significant biomarkers for the study of aging and age-related diseases. Therefore, alterations in the glycosylation patterns of Ighm could be indicative of shifts in immune function, shedding light on immune deficiencies associated with aging or alterations in immune responses.

To gain systematic insights into PTMs in the mouse brain and their changes related to aging, we performed PTMs coregulation network analysis by using the weighted correlation network analysis (WGCNA). Then we performed module-trait association analysis to assess the correlation between each module PTM and aging-relevant phenotypic traits and other characteristics of MCP samples. For glycans, the eigengene dendrogram (Fig. 4f, bottom) summarizes inter-module relatedness and guided module merging (mergeCutHeight=0.25). Trait associations were then tested by correlating module eigengenes with Age and Sex (Fig. 4g). The network consists of one positively correlated glycan module (MEblue) and one negatively correlated glycan module (MEyellow) with significant association to aging status but no association with sex genotype (Fig. 4f, g). MEblue contained 30 glycans, including undecorated complex/hybrid glycans, paucimannose glycans with relatively simple, neutral structures (H2N2 and H3N2, H4N2, H3N3, H3N4, and H3N6) and bisecting GlcNAc-containing core-fucosylated glycans (H3N4F1, H3N5F1, and H3N6F1). In contrast, the MEyellow module with a strong negative correlation to aging (P<0.01) contained 23 glycans (Fig. 4h), which primarily consisted of sialofucosylated glycans (H9N8F1Sa3, H8N7F1Sa2Sg1, and H9N6F1Sa1Sg1), and highly branched and/or elongated glycans (H4N7, H6N6Sa2, and H6N6Sa1). The above results suggest that decreased sialylation and N-glycan branching or elongation is involved in aging.

In our analysis of the MCP samples, we identified 1823 phosphoproteins with 3779 phosphosites (Fig. 4b). A comparative DE analysis of the elderly and young groups showed that phosphosites upregulation peaked at 12M (Supplementary Fig. 15). The upset map plot revealed a dynamic pattern in phosphosite expression levels, with some initially downregulated then upregulated, and others the opposite (Fig. 5a). The changes in phosphosite relative abundance are mainly due to alterations in phosphorylation modification levels, rather than changes in protein expression (Fig. 5b). As for phosphosites, WGCNA analysis uncovered 22 co-regulated phosphosite modules (Supplementary Fig. 16). Among these, 4 phosphosite modules significantly associated with age, including 1 positively correlated module and 3 negatively correlated modules (P<0.01). Only one module showed a strong association with sex. We selected the modules with the strongest correlation with age (MEpurple and MEsalmon) and the module with the strongest correlation with sex (MEred) for Gene Ontology (GO) analysis in Supplementary Fig. 17. The results showed that the modules positively correlated with age were enriched in the functions related to mRNA, RNA processing and splicing, while the negatively correlated modules were most enriched in actomyosin structure organization, negative regulation of stress fiber assembly and neuronal cell homeostasis. Interestingly, the modules related to sex showed differences in learning, memory and synaptic function. We further conducted site-level pathway enrichment using PTM-SEA, which infers PTM regulators from substrate-specific signatures. As shown in Supplementary Fig. 18, compared with 2M controls, 24M samples displayed positive enrichment of the PI3K-AKT pathway and kinase-centric sets (AKT1, CDK2, SIK2, MAPKAPK5/RSK2), with higher normalized enrichment scores and significance than 12M, indicating progressive, age-dependent activation. Conversely, CK2/CSNK2A2, NLK, and ERK5/MAPK7 signatures were consistently negatively enriched at 12M and 24M. Together, these results indicate a shift toward enhanced AKT/cell-cycle kinase activity with a concomitant attenuation of CK2-linked regulation during physiological aging. Consistent with GO results, PTM-SEA indicates age-progressive activation of AKT/RSK-cell cycle signaling linked to RNA/spliceosomal regulation, with concomitant attenuation of CK2/ERK5/NLK pathways that support actomyosin architecture and neuronal/synaptic homeostasis.

Alterations in glycosylation enzymes and regulators are associated with various pathological processes, including the aging. However, the role of these changes in driving N-glycosylation and their broader implications for aging-related decline in brain function remain poorly understood. Here, we assessed the protein expression patterns of glycosylation enzymes and regulators in the MCP samples based on the proteome (Fig. 5c). In the juvenile stage, accelerated development is supported by Ctbs and Gba2, which facilitate extracellular matrix (ECM) remodeling through glycosaminoglycan and glycolipid degradation, while Fut11 refine glycan complexity to optimize cell adhesion and signaling. Glycosidases B3galt5, B3galt6, St6gal1, and St6galnac2, which are responsible for elongation and sialylation, are highly expressed during middle age and may enhance ECM stability and support tissue repair. Alg1, Alg2, and Alg5 enzymes involved in the synthesis of precursor oligosaccharides for N-glycosylation, play a critical role in proper protein folding and endoplasmic reticulum (ER) quality control. Their high expression in the elderly stage indicates increased ER stress. Concurrently, the elevated expression of Erlec1, a key protein in the ER-associated degradation pathway, suggests an enhanced mechanism to alleviate ER burden by targeting misfolded glycoproteins for degradation. This coordinated response underscores the cellular effort to maintain proteostasis and address the challenges of ER stress associated with aging.

Furthermore, our findings indicate that certain glycosidases exhibit abnormal phosphorylation regulation as age progressing (Fig. 5d). For instance, glycogen synthase (Gys1), which transfers glycosyl residues from UDP-Glc to the non-reducing end of α-1,4-glycans, shows increased phosphorylation levels with aging, resulting in reduced activity. Calnexin, an ER lectin chaperone that binds to monoglycosylated glycans from glucosidase II to maintain ER quality control by retaining misfolded proteins and promoting protein folding, shows a decline in phosphorylation levels with age. The interplay between distinct PTMs, such as phosphorylation, glycosylation, and acetylation, is a confirmed phenomenon that profoundly influences protein function. Here, we identified 205 instances of co-occurring N-glycosylation and phosphorylation events on the same protein in CP and investigated their correlation. In 24M mice, 20 modification pairs demonstrated a significant positive correlation (FDR<0.05), underscoring potential age-dependent regulatory correlation between these modifications (Fig. 5e). The most statistically significant example (FDR=4.29E-4) involves phosphorylation at S794 and N-glycosylation at N264 on Adam17, a disintegrin-metalloprotease-domain sheddase. Adam17 drives inflammatory signaling by converting pro-TNF-α to its soluble form and by shedding EGFR ligands, and sustained activation of this axis contributes to chronic inflammation-a hallmark of aging (Supplementary Fig. 19).

To investigate aging-related molecular changes, we conducted time-series clustering of the proteome, phosphoproteome, and glycoproteome across 2, 12, and 24M, followed by KEGG pathway enrichment analysis (Fig. 5f). In the phosphoproteome, pathways associated with aging, including oxidative phosphorylation, Alzheimer’s disease, and Parkinson’s disease, were enriched early in young mice and remained detectable throughout all age groups. The proteome exhibited significant enrichment in pathways such as long-term depression and peroxisome function, specifically at 24M, suggesting a delayed accumulation compared to phosphorylation-mediated changes. The ECM-receptor interaction pathway showed distinct regulatory patterns: glycoprotein levels increased progressively with age, while phosphorylation peaked at 12M before declining by 24M. Enrichment of the cytoskeleton pathway in muscle cells was observed in both the proteome and phosphoproteome, with an initial increase followed by a decrease; phosphorylation changes, however, declined more gradually. Several pathways were uniquely altered in specific PTM layers: focal adhesion decreased with age in the phosphoproteome, whereas oxidative phosphorylation was broadly enriched. In the glycoproteome, the lysosome pathway increased with age, along with upregulation of antigen processing and ECM-related processes. Overall, these multi-layer proteomic profiles reveal temporally distinct and pathway-specific modulation of PTMs during aging, highlighting the value of integrated PTM mapping in understanding molecular aging mechanisms.

Multiple proteomic comparisons among MCP, MCSF, and MSerum (Fig. 5g) reveal that CP aging contributes to impaired integrity of the blood-CSF barrier (B-CSFB) and neuroinflammation, characterized by glial activation (high Gfap expression), tissue remodeling (elevated IL1138 in CP), and enhanced systemic and central inflammation regulation (elevated IL18BP in serum and CSF). Although the B-CSFB is primarily composed of epithelial cells in the CP, the observed changes in endothelial markers (e.g., Vwf and CD31) may reflect the influence of vascular-associated processes on the microenvironment of the CP. These alterations could indirectly impact barrier integrity, highlighting the interplay between epithelial and vascular components in maintaining the functional stability of the B-CSFB. Tight junction changes show clear time-dependent patterns. Claudin-1 (Cldn1) and Claudin-5 (Cldn5) are significantly upregulated at 12M but decline at 24M, indicating mid-stage barrier strengthening followed by late-stage dysfunction. Claudin-2 (Cldn2) exhibits dynamic regulation, while Claudin-12 (Cldn12) shows sustained age-related upregulation, potentially compensating for barrier alterations (Supplementary Fig. 20). These findings highlight the complex regulation of tight junction proteins in the CP during aging, providing key insights into B-CSFB dysfunction.

Totally, our findings reveal that aging is associated with significant remodeling of glycosylation and phosphorylation patterns, which lead to ECM instability, ER stress, and chronic inflammation. The proteomic changes also impact B-CSFB integrity, increasing permeability and promoting neuroinflammation. By linking specific molecular changes to aging-related dysfunctions, this study advances our understanding of the complex regulatory networks underlying brain aging and highlights potential biomarkers and targets for therapeutic intervention. These findings, derived from our MuPPE platform, highlight its potential for advancing research in neurodegenerative diseases and therapeutic interventions.

Multi-dimensional insights of arsenic MoA revealed by MuPPE

Arsenic therapy has shown remarkable efficacy in treating acute promyelocytic leukemia (APL) by inducing apoptosis and promoting the differentiation of malignant cells. However, the specific mechanisms behind these effects, particularly regarding PTMs, have not yet been clearly reported. Further investigation at different molecular layers is essential to elucidate these mechanisms and advance our understanding of arsenic’s therapeutic potential in leukemia treatment. Based on the feasibility of MuPPE, we subsequently investigated the MoA of the arsenic on NB4 cell line. The NB4 cells were incubated with the arsenic of different concentrations (0~10 μM) for 6 h before cell lysis and then the samples were transferred to the MuPPE workflow for further multi-level proteomic profiling (Fig. 6a).

This whole MuPPE workflow was achieved within 4 h and totally ~5600 protein groups were identified. Further quantitative analysis revealed that 105 protein groups exhibited differential expression. Among these proteins, 15 differentially expressed protein groups were consistent with previous report, including the thioredoxin-like protein 1 emphasized in previous study. We also observed a distinct molecular landscape at the PTMs-level compared to the global proteome. Consistently, further analysis revealed that glycosylated and phosphorylated sites exhibit more abundant differential expression, thereby providing valuable insights at the PTMs-level (Fig. 6c, d). These findings align with the expectation that PTMs-based proteomic drug profiling is more informative than conventional single proteome analyses. Over 30% of glycoproteins with differentially expressed glycopeptides were categorized as enzymes (Fig. 6c, insets), while kinases accounted for a higher proportion of proteins associated with upregulated phosphorylated peptides (Fig. 6d, insets). These observations implicate potential functional variations in enzymes. It is worth mentioning that as the drug concentrations increase, the number of glycopeptides and phosphopeptides changes exhibits an opposite trend, while the total number of protein groups remains almost unchanged (Fig. 6e middle). Among the total glycopeptides and phosphopeptides identified, the number of differentially regulated ones tends to increase in a drug concentration-dependent manner. (Fig. 6e left and right). We also focused on glycopeptides and phosphopeptides that emerged or vanished upon drug treatment. As the arsenic concentration increased, the number of vanished glycopeptides exceeded that of emerged ones, whereas phosphopeptides showed the opposite trend. This indicates that these two PTMs are differentially regulated by the arsenic (Fig. 6f). Additionally, high-mannose glycans (Man5 to Man9) exhibited significant structural alterations (Fig. 6g). Kinase-substrate enrichment analysis (KSEA) revealed that arsenic treatment regulated a higher proportion of kinases from the AGC and CMGC families (Fig. 6h).

Building on the multi-omics dataset analysis, we propose a mechanistic model where arsenic exerts its effects through a membrane-to-nucleus signaling cascade (Fig. 6i), primarily mediated by glycosylation-modulated membrane proteins and phosphorylation-driven PI3K-AKT-mTOR pathway perturbation. The glycoproteomic data revealed that arsenic induces glycosylation alterations in membrane-anchored receptors (e.g., components of the PI3K-AKT-mTOR pathway upstream regulators). These glycosylation changes might act as molecular switches to modulate membrane protein stability, localization, and ligand-binding capacity. We also observed that the arsenic treatment significantly impacted the cell cycle and chromosomal organization pathways, particularly in the regulation of the proteome and phosphoproteome during the M phases. This suggests that arsenic exerts profound effects on these two critical signaling pathways, potentially by arresting the cell cycle and disrupting chromosomal functions, thereby promoting apoptosis. Flow cytometry analysis (Fig. 6j and Supplementary Figs. 21 and 22) revealed that arsenic-treated NB4 cells accumulate in the G2/M phase, which is consistent with the phosphorylation changes in cell cycle regulators (e.g., CDK, CCNB proteins in Fig. 6i), directly supporting this G2/phase arrest. GO enrichment results of nuclear functions in the key modules, combined with phosphoproteomic changes in chromatin-associated proteins (e.g., histone modifiers), suggest arsenic might disrupt genome-wide transcriptional programs and link membrane-to-nucleus signaling to nuclear architecture and function.

Focusing on IGF2R, a key membrane protein identified in the PI3K-AKT-mTOR pathway, we interrogated its individual proteomic, glycoproteomic, and phosphoproteomical alterations (Fig. 6k). At the global protein level, IGF2R abundance showed no significant fold-change between arsenic-treated and control groups. However, its glycoproteomic and phosphoproteomic data revealed notably PTMs-specific alterations. We identified 13 differential PTMs sites, including 12 N-glycosylation sites and 1 phosphorylation site. Upregulated and downregulated PTMs were denoted by upward and downward triangles, respectively. Without glycoproteomic/phosphoproteomic data, IGF2R would appear unchanged at the protein level, masking its potential role as a PTMs-driven hub. Thus, arsenic exerts its effects not by altering protein abundance broadly but by rewiring PTM-mediated signaling networks-a mechanism only detectable via multi-omics profiling.

Methods
Cell lines, mouse samples, and human samples source
NB4 cell line: NB4 acute promyelocytic leukaemia cells was purchased from the American Type Culture Collection (Manassas, VA) and cultured in DMEM (Gibco) containing 10% FBS (Gibco), 100 units/mL penicillin, and 50 units/mL streptomycin at 37°C in a CO2 incubator (95% relative humidity, 5% CO2). All reagents were purchased from Sigma unless otherwise indicated.

The study adhered to the guidelines set by the committee. For all of our animal experiments, we used mixed sex groups in the same ratio of male and female C57BL/6J mice in the range of 20-30×g of body weight up to 24M of age. Animals were kept under standard conditions in a specific pathogen-free facility at 20-24°C and 45-65% humidity on a 12 hours light/dark cycle and had access to food and water ad libitum. Mouse brain, heart, liver, and kidney tissues (utilized for MuPPE performance validation), along with mice CP, CSF, and serum (used for MuPPE practical applications), were carefully dissected and stored at −80°C. For detailed implementation steps, please refer to the Supporting Information (SI).

Processing of different biological samples
To obtain cell lysate from untreated and treated cells with the arsenic (0 or 10 μM), cell suspension was centrifuged at 1000×g for 5 min at 4°C, washed with cold PBS (phosphate-buffered saline, without calcium or magnesium) and pelleted before resuspension in lysis buffer (20 mM Tris, 150 mM KCl, 10 mM CaCl2, pH 8, 1% cocktail). Subsequently, the cells were lysed using lysis buffer through three freeze-thaw cycles in liquid nitrogen. The lysis buffer was snap-frozen, followed by thawing at 37°C using a metal bath until about half of the suspension was thawed. Then the samples were transferred onto ice until the entire content was thawed, followed by sonication using a probe. Cell debris was then removed by centrifuging at 14,000 g for 5 min at 4°C and the protein concentration was tested by using a BCA protein assay kit (Thermo Fisher Scientific, San Jose, CA, USA).

Frozen mouse tissues were resuspended in ice-cold 20 mM HEPES, 250 mM KCl, 250 mM NaCl, 10 mM CaCl2, pH 7.4 buffer, and subsequently homogenization was achieved using ceramic beads and an automated homogenizer with 2×15 s pulses, followed by sonication using a probe. Homogenates were clarified by centrifugation for 10 min at 14,000 g at 4°C and protein concentration was determined by the BCA protein assay kit.

The human or mice CSF and serum samples were diluted fourfold and 50-fold, respectively, with 1% SDS in 100 mM ammonium bicarbonate. Protein concentration was determined using a Nanodrop spectrophotometer, and 50 μg of protein from each sample was prepared for subsequent analysis on the MuPPE platform.

MuPPE process
The supernatant of cell lysate, tissues lysates, or other body fluid samples were immediately reduced with dithiothreitol (DTT, final concentration 10 mM) and heated for 10 min simultaneously, and then alkylated with iodoacetamide (IAA, final concentration 40 mM) in dark for 30 min before further processing. Aggregation was induced by the addition of acetonitrile and different beads were added to solution followed by mixing the beads solution uniformly. The mixed solution was allowed to settle for 10 min and then the beads were separated through centrifugation. After washed with 80% (v/v) ethanol, the beads were retained and the supernatant was removed. For eluting the beads bound protein aggregates, the 8 M urea solution was added and the samples were subjected to ultrasonic treatment for better solubilization. Then, the mixture was diluted with 5 mM NH4HCO3 to reduce the urea concentration below 2 M. Trypsin (enzyme-to-protein ratio, 1:50, w/w) was added for digestion at 37°C. For samples with higher protein content (e.g., cell lysates, tissue homogenates), digestion was performed for 4 hours to ensure sufficient proteolysis of complex protein components. For samples with lower protein concentrations (e.g., CSF), a shorter digestion time of 2 hours was used to avoid unnecessary over-digestion. And then the obtained samples were used for the subsequent enrichment experiments based on individual experimental purposes. The detailed enrichment procedures were provided in Supplementary Information.

Proteome identification
The peptides of flow-through from phosphopeptides or glycopeptides enrichment procedure for proteomic profiling were desalted using the C18 cartridge and dried down, and reconstituted in 0.1% FA prior to LC-MS/MS analysis.

In-solution digestion of human serum
One microliter of human serum was diluted 10 times with 8 M urea in 50 mM NH4HCO3 and denatured by reduction with 20 mM (final concentration) DTT for 45 min and alkylation with 40 mM (final concentration) IAA for 30 min at room temperature. Then, the mixture was diluted with 50 mM NH4HCO3 to reduce the urea concentration below 2 M. Then the sample was digested with trypsin at an enzyme-to-protein ratio of 1:50 (w/w) at 37°C overnight. Samples were acidified with formic acid at 1% (v/v) final concentration and the tryptic peptides were subsequently desalted using the C18 cartridge and then lyophilized.

MS data analysis
For proteomic data processing, DIA spectra were analyzed with Spectronaut (v19.0.240606.62635) using the default settings. Briefly, digestion enzyme specificity was set to Trypsin/P and specific. Upto 2 missed cleavages were allowed. Search criteria included carbamidomethylation of cysteine as a fixed modification, as well as oxidation of methionine and acetylation (protein N-terminus) as variable modifications. The false discovery rate (FDR) was set to 1% at the PSM, peptide, and protein level. Precursor and protein Q value cutoff was set to 1%. DIA files were analyzed in Direct-DIA mode, thus without spectral library, using the same settings for quantitative analysis. Direct-DIA was performed by Direct DIA+ Deep.

Glycoproteomic data were processed using Byonic software (v5.4.10, Protein Metrics) with a built-in database of 309 human N-glycans. The parameters for Byonic were set as the following: precursor mass tolerance was 6 ppm; fragment mass tolerance was 20 ppm; The digestion specificity was tryptic fully specific. The missed cleavages were set 2. Carbamidomethylation at C was set as fixed modification. Oxidation at M and deamidated at N were set as variable modifications. The total common max and total rare max were both set as 1. Quantification of intact N-glycopeptides was performed by using Byologic software (5.4.10, Protein Metrics), which uses inputs from both MS1 raw data and Byonic search results to determine glycopeptide intensities. The Byologic quantification results were filtered to score >150. For quantitative analysis of N-glycan modifications, glycopeptides were grouped by their glycan composition or glycan type, and the relative abundance of glycan modification for each glycan category was determined by the summed intensities of glycopeptides for the glycan category normalized by total intensities of glycopeptides for all N-glycan modifications in each sample.

For phosphoproteomic data processing, the raw data were processed using Spectronaut (v19.0.240606.62635) via the direct DIA+ workflow with mostly default settings against different sequence database (Mus musculus or Homo sapiens) from UniProt. Phosphorylation (STY) was set as a variable modification with PTMs localization activated, and the site confidence score cutoff was set to 0.75. Stringent data filtering was applied with a precursor q-value, precursor PEP, protein q-value cutoff (experiment), protein q-value cutoff (run), and protein PEP cutoff all set to 0.01.

Phosphosite/Glycan modification coregulation network analysis
A data-driven network analysis of phosphosite and glycan modifications was conducted using the WGCNA algorithm, facilitating the construction of a phosphosite/glycan network in the MCP samples from modification abundance profiles. We used WGCNA package in R to calculate a correlation matrix for all pairwise correlations of phosphosites/glycan modification abundances across all samples and then transformed it into a weighted adjacency matrix with a soft threshold power of 9 for phosphosites and 3 for glycan according to the scale-free topology criterion. The weighted adjacency matrix was used to generate a topological overlap (TO) matrix, and hierarchical clustering of phosphosites/glycans was performed using 1-TO as the distance measure. Modules of coregulated phosphosite/glycan modifications were identified using dynamic tree-cutting with the following parameters: minimal module size=50/10, deepSplit=2, merge cut height=0.25, and a reassignment threshold of P<0.01. Phosphosite or glycan coregulation module networks were graphically depicted by using the Cytoscape v.3.10.2 software. For each module in the phosphosite/glycan coregulation network, we calculated the module eigenphosphosite or eigenglycan as the first principal component of the coregulated phosphosites or glycans. Pearson correlations between each phosphosite/glycan and its module eigenphosphosite or eigenglycan determined module membership (kME), indicating intra-modular connectivity. Module-trait associations with aging and sex were analyzed using biweight midcorrelations and corresponding P-values.

Pathway temporal clustering and visualization
Proteome, phosphoproteome, and glycoproteome profiles across 2, 12, and 24M were Z-score normalized and subjected to fuzzy c-means clustering using Mfuzz (R, v2.60.0) to capture temporal expression patterns. Cluster-specific KEGG enrichment was performed with clusterProfiler (v4.10.0), retaining pathways with FDR<0.05, FC>1.5. The resulting heatmap integrates three layers of information: (i) Membership plots (left), showing cluster-wise temporal trends with membership values; (ii) Expression heatmaps (middle), displaying Z-scored expression values across 2, 12, and 24M; and (iii) KEGG pathways (right), annotated for each cluster, with functionally relevant pathways highlighted. Results were visualized as integrated membership trajectories, expression heatmaps, and enriched pathways using ggplot2 and ComplexHeatmap, ensuring full reproducibility in R (v4.3.1).

PTM-SEA analysis
For PTM-SEA analysis, phosphosites were ranked based on signed effect sizes derived from moderated t-tests. Enrichment analyses were then performed against PTMsigDB, supplemented with in-house curated glycosite datasets, to retain directional regulatory information (upregulation vs. downregulation). Normalized enrichment scores (NES) and P-values were reported for the results. The entire workflow was implemented in R, utilizing limma for statistical calculations and ssGSEA 2.0 for enrichment analyses.

Statistical information
Significance of difference in group means between each group was assessed by unpaired two-tailed Student’s t test, limma moderated t test, or Kruskal-Wallis test as indicated. For data visualization, heatmaps with Z-score values of normalized abundances were generated using the Heatmapper tool. Phosphosite or glycan coregulation network analysis was performed using the WGCNA algorithm to define network modules. Module-trait association was assessed by biweight midcorrelation between each module eigenphosphosite or eigenglycan and each trait with P<0.01 as the confidence threshold. Enrichment analyses were performed with one-sided Fisher’s exact test to calculate P values. Correction for multiple comparisons was performed using the q value or Benjamini-Hochberg FDR adjustment method as indicated.

Ethics statement
All experiments involving cell lines, animals, and human samples complied with institutional and national guidelines. NB4 cells were obtained from ATCC and cultured under standard conditions. Animal studies were approved by the Ethics Committee of the Center for Excellence in Molecular Cell Science (protocol 2025-001), using mixed-sex C57BL/6J mice housed under specific pathogen-free conditions. Mouse tissues and biofluids were collected following approved procedures and stored at −80°C. Human plasma and CSF studies were approved by the Ethics Committee of the Gan Jiang Chinese Medicine Innovation Center (protocol GJCMIC2025-008). Written informed consent was obtained from all participants involved in this study. All samples were anonymized and stored at −80°C until analysis.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript, Dong, Xiong et al present a new experimental approach to simultaneously analyse the glyco- and the\nphospho-proteome. In this regard, the title of the manuscript is misleading, since it refers to “Multiple PTMs-Proteomics” but\nthe workflow is only presented and validated with two PTMs.\nEven though they show in two independent datasets that the workflow is useful for this purpose, I consider that this\nmanuscript has currently two weaknesses: there is not a comprehensive benchmark to demonstrate the novelty of this\nstrategy against already published methodologies, and, there is not clear information or guidelines on how to integrate the\nthree layers of information (proteome, phosphoproteome and glycoproteome) that can be obtained with MuPPE.\nBelow you can find some major and minor points that might help strengthen the manuscript.\nMajor points.\n1. It is unclear from the results, the main methods and the supplementary material how the protein digestion is performed. In\nthis regard, several questions must be addressed:\na. Which is the concentration of DTT and IAA employed in this protocol? DTT can block IAA, and therefore is not compatible\nwith simultaneous reduction-alkylation, and DTT must be removed from the sample prior to addition to IAA. It is unclear how\nthis step is performed in this protocol. Please explain, and, in the case that IAA was directly added to a solution containing\nDTT, please ensure that you obtain complete carbamydomethylation.\nb. Which type of beads are used for protein aggregation capture-based digestion? The manufacturer is not indicated.\nc. Why do the authors perform the release of the proteins from the beads prior to digestion? This is not how the PAC protocol\nis stated in its original publication.\nd. In Figure 1, the authors compare the PAC digestion to in-solution digestion. However, it is not clear if the in-solution\ndigestion would be compatible with the downstream protocol they present (for instance: is desalting required after in-solution\ndigestion and prior to enrichment?). They should clarify.\ne. Regarding the reasons for choosing PAC as a digestion strategy, the authors indicate that “Integrating the PAC approach\ninto the MuPPE workflow enables glycosylation identification in CSF by promoting protein aggregation through the addition\nof organic solvents, thus increasing protein recovery during extraction”, and they refer to reference 17. I was not able to find\nthe corresponding data in that referenced publication to validate this affirmation. They should provide experimental\nvalidation for this.\n2. The manuscript presents several comparisons against published datasets, such as the one showed in figures 2d and 3d.\nIt is not clear from the text how this benchmark has been done? How did they retrieve the data from the published studies?\nDid they reanalize the published datasets in similar conditions as the ones generated for this work? If not, why? The authors\nshould expand of potential sources of the difference in performance: is it due to worse recovery, different acquisition\nstrategies, different MS instrumentation?\nMinor point regarding these analyses: which works are they referring to? For instance, ref 1 to 4 in figure 3d do not match\nreferences 1 to 4 in the bibliography.\n3. The initial purpose of this work is to present a workflow that integrates proteomics analysis at three levels: full proteome,\nglycoproteome and phosphoproteome. However, the two biological experiments used as proof-of-concept of this work fail to\nshow how to properly integrate and analyse these three layers simultaneously.\n4. Also, in this regard: are protein levels accounted to correct for changes at PTM level? Figure 6b refers to such issue, but it\nis not clear if they account for protein level contribution further or they just dismiss it based on this analysis.\nMinor points\n- The number of points used to generate some of the graphs is missing (for instance in figure 3d).\n- How are the CVs calculated in figure 1f?\n- There are several typos in the figures:\no figure 2h – Uniport instead of Uniprot.\no Figure 6g – Founction instead of Function\n- In the methods section it is said: “Trypsin (enzyme-to-protein ratio, 1:50, wt/wt) was added and proteins were digested at 37\n°C for different time based on the complexity of the samples”. How do they define complexity and how does it impact\ndigestion time?\n- Even if brief, some description of the LC-MSMS methods should be stated in the main methods section.\n- Figure 3b: x-axis is missing\n- In supplementary material:\no The dimensions of the column are not specified\no The information for the DIA acquisition is missing: mass range, number of windows, window size, injection time", "reviewer_2": "This manuscript by Dong et al. presents MuPPE (Multi-level PTMs-Proteomic Enrichment), a platform for simultaneous and\nsequential enrichment of the proteome, glycoproteome, and phosphoproteome from a single biological sample. The authors\ndemonstrate the utility of MuPPE across diverse sample types, including serum, CSF, cell lines, and mouse tissues, and\napply it to study arsenical drug response and aging. However, several concerns remain regarding the platform’s novelty,\nanalytical rigor, and generalizability.\n1. While MuPPE improves upon previous serial enrichment methods by being more time-efficient (4 h vs. 24 h) and\ncompatible with low-input samples, it does not provide a clear conceptual advancement over prior work, such as that by\nFang et al. (Anal. Chem., 2019) and Abelin et al. (Nat. Commun., 2023). The manuscript would benefit from a more explicit\ndiscussion of how MuPPE differentiates itself from these established platforms.\n2. The study exclusively uses label-free quantification. For broader applicability, it is important to demonstrate or at least\ndiscuss MuPPE’s compatibility with isobaric labeling strategies such as TMT. Without this, the platform is likely to remain\nlimited to exploratory analyses, restricting its potential for large-scale, multiplexed quantitative studies.\n3. Although MuPPE simplifies PTM workflows, it identifies substantially fewer phosphopeptides compared to state-of-the-art\nphosphoproteomic methods. This limitation may significantly reduce its utility for phosphorylation-centric studies. The\nauthors should benchmark their phosphopeptide recovery against existing methods and address whether the sequential\ndesign inherently limits phosphopeptide enrichment. Reduced detectability could profoundly impact biological discovery and\ninterpretation.\n4. The sequential nature of MuPPE leads to unequal input across glyco-, phospho-, and total proteome fractions, introducing\npotential biases in quantitation and reproducibility. The manuscript should address whether and how these differences are\nnormalized or controlled.\n5. The case studies using NB4 cells and aging mice are promising, but the biological insights remain largely descriptive.\nThere is minimal integration across PTM layers, and mechanistic interpretation is limited. Given that this manuscript is\nprimarily focused on technology development, a more in-depth comparison with existing platforms and a stronger discussion\nof\nbiological implications would be highly beneficial for readers.\n6. The manuscript requires extensive language polishing. For example, there are several sentences starting with “And”."}, "author_response": "RESPONSES TO REFEREES\n(All Fig.s, page numbers, etc. refer to the REVISED VERSION of the manuscript)\n*******************************************************************\nReviewer #1:\nIn this manuscript, Dong, Xiong et al present a new experimental approach to simultaneously\nanalyse the glyco- and the phospho-proteome. In this regard, the title of the manuscript is\nmisleading, since it refers to “Multiple PTMs-Proteomics” but the workflow is only presented and\nvalidated with two PTMs.\nResponse:\nWe appreciate the reviewer’s critical comment on the title’s accuracy. The original title, “A\nUniversal, Concise, and Versatile Platform for Enhanced Multiple PTM-Proteomics”, indeed\nimplied a broader scope of PTM analyses than what was experimentally validated in this study. To\naddress this, we have revised the title to: “A Versatile Platform for Sequential Glyco-, Phospho-,\nand Proteomics with Multi-PTMs Integration”. This revision better aligns with our core findings\nwhile acknowledging the platform’s potential for expanding to other PTMs. We have attached a\nmarked-up revised manuscript incorporating all changes. Every claim about “multiple PTMs” has\nbeen critically re-evaluated against experimental evidence. We are grateful for the opportunity to\nrefine our work’s precision.\nEven though they show in two independent datasets that the workflow is useful for this purpose, I\nconsider that this manuscript has currently two weaknesses: there is not a comprehensive\nbenchmark to demonstrate the novelty of this strategy against already published methodologies,\nand, there is not clear information or guidelines on how to integrate the three layers of information\n(proteome, phosphoproteome and glycoproteome) that can be obtained with MuPPE.\nResponse:\nWe highly appreciate the reviewer for the constructive comments. We fully acknowledge the\nconcerns regarding the lack of comprehensive benchmarking against existing methodologies and\nthe unclear integration of the three omics layers (proteome, phosphoproteome, and glycoproteome)\nenabled by MuPPE. To address these, we have undertaken substantial experimental and analytical\nrefinements, as detailed below.\n1. Comprehensive Benchmarking Against State-of-the-Art Platforms:\nIn the original manuscript, we compared MuPPE with in-solution digestion in Fig. 1, demonstrating\nits superiority in precursor/peptide/protein identification counts and quantitative precision on\nserum samples. However, we acknowledge that these comparisons primarily focused on proteomic\n1\nprofiling rather than systematic multi-PTMs benchmarking against specialized methods. To\naddress this gap, we have selected four representative studies (Nat. Commun., 2023, 14, 1851; Nat.\nProtoc., 2020, 15, 161; ACS Chem. Biol. 2019, 14, 58; Anal. Chem. 2020, 92, 1842; J. Chromatogr.\nA, 2025, 1739, 465525) involving simultaneous enrichment and identification of glycosylation and\nphosphorylation, along with one work (Nat. Commun., 2023, 14, 1851) enabling multi-PTMs\nenrichment, for comprehensive benchmarking against our MuPPE platform. We have summarized\nand visualized the workflow comparisons of all these platforms, presenting a side-by-side\nschematic illustration to highlight key differences (Fig. 1 and Fig. S7 in the revised version).\nFig. 1. (Fig. S7 in the revised version) Summary diagram of the sequential enrichment process for\ndifferent glycopeptides and phosphopeptides within state-of-the-art platforms according to the\nreports.\nIn Fig. 2. (Fig. 3c in the revised version), we specially present a comparative analysis of workflow\ntime durations. The bar chart quantifies total processing time for MuPPE, state-of-the-art multi-\nPTMs workflows, and conventional glyco/phosphoproteomic methods. This direct comparison\nprovides a clear quantitative basis for MuPPE’s time-saving advantages in multi-PTM analysis,\nhighlighting its efficiency in accelerating experimental workflows.\nprotein extraction to enrichment, showcasing reduced sample transfers and time efficiency.\nWe summarized and defined comparative parameters covering on-line proteolytic digestion,\nworkflow duration, trace sample applicability, sample type compatibility, accuracy &\nreproducibility, transfer times, identified omics types, labeling experiment feasibility, and\nidentification counts, all systematically presented in Table 1 and Fig. 3 (Fig. 3d in the revised\nversion).\nThe radar plot benchmarks MuPPE against five representative platforms across five performance\nparameters. MuPPE achieves the most extensive coverage in “on-bead digestion, trace sample\napplicability, sample compatibility, time efficiency, and minimized transfers”. These results\nunderscore its unique capacity for integrated PTMs enrichment, strong adaptability to\nlimited/heterogeneous samples, and capability for simultaneous multi-omics profiling. Shorter\nworkflow durations accelerate high-throughput analysis, reduced transfer steps minimize sample\nloss and technical variability, and lower protein input benefits to low-abundance samples (e.g.,\nclinical biopsies), firmly positioning it as a transformative tool for complex biological\ninvestigations.\nTable 1. The benchmarking parameters summary for the comparison of each platform\nproteomics methods across parameters, demonstrating MuPPE’s superior performance.\nFinally, to ensure methodological comparability, we benchmarked MuPPE against the\nconventional in-solution approach and two previously reported strategies under strictly matched\nconditions, including identical sample input, mass spectrometric acquisition parameters, database\nsearch settings, and data processing workflows. MuPPE yielded a substantially higher number of\nglycopeptide-spectrum matches than all three reference methods (Fig. 4e). Notably, the number of\nidentified proteins and phosphopeptides did not differ significantly between MuPPE and the other\nmethods, indicating that the integrated multi-omics enrichment design did not compromise\nproteome or phosphoproteome coverage (Fig. 4f and 4g). The primary advantage of MuPPE in\nglycoproteomics lies in its enhanced glycan coverage and structural diversity, as well as its superior\ncapacity to capture a greater proportion of medium-length and structurally complex glycopeptides\n(Fig. 4h and 4i). Peptide length distribution analysis (Fig. 4j) revealed that MuPPE achieved a more\nuniform distribution of identified peptide lengths and a higher overall number of peptides compared\n4\nto the other three methods. This reflects MuPPE’s superiority in capturing peptides across a broader\nsize range, enabling more comprehensive proteomic coverage. Additionally, by comparing the\noverall composition of glycan units identified by the four methods (Fig. 4k), MuPPE enabled\nimproved characterization of glycan structural features. These combined results highlight MuPPE’s\nenhanced capability to dissect the complexity of glycoproteomes at both peptide- and glycan-\nstructural levels, providing deeper insights into glycosylation biology.\nFig. 4. (Fig. 3 e-k in the revised version) e-h Quantitative comparisons across enrichment methods:\nglycopeptide spectrum matches (PSMs) (e), phosphopeptides (f), proteins (g), and glycans (h). i\nDistribution of glycan types identified across methods, showing relative proportions of high-\nmannose, fucosylated, sialylated, and complex glycans. j Peptide length distribution for\nglycopeptides captured by MuPPE versus conventional approaches. k Comparison of glycan\nstructural features, including glycan unit number across enrichment strategies.\nIn summary, the revised manuscript now (i) establishes MuPPE’s advantages through a structured,\nlike-for-like benchmark against state-of-the-art platforms (Fig. 3 c-k, Supplementary Fig. S7) and\n(ii) provides concrete, method-level instructions for integrating the three molecular layers\n(Methods; Supplementary Methods). We believe these changes directly address the reviewer’s\nconcerns and strengthen the manuscript’s clarity, novelty, and practical utility.\nManuscript changes: Page 10, line 245-279 revised manuscript:\nCollectively, the MuPPE workflow facilitates multi-level proteomic analysis by enabling\nsequential analysis of the glycoproteome, followed by the phosphoproteome from just single\nbiological sample, previously only feasible through parallel processing of multiple sample aliquots.\nefficiency. MuPPE excelled in time efficiency (total workflow < 4 hours vs. more than 32 hours\nfor others, Fig. 3c) and sample transfers (2 transfers vs. 5-10 transfers for others), positioning it as\na leading method. These comparisons fully demonstrate the efficiency and advantages of MuPPE\nin multi-levels proteomic research. A radar plot (Fig. 3d) benchmarked MuPPE against five state-\nof-the-art methods15, 31, 34-36, evaluating parameters such as on-bead digestion, trace sample\napplicability, sample compatibility, time efficiency, and minimized transfers numbers.\nWe compared MuPPE with conventional in-solution digestion protocols at the proteome level (Fig.\n1), demonstrating its superiority in precursor, peptide, and protein identifications, as well as\nquantitative precision in serum samples. However, these comparisons primarily addressed\nproteomic profiling rather than systematic multi-PTMs benchmarking against dedicated\nenrichment strategies. To fill this gap, we evaluated MuPPE alongside four representative studies\nthat reported simultaneous enrichment and identification of glycosylation and phosphorylation,\nincluding one work15 that enabled broader multi-PTMs capture. We summarized and visualized the\nworkflows of all platforms and presented a side-by-side schematic to highlight methodological\ndistinctions (Fig. S7). To ensure methodological comparability, we benchmarked MuPPE against\nthe conventional in-solution approach and two previously reported strategies31, 34 under strictly\nmatched conditions, including identical sample input, mass spectrometric acquisition parameters,\ndatabase search settings, and data processing workflows. MuPPE yielded a substantially higher\nnumber of glycopeptide-spectrum matches than all three reference methods (Fig. 3e). Notably, the\nnumber of identified proteins and phosphopeptides did not differ significantly between MuPPE and\nthe other methods, indicating that the integrated multi-omics enrichment design did not\ncompromise proteome or phosphoproteome coverage (Fig. 3f and 3g). The primary advantage of\nMuPPE in glycoproteomics lies in its enhanced glycan coverage and structural diversity, as well as\nits superior capacity to capture a greater proportion of medium-length and structurally complex\nglycopeptides (Fig. 3h-3i). Peptide length distribution analysis (Fig. 3j) revealed that MuPPE\nachieved a more uniform distribution of identified peptide lengths and a higher overall number of\npeptides compared to the other three methods. This reflects MuPPE’s superiority in capturing\npeptides across a broader size range, enabling more comprehensive proteomic coverage.\nAdditionally, by comparing the overall composition of glycan units identified by the four methods\n(Fig. 3k), MuPPE enabled improved characterization of glycan structural features. These combined\nresults highlight MuPPE’s enhanced capability to dissect the complexity of glycoproteomes at both\npeptide- and glycan-structural levels, providing deeper insights into glycosylation biology.\nSupplementary Data: Page 10-11, line 277-308.\nBenchmark workflow\nmg maltose beads for 10 min, followed by addition of 63 μL acetonitrile. Samples were washed\nthree times with 200 μL of 80% ethanol, then resolubilized in 8 μL of 6 M urea and diluted with\n40 μL of ammonium bicarbonate. Proteins were digested with 0.5 μg trypsin for 2.5 h, and the\nreaction was quenched with 2.6 μL TFA and 206.3 μL ACN. After 15 min incubation,\nglycopeptides were captured by centrifugation. Maltose beads were washed three times with 50 μL\nof 80% ACN/1% TFA and eluted with 30 μL of 30% ACN/1% FA (×3). Flow-through and wash\nfractions were combined, vacuum-dried, and incubated with 0.5 mg IMAC beads in 50 μL 80%\nACN/6% TFA for 1 h. IMAC beads were sequentially washed with 50 μL 80% ACN/6% TFA, 2\n× 100 μL 50% ACN/6% TFA/200 mM NaCl, 2 × 200 μL 30% ACN/0.1% FA, and 2 × 100 μL 30%\nACN. Phosphopeptides were finally eluted twice with 30 μL 10% ammonia solution and pooled.\nIn-solution digestion: Mouse brain lysates (20 μg) were denatured in 8 M urea with protease\ninhibitor cocktail (final 1 μg/μL). Proteins were reduced with 1 μL 200 mM DTT and alkylated\nwith 1 μL 400 mM IAA. After dilution with 140 μL ammonium bicarbonate, proteins were digested\nwith 0.5 μg trypsin. Digestion was quenched, and peptides were desalted using C18 SPE columns:\ncolumns were activated, samples loaded, washed with 20 mL water/0.1% FA, and eluted with 2 ×\n1 mL 50% ACN/0.1% FA. Eluates were vacuum-dried. For glycopeptide enrichment, 20 μg\ndesalted peptides were dissolved in 30 μL 80% ACN/1% TFA and incubated with 0.6 mg maltose\nbeads for 15 min. Beads were washed (80% ACN/1% TFA, 50 μL × 3) and eluted (30% ACN/1%\nFA, 30 μL × 3). For phosphopeptide enrichment, 20 μg peptides were dissolved in 50 μL 80%\nACN/6% TFA and incubated with 0.5 mg IMAC beads for 1 h. After washing as described above,\nphosphopeptides were eluted twice with 30 μL 10% ammonia solution and pooled.\nReference methods: For benchmarking, two reported workflows were included. In method 17, 20\nμg digested peptides were dissolved in 50 μL 80% ACN/1% TFA, loaded onto a maltose-packed\ntip column, and processed as described above. The flow-through was dried, resolubilized in 50 μL\n80% ACN/6% TFA, and incubated with 0.5 mg IMAC beads, followed by washing and elution as\nabove. In method 28, 20 μg desalted peptides were dissolved in 50 μL 80% ACN/6% TFA and\nincubated with 0.5 mg IMAC beads for 1 h. Beads were washed as above, and phosphopeptides\nwere eluted with 30 μL 10% ammonia (twice). The IMAC flow-through was concentrated, loaded\nonto OASIS MAX columns (1 mg), equilibrated with 95% ACN/1% TFA, and subjected to\nsequential washing and elution according to the original protocol.\nAdded new Fig.3c-k and Fig. S7.\n2. Systematic Integration of Proteome, Phosphoproteome, and Glycoproteome Data\nTo address the review’s concern regarding systematic integration of proteome, phosphoproteome,\nand glycoproteome data, we have undertaken a comprehensive re-analysis to uncover cross-omics\nassociations of aging and arsenic-treated NB4 cells dataset, as detailed below:\n7\n(ⅰ) For the aging dataset, we performed multi-layered KEGG pathway enrichment analyses (Fig. 5.\nand Fig. 5f in the revised version), allowing us to identify both shared and unique biological\nprocesses and pathways regulated at the total protein, phosphorylation, and glycosylation levels.\nNotably, pathways related to synaptic signaling, cell adhesion, extracellular matrix organization,\nand immune response were consistently enriched across all three proteome, phosphoproteome, and\nglycoproteome layers, suggesting that these fundamental processes are subject to coordinated\nmulti-PTM regulation. For example, the convergence of synaptic signaling and cell adhesion\npathways in proteome, phosphoproteome, and glycoproteome highlights their critical role in neural\nplasticity and tissue remodeling during aging and disease.\nIn contrast, metabolic pathways (e.g., energy metabolism, lipid metabolism) were predominantly\nenriched in the total proteome and phosphoproteome layers, whereas pathways associated with\nimmune modulation and glycan biosynthesis were more specific to the glycoproteome, reflecting\nthe specialized roles of different PTMs.\nThese findings demonstrate that multi-omics integration not only validates known biological\nthemes but also uncovers PTM-specific and multi-layered regulatory networks that would be\nmissed by single-omics analysis alone. The results and biological implications of this multi-omics\nintegration have been detailed in the revised Results and Discussion sections.\nFig. 5. (Fig. 5f in the revised version) Integrated KEGG enrichment analyses of proteome,\nphosphoproteome, and glycoproteome datasets.\n8\n(ⅱ) Based on multi-omics data analysis, we propose a mechanism where arsenic acts via a\nmembrane-to-nucleus signaling cascade. It modulates membrane proteins through glycosylation\nand perturbs the PI3K-AKT-mTOR pathway via phosphorylation, impacting cell cycle and\nchromosomal organization, possibly arresting the cell cycle and inducing apoptosis (supported by\nflow cytometry showing G2/M phase accumulation and phosphoproteomic changes in cell cycle\nregulators). Also, arsenic may disrupt genome-wide transcription by affecting chromatin-\nassociated proteins. Taking IGF2R, a key membrane protein in the PI3K-AKT-mTOR pathway, as\nan example, although its global protein level shows no significant change, multi-omics data reveal\n13 differential PTMs sites (12 N-glycosylation and 1 phosphorylation). This shows arsenic exerts\neffects by rewiring PTMs-mediated signaling networks, a mechanism only detectable through\nmulti-omics profiling.\nFig. 6. (Fig. 6i-k in the revised version) a Schematic of PI3K-AKT-mTOR pathway perturbation\nby arsenic, with downstream effects on cell cycle and chromatin organization. Proteome (green),\nglycoproteome (orange), and phosphoproteome (blue) alterations are mapped. b Flow cytometry\nanalysis of NB4 cells treated with increasing arsenic concentrations, showing dose-dependent\nG2/M phase accumulation. c Differential expression of protein relative abundance, glycosylation\nsites and phosphorylation sites of IGF2R protein with the log fold change values. Different\n2\nsymbols representing up-regulation (upward triangles), down- regulation (downward triangles),\nand the color intensity indicating the significance of the change.\nIn summary, across both applications, the organismal aging cohort and the arsenic-perturbed NB4\nmodel, our cross-omics framework reveals a coherent picture of multi-layer regulation: (i) system-\nlevel convergence of synaptic signaling, cell-adhesion/extracellular-matrix, and immune programs\nacross the proteome, phosphoproteome, and glycoproteome in aging, and (ii) The IGF2R case\nexemplifies this principle, where negligible protein-level change (log2FC≈0) co-occurs with\npronounced site-specific phosphorylation and glycosylation remodeling, indicating pathway\nrewiring that would be invisible to any single layer. Taken together, these results demonstrate the\nrobustness and generalizability of our integrative strategy, provide mechanistic links from PTMs\n9\nremodeling to pathway activity and phenotype, and address the reviewer’s request for systematic\ncross-omics integration and biological interpretation.\nManuscript changes:\nPage 17-18, Line 431-446 in revised manuscript:\nTo investigate aging-related molecular changes, we conducted time-series clustering of the\nproteome, phosphoproteome, and glycoproteome across 2, 12, and 24 M, followed by KEGG\npathway enrichment analysis (Fig. 5f). In the phosphoproteome, pathways associated with aging—\nincluding oxidative phosphorylation, Alzheimer’s disease, and Parkinson’s disease—were\nenriched early in young mice and remained detectable throughout all age groups. The proteome\nexhibited significant enrichment in pathways such as long-term depression and peroxisome\nfunction specifically at 24 M, suggesting a delayed accumulation compared to phosphorylation-\nmediated changes. The ECM-receptor interaction pathway showed distinct regulatory patterns:\nglycoprotein levels increased progressively with age, while phosphorylation peaked at 12 M before\ndeclining by 24 M. Enrichment of the cytoskeleton pathway in muscle cells was observed in both\nthe proteome and phosphoproteome, with an initial increase followed by a decrease;\nphosphorylation changes, however, declined more gradually. Several pathways were uniquely\naltered in specific PTM layers: focal adhesion decreased with age in the phosphoproteome, whereas\noxidative phosphorylation was broadly enriched. In the glycoproteome, the lysosome pathway\nincreased with age, along with upregulation of antigen processing and ECM-related processes.\nOverall, these multi-layer proteomic profiles reveal temporally distinct and pathway-specific\nmodulation of PTMs during aging, highlighting the value of integrated PTM mapping in\nunderstanding molecular aging mechanisms.\nPage 28-29, Line 722-731 in revised manuscript:\nPathway temporal clustering and visualization: Proteome, phosphoproteome, and glycoproteome\nprofiles across 2, 12, and 24 M were Z-score normalized and subjected to fuzzy c-means clustering\nusing Mfuzz (R, v2.60.0) to capture temporal expression patterns. Cluster-specific KEGG\nenrichment was performed with clusterProfiler (v4.10.0), retaining pathways with FDR < 0.05, FC>\n1.5. The resulting heatmap integrates three layers of information: (i) Membership plots (left),\nshowing cluster-wise temporal trends with membership values; (ii) Expression heatmaps (middle),\ndisplaying Z-scored expression values across 2 M, 12 M, and 24 M; and (iii) KEGG pathways\n(right), annotated for each cluster, with functionally relevant pathways highlighted. Results were\nvisualized as integrated membership trajectories, expression heatmaps, and enriched pathways\nusing ggplot2 and ComplexHeatmap, ensuring full reproducibility in R (v4.3.1).\nIntegrated proteome, glycoproteome, and phosphoproteome analysis of aging mouse samples\nand NB4 cells using the MuPPE platform: For each aging mouse sample (choroid plexus, CSF,\nand serum) and for NB4 cell lysates, total protein concentration was quantified using the BCA\nProtein Assay Kit (Beyotime, China) following the manufacturer’s instructions. Exactly 50 μg of\ntotal protein from each sample was used as input for the subsequent MuPPE workflow. In MuPPE\nprocess. Denaturation, PAC and on-line digestion on Click-Maltose, Then, adjust the tryptic\nsample solution to 80% ACN/1% trifluoroacetic acid (TFA) by adding ACN and TFA and incubate\nfor 10 min for glycopeptides enrichment. Finally, adjust the flow-through condition for\nphosphopeptide enrichment according to the manufacturer’s instructions, and then incubate for\n10 minutes to facilitate phosphopeptide enrichment. The remaining (flow-through) peptides not\nbound in the glyco/phospho enrichment steps were retained for proteomics. All peptide fractions\nwere analyzed using high-resolution LC-MS/MS platforms.\nFor data processing and integration across the three layers, contaminants and decoys were removed\nand PTMs sites were required to meet localization probability thresholds defined by the respective\nsearch engines; raw intensities were normalized at the run level (total ion current, TIC) and\nsubsequently log2-transformed. To ensure both depth and reliability, layer-specific coverage\nthresholds were applied: features in the proteome and phosphoproteome were retained only if\nquantified in ≥80% of replicates within at least one group, whereas the glycoproteome retained\nfeatures quantified in ≥50% of replicates, acknowledging the higher intrinsic sparsity of\nglycopeptide data. Missing values were then handled in a layer-specific manner: for the proteome\nand phosphoproteome, partially missing entries were imputed within layer on log2-transformed\ndata using the k-nearest neighbors algorithm (KNN, k = 5), while for the glycoproteome, missing\nvalues were treated as true non-detections and replaced with 0 at the raw scale (followed by ε + log2\ntransformation to avoid undefined values). Finally, for cross-layer comparability, all features were\nZ-scored within each layer (per feature across samples) before integration and downstream\nstatistical analyses.\nBelow you can find some major and minor points that might help strengthen the manuscript.\nMajor points.\n1. It is unclear from the results, the main methods and the supplementary material how the protein\ndigestion is performed. In this regard, several questions must be addressed:\na. Which is the concentration of DTT and IAA employed in this protocol? DTT can block IAA,\nand therefore is not compatible with simultaneous reduction-alkylation, and DTT must be\nremoved from the sample prior to addition to IAA. It is unclear how this step is performed in this\n11\nprotocol. Please explain, and, in the case that IAA was directly added to a solution containing\nDTT, please ensure that you obtain complete carbamydomethylation.\nResponse:\nWe appreciate the reviewer’s concern about the use of DTT and IAA in our protein digestion\nprotocol. In our study, we adopted an online processing workflow and added an excess amount of\nIAA without an additional step to remove DTT. Here is our explanation for this operation: In the\nMuPPE protocol, we adopted a single-pot reduction-alkylation strategy by adding excess\niodoacetamide (IAA) directly after DTT treatment. This decision was based on eliminating the\nultrafiltration step (to remove DTT) streamlines sample processing, reducing potential sample loss\nand variability, which is critical for trace sample analysis. During the operation of the process,\nprotein aggregation begins after IAA alkylation, which removes excess IAA and reduces the\npossibility of IAA side reactions. This protocol also aligns with current best practices in high-\nthroughput proteomics (Molecular & Cellular Proteomics, 2020, 19, 209-222; Nature\nCommunications, 2023, 14, 1851; Cell, 2020, 182, 245-261). We have updated the Methods section\n(Page 26, lines 654-656): The supernatant of cell lysate, tissues lysates, or other body fluid samples\nwere immediately reduced with dithiothreitol (DTT, final concentration 10 mM) and heated for 10\nmin simultaneously, and then alkylated with iodoacetamide (IAA, final concentration 40 mM) in\ndark for 30 min before further processing.\nb. Which type of beads are used for protein aggregation capture-based digestion? The manufacturer\nis not indicated.\nResponse:\nWe apologize for the omission of details regarding the beads used for protein aggregation capture-\nbased digestion. In the revised manuscript, we have supplemented this information in the revised\nSupplementary Information: Click-Mal, Click-Cys and HBS beads employed were both home-\nmade according to our previous work (Chem. Eur. J., 2009, 15, 12618-12626; J. Chromatogr. A,\n2012, 1228, 175-182; Anal. Chem., 2017, 89, 3966-3972). The commercial ZIC-HILIC beads were\nobtained from Merck's SeQuant® ZIC®-HILIC HPLC columns. NH beads and SAX beads were\n2\nobtained from Zhejiang Acchrom Technology Co., Ltd.\nThis update has been added to the “Methods” in revised Supplementary Information (Page 5, lines\n112-116) section to ensure clarity and reproducibility of the workflow. Thank you for drawing our\nattention to this oversight.\nc. Why do the authors perform the release of the proteins from the beads prior to digestion? This is\nnot how the PAC protocol is stated in its original publication.\nResponse:\ndescribes adding digestion buffer (50 mM HEPES, pH 8.5) directly to the beads for on-bead\ndigestion. Tubes were removed from the magnetic rack and 400 µL of digestion buffer (50 mM\nHEPES, pH 8.5) was added to the tubes.\nIn our workflow, we introduced a slight modification: after protein aggregation capture, we\nreleased proteins from the beads using 8 M urea solution prior to digestion. This adjustment was\nintended to enhance protein solubility-urea, as a strong denaturant, helps disrupt residual\naggregates and unfold proteins more thoroughly, thereby improving trypsin accessibility and\ndigestion efficiency, especially for proteins with high hydrophobicity or complex secondary\nstructures. We hypothesized this would increase peptide yield and coverage, which aligns with our\ngoal of maximizing detection sensitivity. Thank you for highlighting this detail, which has helped\nus improve the reproducibility of our protocol description.\nd. In Fig. 1, the authors compare the PAC digestion to in-solution digestion. However, it is not\nclear if the in-solution digestion would be compatible with the downstream protocol they present\n(for instance: is desalting required after in-solution digestion and prior to enrichment?). They\nshould clarify.\nResponse:\nWe appreciate the reviewer’s insightful question regarding the compatibility of in-solution\ndigestion with our downstream workflow.\nIn our study, the in-solution digestion protocol (used for comparison with PAC digestion in Fig. 1)\nstrictly follows conventional procedures to ensure comparability with established workflows. As\npart of these standard steps, desalting is indeed performed after in-solution digestion and prior to\nPTMs enrichment. This step is critical for conventional proteome workflow with removing residual\ndetergents, urea, trypsin, and salts that could interfere with downstream enrichment.\nWe apologize for omitting this detail in the original manuscript. The desalting step for in-solution\ndigestion, as a standard component of traditional protocols, has now been clarified in the revised\n“Fig. 1a” section to ensure full reproducibility. Thank you for highlighting this oversight, which\nhas helped strengthen the clarity of our procedural descriptions.\nThank you for prompting us to elaborate on this critical procedural detail.\ne. Regarding the reasons for choosing PAC as a digestion strategy, the authors indicate that\n“Integrating the PAC approach into the MuPPE workflow enables glycosylation identification in\nCSF by promoting protein aggregation through the addition of organic solvents, thus increasing\nprotein recovery during extraction”, and they refer to reference 17. I was not able to find\nthe corresponding data in that referenced publication to validate this affirmation. They should provide\nexperimental validation for this.\nWe sincerely apologize for the inappropriate citation placement in the original manuscript, which\nled to confusion. We have revised this part to clarify the reference context and provide additional\nexperimental rationale, as follows:\nFirst, the authors have demonstrated that organic solvents (e.g., ethanol) can induce protein\naggregation in biological samples, thereby enhancing protein recovery-this mechanism supports\nthe core principle of the PAC approach we adopted. Our original citation of Reference 17 was\nmisplaced, and this error has been corrected in the revised manuscript: “These results illustrated\nthe improved glycopeptide enrichment capacity and glycosylation identification efficiency in CSF\nsamples compared to previous benchmarks29, 30” (Page 7, lines 187-188)\nSecond, regarding the application of our MuPPE platform to cerebrospinal fluid (CSF) samples:\nCSF is a clinically valuable but challenging matrix due to its low protein concentration (~-0.1-0.3\nmg/mL) and limited sample availability. Our motivation for integrating PAC into MuPPE was to\naddress these limitations, enabling deep multi-omics analysis of trace samples such as CSF. To\nvalidate this, we compared the result of our method with previously reported CSF proteomics in\nFig. S2 with less sample input and relative more identification results.\nThank you for highlighting this citation issue, which has helped improve the accuracy and rigor of\nour manuscript.\n2. The manuscript presents several comparisons against published datasets, such as the one\nshowed in Fig.s 2d and 3d. It is not clear from the text how this benchmark has been done? How\ndid they retrieve the data from the published studies? Did they reanalize the published datasets in\nsimilar conditions as the ones generated for this work? If not, why? The authors should expand of\npotential sources of the difference in performance: is it due to worse recovery, different acquisition\nstrategies, different MS instrumentation? Minor point regarding these analyses: which works are\nthey referring to? For instance, ref 1 to 4 in Fig. 3d do not match references 1 to 4 in the\nbibliography.\nResponse:\nWe sincerely appreciate the reviewer’s insightful comments regarding the comparative data, which\nhave significantly helped improve the quality of our manuscript. We agree that the differences in\nidentification numbers observed in Fig. 2d may arise from variations in analytical conditions across\nstudies. For instance, the fragmentation modes used by different teams varied, with most employing\nHCD and EThcD-MS/MS, while a few used only HCD or EThcD-MS/MS, and some involved CID\nand ETciD-MS/MS. Additionally, in data-dependent acquisition (DDA) strategies, there were\ndifferences in precursor selection and fragmentation parameter settings, which could also\ncontribute to the variability. In terms of search engines, the developer teams utilized 9 different\nglycopeptide search engines, such as IQ-GPA, Protein Prospector, glyXtoolMS, and Byonic.\nSugarQB/Sequest HT, Mascot, etc., with some teams combining preprocessing or post-processing\ntools for auxiliary identification. These differences in search engines and associated tools could\nfurther lead to variations in the number of identified glycopeptides. Considering the rigor of the\ndata, we have decided to remove this comparative data from Fig. 2d to avoid misleading readers.\nAnd for the similar reason, we also delete Fig. 3d. Thank you again for your valuable reminder,\nwhich has enhanced the rigor of our manuscript.\nGiven the limited number of existing studies on CSF glycosylation that can be referenced, our\nintention here is merely to compare the CSF sample volumes used and the identification results\nacross different workflows. For clarity, we have summarized the relevant parameters from these\nstudies in the table below, and this information will be supplemented in the Supplementary\nInformation (SI) section to provide more comprehensive context：\nRemova\nCSF Enrichme\nl of high No. of No. of\nTitle Journal Year Volume nt\nabundan glycopep glycopro.\n/mL Materials\nt protein\nIn-depth Site-specific\nAnalysis of N- HILIC and\nglycoproteome in Molecular & boronic\nRef.\nHuman Cerebrospinal Cellular 2021 No 1 acid 2893 285\nLandscape Changes in t\nAlzheimer's Disease\nIn-depth\nCharacterization of the\nCerebrospinal Fluid Molecular & magnetic\nRef.\n(CSF) Proteome Cellular 2014 Yes 3 hydrazide 1121 520\nthe CSF Proteome\nResource (CSF-PR)\nBoost-DiLeu Enhanced\nIsobaric N, N-Dimethyl\nLeucine Tagging\nnot\nRef. Strategy for Analytical SAX-\ned\nQuantitative\nGlycoproteomic\nAnalysis\nThis work No 0.142 Click-Mal 5018 251\n3. The initial purpose of this work is to present a workflow that integrates proteomics analysis at\nthree levels: full proteome, glycoproteome and phosphoproteome. However, the two biological\nexperiments used as proof-of-concept of this work fail to show how to properly integrate and\nanalyse these three layers simultaneously.\nResponse:\nThank you for underscoring that, while the stated goal of this work is a tri-layer workflow (global\nproteome, glycoproteome, and phosphoproteome), the two proof-of-concept studies in their initial\n15\nform did not convincingly demonstrate simultaneous integration and analysis across all three layers.\nWe fully recognize this concern.\nTo address this directly, we (i) expanded the methodological description with a unified cross-layer\nprocessing pipeline, and (ii) added new integrative analyses/visualizations that make cross-layer\nbehavior explicit (in the revised version Fig. 5 and Fig. 6).\nFor data processing and integration across the three layers, contaminants and decoys were removed\nand PTM sites were required to meet localization probability thresholds defined by the respective\nsearch engines; raw intensities were normalized at the run level (total ion current, TIC) and\nsubsequently log -transformed. To ensure both depth and reliability, layer-specific coverage\n2\nthresholds were applied: features in the proteome and phosphoproteome were retained only if\nquantified in ≥80% of replicates within at least one group, whereas the glycoproteome retained\nfeatures quantified in ≥50% of replicates, acknowledging the higher intrinsic sparsity of\nglycopeptide data. Missing values were then handled in a layer-specific manner: for the proteome\nand phosphoproteome, partially missing entries were imputed within layer on log -transformed\n2\ndata using the k-nearest neighbors algorithm (KNN, k=5), while for the glycoproteome, missing\nvalues were treated as true non-detections and replaced with 0 at the raw scale (followed by ε + log\n2\ntransformation to avoid undefined values). Finally, for cross-layer comparability, all features were\nZ-scored within each layer (per feature across samples) before integration and downstream\nstatistical analyses.\nWe fully agree that the primary objective of this study is to establish and validate a workflow\ncapable of simultaneously integrating global proteome, glycoproteome, and phosphoproteome\nanalyses. In the current version, the NB4 cell and aging mouse experiments were designed as proof-\nof-concept case studies to demonstrate the technical feasibility and versatility of the MuPPE\nplatform across distinct sample types. Admittedly, the main emphasis of these experiments was to\nillustrate the robustness of the workflow, sample compatibility, and multi-layer data acquisition,\nrather than to conduct an in-depth mechanistic investigation or provide exhaustive cross-omics\nbiological interpretation.\nNevertheless, in the revised version, we have explicitly incorporated time-course, cross-layer\nanalyses that illustrate how the three datasets converge and diverge across biological processes\nduring aging. The Fig. 7. displays, for each layer, (left) membership trajectories across ages\n(2M→12M→24M) and (middle) expression Z-scores for the leading-edge genes, with the KEGG\nterms on the right. This visualization makes the simultaneous integration tangible:\n ECM-receptor interaction is jointly highlighted in glycoproteome and phosphoproteome,\nindicating coordinated late-life remodeling of extracellular interfaces that is not apparent\nfrom proteome alone.\n Cytoskeleton in muscle cells appears in proteome and phosphoproteome, consistent with\nphosphorylation-driven cytoskeletal control coupled to abundance shifts.\n16\n Oxidative phosphorylation and neurodegeneration-related terms are phospho-dominant,\nsuggesting early signaling reprogramming that precedes broad abundance/glyco changes,\nwhereas lysosome and vitamin digestion/absorption are glyco-dominant, consistent with\nglycan-dependent trafficking/turnover.\nWe believe these revisions and clarifications will help align the manuscript with its methodological\nfocus, while also making the integrative analysis more transparent and informative to readers.\nFig. 7. (Fig. 5f in the revised version) Integrated KEGG enrichment analyses of proteome,\nphosphoproteome, and glycoproteome datasets.\nLeveraging multi-omics data analysis, we also put forward a mechanism wherein arsenic functions\nthrough a membrane-to-nucleus signaling cascade. It regulates membrane proteins via\nglycosylation and disrupts the PI3K-AKT-mTOR pathway through phosphorylation, thereby\ninfluencing the cell cycle and chromosomal organization. This potentially leads to cell cycle arrest\nand apoptosis induction, which is supported by flow cytometry results demonstrating G2/M phase\naccumulation and phosphoproteomic alterations in cell cycle regulators. Additionally, arsenic\nmight interfere with genome-wide transcription by acting on chromatin-associated proteins. Taking\nIGF2R, a crucial membrane protein in the PI3K-AKT-mTOR pathway, as an instance, even though\nthere is no notable change in its overall protein level, multi-omics data uncover 13 differential\narsenic exerts its effects by reconfiguring PTMs-mediated signaling networks, a mechanism that\ncan only be detected via multi-omics profiling.\nFig. 8. (Fig. 6i-k in the revised version) a Schematic of PI3K-AKT-mTOR pathway perturbation\nby arsenic, with downstream effects on cell cycle and chromatin organization. Proteome (green),\nglycoproteome (orange), and phosphoproteome (blue) alterations are mapped. b Flow cytometry\nanalysis of NB4 cells treated with increasing arsenic concentrations, showing dose-dependent\nG₂/M phase accumulation. c Differential expression of protein relative abundance, glycosylation\nsites and phosphorylation sites of IGF2R protein with the log2 fold change values. Different\nsymbols representing up-regulation (upward triangles), down- regulation (downward triangles),\nand the color intensity indicating the significance of the change.\nManuscript changes:\nPage 17-18, Line 431-446 in revised manuscript:\nTo investigate aging-related molecular changes, we conducted time-series clustering of the\nproteome, phosphoproteome, and glycoproteome across 2, 12, and 24 M, followed by KEGG\npathway enrichment analysis (Fig. 5f). In the phosphoproteome, pathways associated with aging-\nincluding oxidative phosphorylation, Alzheimer’s disease, and Parkinson’s disease-were enriched\nearly in young mice and remained detectable throughout all age groups. The proteome exhibited\nsignificant enrichment in pathways such as long-term depression and peroxisome function\nspecifically at 24 M, suggesting a delayed accumulation compared to phosphorylation-mediated\nchanges. The ECM-receptor interaction pathway showed distinct regulatory patterns: glycoprotein\nlevels increased progressively with age, while phosphorylation peaked at 12 M before declining by\nand phosphoproteome, with an initial increase followed by a decrease; phosphorylation changes,\nhowever, declined more gradually. Several pathways were uniquely altered in specific PTM layers:\n18\nfocal adhesion decreased with age in the phosphoproteome, whereas oxidative phosphorylation\nwas broadly enriched. In the glycoproteome, the lysosome pathway increased with age, along with\nupregulation of antigen processing and ECM-related processes.\nPage 21, Line 514-539 in revised manuscript:\nBuilding on the multi-omics dataset analysis, we propose a mechanistic model where arsenic exerts\nits effects through a membrane-to-nucleus signaling cascade (Fig. 6i), primarily mediated by\nglycosylation-modulated membrane proteins and phosphorylation-driven PI3K-AKT-mTOR\npathway perturbation. The glycoproteomic data revealed that arsenic induces glycosylation\nalterations in membrane-anchored receptors (e.g., components of the PI3K-AKT-mTOR pathway\nupstream regulators). These glycosylation changes might act as “molecular switches” to modulate\nmembrane protein stability, localization, and ligand-binding capacity. We also observed that the\narsenic treatment significantly impacted the cell cycle and chromosomal organization pathways,\nparticularly in the regulation of the proteome and phosphoproteome during the M phases. This\nsuggests that arsenic exerts profound effects on these two critical signaling pathways, potentially\nby arresting the cell cycle and disrupting chromosomal functions, thereby promoting apoptosis.\nFlow cytometry analysis (Fig. 6j) showed the G2/M phase accumulation, consistent with the\nphosphorylation changes in cell cycle regulators (e.g., CDK, CCNB proteins in Fig. 6j) directly\nsupport this arrest. GO enrichment results of nuclear functions in the key modules, combined with\nphosphoproteomic changes in chromatin-associated proteins (e.g., histone modifiers), suggest\narsenic might disrupt genome-wide transcriptional programs and link membrane-to-nucleus\nsignaling to nuclear architecture and function.\nFocusing on IGF2R, a key membrane protein identified in the PI3K-AKT-mTOR pathway, we\ninterrogated its individual proteomic, glycoproteomic, and phosphoproteomic alterations (Fig. 6k).\nAt the global protein level, IGF2R abundance showed no significant fold-change between arsenic-\ntreated and control groups. However, its glycoproteomic and phosphoproteomic data revealed\nnotably PTMs-specific alterations. we identified 13 differential PTMs sites, including 12 N-\nglycosylation sites and 1 phosphorylation site. Upregulated and downregulated PTMs were denoted\nby upward and downward triangles, respectively. Without glycoproteomic/phosphoproteomic data,\nIGF2R would appear “unchanged” at the protein level, masking its potential role as a PTMs-driven\nhub. Thus, arsenic exerts its effects not by altering protein abundance broadly but by rewiring PTM-\nmediated signaling networks-a mechanism only detectable via multi-omics profiling.\nPage 28-29, Line 722-731 in revised manuscript:\nPathway temporal clustering and visualization: Proteome, phosphoproteome, and glycoproteome\nprofiles across 2, 12, and 24 M were Z-score normalized and subjected to fuzzy c-means clustering\nusing Mfuzz (R, v2.60.0) to capture temporal expression patterns. Cluster-specific KEGG\nenrichment was performed with clusterProfiler (v4.10.0), retaining pathways with FDR < 0.05, FC>\n1.5. The resulting heatmap integrates three layers of information: (i) Membership plots (left),\nshowing cluster-wise temporal trends with membership values; (ii) Expression heatmaps (middle),\ndisplaying Z-scored expression values across 2 M, 12 M, and 24 M; and (iii) KEGG pathways\n(right), annotated for each cluster, with functionally relevant pathways highlighted. Results were\nvisualized as integrated membership trajectories, expression heatmaps, and enriched pathways\nusing ggplot2 and ComplexHeatmap, ensuring full reproducibility in R (v4.3.1).\n4. Also, in this regard: are protein levels accounted to correct for changes at PTM level? Fig. 6b\nrefers to such issue, but it is not clear if they account for protein level contribution further or they\njust dismiss it based on this analysis.\nResponse:\nThank you for explicitly raising whether PTM measurements were corrected for underlying protein\nabundance—a critical point for discriminating true PTM-specific regulation from changes that\nmerely track total protein levels. As you noted, Original Fig. 6b gestures at this issue, but our initial\ntext did not make sufficiently clear how protein-level contributions were handled beyond that figure.\nWe agree that changes at the PTM level can be influenced by underlying protein abundance, and it\nis critical to distinguish genuine PTM-specific regulation from mere reflection of global protein\nexpression changes. We wish to emphasize that this correction was already performed in our initial\nsubmission, although we have now made it more explicit in the revised version.\nFig. 9 (Fig. 5b in the revised version) plots site-level log₂ fold-changes (glyco- and phospho-\npeptides) against the matched parent-protein log₂ fold-changes across contrasts. This analysis helps\nto visually identify whether most PTM changes are simply tracking with total protein level or occur\nindependently. The clouds are centered near zero on the protein axis, and PTM-regulated sites\npredominantly populate the off-diagonal quadrants with parent proteins showing no detectable\nchange, indicating weak concordance between PTM and protein variation. Based on this diagnostic,\nwe concluded that the observed PTM dynamics are largely independent of protein abundance in\nour data.\nBecause protein division necessarily discards sites lacking complete protein co-quantification\n(reducing coverage), and because the diagnostic shows minimal protein-PTMs coupling, we report\nsite-level PTMs quantification (without protein division) as the primary analysis to maximize\ncoverage and comparability across layers. To ensure transparency, we supply both the unadjusted\nand protein-adjusted PTM results, together with matched parent-protein values, in Supplementary\nData X; Methods and Fig. legends have been revised to state this decision rule explicitly. Thus, we\ndid not “dismiss” protein contribution; rather, we tested it, quantified its impact, and demonstrated\nrobustness of all conclusions to protein adjustment.\nit shows that PTMs changes are not driven by protein-level shifts in our data. All primary PTM\nanalyses therefore use peptide/site-level quantification, with matched protein values reported in\nSupplementary Date.\nManuscript changes:\nPage 13, Line 334-341 in revised manuscript:\nWe assessed potential protein-level confounding by pairing each quantified PTM site with its\nparent protein and plotting site log₂ fold-change versus protein log₂ fold-change for each contrast\n(Fig. S13). The changes in N-glycopeptide levels between groups were not significantly correlated\nwith the corresponding unmodified protein levels (R2 = 0.21). As a rule, when PTM regulation was\nnot coupled to protein abundance (i.e., regulated sites with parent proteins showing no detectable\nchange), we performed downstream analyses on site-level intensities after standard sample-level\nnormalization. Because dividing by protein restricts coverage to sites with complete protein co-\nquantification, protein-division was not used for the primary results.\nSupplementary Inf: Pages 11-12, lines310-322.\nNormalization of PTMs Abundance to Protein Level: To account for potential changes in total\nprotein expression and to accurately assess site-specific regulation, we normalized the abundance\nof each PTMs site (phosphorylation or glycosylation) to the corresponding parent protein\nabundance in each sample. Specifically, for every quantified PTMs site and sample, we calculated\n21\nthe normalized value as the log₂-transformed ratio of the PTMs site intensity to the parent protein\nintensity:\nNormalized PTM abundance=log (PTM site intensity)-log (protein intensity);\n2 2\nWe assessed potential protein-level confounding by pairing each quantified PTMs site with its\nparent protein and plotting site log₂ fold-change versus protein log₂ fold-change for each contrast.\nAs a rule, when PTMs regulation was not coupled to protein abundance (i.e., regulated sites with\nparent proteins showing no detectable change), we performed downstream analyses on site-level\nintensities after standard sample-level normalization. Because dividing by protein restricts\ncoverage to sites with complete protein co-quantification, protein-division was not used for the\nprimary results; matched protein values are provided in Supplementary Data.\nMinor points\n- The number of points used to generate some of the graphs is missing (for instance in Fig. 3d).\nResponse:\nWe thank the reviewer for the valuable comment and sincerely apologize for the oversight. In the\nrevised manuscript, Fig. 3d has been removed, and thus the above issue has been avoided.\n- How are the CVs calculated in Fig. 1f?\nResponse:\nWe sincerely appreciate the reviewer’s query regarding the calculation of CVs in Fig. 1f. Our\nproteomic data was acquired using data-independent acquisition (DIA) and searched with\nSpectronaut, a high-performance DIA proteomics software. The CV values were directly extracted\nfrom the results generated by Spectronaut. Spectronaut quantifies proteins at two levels: the peptide\n(minor) group and the protein (major) group. For the peptide level, quantification is based on\nprecursors, while for the protein level, it is derived from the peptide-level quantification. By default,\nSpectronaut performs label-free quantification (LFQ) using either the maxLFQ or top N strategy.\nPrecursors are quantified by calculating the area under the curve within the MS XIC (extracted\n2\nion chromatogram) integration boundaries. The coefficient of variation (CV) is a measure of\nrelative variability. Mathematically, the CV is calculated as the ratio of the standard deviation (σ)\nof a sample to its mean (μ), expressed as a percentage, i.e.,\nCV=σ/μ×100%. Spectronaut calculates the standard deviation and mean for the peptide or protein\nquantification values within a set of replicates. The standard deviation represents the dispersion of\nthe data points around the mean value. A lower standard deviation indicates that the data points are\ncloser to the mean, and thus, the data is more consistent. The mean is the arithmetic average of the\nquantification values for a particular peptide or protein across the replicates. A lower CV indicates\nmore consistent and reproducible results.\nreproducibility and stability. In our study, the lower CV of MuPPE (average 7.8%) compared to\nconventional serial enrichment methods (average 12.5%) directly demonstrates that MuPPE\nminimizes technical variability from sample handling and processing steps, confirming its superior\nreproducibility.\n- There are several typos in the Fig.s:\n- Fig. 2h - Uniport instead of Uniprot.\n- Fig. 6g - Founction instead of Function\n- Fig. 3b: x-axis is missing\nResponse:\nWe sincerely appreciate the reviewer for identifying these typos, which have now been corrected\nin the revised manuscript:\nIn Fig. 2i (Original Fig. 2h), “Uniport” has been revised to “UniProt”.\nIn Fig. 5g (Original Fig. 6g), “Founction” has been corrected to “Function”.\nIn Fig. 3b, x-axis is added with “The sample input of mouse brain protein (g)”\nThese errors were unintended and have been carefully addressed to ensure accuracy. Thank you\nagain for your meticulous review, which helps improve the quality of our manuscript.\n- In the methods section it is said: “Trypsin (enzyme-to-protein ratio, 1:50, wt/wt) was added and\nproteins were digested at 37°C for different time based on the complexity of the samples”. How do\nthey define complexity and how does it impact digestion time?\nResponse:\nWe sincerely apologize for the confusion caused by the imprecise wording in our original\ndescription. What we intended to convey is that for samples of relatively complex types with higher\nprotein content, an appropriate extension of digestion time can be considered; whereas for samples\nwith lower protein concentrations, such as CSF, a shorter digestion time may be sufficient.\nWe recognize that the original expression might mislead readers, so we have decided to revise it to\nthe following in the Methods section for greater clarity: “Trypsin (enzyme-to-protein ratio, 1:50,\nwt/wt) was added for digestion at 37°C. For samples with higher protein content (e.g., cell lysates,\ntissue homogenates), digestion was performed for 4 hours to ensure sufficient proteolysis of\ncomplex protein components. For samples with lower protein concentrations (e.g., CSF), a shorter\ndigestion time of 2 hours was used to avoid unnecessary over-digestion.”\nThis revision aims to accurately reflect the relationship between sample characteristics and\ndigestion time, ensuring that readers can better understand and reproduce our experimental\nprocedures. Thank you again for your careful review, which helps us improve the accuracy and\nclarity of the manuscript.\n23\n- Even if brief, some description of the LC-MSMS methods should be stated in the main methods\nsection.\nResponse:\nWe appreciate the reviewer’s important suggestion. To enhance the transparency and\nreproducibility of our workflow, we have supplemented a concise description of the LC-MS/MS\nmethods in the Supplementary Information “Methods” section (Page 7-8, Line 174-215), as follows:\nLC-MS/MS analysis of different proteomic samples\nSamples were loaded onto capillary analytical column (150 µm inner diameter, 15 cm length)\npacked in-house with C18 (1.9 mm) ReproSil particles (Dr. Maisch GmbH), with an EASY-nLC\n1200 system (Thermo Fisher Scientific) coupled to the MS (Orbitrap 480, Thermo Fisher\nScientific). The column oven maintained column temperature at 50°C. The mobile phases consisted\nof 0.1% FA (A) and 0.1% formic acid and 80% ACN (B).\nFor the proteomic samples, peptides were separated through a gradient of up to 90% buffer B over\n20 min (for NB4 dataset) /40 min (CP dataset) / 60 min (aging serum dataset and aging CSF dataset)\nat a flow rate of 1 mL/min. The gradient of the mobile phase started from 3% B to 15% B for 0.5\nmin and then was increased linearly to 50% B in 15 min, to 90% in 1.5 min, and maintained for 3\nmin. The LC-MS/MS system was operated in data-independent MS/MS acquisition mode. The full\nmass scan acquired in the Orbitrap mass analyzer was from m/z 350 to 1500 with a resolution of\n60000 (m/z 200). The MS/MS scans were also acquired by using an Orbitrap with a 30000\nresolution (m/z 200), and the AGC target was set as custom. The spray voltage and the temperature\nof the ion transfer capillary were set to 2.6 KV and 320°C, respectively. The normalized collision\nenergy for HCD and dynamic exclusion was set as 32% and 30 s, respectively.\nFor the glycoproteomic samples, peptides were separated through a gradient of up to 90% buffer B\nover 120 min at a flow rate of 600 nL/min. The gradient of the mobile phase started from 3% B to\n10% B for 6 min and then was increased linearly to 40% B in 10 min, to 90% in 4 min, and\nmaintained for 10 min. The LC-MS/MS system was operated in data-dependent MS/MS acquisition\nmode. The full mass scan acquired in the Orbitrap mass analyzer was from m/z 350 to 1500 with a\nresolution of 60000 (m/z 200). The MS/MS scans were also acquired by using an Orbitrap with a\n30000 resolution (m/z 200), and the AGC target was set as standard. The spray voltage and the\ntemperature of the ion transfer capillary were set to 2.6 KV and 320°C, respectively. The\nnormalized collision energy for HCD and dynamic exclusion was set as 20%, 30%, 40% and 30 s,\nrespectively.\nFor the phosphoproteomic samples, peptides were separated through a gradient of up to 90% buffer\nB over 60 min at a flow rate of 600 nL/min. The gradient of the mobile phase started from 1% B\nto 5% B for 3 min and then was increased linearly to 32% B in 50 min, to 90% in 2 min, and\nmaintained for 5 min. The LC-MS/MS system was operated in data-independent MS/MS\n24\nacquisition mode. The full mass scan acquired in the Orbitrap mass analyzer was from m/z 350 to\n1500 with a resolution of 60000 (m/z 200). The MS/MS scans were also acquired by using an\nOrbitrap with a 30000 resolution (m/z 200), and the AGC target was set as custom. The spray\nvoltage and the temperature of the ion transfer capillary were set to 2.6 KV and 320°C, respectively.\nThe normalized collision energy for HCD and dynamic exclusion was set as 32% and 30 s,\nrespectively.\nFor TMT labeling proteomic and phosphoproteomic samples: peptides were separated through a\ngradient of up to 90% buffer B over 90 min at a flow rate of 600 nL/min. The gradient of the mobile\nphase started from 2% to 5% B for 1 min and then was increased linearly to 40% B in 70 min, to\n45% in 10 min, to 100% in 5 min, and maintained for 5 min. The LC-MS/MS system was operated\nin data-dependent MS/MS acquisition mode. MS1 scans (350-1500 m/z) were acquired at a\nresolution of 60,000 with an AGC target of 200 % and a maximum injection time of 5 ms. MS/MS\nscans were acquired with 0.7 m/z isolation windows (scan range 110-3000 m/z), 59 windows, a\nmaximum injection time of 100 ms and an AGC target of 300 %. The spray voltage and the\ntemperature of the ion transfer capillary were set to 2.6 KV and 320 °C, respectively. The\nnormalized collision energy for HCD and dynamic exclusion was set as 34% and 30 s, respectively.\n- In supplementary material:\no The dimensions of the column are not specified\no The information for the DIA acquisition is missing: mass range, number of windows, window\nsize, injection time\nResponse:\nThank you for your valuable comment. We have supplemented the relative information and the\nrelevant information on DIA acquisition in Supplementary Information “Methods”. The details are\nas follows:\nSupplementary Inf: Page 7, lines 175-177\n“Samples were loaded onto capillary analytical column (150 µm inner diameter, 15 cm length)\npacked in-house with C18 (1.9 mm) ReproSil particles (Dr. Maisch GmbH), with an EASY-nLC\n1200 system (Thermo Fisher Scientific) coupled to the MS (Orbitrap 480, Thermo Fisher\nScientific).”\nSupplementary Inf: Page 7, lines 179-187\n“For the proteomic samples, peptides were separated through a gradient of up to 90% buffer B over\n20 min at a flow rate of 1 mL/min. The gradient of the mobile phase started from 3% B to 15% B\nfor 0.5 min and then was increased linearly to 50% B in 15 min, to 90% in 1.5 min, and maintained\nfor 3 min. The LC-MS/MS system was operated in data-independent MS/MS acquisition mode.\nThe full mass scan acquired in the Orbitrap mass analyzer was from m/z 350 to 1500 with a\n25\nresolution of 60000 (m/z 200). The MS/MS scans were also acquired by using an Orbitrap with a\n30000 resolution (m/z 200), and the AGC target was set as custom. The spray voltage and the\ntemperature of the ion transfer capillary were set to 2.6 KV and 320 °C, respectively. The\nnormalized collision energy for HCD and dynamic exclusion was set as 32% and 30 s, respectively.\nSupplementary Inf: Page 7-8, lines 197-205\nFor the phosphoproteomic samples, peptides were separated through a gradient of up to 90% buffer\nB over 900 min at a flow rate of 600 nL/min. The gradient of the mobile phase started from 1% B\nto 5% B for 3 min and then was increased linearly to 32% B in 50 min, to 90% in 2 min, and\nmaintained for 5 min. The LC-MS/MS system was operated in data-independent MS/MS\nacquisition mode. The full mass scan acquired in the Orbitrap mass analyzer was from m/z 350 to\n1500 with a resolution of 60000 (m/z 200). The MS/MS scans were also acquired by using an\nOrbitrap with a 30000 resolution (m/z 200), and the AGC target was set as custom. The spray\nvoltage and the temperature of the ion transfer capillary were set to 2.6 KV and 320 °C, respectively.\nThe normalized collision energy for HCD and dynamic exclusion was set as 32% and 30 s,\nrespectively.”\n26\n*******************************************************************\nReviewer #2:\nThis manuscript by Dong et al. presents MuPPE (Multi-level PTMs-Proteomic Enrichment), a\nplatform for simultaneous and sequential enrichment of the proteome, glycoproteome, and\nphosphoproteome from a single biological sample. The authors demonstrate the utility of MuPPE\nacross diverse sample types, including serum, CSF, cell lines, and mouse tissues, and apply it to\nstudy arsenical drug response and aging. However, several concerns remain regarding the\nplatform’s novelty, analytical rigor, and generalizability.\n1. While MuPPE improves upon previous serial enrichment methods by being more time-\nefficient (4 h vs. 24 h) and compatible with low-input samples, it does not provide a clear\nconceptual advancement over prior work, such as that by Fang et al. (Anal. Chem., 2019) and\nAbelin et al. (Nat. Commun., 2023). The manuscript would benefit from a more explicit discussion\nof how MuPPE differentiates itself from these established platforms.\nResponse:\nWe sincerely appreciate your valuable suggestion. We acknowledge that this aspect needs further\nclarification, and thus, in the revised manuscript, we have provided a detailed comparison of\nMuPPE with several currently reported works. We have selected four representative studies (Nat.\nCommun., 2023, 14, 1851; Nat. Protoc., 2020, 15, 161; ACS Chem. Biol. 2019, 14, 58; Anal. Chem.\n2020, 92, 1842; J. Chromatogr. A, 2025, 1739, 465525) involving simultaneous enrichment and\nidentification of glycosylation and phosphorylation, along with one work (Nat. Commun., 2023,\n14, 1851) enabling multi-PTMs enrichment, for comprehensive benchmarking against our MuPPE\nplatform. We have summarized and visualized the workflow comparisons of all these platforms,\npresenting a side-by-side schematic illustration to highlight key differences (Fig. 1, and Fig. S5 in\nthe revised version).\nIn the original manuscript, we compared MuPPE with in-solution digestion in Fig. 1, demonstrating\nits superiority in precursor/peptide/protein identification counts and quantitative precision on\nserum samples. However, we acknowledge that these comparisons primarily focused on proteomic\nprofiling rather than systematic multi-PTMs benchmarking against specialized methods. To\naddress this gap, we have selected four representative studies (Nat. Commun., 2023, 14, 1851; Nat.\nProtoc., 2020, 15, 161; ACS Chem. Biol. 2019, 14, 58; Anal. Chem. 2020, 92, 1842; J. Chromatogr.\nA, 2025, 1739, 465525) involving simultaneous enrichment and identification of glycosylation and\nphosphorylation, along with one work (Nat. Commun., 2023, 14, 1851) enabling multi-PTMs\nenrichment, for comprehensive benchmarking against our MuPPE platform. We have summarized\nand visualized the workflow comparisons of all these platforms, presenting a side-by-side\nschematic illustration to highlight key differences (Fig. 1 and Fig. S7 in the revised version).\ndifferent glycopeptides and phosphopeptides within state-of-the-art platforms according to the\nreports.\nIn Fig. 2. (Fig. 3c in the revised version), we specially present a comparative analysis of workflow\ntime durations. The bar chart quantifies total processing time for MuPPE, state-of-the-art multi-\nPTMs workflows, and conventional glyco/phosphoproteomic methods. This direct comparison\nprovides a clear quantitative basis for MuPPE’s time-saving advantages in multi-PTM analysis,\nhighlighting its efficiency in accelerating experimental workflows.\nprotein extraction to enrichment, showcasing reduced sample transfers and time efficiency.\nWe summarized and defined comparative parameters covering on-line proteolytic digestion,\nworkflow duration, trace sample applicability, sample type compatibility, accuracy &\nreproducibility, transfer times, identified omics types, labeling experiment feasibility, and\nidentification counts, all systematically presented in Table 1 and Fig. 3 (Fig. 3d in the revised\nversion).\nThe radar plot benchmarks MuPPE against five representative platforms across five performance\nparameters. MuPPE achieves the most extensive coverage in “on-bead digestion, trace sample\napplicability, sample compatibility, time efficiency, and minimized transfers”. These results\nunderscore its unique capacity for integrated PTMs enrichment, strong adaptability to\nlimited/heterogeneous samples, and capability for simultaneous multi-omics profiling. Shorter\nworkflow durations accelerate high-throughput analysis, reduced transfer steps minimize sample\nloss and technical variability, and lower protein input benefits to low-abundance samples (e.g.,\nclinical biopsies), firmly positioning it as a transformative tool for complex biological\ninvestigations.\nTable 1. The benchmarking parameters summary for the comparison of each platform\nproteomics methods across parameters, demonstrating MuPPE’s superior performance.\nFinally, to ensure methodological comparability, we benchmarked MuPPE against the\nconventional in-solution approach and two previously reported strategies under strictly matched\nconditions, including identical sample input, mass spectrometric acquisition parameters, database\nsearch settings, and data processing workflows. MuPPE yielded a substantially higher number of\nglycopeptide-spectrum matches than all three reference methods (Fig. 4e). Notably, the number of\nidentified proteins and phosphopeptides did not differ significantly between MuPPE and the other\nmethods, indicating that the integrated multi-omics enrichment design did not compromise\nproteome or phosphoproteome coverage (Fig. 4f and 4g). The primary advantage of MuPPE in\nglycoproteomics lies in its enhanced glycan coverage and structural diversity, as well as its superior\ncapacity to capture a greater proportion of medium-length and structurally complex glycopeptides\n(Fig. 4h and 4i). Peptide length distribution analysis (Fig. 4j) revealed that MuPPE achieved a more\nuniform distribution of identified peptide lengths and a higher overall number of peptides compared\n30\nto the other three methods. This reflects MuPPE’s superiority in capturing peptides across a broader\nsize range, enabling more comprehensive proteomic coverage. Additionally, by comparing the\noverall composition of glycan units identified by the four methods (Fig. 4k), MuPPE enabled\nimproved characterization of glycan structural features. These combined results highlight MuPPE’s\nenhanced capability to dissect the complexity of glycoproteomes at both peptide- and glycan-\nstructural levels, providing deeper insights into glycosylation biology.\nFig. 4. (Fig. 3 e-k in the revised version) e-h Quantitative comparisons across enrichment methods:\nglycopeptide spectrum matches (PSMs) (e), phosphopeptides (f), proteins (g), and glycans (h). i\nDistribution of glycan types identified across methods, showing relative proportions of high-\nmannose, fucosylated, sialylated, and complex glycans. j Peptide length distribution for\nglycopeptides captured by MuPPE versus conventional approaches. k Comparison of glycan\nstructural features, including glycan unit number across enrichment strategies.\nIn summary, the revised manuscript now (i) establishes MuPPE’s advantages through a structured,\nlike-for-like benchmark against state-of-the-art platforms (Fig. 3 c-k, Supplementary Fig. S7) and\n(ii) provides concrete, method-level instructions for integrating the three molecular layers\n(Methods; Supplementary Methods). We believe these changes directly address the reviewer’s\nconcerns and strengthen the manuscript’s clarity, novelty, and practical utility.\nManuscript changes: Page 10, line 245-279 revised manuscript:\nCollectively, the MuPPE workflow facilitates multi-level proteomic analysis by enabling\nsequential analysis of the glycoproteome, followed by the phosphoproteome from just single\nbiological sample, previously only feasible through parallel processing of multiple sample aliquots.\nefficiency. MuPPE excelled in time efficiency (total workflow < 4 hours vs. more than 32 hours\nfor others, Fig. 3c) and sample transfers (2 transfers vs. 5-10 transfers for others), positioning it as\na leading method. These comparisons fully demonstrate the efficiency and advantages of MuPPE\nin multi-levels proteomic research. A radar plot (Fig. 3d) benchmarked MuPPE against five state-\nof-the-art methods15, 31, 34-36, evaluating parameters such as on-bead digestion, trace sample\napplicability, sample compatibility, time efficiency, and minimized transfers numbers.\nWe compared MuPPE with conventional in-solution digestion protocols at the proteome level (Fig.\n1), demonstrating its superiority in precursor, peptide, and protein identifications, as well as\nquantitative precision in serum samples. However, these comparisons primarily addressed\nproteomic profiling rather than systematic multi-PTMs benchmarking against dedicated\nenrichment strategies. To fill this gap, we evaluated MuPPE alongside four representative studies\nthat reported simultaneous enrichment and identification of glycosylation and phosphorylation,\nincluding one work15 that enabled broader multi-PTMs capture. We summarized and visualized the\nworkflows of all platforms and presented a side-by-side schematic to highlight methodological\ndistinctions (Fig. S7). To ensure methodological comparability, we benchmarked MuPPE against\nthe conventional in-solution approach and two previously reported strategies31, 34 under strictly\nmatched conditions, including identical sample input, mass spectrometric acquisition parameters,\ndatabase search settings, and data processing workflows. MuPPE yielded a substantially higher\nnumber of glycopeptide-spectrum matches than all three reference methods (Fig. 3e). Notably, the\nnumber of identified proteins and phosphopeptides did not differ significantly between MuPPE and\nthe other methods, indicating that the integrated multi-omics enrichment design did not\ncompromise proteome or phosphoproteome coverage (Fig. 3f and 3g). The primary advantage of\nMuPPE in glycoproteomics lies in its enhanced glycan coverage and structural diversity, as well as\nits superior capacity to capture a greater proportion of medium-length and structurally complex\nglycopeptides (Fig. 3h-3i). Peptide length distribution analysis (Fig. 3j) revealed that MuPPE\nachieved a more uniform distribution of identified peptide lengths and a higher overall number of\npeptides compared to the other three methods. This reflects MuPPE’s superiority in capturing\npeptides across a broader size range, enabling more comprehensive proteomic coverage.\nAdditionally, by comparing the overall composition of glycan units identified by the four methods\n(Fig. 3k), MuPPE enabled improved characterization of glycan structural features. These combined\nresults highlight MuPPE’s enhanced capability to dissect the complexity of glycoproteomes at both\npeptide- and glycan-structural levels, providing deeper insights into glycosylation biology.\nSupplementary Data: Page 10-11, line 277-308.\nBenchmark workflow\nmg maltose beads for 10 min, followed by addition of 63 μL acetonitrile. Samples were washed\nthree times with 200 μL of 80% ethanol, then resolubilized in 8 μL of 6 M urea and diluted with\n40 μL of ammonium bicarbonate. Proteins were digested with 0.5 μg trypsin for 2.5 h, and the\nreaction was quenched with 2.6 μL TFA and 206.3 μL ACN. After 15 min incubation,\nglycopeptides were captured by centrifugation. Maltose beads were washed three times with 50 μL\nof 80% ACN/1% TFA and eluted with 30 μL of 30% ACN/1% FA (×3). Flow-through and wash\nfractions were combined, vacuum-dried, and incubated with 0.5 mg IMAC beads in 50 μL 80%\nACN/6% TFA for 1 h. IMAC beads were sequentially washed with 50 μL 80% ACN/6% TFA, 2\n× 100 μL 50% ACN/6% TFA/200 mM NaCl, 2 × 200 μL 30% ACN/0.1% FA, and 2 × 100 μL 30%\nACN. Phosphopeptides were finally eluted twice with 30 μL 10% ammonia solution and pooled.\nIn-solution digestion: Mouse brain lysates (20 μg) were denatured in 8 M urea with protease\ninhibitor cocktail (final 1 μg/μL). Proteins were reduced with 1 μL 200 mM DTT and alkylated\nwith 1 μL 400 mM IAA. After dilution with 140 μL ammonium bicarbonate, proteins were digested\nwith 0.5 μg trypsin. Digestion was quenched, and peptides were desalted using C18 SPE columns:\ncolumns were activated, samples loaded, washed with 20 mL water/0.1% FA, and eluted with 2 ×\n1 mL 50% ACN/0.1% FA. Eluates were vacuum-dried. For glycopeptide enrichment, 20 μg\ndesalted peptides were dissolved in 30 μL 80% ACN/1% TFA and incubated with 0.6 mg maltose\nbeads for 15 min. Beads were washed (80% ACN/1% TFA, 50 μL × 3) and eluted (30% ACN/1%\nFA, 30 μL × 3). For phosphopeptide enrichment, 20 μg peptides were dissolved in 50 μL 80%\nACN/6% TFA and incubated with 0.5 mg IMAC beads for 1 h. After washing as described above,\nphosphopeptides were eluted twice with 30 μL 10% ammonia solution and pooled.\nReference methods: For benchmarking, two reported workflows were included. In method 17, 20\nμg digested peptides were dissolved in 50 μL 80% ACN/1% TFA, loaded onto a maltose-packed\ntip column, and processed as described above. The flow-through was dried, resolubilized in 50 μL\n80% ACN/6% TFA, and incubated with 0.5 mg IMAC beads, followed by washing and elution as\nabove. In method 28, 20 μg desalted peptides were dissolved in 50 μL 80% ACN/6% TFA and\nincubated with 0.5 mg IMAC beads for 1 h. Beads were washed as above, and phosphopeptides\nwere eluted with 30 μL 10% ammonia (twice). The IMAC flow-through was concentrated, loaded\nonto OASIS MAX columns (1 mg), equilibrated with 95% ACN/1% TFA, and subjected to\nsequential washing and elution according to the original protocol.\nAdded new Fig.3c-k and Fig. S7.\n2. The study exclusively uses label-free quantification. For broader applicability, it is important\nto demonstrate or at least discuss MuPPE’s compatibility with isobaric labeling strategies such as\nTMT. Without this, the platform is likely to remain limited to exploratory analyses, restricting its\npotential for large-scale, multiplexed quantitative studies.\nWe appreciate the reviewer’s valuable suggestion regarding the compatibility of MuPPE with\nisobaric labeling strategies, which is critical for expanding its applicability in large-scale\nmultiplexed studies. We agree that addressing this aspect enhances the platform’s utility, and we\nhave supplemented both experimental data and discussion to clarify this point.\nIn the revision, we developed and validated a fully TMT-compatible implementation of MuPPE,\nand we provide a step-by-step protocol in the Supplementary Information. Methodologically, we\nadopted a label-then-enrich design: individual samples are digested in 100 mM TEAB (substituted\nfor ammonium bicarbonate to preserve NHS-ester reactivity), TMT-labeled, pooled in equal\namounts, and then subjected to MuPPE’s sequential glycopeptide-first and phosphopeptide\nenrichments. Processing all channels as a single pooled sample ensures identical downstream\nhandling and thus preserves inter-channel ratio fidelity, while retaining MuPPE’s orthogonal\nfractionation logic. We did not include a TMT-based glycoproteomics analysis. At present, robust\nand widely validated pipelines in our environment for reporter-ion-based, site-resolved N-\nglycopeptide quantification-with rigorous multi-level FDR control (peptide/glycan/site) and\ncomprehensive interference corrections (isotopic impurity and co-isolation)-are not yet available.\nWe will incorporate glyco-TMT once such tools are mature and have been validated in our\nlaboratory.\nEvidence of TMT compatibility (Fig. 5). For each feature, within-run log₂(MuPPE/In-solution)\nratios were computed across three matched 2-plex TMT experiments, and statistical significance\nwas assessed using paired t-tests with Benjamini-Hochberg FDR control. The resulting volcano\nplots show a systematic rightward shift for both the proteome and phosphoproteome, indicating\nthat MuPPE yields higher quantitative intensities for the majority of features relative to the in-\nsolution workflow. These results demonstrate that MuPPE is fully compatible with isobaric\nmultiplexing and strengthen its suitability for both exploratory and large-scale quantitative studies.\nVolcano plots for the global proteome (left) and phosphoproteome (right). Each point represents a\nprotein group or a phosphopeptide. The x-axis shows within-run log₂ intensity ratios (MuPPE/In-\nsolution) computed after isotopic correction; the y-axis shows -log₁₀ P from paired t-tests across\nthree matched 2-plex TMT runs. Blue stars indicate features significantly higher in MuPPE at the\nstated thresholds; grey points are not significant. Under equal input and TMT multiplexing, MuPPE\nyields higher and more consistent quantitative signal than the in-solution workflow across both the\nproteome and phosphoproteome layers.\nManuscript changes:\nPage 4, Line 128-130 in revised manuscript:\nAdditionally, we compared the compatibility of the in-solution method and the MuPPE method\nwith TMT labeling experiments, and confirmed that MuPPE is compatible with TMT-based\nmultiplexed quantification (Fig. S1).\nSupplementary Inf: Page 8, Lines 206-215.\nFor TMT labeling proteomic and phosphoproteomic samples: peptides were separated through a\ngradient of up to 90% buffer B over 90 min at a flow rate of 600 nL/min. The gradient of the mobile\nphase started from 2% to 5% B for 1 min and then was increased linearly to 40% B in 70 min, to\n45% in 10 min, to 100% in 5 min, and maintained for 5 min. The LC-MS/MS system was operated\nin data-dependent MS/MS acquisition mode. MS1 scans (350-1500 m/z) were acquired at a\nresolution of 60,000 with an AGC target of 200 % and a maximum injection time of 5 ms. MS/MS\nscans were acquired with 0.7 m/z isolation windows (scan range 110-3000 m/z), 59 windows, a\nmaximum injection time of 100 ms and an AGC target of 300 %. The spray voltage and the\n35\ntemperature of the ion transfer capillary were set to 2.6 KV and 320 °C, respectively. The\nnormalized collision energy for HCD and dynamic exclusion was set as 34% and 30 s, respectively.\nSupplementary Inf: Page 8-9, Lines 217-250.\nTMT labeling experimental process: For the MuPPE-TMT workflow, approximately 20 μg of\nmouse brain proteins (lysed in 8 M urea/HEPES) were reduced with 1.1 μL of 200 mM DTT (final\nconcentration 10 mM, 37 °C, 60 min) and alkylated with 1.12 μL of 800 mM IAA (final\nconcentration 40 mM, dark, 30 min). Each sample was incubated with 0.6 mg maltose beads (5 μL\nsuspension in water) and 63 μL acetonitrile (final concentration 70%) for 10 min, centrifuged at\n4000 rpm for 4 min, and washed three times with 200 μL of 80% ethanol. Beads were resolubilized\nin 8 μL of 6 M urea, sonicated to disperse, diluted with 40 μL of 100 mM TEAB, and digested with\n2 μL trypsin (0.5 μg, 1:40) at 37 °C for 2 h. The digested peptides were labeled with TMT reagents\nby dissolving 40 μg of TMT in 2 μL anhydrous acetonitrile, incubating with 50 μL peptide solution\nat room temperature for 1 h, and quenching with 1 μL of 5% hydroxylamine for 15 min.\nFor the In-solution-TMT workflow, 20 μg of mouse brain proteins (8 M urea/HEPES) were reduced\nwith 1.1 μL of 200 mM DTT (10 mM, 37 °C, 60 min), alkylated with 1.12 μL of 800 mM IAA (40\nmM, dark, 30 min), and digested with 2 μL trypsin (0.5 μg, 1:40) overnight at 37 °C. Digestion\nwas stopped by adding 2 μL of 1% TFA (final concentration 0.1%). Peptides were desalted with\n0.75 mg C18 tips using the following steps: activation with 50 μL 50% acetonitrile/0.1% FA,\nequilibration with 50 μL 0.1% FA, sample loading in 30 μL 0.1% FA, washing with 50 μL 0.1%\nFA (×2), and elution with 30 μL 50% acetonitrile/0.1% FA (×2). The eluates were vacuum dried\nand subjected to TMT labeling as described above (2 μL TMT reagent in 2 μL anhydrous\nacetonitrile, 50 μL peptide solution, 1 h incubation at room temperature, and quenching with 1 μL\nof 5% hydroxylamine for 15 min).\nFor glycopeptide enrichment, the TMT-labeled digests from both MuPPE and in-solution\nworkflows were adjusted with 2.37 μL TFA and 189.5 μL acetonitrile to maltose-binding\nconditions and incubated with 0.6 mg maltose beads for 15 min. Bound glycopeptides were washed\nthree times with 50 μL of 80% acetonitrile/1% TFA, and eluted three times with 30 μL of 30%\nacetonitrile/1% FA. The flow-through and wash fractions were combined, dried, and resolubilized\nin 200 μL of 80% acetonitrile/6% TFA for phosphopeptide enrichment. IMAC tips containing 1\nmg beads were equilibrated with 100 μL of 80% acetonitrile/6% TFA, loaded with 100 μL of\nsample solution, and sequentially washed with 50 μL 80% acetonitrile/6% TFA, 2 × 100 μL 50%\nacetonitrile/6% TFA/200 mM NaCl, 2 × 100 μL 30% acetonitrile/0.1% FA, and 2 × 50 μL 30%\nacetonitrile. Bound phosphopeptides were eluted twice with 30 μL of 10% ammonia, vacuum dried,\nand stored at -80 °C.\nFinally, glycopeptide and phosphopeptide eluates, as well as protein fractions from the combined\nflow-throughs, were dried, resolubilized in 30 μL of 0.1% FA, desalted with 0.75 mg C18 tips\n(activation: 50 μL 50% acetonitrile/0.1% FA; equilibration: 50 μL 0.1% FA; loading: 30 μL 0.1%\nFA; wash: 2 × 50 μL 0.1% FA; elution: 2 × 30 μL 50% acetonitrile/0.1% FA), dried again, and\nstored at -80 °C until LC–MS/MS analysis.\nAdded new Fig. S1\n3. Although MuPPE simplifies PTM workflows, it identifies substantially fewer phosphopeptides\ncompared to state-of-the-art phosphoproteomic methods. This limitation may significantly reduce\nits utility for phosphorylation-centric studies. The authors should benchmark their phosphopeptide\nrecovery against existing methods and address whether the sequential design inherently limits\nphosphopeptide enrichment. Reduced detectability could profoundly impact biological discovery and\ninterpretation.\nResponse:\nWe appreciate the reviewer’s concern that MuPPE may identify fewer phosphopeptides than state-\nof-the-art, phospho-dedicated workflows, and we agree that fair benchmarking and an explicit\ndiscussion of any sequential-design effect are essential. We acknowledge that specialized, ultra-\ndeep phosphoproteomics (e.g., mg-scale inputs, multi-round IMAC/TiO₂, and extensive offline\nfractionation) can yield larger site catalogs than any single-shot, multi-omics workflow. MuPPE is\ndesigned to balance depth with integrative coverage (proteome/glyco/phospho) and sample\nefficiency.\nComprehensive Benchmarking Against State-of-the-Art Platforms: To avoid confounding by input\namounts and fractionation depth, we performed matched comparisons (identical starting input,\nsingle-shot LC-MS/MS, the same search/filters and FDR, and identical site-localization thresholds)\nbetween MuPPE and two commonly used IMAC-based reference workflows. Across biological\nreplicates, the numbers of identified phosphopeptides and localized sites did not differ significantly\nbetween MuPPE and the references (Fig. 4. above). These results indicate that, under matched\nmicro-scale and single-shot conditions, MuPPE does not compromise phosphoproteome coverage.\nSequential-design assessment: We explicitly evaluated order effects in our tandem HILIC-IMAC\nstudy (“Tandem HILIC-IMAC strategy for simultaneous N-glycoproteomics and\nphosphoproteomics in aging mouse brain.” J. Chromatogr. A, 2025, 1739). Running HILIC-IMAC\n(glyco-first) yielded more phosphopeptides and slightly more glycopeptides than IMAC-HILIC.\nCritically, glycan-diagnostic signals (HexNAc/NeuAc) in the phosphopeptide fraction were lower\nwith HILIC-IMAC than with IMAC-HILIC, indicating far less glycopeptide carryover. Conversely,\nphosphopeptide carryover into the glycopeptide fraction was minimal with HILIC-IMAC.\nMechanistically, depleting glycopeptides first does not introduce conditions that favor\nphosphopeptide loss, consistent with the minimal phospho- signal observed in glycol- fractions\n(Fig. 6).\ncontaining MS2 spectra and the total MS2 spectra in PP fraction using HILIC-IMAC strategy and\nIMAC-HILIC strategy. b The number of phosphopeptides in GP fraction using HILIC-IMAC\nstrategy and IMAC-HILIC strategy. c The number of identified target peptides using HILIC-IMAC\nstrategy and IMAC-HILIC strategy (J. Chromatogr. A, 2025, 1739).\nFinally, TMT label-then-enrich validation and quantitative behavior: We conducted three matched\n2-plex TMT experiments. Within-run paired analyses show a systematic right-shift in volcano plots\nfor both proteome and phosphoproteome layers, consistent with higher quantitative intensities for\nthe majority of features relative to in-solution workflows at the same input (Fig. 5. above).\nCollectively, these results show that MuPPE does not intrinsically sacrifice phosphoproteome\nperformance. Under strictly matched, single-shot conditions, phosphopeptide and localized-site\nyields are comparable to IMAC references; our tandem HILIC-IMAC study show that ordering\nHILIC→IMAC with markedly lower glycan carryover in the PP fraction and minimal\nphosphopeptide carryover to the GP fraction. In addition, TMT label-then-enrich runs preserve\nratio fidelity and increase quantitative signal. Thus, perceived deficits versus ultra-deep,\nfractionated phospho-dedicated pipelines primarily reflect scale/fractionation, not a limitation of\nMuPPE; when maximal site catalogs are needed, MuPPE can be paired with deeper fractionation\nwithout altering its integrative design.\n4. The sequential nature of MuPPE leads to unequal input across glyco-, phospho-, and total\nproteome fractions, introducing potential biases in quantitation and reproducibility. The manuscript\nshould address whether and how these differences are normalized or controlled.\nResponse:\nWe thank the reviewer for highlighting the risk that a sequential workflow could yield unequal\nmaterial input across the glyco-, phospho-, and total proteome fractions, potentially biasing\nquantitation and reproducibility. Our study design and analysis pipeline explicitly mitigate these\nrisks at the experiment, normalization, and quality-control levels, as detailed below.\n(e.g., 50 µg per mouse) with constant material-to-sample ratios at every enrichment step, and\nmatched LC-MS acquisition time/gradients/instrument settings (Supplementary Inf: “LC-MS/MS\nanalysis of different proteomic samples”; “Integrated proteome, glycoproteome, and\nphosphoproteome analysis of aging mouse samples and NB4 cells using the MuPPE platform”\nsection). Injections are randomized and interleaved with blank/QC runs to monitor carryover and\ndrift. These controls ensure that any post-enrichment differences reflect expected yield\ncharacteristics of each chemistry rather than arbitrary input disparities.\nData Normalization: For all three omics layers, we performed log transformation and median\n2\nnormalization prior to downstream analyses. This step further reduces the impact of absolute\nenrichment efficiency differences and enables reliable comparison across fractions.\nQuantitative Consistency Verification: We systematically assessed the quantitative reproducibility\nof each omics dataset by calculating pairwise Pearson correlation coefficients among biological\nreplicates. As shown in Fig. 7, all three omics datasets displayed high correlation (Proteome: 0.97-\n1.00, Phosphoproteome: 0.83-0.96, Glycoproteome: 0.76-0.87), confirming strong consistency and\nthat the sequential workflow does not introduce systematic quantitation bias.\nLiterature Validation: Our previous publication (Ding et al., J. Chromatogr. A 2025, 1739.)\nsystematically validated this approach by tandem enrichment and integrated analysis of proteome,\nglycoproteome, and phosphoproteome in mouse brain tissue, achieving technical repeatability with\na median Pearson’s r of 0.98. This further demonstrates that the tandem strategy provides high\nreliability and quantitative reproducibility for multi-omics integration.\nTogether, these measures ensure that the observed quantitative differences truly reflect biological\nvariation rather than technical artifacts introduced by the sequential workflow.\nFig. 7. Pairwise Pearson correlation matrices of quantitative data among biological replicates for\nthe proteome, phosphoproteome, and glycoproteome datasets after normalization. Each cell shows\nthe Pearson correlation coefficient (r) between two samples, with the corresponding scatter plots\nand distribution curves. The consistently high correlation coefficients (Proteome: 0.97-1.00,\nreproducibility and confirm that the sequential enrichment strategy does not introduce systematic\nbias (Data shown are from choroid plexus samples in the mouse aging cohort).\nManuscript changes:\nSupplementary Data: Pages 9-10, lines 252-275\nIntegrated proteome, glycoproteome, and phosphoproteome analysis of aging mouse samples\nand NB4 cells using the MuPPE platform: For each aging mouse sample (choroid plexus, CSF,\nand serum) and for NB4 cell lysates, total protein concentration was quantified using the BCA\nProtein Assay Kit (Beyotime, China) following the manufacturer’s instructions. Exactly 50 μg of\ntotal protein from each sample was used as input for the subsequent MuPPE workflow. In MuPPE\nprocess. Denaturation, PAC and on-line digestion on Click-Maltose, Then, adjust the tryptic\nsample solution to 80% acetonitrile (ACN)/1% trifluoroacetic acid (TFA) by adding ACN and TFA\nand incubate for 10 min for glycopeptides enrichment. Finally, adjust the flow-through condition\nfor phosphopeptide enrichment according to the manufacturer's instructions, and then incubate for\n10 minutes to facilitate phosphopeptide enrichment. The remaining (flow-through) peptides not\nbound in the glyco/phospho enrichment steps were retained for proteomics. All peptide fractions\nwere analyzed using high-resolution LC-MS/MS platforms.\nFor data processing and integration across the three layers, contaminants and decoys were removed\nand PTM sites were required to meet localization probability thresholds defined by the respective\nsearch engines; raw intensities were normalized at the run level (total ion current, TIC) and\nsubsequently log2-transformed. To ensure both depth and reliability, layer-specific coverage\nthresholds were applied: features in the proteome and phosphoproteome were retained only if\nquantified in ≥80% of replicates within at least one group, whereas the glycoproteome retained\nfeatures quantified in ≥50% of replicates, acknowledging the higher intrinsic sparsity of\nglycopeptide data. Missing values were then handled in a layer-specific manner: for the proteome\nand phosphoproteome, partially missing entries were imputed within layer on log2-transformed\ndata using the k-nearest neighbors algorithm (KNN, k=5), while for the glycoproteome, missing\nvalues were treated as true non-detections and replaced with 0 at the raw scale (followed by ε + log2\ntransformation to avoid undefined values). Finally, for cross-layer comparability, all features were\nZ-scored within each layer (per feature across samples) before integration and downstream\nstatistical analyses.\n5. The case studies using NB4 cells and aging mice are promising, but the biological insights\nremain largely descriptive. There is minimal integration across PTM layers, and mechanistic\ninterpretation is limited. Given that this manuscript is primarily focused on technology\n40\ndevelopment, a more in-depth comparison with existing platforms and a stronger discussion of\nbiological implications would be highly beneficial for readers.\nResponse:\nWe thank the reviewer for this valuable suggestion. In the revision we substantially strengthened\n(1) cross-layer PTMs integration and mechanistic interpretation for both the aging mouse cohorts\nand NB4 cells, and (2) head-to-head context with existing platforms.\nTo address the review’s concern regarding systematic integration of proteome, phosphoproteome,\nand glycoproteome data, we have undertaken a comprehensive re-analysis to uncover cross-omics\nassociations of aging and arsenic-treated NB4 cells dataset, as detailed below:\n(ⅰ) For the aging dataset, we performed multi-layered KEGG pathway enrichment analyses (Fig. 8.\nFig. 5f in the revised version), allowing us to identify both shared and unique biological\nprocesses and pathways regulated at the total protein, phosphorylation, and glycosylation levels.\nNotably, pathways related to synaptic signaling, cell adhesion, extracellular matrix organization,\nand immune response were consistently enriched across all three proteome, phosphoproteome, and\nglycoproteome layers, suggesting that these fundamental processes are subject to coordinated\nmulti-PTM regulation. For example, the convergence of synaptic signaling and cell adhesion\npathways in proteome, phosphoproteome, and glycoproteome highlights their critical role in neural\nplasticity and tissue remodeling during aging and disease.\nIn contrast, metabolic pathways (e.g., energy metabolism, lipid metabolism) were predominantly\nenriched in the total proteome and phosphoproteome layers, whereas pathways associated with\nimmune modulation and glycan biosynthesis were more specific to the glycoproteome, reflecting\nthe specialized roles of different PTMs.\nThese findings demonstrate that multi-omics integration not only validates known biological\nthemes but also uncovers PTM-specific and multi-layered regulatory networks that would be\nmissed by single-omics analysis alone. The results and biological implications of this multi-omics\nintegration have been detailed in the revised Results and Discussion sections.\nphosphoproteome, and glycoproteome datasets.\n(ⅱ) Based on multi-omics data analysis, we propose a mechanism where arsenic acts via a\nmembrane-to-nucleus signaling cascade. It modulates membrane proteins through glycosylation\nand perturbs the PI3K-AKT-mTOR pathway via phosphorylation, impacting cell cycle and\nchromosomal organization, possibly arresting the cell cycle and inducing apoptosis (supported by\nflow cytometry showing G2/M phase accumulation and phosphoproteomic changes in cell cycle\nregulators). Also, arsenic may disrupt genome-wide transcription by affecting chromatin-\nassociated proteins. Taking IGF2R, a key membrane protein in the PI3K-AKT-mTOR pathway, as\nan example, although its global protein level shows no significant change, multi-omics data reveal\n13 differential PTMs sites (12 N-glycosylation and 1 phosphorylation). This shows arsenic exerts\neffects by rewiring PTM-mediated signaling networks, a mechanism only detectable through multi-\nomics profiling.\neffects on cell cycle and chromatin organization. Proteome (green), glycoproteome (orange), and\nphosphoproteome (blue) alterations are mapped. b Flow cytometry analysis of NB4 cells treated\nwith increasing arsenic concentrations, showing dose-dependent G2/M phase accumulation. c\nDifferential expression of protein relative abundance, glycosylation sites and phosphorylation sites\nof IGF2R protein with the log2 fold change values. Different symbols representing up-regulation\n(upward triangles), down-regulation (downward triangles), and the color intensity indicating the\nsignificance of the change.\nIn summary, across both applications, the organismal aging cohort and the arsenic-perturbed NB4\nmodel, our cross-omics framework reveals a coherent picture of multi-layer regulation: (i) system-\nlevel convergence of synaptic signaling, cell-adhesion/extracellular-matrix, and immune programs\nacross the proteome, phosphoproteome, and glycoproteome in aging, and (ii) The IGF2R case\nexemplifies this principle, where negligible protein-level change (log2FC≈0) co-occurs with\npronounced site-specific phosphorylation and glycosylation remodeling, indicating pathway\nrewiring that would be invisible to any single layer. Taken together, these results demonstrate the\nrobustness and generalizability of our integrative strategy, provide mechanistic links from PTMs\nremodeling to pathway activity and phenotype, and address the reviewer’s request for systematic\ncross-omics integration and biological interpretation.\nWe sincerely appreciate your constructive suggestion regarding strengthening the discussion of\nbiological implications, which has been highly instrumental in enhancing the depth and impact of\nour manuscript. In response to this comment, we have supplemented both the Results and\nDiscussion sections with dedicated content to elaborate on the biological implications of our\nfindings.\nManuscript changes:\nPage 17-18, Line 431-446 in revised manuscript:\nproteome, phosphoproteome, and glycoproteome across 2, 12, and 24 M, followed by KEGG\npathway enrichment analysis (Fig. 5f). In the phosphoproteome, pathways associated with aging-\nincluding oxidative phosphorylation, Alzheimer’s disease, and Parkinson’s disease-were enriched\nearly in young mice and remained detectable throughout all age groups. The proteome exhibited\nsignificant enrichment in pathways such as long-term depression and peroxisome function\nspecifically at 24 M, suggesting a delayed accumulation compared to phosphorylation-mediated\nchanges. The ECM-receptor interaction pathway showed distinct regulatory patterns: glycoprotein\nlevels increased progressively with age, while phosphorylation peaked at 12 M before declining by\nand phosphoproteome, with an initial increase followed by a decrease; phosphorylation changes,\nhowever, declined more gradually. Several pathways were uniquely altered in specific PTM layers:\nfocal adhesion decreased with age in the phosphoproteome, whereas oxidative phosphorylation\nwas broadly enriched. In the glycoproteome, the lysosome pathway increased with age, along with\nupregulation of antigen processing and ECM-related processes.\nPage 21, Line 514-539 in revised manuscript:\nBuilding on the multi-omics dataset analysis, we propose a mechanistic model where arsenic exerts\nits effects through a membrane-to-nucleus signaling cascade (Fig. 6i), primarily mediated by\nglycosylation-modulated membrane proteins and phosphorylation-driven PI3K-AKT-mTOR\npathway perturbation. The glycoproteomic data revealed that arsenic induces glycosylation\nalterations in membrane-anchored receptors (e.g., components of the PI3K-AKT-mTOR pathway\nupstream regulators). These glycosylation changes might act as “molecular switches” to modulate\nmembrane protein stability, localization, and ligand-binding capacity. We also observed that the\narsenic treatment significantly impacted the cell cycle and chromosomal organization pathways,\nparticularly in the regulation of the proteome and phosphoproteome during the M phases. This\nsuggests that arsenic exerts profound effects on these two critical signaling pathways, potentially\nby arresting the cell cycle and disrupting chromosomal functions, thereby promoting apoptosis.\nFlow cytometry analysis (Fig. 6j) showed the G2/M phase accumulation, consistent with the\nphosphorylation changes in cell cycle regulators (e.g., CDK, CCNB proteins in Fig. 6j) directly\nsupport this arrest. GO enrichment results of nuclear functions in the key modules, combined with\nphosphoproteomic changes in chromatin-associated proteins (e.g., histone modifiers), suggest\narsenic might disrupt genome-wide transcriptional programs and link membrane-to-nucleus\nsignaling to nuclear architecture and function.\nFocusing on IGF2R, a key membrane protein identified in the PI3K-AKT-mTOR pathway, we\ninterrogated its individual proteomic, glycoproteomic, and phosphoproteomic alterations (Fig. 6k).\nAt the global protein level, IGF2R abundance showed no significant fold-change between arsenic-\ntreated and control groups. However, its glycoproteomic and phosphoproteomic data revealed\nnotably PTMs-specific alterations. we identified 13 differential PTMs sites, including 12 N-\nglycosylation sites and 1 phosphorylation site. Upregulated and downregulated PTMs were denoted\nby upward and downward triangles, respectively. Without glycoproteomic/phosphoproteomic data,\nIGF2R would appear “unchanged” at the protein level, masking its potential role as a PTMs-driven\nhub. Thus, arsenic exerts its effects not by altering protein abundance broadly but by rewiring PTM-\nmediated signaling networks-a mechanism only detectable via multi-omics profiling.\nPage 28-29, Line 722-731 in revised manuscript:\nPathway temporal clustering and visualization: Proteome, phosphoproteome, and glycoproteome\nprofiles across 2, 12, and 24 M were Z-score normalized and subjected to fuzzy c-means clustering\nusing Mfuzz (R, v2.60.0) to capture temporal expression patterns. Cluster-specific KEGG\nenrichment was performed with clusterProfiler (v4.10.0), retaining pathways with FDR < 0.05, FC>\n1.5. The resulting heatmap integrates three layers of information: (i) Membership plots (left),\nshowing cluster-wise temporal trends with membership values; (ii) Expression heatmaps (middle),\ndisplaying Z-scored expression values across 2 M, 12 M, and 24 M; and (iii) KEGG pathways\n(right), annotated for each cluster, with functionally relevant pathways highlighted. Results were\nvisualized as integrated membership trajectories, expression heatmaps, and enriched pathways\nusing ggplot2 and ComplexHeatmap, ensuring full reproducibility in R (v4.3.1).\nAdded new integrative Figure. (Fig. 5 and 6 in the revised version).\n6. The manuscript requires extensive language polishing. For example, there are several\nsentences starting with “And”.\nResponse:\nWe will carefully review and polish the language of the manuscript to improve its quality,\neliminating sentences starting with “And” and ensuring a more fluent and professional writing style.\n*******************************************************************\nReviewer #1:\nI have read with interest the extensive revision of this manuscript, in which I think the authors\nhave done an excellent job addressing all my previous concerns.\nSome minor points:\n1) Figure 4f-I assume that “ns” on top of the figure stands for “not significant.” However, I\ndon’t think such a statement is relevant when comparing the number of phosphopeptides. If the\nauthors wish to retain it, they should indicate which type of statistical test was used to derive\nthat conclusion.\nResponse:\nThank you for the suggestion. We agree that “ns” labels are unnecessary on a summary count\nplot. We have removed all “ns” annotations from Fig. 3f. The main text has been updated\naccordingly.\nManuscript changes:\nFig. 3f “ns” annotations have been removed.\nFig. 3. Serialized analysis of glycoproteome, phosphoproteome and proteome using MuPPE\nPage 10, Line 268-270 in revised manuscript:\n“Notably, the number of identified proteins and phosphopeptides were comparable between\nMuPPE and the other methods, indicating that the integrated multi-omics enrichment design\ndid not compromise proteome or phosphoproteome coverage (Fig. 3f and 3g).”\n2) Figure 5-The authors perform a KEGG enrichment analysis to derive biological insights into\nthe pathways regulated at the protein, phosphoproteome, and glycoproteome levels. However,\nthis type of analysis does not consider the regulatory layers of the phospho- and glyco-\nproteomes. For instance, when considering a glycoprotein for such enrichment, how do the\nauthors account for cases where a protein contains both glyco-sites that are up-regulated and\ndown-regulated (as shown in Figure 6C)? There are annotation tools, such as PTM-SEA, that\ntake this into account and provide more meaningful information for this type of data.\nResponse:\nThank you for this insightful comment. We agree that gene-centric KEGG can mask site-level\nregulation in phospho- and glycoproteomes, particularly when a protein harbors both up- and\ndown-regulated sites. We now perform a PTM-aware, site-centric analysis for the\nphosphoproteome. Specifically, we computed site-level moderated t statistics (older vs 2M) and\nran PTM-SEA (ssGSEA2.0) against PTMsigDB kinase/motif/pathway signatures. This\npreserves directionality at each phosphosite, so proteins with mixed regulation contribute\nappropriately to both up/down signatures. Results are reported as normalized enrichment scores\n(NES) with P-value and are shown in Supplementary Fig. S18. Compared with 2 M, 24 M\nshow positive enrichment of PI3K-Akt pathway and kinase-centric signatures AKT1, CDK2,\nSIK2, MAPKAPK5/RSK2, with larger NES and significance than 12 M, indicating progressive\nactivation with age. In contrast, CK2/CSNK2A2, NLK, and ERK5/MAPK7 signatures are\nnegatively enriched at both ages. These results indicate a shift toward AKT/ cell-cycle kinase\nactivity with a concomitant decline of CK2-mediated regulation during physiological aging.\nConsistent with GO results, PTM-SEA indicates age-progressive activation of AKT/RSK-cell-\ncycle signaling linked to RNA/spliceosomal regulation, with concomitant attenuation of\nCK2/ERK5/NLK pathways that support actomyosin architecture and neuronal/synaptic\nhomeostasis. These results indicate a shift toward AKT/ cell-cycle kinase activity with a\nconcomitant decline of CK2-mediated regulation during physiological aging.\nAs for glycoproteome; there is currently no curated, PTM-SEA-style library for site-resolved,\ndirectionality-aware N-glycosylation signatures comparable to PTMsigDB. Therefore, we\nretained the protein-centric KEGG analysis for the glycoproteome.\nManuscript changes:\nAdded new Fig. S18.\nFig. S18. PTM signature enrichment analysis (PTM-SEA) bubble plot for 12 M/2 M and 24\nM/2 M contrasts. Circles are coloured by normalized enrichment scores (NES) (red, positive\nenrichment; blue, negative) and scaled by significance (bubble area, proportional to −log P-\n10\nvalue); signature names are shown on the left.\nPage 16-17, Line 404-414 in revised manuscript:\n“We further conducted site-level pathway enrichment using PTM-SEA, which infers PTM\nregulators from substrate-specific signatures56. As shown in Fig. S18, relative to 2 M controls,\n(AKT1, CDK2, SIK2, MAPKAPK5/RSK2), with higher normalized enrichment scores and\nsignificance than 12 M, indicating progressive, age-dependent activation. Conversely,\nCK2/CSNK2A2, NLK and ERK5/MAPK7 signatures were consistently negatively enriched at\nactivity with a concomitant attenuation of CK2-linked regulation during physiological aging57.\nConsistent with GO results, PTM-SEA indicates age-progressive activation of AKT/RSK-cell-\ncycle signaling linked to RNA/spliceosomal regulation, with concomitant attenuation of\nCK2/ERK5/NLK pathways that support actomyosin architecture and neuronal/synaptic\nhomeostasis.”\nPage 30, Line 745-749 in revised manuscript:\n“For PTM-SEA analysis, phosphosites were ranked based on signed effect sizes derived from\nmoderated t-tests. Enrichment analyses were then performed against PTMsigDB, supplemented\nwith in-house curated glycosite datasets, to retain directional regulatory information\n(upregulation vs. downregulation). Normalized enrichment scores (NES) and P-values were\nreported for the results. The entire workflow was implemented in R, utilizing limma for\nstatistical calculations and ssGSEA2.0 for enrichment analyses.”\n3) There is a typo in the legend of Figure 5g – “Founction” should be corrected to “Function.”\nResponse:\nWe sincerely thank the reviewer for spotting the typo; which have now been corrected in the\nrevised manuscript Fig. 5g.\nManuscript changes:\nFig. 5g “Founction” has been corrected to “Function”.\nFig. 5. Phosphoproteome, glycoproteome and proteome integrative analysis of mice aging\ncohort.\n4) In the supplementary information:\n“PTMs sites were required to meet localization probability thresholds defined by the respective\nsearch engines.” Please specify these thresholds.\nResponse:\nThank you for pointing this out, and we apologize for the lack of specificity. We have revised\nthe Methods to explicitly report the engine-specific thresholds used for site localization\nconfidence.\nPhosphoproteome: We reported Class-I phosphosites only, defined as PTM Localization\nProbability≥0.75 with site q-value ≤0.01 (and peptide/precursor q-value≤0.01). A sensitivity\nanalysis at a stricter 0.90 localization threshold produced the same qualitative results.\nGlycoproteome: We controlled 2D-FDR=1% at the glyco-PSM level. For confident site\nassignment, we required Byonic Score≥150, |LogProb|≥1 to disambiguate the N-site, and\ndiagnostic evidence (oxonium ions and≥2Y-ions) consistent with the assigned composition.\nWe have inserted the sentence in the Methods to make these thresholds explicit.\nManuscript changes:\nPage 10, Line 267-270 in revised Supplementary Information:\n“PTM site localization used engine-specific criteria: for the phosphoproteome we retained\nClass-I sites with Localization Probability>0.75 and Site q-value <0.01 (peptide/precursor\nq<0.01); results were robust at a stricter 0.90 cutoff. For the glycoproteome we controlled 2D-\nFDR=1% at the glyco-PSM level and required Byonic Score>150, |LogProb|>1;”\n“Raw intensities were normalized at the run level (total ion current, TIC).” Please provide a\nmore detailed explanation of this normalization strategy.\nResponse:\nThank you for the comment, and we apologize for not describing the normalization clearly. We\nused run-level total-ion-current (TIC) normalization: after contaminant/decoy removal and ID\nfilters, we computed each run’s TIC as the sum of precursor intensities, scaled all intensities in\nthat run by αᵣ = median (TIC)/TICᵣ to equalize total signal across runs, and then applied a log₂\ntransform (ε was added only where zeros can occur). This step precedes any missing-value\nhandling and is applied identically to proteome, phosphoproteome, and glycoproteome layers.\nWe have inserted the sentence in the Methods to make these thresholds explicit.\nManuscript changes:\nPage 10, Line 270-272 in revised Supplementary Information:\n“Raw intensities were run-level TIC-normalized by scaling each run’s total precursor signal to\nthe cohort median (scale factor α_r=median (TIC)/TIC_r) and then log₂-transformed.”\n*******************************************************************\nReviewer #2:\nThe reviewer’s comments have been fully addressed in the revised manuscript.\nResponse:\nWe thank you for their positive evaluation and confirmation that all previous comments have\nbeen adequately addressed.\n*******************************************************************\nReviewer #3:\nRemarks to Author:\nThe authors present a platform for the serial analysis of the proteome, glycoproteome, and\nphosphoproteome for both tissue and liquid samples. They provide a comparison against other\nmethods for post-translational modification analysis and show that their method significantly\nimproves processing time, the number of transfers needed, and other parameters. The authors\nvalidate their method on human serum and mouse brain tissue samples, while also applying\ntheir method to aged animals and an arsenic-treated cell line.\nThis review is primarily focused on the manuscript from an aging perspective. The authors\nprovide an analysis of age-associated changes in post-translation modifications which may have\nimportant implications, especially given how the immune system is known to shift with aging\n(Fulop et al. Clin Rev Allergy Immunol. 2021) and how post-translation modifications can\nmodulate immunity (Mattingly et al. Immune Netw. 2025, Liu et al. Immunity. 2016).\nThe changes in antibody-associated glycopeptides with age are intriguing and could have\nimplications for modern immunotherapies and anti-aging approaches. Recent literature has\nlinked the IgG antibody to aging and tissue fibrosis (Yu et al. Cell Metab. 2024). There is also\nliterature discussing the role of Fc fucosylation on antibody behavior (Golay et al. Front.\nImmunol. 2022., Saporiti et al. Commun. Biol. 2023.), but the age-associated impact of post-\ntranslational modifications on antibody function is less studied.\nResponse:\nWe sincerely thank you for the thorough and insightful evaluation of our manuscript. We\nappreciate the positive feedback on the strengths of our study and the constructive suggestions\nthat have helped us improve its clarity and impact. All reviewer-recommended references have\nbeen added at the relevant locations and are now cited as [42], [52], [53], [54], and [55].\nMain Comments:\nIn Figure 5G, one drawback is that for two of the tissues analyzed (MCSF and Mserum) only\nthe proteome and glycoproteome are shown which takes away from the claim that the platform\nenables easy integration of all three levels.\nResponse:\nWe sincerely thank the reviewer for this insightful comment regarding the phosphoproteome\ndata of MCSF and Mserum samples in Fig. 5g. We fully agree with the reviewer’s observation\nthat phosphoproteome data is absent for these two samples, and we greatly appreciate the\nchance to clarify the underlying reason.\nThe lack of a phosphoproteome layer for MCSF and Mserum in Fig. 5g does not stem from a\nlimitation of our integrative platform, but rather from an intrinsic constraint of biofluid matrices.\nPrior studies have established that phosphoproteome coverage in biofluids-particularly\nplasma/serum and cerebrospinal fluid (CSF)-is intrinsically limited: even with dedicated\nphosphopeptide enrichment and high-resolution MS-MS, stable yields are typically only on the\norder of tens to a few hundred phosphopeptides. Consistent with those literatures (listed as\nbelow), our own pilot runs from 1 µL serum input yielded only ~7-20 unique phosphopeptides.\nThus, our data did not exceed the known matrix-imposed ceiling and were insufficient for\nrobust downstream bioinformatic analysis; accordingly, we did not include these\nphosphoproteome layers in the main figures. We hope this clarification addresses the reviewer’s\nconcern.\nWe have summarized the relevant studies below:\n Ref. 1: J. Proteom. Res. (2010, 9: 1385-1391) reported 127 phosphopeptides from human\nplasma and 123 from CSF using IMAC/TiO₂ enrichment with EDTA co-injection;\n Ref. 2: Clin. Proteom. (2018, 15: 29) identified 111 phosphotyrosine (pY) peptides from\nCSF via anti-pY enrichment;\n Ref. 3: Proteomics (2022, 22: 2100216) performed a head-to-head comparison on serum\ndigests and found that, even across different enrichment media/strategies and higher\ninjection amounts, the total number of identified phosphopeptides ranged only from ~19\nto 147;\n Ref. 4: Anal. Chem. (2024, 96: 8254-8262) recent work on serum further confirmed this\nconstraint: conventional phosphoproteomics yielded only 172 phosphopeptides.\nFrom the text, Figures 4F and 4G should demonstrate that the MEblue and MEyellow modules\nare positively and negatively correlated with aging, but Figure 4F itself does not seem to\nprovide information relevant to the module correlations. It is not entirely clear how the structure\nof the dendrogram provides additional information.\nResponse:\nWe thank the reviewer for this insightful observation. The reviewer is correct in noting that the\ndirect demonstration of module-trait correlations is contained within Fig. 4g. We apologize for\nany lack of clarity in the original manuscript text regarding the specific role of Fig. 4f. Fig. 4f\nand 4g are designed to present two sequential, yet distinct, steps in our Weighted Gene Co-\nexpression Network Analysis (WGCNA):\nFig. 4f (Module detection and structure); Top: hierarchical clustering dendrogram of\nglyco(features) using TOM-based dissimilarity; colored bars indicate DynamicTreeCut\nmodules. Bottom: clustering of module eigengenes (MEs); branch heights reflect 1-correlation\namong MEs. This panel documents module formation and inter-module relatedness and is not\na trait correlation.\nFig. 4g (Module-trait correlations); Pearson correlations between each module eigengene and\ntraits (Age, Sex). Cells show r with FDR-adjusted P-value in parentheses; asterisks denote\nsignificance. MEblue is positively correlated with Age, whereas MEyellow is negatively\ncorrelated. We have revised the manuscript text to more precisely describe the purposes of these\nfigures.\nManuscript changes:\nPage 14, Line 361-364 in revised manuscript:\n“For glycans, the eigengene dendrogram (Fig. 4f, bottom) summarizes inter-module relatedness\nand guided module merging (mergeCutHeight = 0.25). Trait associations were then tested by\ncorrelating module eigengenes with Age and Sex (Fig. 4g).”\nThe flow cytometry analysis in Figure 6j provides validation of changes to cell cycle regulators,\nbut the specific markers, fluorophores, and staining protocol, all of which can significantly\nimpact the results and are important for reproducibility, are not mentioned in the manuscript.\nResponse:\nThank you for pointing this out. We fully agree that reproducibility requires detailed\nmethodological information. In response, we have added a detailed description in the\nSupplementary Information (SI) titled “Flow cytometry analysis”.\nManuscript changes:\nPage 12, Line 330-339 in revised Supplementary Information:\n“Flow cytometry analysis: Cellular DNA was stained with propidium iodide (PI) to\nassess cell cycle distribution and apoptosis. Briefly, harvested cells were washed with\nphosphate-buffered saline (PBS) and fixed in pre-chilled 70% ethanol at 4°C for 12\nhours. After removing the ethanol, the cells were resuspended in a staining buffer\ncontaining PI and RNase A (0.5 mL per tube) and then were incubated at 37°C in the\ndark for 30 minutes. PI fluorescence was detected in the B690-PC5.5 channel of a flow\ncytometer (excitation wavelength, 488 nm), while forward and side scatter\ncharacteristics were simultaneously analyzed. Cell cycle phases and apoptotic cells\nwere determined based on DNA content distribution, where the G0/G1 peak set to 1,\nthe G2/M peak as approximately 2, and the sub-G1 peak was identified as indicative of\napoptosis. Detection was performed using a flow cytometer (Beckman Coulter\nCytoFLEX S).”\nMinor Comments:\nThe authors do not provide the number of samples used to generate the statistics and/or plots in\nFigures 1 (C – D, E – G) and 3 (E – K).\nResponse:\nWe thank you for your kind reminder. We have now added the sample size (n) to all relevant\nfigure legends. Specifically, for Fig. 1c-g, n=2 technical replicates per method; For Fig. 3e-k,\nn=3 technical replicates per method.\nManuscript changes:\nPage 6, Line 146-147 in revised manuscript:\n“For Fig. 1c-g, n=2 technical replicates per method.”\nPage 12, Line 291-292 in revised manuscript:\n“For Fig. 3e-k, n=3 technical replicates per method.”\nLines 300-301 indicate that “The limited availability of mammalian samples representing\nphysiological aging poses additional challenges for related analyses.” How does the paper\naddress the limited availability of mammalian samples? This line may not be necessary or there\nshould be more clarity in the text.\nResponse:\nWe appreciate the comment. Our original wording was overly general. Here, “limited\navailability” specifically refers to the minute tissue mass obtained from physiologically aged\nmammalian brain regions-for example, mouse choroid plexus-where the per-animal material\nand peptide yield frequently fall below conventional proteomics input requirements.\nManuscript changes:\nPage 12, Line 300-302 in revised manuscript:\n“In studies of physiological aging, certain mammalian brain regions yield minute tissue mass\nand limited peptide amounts, posing practical challenges for conventional proteomics input and\nstatistical stability.”\nIn Lines 581-582, the authors state that “…MuPPE has revealed temporal and spatial PTMs\nchanges…”, but no spatial information was provided in the study. The authors may be referring\nto tissue-specific changes, but the current language could be made clearer.\nResponse:\nThank you for this insightful suggestion. We have revised the wording to “temporal and tissue-\nspecific PTM changes” (Page 25, Line 595) to accurately reflect our findings.\nManuscript changes:\nPage 25, Line 594 in revised manuscript:\n“…temporal and tissue-specific PTM changes…”\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse:\nWe thank you for your co-review efforts and for supporting the evaluation of our manuscript.\n*******************************************************************\nReviewer #4:\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse:\nWe thank you for your co-review efforts and for supporting the evaluation of our manuscript.\nRemarks to Author:\nThe authors present a platform for the serial analysis of the proteome, glycoproteome, and\nphosphoproteome for both tissue and liquid samples. They provide a comparison against other\nmethods for post-translational modification analysis and show that their method significantly\nimproves processing time, the number of transfers needed, and other parameters. The authors\nvalidate their method on human serum and mouse brain tissue samples, while also applying\ntheir method to aged animals and an arsenic-treated cell line.\nThis review is primarily focused on the manuscript from an aging perspective. The authors\nprovide an analysis of age-associated changes in post-translation modifications which may have\nimportant implications, especially given how the immune system is known to shift with aging\n(Fulop et al. Clin Rev Allergy Immunol. 2021) and how post-translation modifications can\nmodulate immunity (Mattingly et al. Immune Netw. 2025, Liu et al. Immunity. 2016).\nThe changes in antibody-associated glycopeptides with age are intriguing and could have\nimplications for modern immunotherapies and anti-aging approaches. Recent literature has\nlinked the IgG antibody to aging and tissue fibrosis (Yu et al. Cell Metab. 2024). There is also\nliterature discussing the role of Fc fucosylation on antibody behavior (Golay et al. Front.\nImmunol. 2022., Saporiti et al. Commun. Biol. 2023.), but the age-associated impact of post-\ntranslational modifications on antibody function is less studied.\nMain Comments:\nIn Figure 5G, one drawback is that for two of the tissues analyzed (MCSF and Mserum) only the\nproteome and glycoproteome are shown which takes away from the claim that the platform\nenables easy integration of all three levels.\nFrom the text, Figures 4F and 4G should demonstrate that the MEblue and MEyellow modules\nare positively and negatively correlated with aging, but Figure 4F itself does not seem to provide\ninformation relevant to the module correlations. It is not entirely clear how the structure of the\ndendrogram provides additional information.\nThe flow cytometry analysis in Figure 6j provides validation of changes to cell cycle regulators,\nbut the specific markers, fluorophores, and staining protocol, all of which can significantly impact\nthe results and are important for reproducibility, are not mentioned in the manuscript.\nMinor Comments:\nThe authors do not provide the number of samples used to generate the statistics and/or plots in\nFigures 1 (C – D, E – G) and 3 (E – K).\nLines 300-301 indicate that “The limited availability of mammalian samples representing\nphysiological aging poses additional challenges for related analyses.” How does the paper\naddress the limited availability of mammalian samples? This line may not be necessary or there\nshould be more clarity in the text.\nIn Lines 581-582, the authors state that “…MuPPE has revealed temporal and spatial PTMs\nchanges…”, but no spatial information was provided in the study. The authors may be referring\nto tissue-specific changes, but the current language could be made clearer.\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is\npart of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse:\nWe thank you for your co-review efforts and for supporting the evaluation of our manuscript."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed all my comments in this revised version.", "reviewer_3": "The authors have adequately addressed the reviewers comments", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}]

Decision letter