High-throughput chemical proteomics workflow for profiling protein citrullination dynamics

Domain: chemistry
Published: 2026-02-21
Editorial outcome: minor_revision

Abstract
Citrullination is a post-translational modification implicated in autoimmune and inflammatory diseases, yet its low abundance and lack of effective enrichment tools have limited proteome-wide analysis. Here, we develop a robust chemical proteomics workflow with improved specificity and throughput. This method builds upon glyoxal-based derivatization and incorporates a cleavable biotin linker for efficient peptide enrichment, release, and identification via mass spectrometry. Benchmarking demonstrates a >10-fold increase in the detection of citrullinated peptides at sub-0.1% abundance. Applying this workflow to primary human neutrophils, we successfully monitor dynamic regulation, quantifying dose-dependent activation and inhibition by the PAD4 inhibitor GSK484. Furthermore, stimulation with the fungal pathogen Candida albicans reveals a “core citrullinome” conserved across distinct stimuli. Notably, extensive citrullination of linker histone H1 and structural proteins like lamin B1 suggests broad remodeling of cell architecture during NET formation. This workflow enables proteome-wide mapping of citrullination sites and facilitates its study across diverse biological contexts.

Introduction
Protein post-translational modifications (PTMs) are essential regulators of cellular processes such as gene expression, structural integrity, and immune responses1,2. Despite their biological significance, many PTMs remain challenging to study due to the lack of effective detection and analytical tools. Among these, protein citrullination has gained increasing attention for its roles in health and disease3, yet remains underexplored.

Citrullination, catalyzed by peptidylarginine deiminases (PADs), converts the guanidyl group of arginine residues into a ureido group, resulting in a mass increase of 0.98 Da and the loss of positive charge4. This modification can profoundly alter protein structure and function, influencing apoptosis5, gene regulation6, structural support7, and immune response8. A prominent example is histone hypercitrullination, which drives chromatin decondensation during neutrophil extracellular trap (NET) formation9. Aberrant citrullination is implicated in several diseases such as rheumatoid arthritis (RA)4, neurodegenerative disorders10, cancer11, and viral infection12. Notably, anti-citrullinated protein antibodies (ACPAs) serve as diagnostic tools for RA, highlighting the clinical relevance of citrullination and its potential as a therapeutic target13.

Developing enrichment strategies coupled with mass spectrometry has been a turning point in PTM analysis, transforming our ability to study modifications such as phosphorylation at scale2. Citrullination, however, presents unique analytical challenges that make enrichment particularly critical. The 0.98 Da mass difference to arginine is identical to that of deamidation on Gln/Asn, complicating confident identification, particularly when multiple potential sites are present in a peptide. In addition, errors in assigning monoisotopic peptide mass can fall within set mass tolerances and increase the false positive rate during database searching14,15. Although advanced fragmentation techniques16, search strategies15,17, statistical models leveraging neutral loss patterns16,17, and deep learning-based tools18 have improved identification, detecting low-abundance citrullinated peptides in complex mixtures remains difficult. Deep proteomic profiling via orthogonal separation prior to MS measurement can enhance coverage14,19, but is time- and resource-intensive, limiting scalability. As such, effective enrichment prior to MS remains essential for enabling sensitive and high-throughput citrullinome profiling.

Although antibody-based enrichment has proven effective for certain PTMs, such as phosphotyrosine and ubiquitination20,21, no anti-pan-citrullination antibodies currently exist that can robustly and efficiently enrich citrullinated peptides22. As a result, chemical derivatization strategies have become the primary approach for citrullination enrichment23–28. These methods leverage the reactivity of citrulline’s ureido group with glyoxals under acidic conditions28 and typically use conjugation to agarose beads23 or biotin moieties25–27 for downstream capture. However, existing strategies suffer from several limitations, including poor peptide fragmentation25, loss of site-specific information26, harsh elution requirements for biotinylated peptides from streptavidin beads27, and relying on in-house synthesized probes27. Critically, none are readily compatible with high-throughput workflows, posing a major barrier to broader implementation in citrullination proteomics.

In this work, we develop a high-throughput chemical workflow for global citrullinome profiling with high sensitivity and reproducibility to address current limitations (Fig. 1a). This workflow combines glyoxal-based derivatization of peptides using a clickable and cleavable linker, followed by biotin-based enrichment and on-bead cleavage of derivatized peptides for downstream analysis via mass spectrometry. We systematically optimize derivatization, enrichment, and database search strategies. All reagents are commercially available, and the workflow is validated in a 96-well format for streamlined scalability with standard LC-MS/MS pipelines. Using this workflow on the mouse brain expands citrullinome coverage, including proteins involved in myelin formation and synaptic vesicle transport, relative to a previous study27. In ionomycin-activated human neutrophils, dose-dependent profiling reveals dynamic citrullination during early neutrophil extracellular trap (NET) formation, with site-specific regulation across all core and linker histones. Notably, linker histone H1 shows pronounced citrullination, implicating its major role in NET biology. We also observe lamin B citrullination within direct DNA-binding region, suggesting its involvement in nuclear lamina destabilization, aligning with previous reports of lamina rupture during NET formation29 and expanding citrullination’s role beyond chromosome decondensation.

Results
A clickable-cleavable derivatization strategy for global profiling of citrullination

Biotin-conjugated chemical probes represent a primary approach for enriching citrullinated proteins and peptides from complex proteomes25–27. While probe designs vary, they all exploit glyoxals’ reactivity with the citrulline ureido group under acidic conditions28. However, they face two key limitations: (1) the bulky derivatized moiety often impairs peptide fragmentation during tandem MS, reducing identification and site-localization25,26; (2) probes showing improved fragmentation in tandem MS, e.g., a recent biotin thiol probe27, still require harsh elution of derivatized peptides from streptavidin beads and are not commercially available.

To address these issues, we designed a two-step derivatization strategy compatible with tandem MS and large-scale citrullinome profiling using commercially available reagents (Fig. 1b; Supplementary Fig. 1a): 4-azido-phenylglyoxal (APG), which reacts with citrulline and introduces a clickable azide handle (+158.01 Da), and Dde-PEG-biotin alkyne, a cleavable biotin with an alkyne moiety (+809.35 Da after click reaction)30,31. The final product after hydrazine cleavage leaves a +213 Da mass tag compatible with MS detection.

We first developed the derivatization using the synthetic citrullinated peptide SAVRA[R(Cit)]SSVPGVR. Optimal derivatization was achieved under 50% TFA with an APG-to-peptide ratio up to 1000 (Supplementary Fig. 1b–c). MALDI-TOF MS confirmed the masses of the intermediate and final product at each step (Fig. 1c), including a +131 Da shift after APG labeling due to N₂ loss during ionization32 (Supplementary Fig. 1d), and a +809 Da shift after click reaction, with characteristic PEG fragmentation33 (Supplementary Fig. 1e). Hydrazine cleavage yielded the final +213 Da tag. No modification was observed in control peptides containing native arginine, confirming its specificity toward citrulline (Supplementary Fig. 1f). To test broader applicability, we applied the workflow to a pool of 185 synthetic citrullinated peptides (CitPool)34 and matched arginine controls (ArgPool), analyzed via LC-Orbitrap MS. MS Fragger open search35 analysis showed a dominant +213 Da mass shift only in the CitPool (Fig. 1d; Supplementary Data 1), demonstrating high specificity across various peptides. Approximately 80% of peptides were recovered, with <1% residual signal from non-derivatized forms (Fig. 1e; Supplementary Data 2).

While modified peptides often generate diagnostic ions or neutral losses (NL) during fragmentation aiding in identification and localization36, no such unique diagnostic ions were observed when examining the fragmentation patterns of derivatized peptides in HCD MS2 spectra (Supplementary Fig. 1g). However, fragment y-ions containing the derivatization showed neutral losses of –17 Da (NH₃), –28 Da (N₂), or –45 Da (combined) (Fig. 1f). These losses appeared in ~35–50% of modified fragment ions and ~18–30% of precursors (Fig. 1g; Supplementary Data 3). Although not universal, incorporating neutral losses into search scoring may aid in more accurate identification and localization from complex samples14.

Evaluation of a high-throughput enrichment workflow reveals robustness and reproducibility in complex proteomes

After validating efficient derivatization on synthetic peptides, we evaluated whether this strategy enables robust enrichment of citrullinated peptides in complex proteomes. To systematically assess sensitivity, efficiency, and reproducibility, we designed benchmark experiments under controlled and biologically relevant conditions. All steps were implemented in a 96-well plate format to support scalability (Fig. 2a).

We first assessed sensitivity and enrichment efficiency by spiking either a single citrullinated peptide or a pool of citrullinated peptides (CitPool) into HeLa tryptic digest, which has low levels of endogenous citrullination (Fig. 2a, Exp. 1–2). The single derivatized peptide was enriched and quantified down to 39.1 fmol/µg of a background proteome (Fig. 2b; Supplementary Data 4). Additionally, the recovery of CitPool was similar (~75–80%) with or without background (Fig. 1e, Fig. 2c; Supplementary Data 5). Enrichment boosted peptide intensity drastically: derivatized peptides accounted for ~50% of total peptide intensity after enrichment, compared to ~0.6% before enrichment, an 83-fold increase (Fig. 2d).

To mimic variable citrullination levels more representative of biological samples (Fig. 2a, Exp. 3), we performed in vitro citrullination of HeLa lysate using recombinant PAD4, followed by tryptic digestion and MS analysis. The resulting hypercitrullinated digest contained ~8–10% citrullinated peptides (by MS1 intensity). This digest was titrated into an untreated digest to produce a dilution series from 0.1–2% citrullination, covering physiological ranges17. Pre- and post-enrichment samples were analyzed to assess identification and quantification across this range.

We also tested whether the previously observed neutral losses in MS2 spectra (–17, –28, –45 Da; Fig. 1g) could improve identification. Using hybrid search from MSFragger37, which incorporates neutral losses (–17, –28, –45 Da) from modified fragment ions into peptide scoring, we observed that peptides uniquely identified by hybrid search correlated more strongly with citrullination levels (Supplementary Fig. 2a–c; Supplementary Data 6), supporting its reliability for subsequent analyses. Across the titration series, enrichment drastically increased both identification and intensity of citrullinated peptides (Fig. 2e, f; Supplementary Data 6). Depending on the initial citrullination level, we observed a 4- to 10-fold gain in peptide identifications and a 53- to 155-fold increase in intensity. Fold enrichment was greatest at lower citrullination levels. Notably, slight saturation occurred at 1–2% citrullination, which exceeds typical levels in vivo17. Motif analysis of enriched peptides revealed similar sequence preferences to unenriched samples, with Asp, Glu, and Gly enriched at the +1 position—consistent with previous findings14,19 (Fig. 2g).

To assess technical reproducibility, we performed 12 replicates using samples with 0.25% citrullination (Fig. 2a, Exp. 4). On average, ~720 derivatized peptides were quantified per replicate, comprising ~45% of total peptide intensity (Fig. 2h–i; Supplementary Data 7). Roughly 400 peptides were consistently detected in ≥75% of replicates, with a median coefficient of variation of ~20% (Fig. 2j–k). These results demonstrate that the workflow is sensitive, reproducible, and well-suited for high-throughput citrullinome analysis, even in complex and low-abundance samples.

Citrullinome profiling of murine brain expands site coverage and reveals distinct patterns from a previous dataset

Citrullination is particularly abundant in the brain among both human17 and mouse27, yet its site-specific landscape and how it may vary across individuals or experimental workflows remains poorly defined. To explore this, we applied our workflow to three independent healthy brain samples of murine C57Bl/6J mice and compared the resulting citrullinome to a previously published dataset generated using an alternative chemical probe (Shi et al.)27. While both studies identified ~200–300 citrullinated peptides per sample, Shi et al. reported a higher proportion of C-terminal citrullination—modifications on the C-terminal arginine of tryptic peptides—accounting for 62–65% of identifications versus 22–27% in our data (Fig. 3a; Supplementary Fig. 3a; Supplementary Data 8). Although the origin of C-terminal citrullination (biologically or technically) remains debated, cleavage after citrulline by trypsin should occur at low rate due to its neutral charge38. Excluding these sites enhances confidence in subsequent comparative and quantitative analyses.

After removing C-terminal sites, our workflow identified 200–300 unique citrullination sites, 2–3 times more than the previous study27 (Fig. 3b). However, only a tiny fraction of these sites, less than 4%, were shared between the two datasets (Fig. 3c). While this could reflect biological variation, differences in workflows (e.g., protein extraction, derivatization, instrumentation) may also play a role. Notably, our biological triplicates showed high internal consistency (Supplementary Fig. 3b), suggesting minimal intra-cohort variability. Protein-level overlap increased to 16.4% (Fig. 3d), with shared proteins enriched in myelin-associated components (Fig. 3e).

To further understand site specificity, we examined the sequence context of citrullinated sites across datasets to assess whether known PAD substrate motifs had been preserved. Given conserved substrate recognition and active sites across human and mouse PADs39, similar sequence preferences are expected. Our data recapitulated known PAD preferences from human brain14,18, with additional enrichment for serine at the +1 position. In contrast, motifs from the previous study were less defined (Fig. 3f), potentially reflecting differences in enrichment or identification strategies.

To explore the functional context of citrullinated sites, we ranked them by abundance across both datasets. Six of our top ten sites—and two from the previous dataset—mapped to myelin basic protein (MBP), a well-established citrullinated target in the brain40 (Fig. 3g, h). Both datasets captured six shared MBP sites; however, our study revealed five additional sites not previously reported, while two were unique to Shi et al. We also identified citrullination on other brain-enriched proteins, including seven sites on synapsin-1 (SYN1), a synaptic vesicle protein involved in neurotransmission (Fig. 3i). These findings suggest that citrullination in the brain may extend beyond myelin formation, potentially modulating broader aspects of neural function.

Dose-dependent citrullinome profiling in activated human neutrophils reveals dynamic regulation of histones and structural proteins during NET formation

Neutrophils defend against pathogens via phagocytosis, degranulation, and formation of neutrophil extracellular traps (NETs)—web-like structures composed of chromatin, histones, and granule proteins released upon activation41. This process is driven by PAD4-mediated citrullination of histones9,42. While histone H3 citrullination is a widely used NET marker, the full scope of protein citrullination during this process remains poorly characterized. This gap limits our understanding of its broader immunological roles, including the potential for citrullinated proteins to act as autoantigens43.

To systematically define the NET-associated citrullinome, we profiled the citrullinome of primary human neutrophils stimulated with ionomycin, a calcium ionophore that activates PAD4 and induces NET formation44 (Fig. 4a). Using a highly sensitive Orbitrap Astral MS, we quantified up to 1,700 citrullinated peptides on 580 proteins across ionomycin doses (0.01–3 µM) (Fig. 4b; Supplementary Fig. 4a; Supplementary Data 9). Citrullinated peptide intensity increased sharply between 0.3–3 µM—coinciding with NET-inducing conditions45 (Supplementary Fig. 4b). Analysing dose-response data for statistical relevance using CurveCurator46 revealed over 400 significantly regulated citrullination sites, including 384 up-regulated and 20 down-regulated sites (Supplementary Data 9), while overall protein abundance remained stable (Supplementary Fig. 4c; Supplementary Data 9), indicating stoichiometric regulation of citrullination.

We readily detected hallmark NET citrullination sites on histone H3 (R17, R26), as well as two additional regulated sites (R42, R49) (Fig. 4c). Notably, R17 and R26 citrullination co-occurred with H3 acetylation (K14, K18) and methylation (K27), suggesting potential PTM crosstalk that may facilitate chromatin decondensation. Beyond H3, we observed broad citrullination across all core histones (H2A, H2B, H3, H4), with prominent up-regulation of linker histone H1 variants (Fig. 4d). Mapping citrullination sites revealed N-terminal citrullination in H2A, H3, and H4, and widespread modification across H1 variants (Fig. 4e), including H1.4 at R54—a DNA-binding site implicated in autoimmunity44,47. H1.10 citrullination was especially highly up-regulated, suggesting an expanded role for H1 variants in chromatin decondensation (Fig. 4f). In contrast, down-regulated sites primarily mapped to secreted granule proteins (e.g., MPO, CTSG, LTF), likely reflecting protein secretion during neutrophil activation rather than active de-citrullination (Supplementary Data 9).

Gene ontology enrichment analysis of up-regulated proteins highlighted roles in nucleosome assembly, chromatin organization, and cytoskeletal remodeling—core processes for chromatin extrusion during NETosis48 (Fig. 4g). Cellular component terms further indicated they are mainly in protein-DNA complexes and nucleosomes, consistent with the molecular functions of its binding to actin and DNA (Supplementary Fig. 4d–e). Strikingly, 10 of the top 15 most regulated proteins are involved in cytoskeleton organization (Fig. 4h), suggesting widespread remodeling of cell architecture via citrullination.

Among these, HCLS1 (HS1, Hematopoietic lineage cell-specific protein), an actin-regulatory protein, had 12 up-regulated citrullination sites (Fig. 4h)—10 of which localized to its N-terminal region overlapping Arp2/3 and F-actin binding domains (residues 27–212) (Supplementary Fig. 4f). Given HCLS1’s role in actin polymerization phagocytosis49, citrullination in this region may disrupt cytoskeletal dynamics during NET formation. Similarly, lamin B1, a major component of the nuclear lamina, also emerged as a highly citrullinated protein, with seven up-regulated sites including two within its direct DNA-binding region (residues 242–276)50 (Fig. 4i; Supplementary Fig. 4g). As lamin B1 anchors chromatin and maintains nuclear shape51,52, citrullination may weaken chromatin-lamina interactions, promoting nuclear rupture as part of NET release29.

To further investigate biophysical features of regulated citrullination, we examined their structural context using NetSurfP-3.053. Compared to (non-modified) arginines, regulated citrullination sites were more frequently solvent-exposed and enriched in disordered or coil regions (~75% vs. ~60%) (Supplementary Fig. 4h–i). These features support citrullination’s role as a regulatory PTM, preferentially targeting accessible and intrinsically disordered regions of proteins54. Given these characteristics and the established role of PAD4 in NET formation, we next examined the contribution of PAD4 activity to the observed citrullinome using the selective inhibitor GSK484.

Treatment with increasing concentrations of GSK48455 (0.3–30 µM) resulted in a dose-dependent reduction in both citrullinated peptide intensities and site detection (Supplementary Fig. 5a–c), with a total of 1568 citrullination sites quantified across all conditions. Among these, over 1100 sites overlapped with the ionomycin-induced dataset (Supplementary Fig. 5d). Within this shared subset, 190 sites were significantly down-regulated by GSK484 treatment, defining a PAD4-dependent citrullinome. Notably, 103 of these sites were also up-regulated during ionomycin stimulation, establishing a core citrullinome directly modulated by PAD4 during neutrophil activation. We also identified 208 sites that were up-regulated by ionomycin but not significantly inhibited by GSK484, suggesting potential contributions from PAD2 activity. Interestingly, the majority of detected sites showed no significant regulation in either dataset, which may reflect lower abundance, stochastic detection, or regulation independent of PAD activity.

To estimate the inhibitory potency of GSK484, we examined the half-maximal effective concentration (pEC50) of regulated citrullination sites, revealing a median EC₅₀ of ~760 nM—approximately three-fold higher than the reported in vitro value55 (Supplementary Fig. 5f). Among the GSK484-responsive substrates were canonical PAD4 targets such as histone H3 (R17, R26), as well as histone H1.10 (R127, R128, R155, R156) and lamin B1 (R197, R220, R221, R276)—the latter previously identified as a highly citrullinated protein during ionomycin stimulation (Supplementary Fig. 5g; Fig. 4f & i). Additional PAD4-dependent sites were found on nuclear RNA- and DNA-binding proteins involved in chromatin organization, including HP1BP3 and HNRNPU (Supplementary Fig. 5h), reinforcing the role of PAD4 in shaping the nuclear citrullinome during NET formation.

Pathogenic stimulation with Candida albicans reveals a conserved NET-associated citrullinome

To further evaluate the biological relevance of our method under physiologically meaningful stimuli, we treated primary human neutrophils with heat-killed Candida albicans (HKCA)—a common opportunistic fungal pathogen known to trigger NET formation through PAD4-dependent mechanisms55,56 (Fig. 5a). Across four biological replicates, we identified 268 citrullination sites, of which approximately 200 were consistently detected in at least two replicates (Fig. 5b). This represents a substantial increase compared to resting or serum-treated controls, which exhibited only ~40 sites, and supports PAD activation upon fungal challenge.

Although the total number of sites was lower than in ionomycin-treated cells, this is consistent with prior reports showing reduced histone H3 citrullination in C. albicans- versus calcium ionophore–stimulated neutrophils56. Importantly, when comparing the HKCA- and ionomycin-induced citrullinomes, we observed a huge overlap of 234 sites across 133 proteins (Fig. 5c, d). This suggests that, despite divergent upstream signaling, the downstream citrullination landscape during NET formation is largely conserved.

Gene ontology enrichment analysis of HKCA-induced citrullinated proteins revealed enrichment in biological processes such as nucleosome assembly, chromatin remodeling, and organelle organization (Fig. 5e)—highly similar to the processes observed in ionomycin-stimulated cells (Fig. 4g). Moreover, comparison of proteins with four or more citrullination sites confirmed extensive overlap between the two datasets, defining a robust, shared NET-associated citrullinome (Fig. 5f). Interestingly, several highly citrullinated proteins in the HKCA condition—such as ACTB, KRT1, and RPS6— while detected in the ionomycin dataset, were not significantly regulated. These may represent constitutively modified proteins present in basal neutrophils, rather than NET-specific citrullination events.

Together, these findings demonstrate that our enrichment platform captures a conserved and biologically meaningful citrullinome across distinct NET-inducing stimuli, reinforcing its utility for dissecting citrullination dynamics in both experimental and disease-relevant contexts.

Methods
Ethical statement
All experiments complied with relevant ethical regulations. Human neutrophils were isolated from peripheral blood donated by an author after obtaining written informed consent. The study adhered to the Declaration of Helsinki. In accordance with the guidelines of the Technical University of Munich, a formal ethics vote was not required for this basic research using human material ex vivo. Animal organs were obtained from mice sacrificed solely for organ removal. This procedure was performed in accordance with the guidelines of the Federation of Laboratory Animal Science Association (FELASA) and approved by the District Government of Upper Bavaria.

Animals
Eight-week-old, male C57Bl/6J mice (n=3) were purchased from Charles River (Charles River Laboratories International, Inc., US). Animals were housed under specific pathogen-free conditions at Animal Core Facility of the School of Medicine and Health, TUM. Housing followed a 12 h light/12 h dark cycle with a temperature of 22 ± 2 °C and a humidity of 55 ± 10%. Animals were controlled daily.

Synthetic peptides
Lyophilized synthetic peptides (GenScript Biotech) with sequences SAVRA[R(Cit)]SSVPGVR and SAVRARSSVPGVR were dissolved in 0.1% formic acid (FA) and further diluted to a final concentration of 10 pmol/µL with water before storage at −20 °C. A pool of 200 synthetic citrullinated peptides and a pool of their unmodified counterparts were described previously (JPT Peptide Technologies)34. These peptides were dissolved in 0.1% FA and 20% acetonitrile (ACN) to a concentration of 200 pmol/µL, followed by dilution to 20 pmol/µL in 0.1% FA.

HeLa lysate, digestion, and desalting
HeLa cells (CCL-2; ATCC, cat# 430641) were lysed in urea buffer (8 M Urea 40 mM Tris-HCl, pH 7.6). Dithiothreitol (DTT) was added to a final concentration of 10 mM, and the lysate was incubated at 30 °C for 30 min with shaking at 400 rpm. Subsequently, chloroacetamide (CAA) was added to 55 mM and incubated at room temperature for 30 min in the dark. The lysate was diluted with 50 mM ammonium bicarbonate and digested with trypsin (enzyme-to-protein ratio of 1:100) in two steps: 1 h at 30 °C followed by overnight incubation at 30 °C with shaking at 400 rpm. The samples were acidified to pH 1–2 with FA and desalted using 200 mg SepPak cartridges (Merck Millipore). Cartridges were equilibrated with 3 mL ACN, 3 mL 0.1% FA in 50% ACN, and twice with 3 mL 0.1% FA. Samples were applied by gravity, and the flow-through was reapplied once. The cartridges were washed twice with 3 mL 0.1% FA and eluted twice with 600 µL 0.1% FA in 60% ACN. For 50 mg SepPak cartridges, wash volumes were adjusted to 1 mL and elution volumes to 200 µL. Eluted samples were dried using a SpeedVac and stored at −80 °C.

Spike-in experiments
Decreasing amounts of SAVRA[R(Cit)]SSVPGVR were spiked into 20 µg of digested HeLa peptides, ranging from 200 pmol to 0.2 pmol in half-step dilutions. A negative control without spiked peptide was included. Additionally, 1 nmol of the citrullinated peptide pool (CitPool) was spiked into 20 µg of HeLa peptides in triplicate. Samples were derivatized, enriched, and desalted for subsequent LC-MS/MS analysis.

In vitro citrullinated HeLa lysate
HeLa lysates were cleaned and digested using the SP3 protocol63. A total of 200 µg of protein was precipitated with ACN on 10 µL of Sera-Mag A and B magnetic beads (GE Healthcare, cat. no. 45152105050250 and cat. no. 65152105050250), followed by washing with ethanol and ACN. For in vitro citrullination, 150 µL of PAD buffer (100 mM Tris, 2.5 mM DTT, 10 mM CaCl₂) was added to the beads, and PAD4 enzyme was added at a 1:50 enzyme-to-protein ratio. The reaction was incubated overnight at 37 °C with shaking at 800 rpm. PAD4 activity was halted by heating at 80 °C for 10 min. DTT (10 mM final concentration) was added and incubated at 37 °C for 1 h, followed by the addition of CAA (55 mM final concentration) and incubation in the dark for 30 min at room temperature. Samples were digested with trypsin (enzyme-to-protein ratio of 1:50) at 37 °C with shaking at 800 rpm overnight. Digests were centrifuged at 20,000 × g for 5 min, and the supernatant was collected. Samples were desalted using 50 mg SepPak cartridges and dried in a SpeedVac. Citrullination content was assessed using LC-MS/MS. Aliquots were prepared by mixing citrullinated peptides with digested HeLa peptides to achieve samples with 0.1%, 0.25%, 0.5%, 1%, and 2% citrullination content (based on intensity) in a total of 100 µg of peptides.

Mouse brain profiling
Eight-week-old male C57Bl6/J mice (Charles River Laboratories International, Inc., US) were euthanized utilizing isoflurane followed by cervical dislocation. Mice were perfused with PBS through the left heart ventricle and brain organs were harvested, washed in ammonium bicarbonate (50 mM, pH 7.8) and frozen in liquid nitrogen. Tissues were lysed using a lysis buffer containing 4% SDS and 40 mM Tris, followed by mechanical disruption with a TissueLyser II (QIAGEN, Germany), sonication using an R230 focused-ultrasound instrument (Covaris Ltd., UK, 300 s duration, 30 s on/off) and heat inactivation at 90 °C for 30 min. Protein quantification was measured using the BCA assay, following the manufacturer’s protocol (Pierce, Thermo Fisher Scientific). Samples were digested and cleaned up using SP3 sample preparation as described above. Peptides were desalted with 50 mg SepPak cartridges and dried in a SpeedVac. A total amount of 400 µg of peptide per sample was enriched and analyzed using the described enrichment workflow.

Neutrophil isolation, treatment, and profiling
Peripheral neutrophils were isolated from human whole blood of one healthy volunteer using the EasySep Direct Human Neutrophil Isolation Kit (STEMCELL Technologies) by immunomagnetic negative selection. Enrichment was carried out on 50 mL of blood as per the manufacturer’s instructions. The cells were washed with HBSS (Hank’s Balanced Salt Solution) without calcium and magnesium (Gibco, Thermo Fisher Scientific). Five million cells were kept as resting cells as control. The rest of the cells were centrifuged and suspended in HBSS with calcium and magnesium (Gibco, Thermo Fisher Scientific) (HBSS (+)). Five million cells in 1 mL HBSS of each treatment were treated with 1 µL of stock dilutions of ionomycin (Sigma-Aldrich; dissolved in DMSO; concentrations: 0.01, 0.03, 0.1, 0.3, 1 mM) and incubated at 37 °C for 30 min with gentle shaking at 700 rpm. For the inhibition experiment, the cells were treated with 1 µL of stock dilutions of GSK48455 (Sigma-Aldrich; dissolved in DMSO; concentrations: 0.3, 1, 3, 5, 10, 30 mM) at 37 °C for 30 min prior to ionomycin activation. Cells were lysed using 2% SDS in 40 mM Tris-HCl (pH 7.6), followed by heat inactivation at 90 °C for 10 min. Samples were acidified to 1% TFA to hydrolyze DNA and neutralized by 3 M Tris buffer. Protein quantification was measured using the BCA assay, following the manufacturer’s protocol (Pierce, Thermo Fisher Scientific). Each sample (200 µg of protein) was digested and cleaned up using SP3 sample preparation as described above. Peptides were desalted with 50 mg SepPak cartridges and dried in a SpeedVac. The peptides were enriched and analyzed using the described enrichment workflow. Dose-response data from citrullinated sequences were evaluated using CurveCurator46.

Neutrophil activation by heat-killed Candida albicans for proteomic analysis
Freshly isolated human neutrophils were prepared as described above and resuspended in HBSS containing calcium and magnesium (HBSS (+)) at a concentration of five million cells cells/mL. Heat-killed Candida albicans (HKCA; InvivoGen, 1 × 10^9 cells) was diluted in 1 mL of sterile, endotoxin-free water, and activation mixtures were prepared by combining the diluted HKCA with human serum (H4522, Merck) and HBSS (+). Control samples received serum64 and HBSS (+) without HKCA. After gentle mixing at room temperature (400 rpm, 20 min), the activation mixture was added to neutrophil suspensions to achieve a multiplicity of infection (MOI) of 10:1 (HKCA: neutrophils). Cells were incubated at 37 °C for 3 h to induce activation. Following incubation, the medium was removed and cells were lysed in 2% SDS in 40 mM Tris-HCl (pH 7.6), heat-inactivated at 90 °C for 10 min, acidified with 1% TFA to hydrolyze DNA, and neutralized with 3 M Tris buffer as described above. Protein concentrations were determined by BCA assay (Pierce, Thermo Fisher Scientific), and 200 µg (protein) of each sample was processed using SP3-based digestion, peptide cleanup, and enrichment workflows as described for the ionomycin-treated samples.

Enrichment of citrullinated peptides
Subsequent steps can be conducted in either 1.5–2 mL tubes or 96-well plate (Step 1: BRAND® 96-well deep well plate Cat# BR701340, Merck; Step 2: Nunc™ 96-Well Cat# 249944, Thermo Fisher; Enrichment: 96-well filter plate Cat# SKU: 360021, Porvair Sciences).
Derivatization Step 1: Peptides were dissolved in 55 µL buffer containing 2.5 µL of 4-azidophenylglyoxal (APG, 400 nmol/µL) (Apollo Scientific) in a 50% TFA/water solution. The reaction was incubated at 50 °C for 3 h, shaking at 1000 rpm. After incubation, the samples were diluted tenfold with water and desalting was performed using SCX StageTips. The samples were dried in a SpeedVac and stored at −20 °C.
Derivatization Step 2: The click buffer was prepared by combining 64 µL of water, 3.9 µL of Dde-biotin-alkyne (20 nmol/µL), 6.25 µL of THPTA (5 nmol/µL), 6.25 µL of CuSO4 (5 nmol/µL), and 31.25 µL of sodium ascorbate (50 nmol/µL). The dried samples from Step 1 were reconstituted using click buffer and incubated for 30 min at room temperature with shaking at 550 rpm. After incubation, the samples were acidified and desalted via SCX StageTips. The samples were dried in a SpeedVac and stored at −20 °C.
Enrichment and Cleavage: Derivatized peptides were enriched using Pierce High-Capacity Streptavidin Agarose beads (Thermo Fisher Scientific). After pre-wetting the wells with 1 mL of ethanol and PBS, 50 µL of beads were added to each well on a 96-well plate. The beads were washed twice with 500 µL PBS. The Step 2 sample was reconstituted with 0.2 % NP-40 in PBS buffer and incubated for 1 h with 400 rpm shaking followed by extensive washing (3 times 1 mL of 0.2% NP-40 in PBS buffer, 3 times 1 mL of PBS buffer and 3 times 1 mL of water). Peptides were eluted with 100 µL of 2% hydrazine in water and incubated for 1 h at room temperature. Then the beads were washed twice with 100 µL of 2% hydrazine in water and the elutions were combined. The eluted peptides were acidified, desalted via SCX StageTips, dried, and stored at −20 °C for MS analysis.

Strong cation exchange (SCX) Desalting
For peptide quantities up to 400 µg, StageTips were constructed using 300 µL tips packed with 4 SCX disks (2.8 mm diameter; IVA Analysentechnik GmbH). The tips were equilibrated with 300 µL ACN followed by 300 µL 0.1% FA via centrifugation at 250 × g for 1 min. Acidified samples were loaded, reapplied once, and washed three times with 300 µL 0.1% FA in 50% ACN. Peptides were eluted with 200 µL of 2% NH₃ in 40% ACN. For smaller sample volumes or enriched synthetic peptides, smaller StageTips with 200 µL tips and 3 SCX disks (0.6 mm diameter) were used, with reduced washing and elution volumes.

LC-MS/MS analysis
Orbitrap Fusion Lumos: Samples (synthetic peptides, in vitro citrullinated HeLa digest, and mouse brain tissues) were dissolved in 0.1% formic acid (FA) and analyzed using an Orbitrap Fusion Lumos mass spectrometer (Thermo Fisher Scientific) coupled to an UltiMate 3000 HPLC system. Sample injection was performed at a drawing speed of 0.5 µL/s. Peptides were initially loaded onto a trap column (ReproSil-pur C18-AQ, 5 µm, 20 mm × 75 µm, self-packed; Dr. Maisch) at a flow rate of 5 µL/min using solvent A (0.1% FA in water). Separation was performed on an analytical column (ReproSil Gold C18-AQ, 3 µm, 50 cm × 75 µm, self-packed; Dr. Maisch) at a flow rate of 300 nL/min. A linear gradient from 4% to 32% of solvent B (0.1% FA and 5% DMSO in ACN) was applied over 50 min (extended to 100 min for mouse brain samples). MS data acquisition was performed in data-dependent acquisition (DDA) mode with a cycle time of 2 seconds. MS1 spectra were acquired in the Orbitrap over a mass range of 360–1300 m/z at a resolution of 60,000, with an AGC target of 100% and a maximum injection time (maxIT) of 118 ms. Precursor ions with intensities above 2.5e4 and charge states of 2–6 were selected for MS2 analysis. Dynamic exclusion was set to 30 s. Fragmentation was performed using higher-energy collisional dissociation (HCD) at a normalized collision energy (NCE) of 35%. MS2 spectra were acquired at a resolution of 30,000 with an isolation window of 1.3 m/z, an AGC target of 150%, and a maxIT of 54 ms.
Orbitrap Astral: Neutrophil samples were dissolved in 0.1% FA and analyzed using a Vanquish Neo UHPLC system (Thermo Fisher Scientific) coupled to an Orbitrap Astral mass spectrometer (Thermo Fisher Scientific) with a Nanospray Flex ion source. The LC was operated in trap-and-elute mode using backward flush. Peptides were trapped on a PepMap Neo Trap Cartridge (300 µm × 5 mm) and separated using an IonOpticks Aurora Rapid analytical column (75 µm × 8 cm) maintained at 50 °C. Mobile phase A was 0.1% FA in water and mobile phase B was 0.1% FA in ACN. The gradient, in total 27 mins (active gradient 22 mins), proceeded as follows: 2% to 6% B over 0.5 min at 750 nL/min, 6% to 8% B over 2 min while decreasing flow to 300 nL/min, then 8% to 32% B over 19 min. The column was washed with 80% B at 750 nL/min for 2 min, followed by equilibration at 2% B. MS data acquisition was performed in positive ion mode using DDA. MS1 scans were acquired in the Orbitrap at 240,000 resolution, every 0.6 s, over a scan range of 360–1,300 m/z. The AGC target was 300% (3e6 charges) with a maxIT of 3 ms. Precursor ions were fragmented using HCD at an NCE of 26%. MS2 scans were acquired in the Astral analyzer over a scan range of 100–2000 m/z using a 1.2 Th isolation window, an AGC target of 300% (3e4 charges), and a maxIT of 5 ms. MS2 selection included charge states 2–6 and undetermined, with dynamic exclusion set to 5 s and monoisotopic precursor selection (MIPS) enabled for peptides.

Database search
Raw data files were analyzed using FragPipe v22 (MSFragger v4.1, IonQuant v1.10.27) against all canonical protein sequences as annotated in the Swiss-Prot referenced database of human (20,435 entries, downloaded 28 June 2024) or mouse (17,212 entries, downloaded 24 July 2024). Strict trypsin was specified as the proteolytic enzyme, and up to three missed cleavage sites were allowed. All the searches were applied with 1% FDR threshold on PSM level using PeptideProphet unless specified elsewhere.
Open database searches on synthetic peptide pools employed default open search settings35. Closed database searches on synthetic citrullinated peptide pool were conducted using default settings with additional variable modifications: deamidation of asparagine/glutamine (Δ mass 0.9840), citrullination (Δ mass 0.9840), first derivative (Δ mass 158.0116, C8H8N2O2), and the final derivative of arginine (Δ mass 213.0538, C11H17N3O2) were set as variable modifications, allowing up to two occurrences per peptide. Database searches on hydrolysis test dataset were conducted using default settings with nonspecific digestion.
Citrullinome of HeLa dilutions, mouse brain, and neutrophils were analyzed using hybrid search in MSFragger37. Hybrid searches were performed by modifying the default search settings in the MSFragger tab using a detailed mass offset file (#Mass: 213.0538, Fragment remainder ions: 168.0211, 185.0477, 196.0273) and enabling the labile search mode. Under the open search options, report mass offset as variable modification by removing the delta mass was selected and LocalizeMassShift (LOS) was enabled. Variable modifications including asparagine/glutamine deamidation (Δ mass 0.9840), and the final derivative of arginine (Δ mass 213.0538) (HeLa, mouse brain, and neutrophils) were permitted up to two occurrences per peptide. Additionally, for the neutrophil dataset, lysine/arginine methylation (Δ mass 14.0156), lysine/arginine dimethylation (Δ mass 28.0313), lysine trimethylation (Δ mass 42.0469), and lysine acetylation (Δ mass 42.0106) were permitted as variable modifications, with up to one occurrence per peptide. C-terminal derivatization was excluded for the down-stream analysis.
Global proteome of HeLa dilutions and neutrophils were analyzed using default closed search settings with additional variable modifications: deamidation of asparagine/glutamine (Δ mass 0.9840) and citrullination (Δ mass 0.9840) with up to two occurrences per peptide allowed and up to 3 miscleavages. For HeLa dilutions dataset, no FDR (100% FDR) was applied. The search results of citrullination identifications were further analyzed using Prosit-Cit at 1% FDR (PSM) for better precision18. For neutrophils dataset, additional variable modifications were added including: lysine/arginine methylation (Δ mass 14.0156), lysine/arginine dimethylation (Δ mass 28.0313), lysine trimethylation (Δ mass 42.0469), and lysine acetylation (Δ mass 42.0106) with up to one occurrence per peptide. C-terminal citrullination was excluded for the down-stream analysis.

MALDI-TOF analysis
For MALDI-TOF experiments, 800 pmol of the synthetic peptide SAVRA[R(Cit)]SSVPGVR dissolved in 0.1% FA underwent derivatization following aforementioned enrichment workflow with below modifications. The peptide was enriched by incubating the sample with 10 µL of Streptavidin Mag Sepharose (Cytiva Life Sciences) at room temperature for 1 h. A StageTip was constructed on a 10 µL pipette tip using one C18 disk (0.5 mm diameter). The tip was conditioned with 20 µL of ACN and 0.1% FA. Half of the reaction mixture was loaded onto the StageTip, which was washed with 20 µL of 0.1% FA before eluting directly onto a MALDI target using 5 µL of α-Cyano-4-hydroxycinnamic acid (HCCA) matrix. Spectra were acquired using a MALDI ultrafleXtreme mass spectrometer (Bruker Daltonik). Data processing and visualization were performed in FlexAnalysis software (Bruker Daltonik) employing baseline subtraction and smoothing algorithms.

Data analysis
Diagnostic ions exploration: Accumulated MS2 spectra were generated from spiked CitPool into HeLa samples using a custom Python script. Briefly, MS2 spectra corresponding to derivatized and unmodified peptides were extracted from the psm output file from the Fragpipe search. The m/z and intensity values were taken from the corresponding mgf file. The intensities of both across m/z 100 to 400 were aggregated within tolerance windows of 0.0001 Da.
MS2 spectra visualization and annotation: Interactive peptide spectral annotator was used to visualize and export annotated MS2 spectra65.
Annotation of neutral loss in MS2 spectra: To identify and count neutral loss-associated fragment ions in a given PSM, theoretical y- and b-ion masses were first computed for the peptide sequence using residue-specific molecular weights66. Briefly, for ions that can carry neutral losses adjusted masses were derived by subtracting the respective neutral loss mass from the theoretical y- and b-ion masses. The same procedure was applied to precursor ions, where expected neutral losses were subtracted from the intact precursor mass to generate adjusted target masses. Experimental spectrum was then searched for peaks matching these adjusted masses, with tolerances set according to the mass analyzer: ±20 ppm for high-resolution Fourier-transform mass spectrometers (FTMS). Matches were counted if their m/z values fell within the defined tolerance window.
Motif Analysis: Potential substrate motifs surrounding identified citrullination sites were analyzed using pLOGO67. Sequences spanning −5 to +5 residues around the citrullination sites were extracted and compared with their respective background frequencies in the human proteome. Significantly up-regulated motifs (p=0.05, Bonferroni corrected) were visualized.
Gene ontology enrichment analysis: This analysis was performed using functional enrichment analysis in STRING database68. Gene names of citrullinated proteins with up-regulated sites in neutrophils dataset were annotated with GO terms. Enrichment was performed and visualized using human proteome as background with following filters: FDR<=0.05, group terms with similarity >= 0.5, strength >= 0.01, minimum count in network of 2. Top 10 terms were shown.
Secondary structure prediction: Secondary structure prediction was carried out using the NetSurfP-3.0 web server (DTU Health Tech, Denmark)53. Canonical fasta sequences of all proteins with a citrullination site found in the neutrophil dataset were retrieved from UniProt using the ID mapping tool to human UniProtKB/Swiss-Prot database (downloaded 13 Jan 2025). Fasta sequences were then submitted to the NetSurfP-3.0 online tool and the output downloaded in csv format. Protein Q9NZV6 (gene name MSRB1) had to be excluded from structure prediction due to the presence of a non-canonical amino acid (selenocysteine U) in its sequence which is not supported by the prediction tool. The resulting prediction data was filtered for arginine residues, and the citrullination sites found in the MS data mapped to their corresponding predicted structures. Non-modified arginine residues among the proteins carrying citrullination sites served as background for secondary structure and the relative solvent accessibility (RSA) comparisons.

Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability
The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org)69 via the MassIVE partner repository with the dataset identifier MSV000097617 [https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?accession= MSV000097617]. All processed data generated in this study are provided in the Supplementary Information, Supplementary Data, and Source Data file. Source data are provided with this paper.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "General\nMeelker González et al. present a proteomics project in which they aim to enable the systemic study of citrullinated peptides\nvia mass spectrometry, with the focus of specifically enriching this modification that has otherwise mainly been studied in a\nnon-enriched (total proteome) context. Such an approach would allow the mass spectrometer to spend more time resolving\ncitrullinated peptides, rather than irrelevant background peptides. In turn, this would enable the study of citrullination in more\nbiologically relevant contexts, and at a scale that would be more affordable to many labs.\nCurrent enrichment approaches for citrullinated peptides often lack specificity, are chemically quite harsh, or result in a\npurified sample that is not suitable for MS analysis. The authors sought to work around this via a two-step derivatization and\nenrichment strategy, ultimately leaving a 213 Da mass remnant for each citrulline. They first validate this strategy,\nbenchmark it in a setting with synthetic and spiked-in hyper-citrullinated HeLa, and finally test out the method in mouse brain\nas well as human primary neutrophils treated with a gradient of ionomycin.\nThe manuscript is well written and logically structured. The authors clearly explain the methodology and prove its efficacy in\na variety of settings, giving equal importance to technical validation and application of their methodology to relevant biology.\nThe strategy would be applicable by other labs, and potentially could be further refined in the future to enhance systems-\nwide profiling of citrullination by MS.\nI have a few queries regarding some of the MS data processing and quality-control throughout the manuscript. Overall, if\nthese points are adequately addressed, I would recommend this manuscript for publication in Nature Communications.\nSpecific points\nThe authors define and search their mass remnants as “first derivative (Δ mass 158.0116), and the\n509 final derivative of arginine (Δ mass 213.0538)”. In addition to mass deltas, the authors should provide the atomic\ncomposition of the derivatives relative to arginine, for the sake of clarity and so that this modification may be defined in other\nsearch engines. If I am correct, the atomic composition of the +213.05 would be C11 H7 N3 O2?\nAs the authors are using data-dependent acquisition and are trying to pinpoint the location of citrullination within the\npeptides, correct localization is of high importance. The authors also mention the importance of localization throughout the\nmanuscript, and in the methods describe that “Localize Mass Shift (LOS) was enabled” within the hybrid search of\nMSFragger. How exactly does the localization in MSFragger work? I was able to find a localization probability in some of the\nMSFragger output, which seems to either be “0” or “1”. Why are there no probabilities in between? While localizing a\nrelatively large (213 Da) and stable PTM should not be too challenging, it would be beneficial if the authors could make an\nestimation/visualization of the distribution of localization confidence for the citrullinated\npeptides, especially since they (rightfully) choose to exclude peptide C-terminal citrullination.\nThe authors should provide an estimate or distribution of overall spectral quality for all complex datasets (figures 2, 3, 4). The\nAstral data (Figure 4) seems to be of high spectral quality, via manual inspection of the RAW data and a quick search via\nMaxQuant. However, based on a manual investigation of some of the MS/MS spectra within e.g. the RAW data generated for\nFigure 3 (YIG_314_L001_105_002_RMG, YIG_314_L001_105_005_RMG, and YIG_314_L001_105_008_RMG) using a\nFusion Lumos, the quality of these MS/MS spectra is overall not very high. Analyzing these files with MaxQuant yields a\nrelatively low MS/MS identification rate (~7%), with average spectral scores of ~83 for all identified peptides, and only\napproximately 30% of peptides exceed an Andromeda score of 100 (which is generally a minimum for a spectrum that would\nbe manually annotatable). For the +213 Da modified peptides, which appear to score lower than unmodified background\npeptides, only about 10% of spectra appear to be in excess of 100 Andromeda score. Based on a (quick and perhaps sub-\noptimal) MaxQuant search of the 3 mouse RAW files I was able to find ~100 unique +213 Da sites, whereas the authors find\n~400 using MSFragger (in both cases excluding peptide C-terminal site assignments). Perhaps MSFragger (in combination\nwith post-processing using PeptideProphet) is more efficient at identifying this modification compared to MaxQuant, but still\nthe difference seems relatively large. Using an alternative search engine on the data and being able to provide numbers in\nthe same ballpark as MSFragger, which could be included as a technical note, would strengthen the claims made by the\nauthors.\nIn relation to the two points above, and since we are looking at DDA data for modified peptide sequences, the authors\nshould provide fully annotated MS/MS spectra for all citrullinated peptides identified in the manuscript, stratified per\nexperiment. Currently there is only one annotated spectrum in Figure 1F, which is missing virtually the entire b-ion series.\nProviding annotated spectra (e.g. online via MS-Viewer, or batch-exported from any DDA search engine) would increase\ntransparency and simultaneously demonstrate sufficient spectral quality to facilitate accurate identification and localization of\ncitrullination within the peptides. Without the ability to manually inspect annotated spectra, and perhaps also an orthogonal\ndata search using a different software, it would be difficult to ascertain the spectral quality and rule out over-interpretation of\nthe data.\nThe authors derivatize citrullination, and search for the 213 Da derivative on Arginine residues. Is it possible for this\nderivative to exist on other amino acid residues? While this is to an extent addressed in Figure 1, it would be important for\nthe authors to perform an in silico entrapment experiment, by searching a subset of the data generated from complex\nsamples (e.g. from Figure 2, 3, or 4) and allowing the 213 Da mass remnant to be assigned to e.g. glutamine and asparagine\nresidues in addition to arginine residues (or ideally allow it on any residue to perform a true localization experiment, but this\nmay be computationally challenging). This would give an indication of the overall false discovery rate of the author’s\nmethodology when applied to complex samples – either because of non-specific chemical derivatization (i.e. in vitro), or in\nrelation to the accuracy of the computational localization (i.e. in silico).\nIn their final experiment (Figure 4), the authors enrich and identify citrullination from primary human neutrophils stimulated\nwith ionomycin, which is a calcium ionophore. Although briefly mentioned in the introduction, a recent landmark publication\nby Rebak et al. (published in Nat. Struct. Mol. Biol., PMID: 38321148) did a very deep dive into citrullination sites identified\nfrom HL60 leukemia cells differentiated into neutrophil-like cells. There, they also used a calcium ionophore to induce\ncitrullination, used a neutrophil-like system, and the study was in human cells. Considering these similarities in both aim of\nthe study and model systems, at a minimum, it would be prudent for the authors to compare their data (as described in Figure\n4) to the citrullinome published by Rebak et al., as there should be a significant overlap (>50% based on a very quick Venn\ndiagram), and it would be relevant to discuss any similarities or differences. The comparison could be done globally, like the\nauthors did in Figure 3. More specifically, it would be interesting to compare histone citrullination (described by the authors\nin 4E, and in part listed by Rebak et al. in their Table 1), as from a quick glance there appears to be a strong overlap there as\nwell.\nIn relation to the point above, the authors should include comparison to Rebak et al. in their discussion. Some of the points\ncurrently in the discussion, e.g. specific citrullinations on histones, were described by Rebak et al. and should as such be\nreferenced. Of course, the authors can find a considerable number of citrullination sites from relatively short (27 – 80 min)\nsingle-shot runs, whereas Rebak et al. used over a day of gradient time per (46-fraction) replicate – a distinction that would\ncertainly be worth mentioning in the context of scalability.\nFor the data table accompanying Figure 4 (i.e. Supplementary Table 9), it would be beneficial to have a list of citrullination\nsites that was identified (i.e. the “combined_site” that is present in Supplementary Table 8), as the current information is quite\ntechnical and verbose and only provided at the PSM level. Providing a more concise and cleaner table containing the sites\n(i.e. UniProt ID, gene name, citrulline position within the UniProt sequence, modified peptide, optionally a sequence window\ncentered around the citrulline) would increase the resource value of the manuscript by making this information more\naccessible to a broader audience.\nSame as above, but for Supplementary Table 6.\nUnless I am mistaken, the methods section does not detail how the mouse RAW data was searched, nor where the library\noriginated from.\nIn figure 4A the authors report their DDA gradient as 22 min. This likely relates to the active gradient, or the time where\npeptides are eluting. The actual gradient length (based on investigating RAW files) is 27 min, so the figure should be\namended to either state “22 min active gradient” or “27 min”.", "reviewer_2": "In this manuscript, Gonzalez et al. describes the development of a high-throughput chemical workflow for global citrullinome\nprofiling, which demonstrates a >10-fold increase in the detection of citrullinated peptides compared to other methods. This\nworkflow increases citrullinome coverage in the analysis of tissues and, when applied to the study of NETs, reveals new\ndynamics of protein citrullination involved in remodeling the neutrophil structure during NETs formation. The manuscript is\ntechnically rigorous and the data and conclusions are not overstated. While the manuscript is better suited to a technical\nreport in a biochemical journal, the study of citrullination dynamics during NET formation makes it highly innovative.\nNevertheless, the study of NETs is also a major caveat of the manuscript.\n1. In the manuscript, NETs were induced with ionomycin, a calcium ionophore that hyperactivates PADs, resulting in global\nhypercitrullination. Ionomycin is obtained from Streptomyces conglobatus, which is not pathogenic to humans. Importantly,\namong the large variety of mechanisms associated with the induction of NETs in humans, none has been linked to calcium\nionophores. The problem is that hypercitrullination induced by ionomycin targets almost everything, including proteins and\ncitrullination sites that are unlikely to be important for the formation of NETs. Under these conditions, it is impossible to\ndistinguish between citrullinated proteins and citrullination sites that are directly involved in the process of NETs from those\nthat are citrullinated bystanders. Thus, all ontology pathways and proteins linked to citrullination-induced NETs formation\ndescribed in the manuscript could be artifacts due to the prominent effect of ionomycin. For instance, despite the general\ndogma, there is rigorous data from the Zychlinsky lab showing that citrullination is not required for the induction of NETs\n(PMID: 28574339). Moreover, MS analysis of NETs induced with RNP immune complexes (PMID: 28649674) only detected\nfew citrullinated proteins (no more than 10) compared to the hundreds described in this manuscript. Certainly, the quality of\nthe MS analysis in PMID: 28649674 is terrible, but not even citrullinated histones, which are easily detected by WB, where\nfound in the NETs citrullinome. Thus, there is concern that the citrullination dynamics described in the manuscript may not\nbe biologically relevant for NETs.\n2. Detecting citrullinated proteins in ionomycin activated neutrophils is not a challenge. It can be done using any MS\napproach. The true test for the workflow described in the manuscript is to determine the citrullination dynamics during\nbiologically significant NET formation. Over the last 20 years, the number of conditions linked to NETs has grown\nexponentially. Some include IL-8, LPS, Candida albicans, Group B Streptococcus, toxin-free S. aureus, RNP immune\ncomplexes (PMID: 28574339, 28649674, 15001782, 17210947) and many others. There are numerous options to consider.\nIf the authors want to use the study of NETs to demonstrate the importance of their novel approach, the manuscript must\ninclude at least two examples of citrullination dynamics in NETs induced by any physiological or pathologic stimuli relevant\nto humans. Importantly, some bacteria secrete pore-forming toxins (e.g., S. aureus) that can induce necrosis and\ncitrullination (PMID: 17210947), which could be confused with NETs. Contamination with pore-forming toxins should\navoided.", "reviewer_3": "Lee and coworkers reported a streamlined chemical proteomics workflow to study protein citrullination, an important protein\npost-translational modification that remains underexplored due to the lack of good methodology for proteome-wide profiling.\nThe authors utilized commercially available 4-azido-phenyl glyoxal as a probe to derivatize citrulline in biological samples.\nThis installed azido group onto citrullinated proteins, allowing click reaction to incorporate a cleavable biotin group for\nenrichment and subsequent release for MS analysis. They thoroughly investigated the MS results, including the\nfragmentation pattern of the derivatized peptides as well as the sensitivity and reproducibility of the detection of\ncitrullinated sites and proteins. They successfully utilized their workflow to study protein citrullination in peptides,\ncell lysates, brain tissues and primary neutrophil cells, and identified previously uncharacterized citrullination sites on proteins\nsuch as on linker histone H1 and lamin B1.\nThe manuscript is well-written and is a pleasure to read. All the data have been well analyzed. Although the reagents used\nin their chemical proteomics workflow have been reported and the chemistry itself is not new, this is the first time protein\ncitrullination has been profiled using both a clickable probe and a cleavable linker. This “clickable-cleavable” platform\nenabled a significant increase in the number of citrullinated peptide detection as compared to the reported workflow (Anal.\nChem. 2022, 94, 17895). The authors also demonstrated the applicability of their workflow for studying protein citrullination\nin a variety of biological samples. This work should be of great interest to a broad readership, including those working on\nprotein post-translational modifications, cell biology, chemical proteomics and mass spectrometry. Yet, there are some\nconcerns that should be addressed prior to publication:\n1. Selectivity of the probe derivatization of citrullination site\nThe peptides were reacted with a high concentration of APG probe (400 mM; from the Method section) in 50% TFA at 50 oC\nfor 3 hours. The reaction condition is quite harsh. The authors should perform an experiment using control and probe-treated\nbiological samples such as cell lysates, and then analyze probe selectivity and any undesired off-target modifications by\nopen search.\n2. Any changes of other protein PTMs under the strongly acidic conditions used for derivatization of citrullination site\nThe harsh reaction condition for probe derivatization may also remove/introduce other protein modifications. The authors\ndiscussed the potential PTM crosstalk between citrullination and histone methylation/acetylation (line 242). While it is\ninteresting, the authors should perform experiments (e.g. proteomics for phosphorylation, acetylation and methylation) on\ncontrol and probe-treated biological samples to study any changes in these PTMs. This can provide important information to\nthe readers about the possibility of using their workflow together with proteomics platforms for other PTMs.\n3. Open search to confirm selectivity of probe modification in the brain tissues and neutrophil samples\nGlyoxal compounds are also known to react with arginine (but under basic condition. e.g. ACS Omega 2018, 3, 14229-\n14235). The authors should perform an open search on their MS data from more complex biological samples (brain tissues\nand neutrophils) to confirm that there are no modifications on arginine.\n4. Profiling downregulated citrullination using their workflow\nAlthough “profiling protein citrullination dynamics” is on the paper title, the authors have not investigated real biological\nsamples with downregulation of citrullination. It is understandable that protein citrullination levels are normally low, so more\nprotein input may be required for experiments on samples with downregulated citrullination. Yet, this is important to\ndemonstrate the feasibility of their workflow to detect protein citrullination below the basal level and disclose the\nexperimental details (particularly the protein input). The authors should perform experiments on samples treated with PAD4\ninhibitors or with PAD4 knockdown.\nMinor:\n- Extended Data Fig.1: The chemical structure of dde-PEG-biotin alkyne is not correct"}, "author_response": "Point-by-point response\nWe thank the reviewers for their time and constructive feedback. In response, we have\nextensively revised the manuscript and performed additional experiments and analyses to\naddress all major concerns. A clean and tracked-changes version of the revised manuscript is\nprovided. Point-by-point responses are detailed below (page numbers refer to the clean\nversion). Major revisions and new data include:\n1. Validation with Pathogen-Induced Citrullinome in Neutrophils (New Fig. 5; Results page 11,\nline 3): We profiled primary human neutrophils stimulated with a fungal pathogen Candida\nalbicans, identifying a conserved “core citrullinome” that substantially overlaps with the\nionomycin-induced dataset (Response to Reviewer #2).\n2. Quantification of Citrullination Down-regulation (New Extended Data Fig. 5; Results page\n10, line 14): Using the PAD4 inhibitor GSK484, we demonstrate dose-dependent\nsuppression of citrullination, strengthening the method’s capacity to detect citrullination\ndynamics (Response to Reviewer #3).\n3. Search Engine Benchmarking and Localization Assessment (Extended Data Fig. 6): We\ncompared MSFragger (hybrid and labile mode), MaxQuant, and Sage, highlighting sensitivity\ngains with MSFragger. We also verified site localization using PTMProphet within the\nFragPipe pipeline (Response to Reviewer #1).\n4. Chemical Selectivity and Side-Reaction Characterization (Discussion page 14, lines 5–13):\nWe directly addressed the +212 Da arginine derivatization, demonstrating that it is mass-\nresolvable from +213 Da derivatization, dose-independent, and computationally filtered\n(Response to Reviewer #3).\n5. Expanded Discussion and Limitations: The Discussion has been updated to incorporate\nnew data and address limitations raised by reviewers, including comments on selectivity,\nPTM compatibility, and computational aspects.\nWe are confident that all major issues have been thoroughly addressed, and we believe the\nrevised manuscript is now suitable for publication.\nReviewer comments\nReviewer #1 (Remarks to the Author):\nGeneral\nMeelker González et al. present a proteomics project in which they aim to enable the systemic\nstudy of citrullinated peptides via mass spectrometry, with the focus of specifically enriching\nthis modification that has otherwise mainly been studied in a non-enriched (total proteome)\ncontext. Such an approach would allow the mass spectrometer to spend more time resolving\ncitrullinated peptides, rather than irrelevant background peptides. In turn, this would enable\nthe study of citrullination in more biologically relevant contexts, and at a scale that would be\nmore affordable to many labs.\nCurrent enrichment approaches for citrullinated peptides often lack specificity, are chemically\nquite harsh, or result in a purified sample that is not suitable for MS analysis. The authors\n1\nsought to work around this via a two-step derivatization and enrichment strategy, ultimately\nleaving a 213 Da mass remnant for each citrulline. They first validate this strategy, benchmark it\nin a setting with synthetic and spiked-in hyper-citrullinated HeLa, and finally test out the\nmethod in mouse brain as well as human primary neutrophils treated with a gradient of\nionomycin.\nThe manuscript is well written and logically structured. The authors clearly explain the\nmethodology and prove its efficacy in a variety of settings, giving equal importance to technical\nvalidation and application of their methodology to relevant biology. The strategy would be\napplicable by other labs, and potentially could be further refined in the future to enhance\nsystems-wide profiling of citrullination by MS.\nWe appreciate the reviewer’s thoughtful summary and positive overall assessment of our work.\nI have a few queries regarding some of the MS data processing and quality-control throughout\nthe manuscript. Overall, if these points are adequately addressed, I would recommend this\nmanuscript for publication in Nature Communications.\nSpecific points\nThe authors define and search their mass remnants as “first derivative (Δ mass 158.0116), and\nthe 509 final derivative of arginine (Δ mass 213.0538)”. In addition to mass deltas, the authors\nshould provide the atomic composition of the derivatives relative to arginine, for the sake of\nclarity and so that this modification may be defined in other search engines. If I am correct,\nthe atomic composition of the +213.05 would be C11 H7 N3 O2?\nThe reviewer is correct. We have added the atomic composition (C11 H7 N3 O2) to the Methods\nsection of the revised manuscript (page 21, lines 22-23) for clarity and reproducibility across\nsearch engines.\nAs the authors are using data-dependent acquisition and are trying to pinpoint the location of\ncitrullination within the peptides, correct localization is of high importance. The authors also\nmention the importance of localization throughout the manuscript, and in the methods\ndescribe that “Localize Mass Shift (LOS) was enabled” within the hybrid search of MSFragger.\nHow exactly does the localization in MSFragger work? I was able to find a localization\nprobability in some of the MSFragger output, which seems to either be “0” or “1”. Why are there\nno probabilities in between? While localizing a relatively large (213 Da) and stable PTM should\nnot be too challenging, it would be beneficial if the authors could make an\nestimation/visualization of the distribution of localization confidence for the citrullinated\npeptides, especially since they (rightfully) choose to exclude peptide C-terminal citrullination.\nWe thank the reviewer for raising this important point. Because localization strategies differ\nfundamentally between search engines, we clarify and address this in two steps:\n1. Clarification of “0” vs “1” (Deterministic Localization): Unlike MaxQuant, which uses a\nprobabilistic algorithm (PTM Score) to report continuous probabilities (0–1), MSFragger’s\ndefault localization is deterministic and score-based. When Localize Mass Shift (LOS) is\nenabled, MSFragger evaluates every possible residue position for the modification and\ncalculates a Hyperscore.\n 1 (Uniquely Localized): A single site has the highest score.\n 0 (Ambiguous): There is a tie between sites or insufficient evidence to distinguish them.\nfact a binary localization flag, not a probability. This explains why the reviewer observed\nonly 0s and 1s.\n2. Probabilistic Validation (PTMProphet): To provide the continuous confidence distribution\nrequested by the reviewer, we re-analyzed representative biological datasets (HeLa, mouse\nbrain, and neutrophils) using PTMProphet in Fragpipe (PMID: 31290668). This tool applies a\nBayesian model to MSFragger results to report per-site probabilities with global false\nlocalization rate (FLR) control.\nAs shown below, all citrullinated peptides exceeded the default confidence threshold\n(>0.5), and the vast majority (>90%) showed localization probabilities >0.9, with medians\nbetween 0.96 and 1.00. This confirms that even though the default output is binary, the\nunderlying site assignments are highly confident.\nThe authors should provide an estimate or distribution of overall spectral quality for all complex\ndatasets (figures 2, 3, 4). The Astral data (Figure 4) seems to be of high spectral quality, via\nmanual inspection of the RAW data and a quick search via MaxQuant. However, based on a\nmanual investigation of some of the MS/MS spectra within e.g. the RAW data generated for\nFigure 3 (YIG_314_L001_105_002_RMG, YIG_314_L001_105_005_RMG, and\nYIG_314_L001_105_008_RMG) using a Fusion Lumos, the quality of these MS/MS spectra is\noverall not very high. Analyzing these files with MaxQuant yields a relatively low MS/MS\nidentification rate (~7%), with average spectral scores of ~83 for all identified peptides, and only\napproximately 30% of peptides exceed an Andromeda score of 100 (which is generally a\nminimum for a spectrum that would be manually annotatable). For the +213 Da modified\npeptides, which appear to score lower than unmodified background peptides, only about 10%\nof spectra appear to be in excess of 100 Andromeda score. Based on a (quick and perhaps sub-\noptimal) MaxQuant search of the 3 mouse RAW files I was able to find ~100 unique +213 Da\nsites, whereas the authors find ~400 using MSFragger (in both cases excluding peptide C-\nterminal site assignments). Perhaps MSFragger (in combination with post-processing using\nPeptideProphet) is more efficient at identifying this modification compared to MaxQuant, but\nstill the difference seems relatively large. Using an alternative search engine on the data and\nbeing able to provide numbers in the same ballpark as MSFragger, which could be included as a\ntechnical note, would strengthen the claims made by the authors.\nWe have addressed the questions regarding (1) spectral quality and (2) search engine sensitivity\nbelow.\n3\n1. Spectral Quality Assessment: We agree that spectral quality is key for modified peptides.\nWe plotted Hyperscore distributions (MSFragger’s main scoring metric) for both derivatized\n(+213 Da) and unmodified peptides across three datasets (See below). We observed that\nderivatized peptides scored slightly lower, particularly in the mouse brain dataset. We\nattribute this to two specific factors: i) Low endogenous citrullination in unstimulated brain,\nand ii) Instrument differences: the Fusion Lumos (brain) produces lower MS2 quality than\nthe Astral (neutrophils). Despite these lower scores, we observed a citrullination motif\nstrongly consistent with that found in the human brain (Fig. 3f), providing orthogonal\nsupport for correct identifications.\n2. MSFragger vs. MaxQuant Sensitivity: The reviewer notes a discrepancy in identifications\n(~400 vs ~100) in the mouse brain dataset. We believe this reflects analytical sensitivity\nrather than specificity.\nMaxQuant does not currently support “labile” fragment searching. The derivatized citrulline\nmoiety (+213 Da) often undergoes neutral loss during fragmentation. MaxQuant might miss\nthe resulting diagnostic ions, whereas MSFragger’s hybrid offset + labile mode is\nspecifically configured to detect these “remainder” ions.\nTo demonstrate that this is not an artifact, we benchmarked MSFragger, MaxQuant, and\nSage (PMID: 37819886) in a controlled HeLa citrullination dilution series (Dataset used in\nFig. 2e). MSFragger consistently reported more PSMs across all input levels (See below).\nWe conclude that MSFragger offers improved sensitivity without sacrificing specificity. We have\nincluded this technical point in the Discussion (page 12, lines 11-18) and Extended Fig. 6.\nIn relation to the two points above, and since we are looking at DDA data for modified peptide\nsequences, the authors should provide fully annotated MS/MS spectra for all citrullinated\n4\npeptides identified in the manuscript, stratified per experiment. Currently there is only one\nannotated spectrum in Figure 1F, which is missing virtually the entire b-ion series.\nProviding annotated spectra (e.g. online via MS-Viewer, or batch-exported from any DDA search\nengine) would increase transparency and simultaneously demonstrate sufficient spectral quality to\nfacilitate accurate identification and localization of citrullination within the peptides. Without\nthe ability to manually inspect annotated spectra, and perhaps also an orthogonal data search\nusing a different software, it would be difficult to ascertain the spectral quality and rule out\nover-interpretation of the data.\nWe fully support the request for transparency and manual inspection. To view any identified\npeptides in our results, we propose to access them interactively via Proteomics Data Viewer\n(PDV, PMID: 30169737), which allows interactive inspection of spectra.\nInstructions for usage:\na) Find scan number and file name in Supplementary Tables.\nb) Open corresponding .mzML and .pepXML files from MassIVE in PDV (available at\nhttps://wenbostar.github.io/PDV).\nc) Use PDV’s GUI to visualize b/y-ion series and neutral losses.\nThis platform provides greater transparency than static figures and allows readers to explore\nfragmentation patterns interactively (see below for example).\nThe authors derivatize citrullination, and search for the 213 Da derivative on Arginine residues.\nIs it possible for this derivative to exist on other amino acid residues? While this is to an extent\naddressed in Figure 1, it would be important for the authors to perform an in silico entrapment\nexperiment, by searching a subset of the data generated from complex samples (e.g. from\nFigure 2, 3, or 4) and allowing the 213 Da mass remnant to be assigned to e.g. glutamine and\nasparagine residues in addition to arginine residues (or ideally allow it on any residue to\nperform a true localization experiment, but this may be computationally challenging). This\nwould give an indication of the overall false discovery rate of the author’s methodology when\n5\napplied to complex samples – either because of non-specific chemical derivatization (i.e. in\nvitro), or in relation to the accuracy of the computational localization (i.e. in silico).\nWe address this concern with chemical and computational evidence.\n1) Chemical selectivity of derivatization: APG selectively reacts with citrulline via the ureido\ngroup under strongly acidic conditions (50% TFA) and with arginine under basic conditions.\nNo plausible chemical mechanism exists for this reaction with canonical amino acid side\nchains such as Q, N, or D. We performed open searches across HeLa, mouse brain, and\nneutrophils (See Reviewer#3 Comment #2). The only recurring delta masses observed were\n+212 Da (Arg side-reaction) and +213 Da (Cit). No other residues consistently showed\nmasses in this range.\n2) Computational localization: We use PTMProphet to model per-site localization with global\nFLR control. The majority of modified sites have high probabilities (>0.9), with no\nenrichment of off-residue assignments. Refer to response point #2 above for the full\nlocalization distribution.\nWhile the reviewer’s proposed entrapment search (e.g. allowing +213 Da on Q/N) is a useful\ntheoretical control, our open search and site-localization results already rule out consistent off-\ntarget derivatization or misassignment. These lines of evidence collectively suggest that the\nlocalized in our datasets.\nIn their final experiment (Figure 4), the authors enrich and identify citrullination from primary\nhuman neutrophils stimulated with ionomycin, which is a calcium ionophore. Although briefly\nmentioned in the introduction, a recent landmark publication by Rebak et al. (published in Nat.\nStruct. Mol. Biol., PMID: 38321148) did a very deep dive into citrullination sites identified from\nHL60 leukemia cells differentiated into neutrophil-like cells. There, they also used a calcium\nionophore to induce citrullination, used a neutrophil-like system, and the study was in human\ncells. Considering these similarities in both aim of the study and model systems, at a minimum,\nit would be prudent for the authors to compare their data (as described in Figure 4) to the\ncitrullinome published by Rebak et al., as there should be a significant overlap (>50% based on\na very quick Venn diagram), and it would be relevant to discuss any similarities or differences.\nThe comparison could be done globally, like the authors did in Figure 3. More specifically, it\nwould be interesting to compare histone citrullination (described by the authors in 4E, and in\npart listed by Rebak et al. in their Table 1), as from a quick glance there appears to be a strong\noverlap there as well.\nWe appreciate the reviewer’s suggestion to compare our dataset with the recent study by\nRebak et al., a major profiling effort in neutrophil-like cells. We have now included this\ncomparison in Extended Data Fig. 7 and updated the Discussion (page 13, lines 19–26), with\nappropriate referencing throughout.\nDespite substantial methodological differences—cell type (culture cell-derived vs. primary\nneutrophils), MS runtime (~1 day vs. 22 min per sample), and sample prep (46-fraction vs.\nsingle-shot)—we observed ~50% overlap in citrullination sites, including histones, supporting\nthe robustness of our approach (see below).\nWhile Rebak et al. generated a comprehensive “citrullinome atlas” with broad site coverage,\nour study emphasizes functional profiling. Using PAD activation and inhibition in a dose-\ndependent design, we identified regulated and PAD-responsive sites, distinguishing functional\n6\ncitrullination from basal modifications. This added regulatory dimension, along with the\nscalability of our workflow, reinforces its utility for studying citrullination dynamics in\nphysiological contexts. While a deeper comparison of e.g. histone citrullination is certainly of\ninterest, we note that our analysis focuses on the subset of functionally regulated histone sites,\nwhich were not statistically defined in the Rebak study.\nTherefore, this distinction between total coverage and regulatory insight highlights the value of\nour workflow for dissecting citrullination dynamics in physiologically relevant settings for larger\nstudies.\nIn relation to the point above, the authors should include comparison to Rebak et al. in their\ndiscussion. Some of the points currently in the discussion, e.g. specific citrullinations on\nhistones, were described by Rebak et al. and should as such be referenced. Of course, the\nauthors can find a considerable number of citrullination sites from relatively short (27 – 80 min)\nsingle-shot runs, whereas Rebak et al. used over a day of gradient time per (46-fraction)\nreplicate – a distinction that would certainly be worth mentioning in the context of scalability.\nWe have addressed this point in the response above and have updated the Discussion\naccordingly (page 13, lines 19–26).\nFor the data table accompanying Figure 4 (i.e. Supplementary Table 9), it would be beneficial to\nhave a list of citrullination sites that was identified (i.e. the “combined_site” that is present in\nSupplementary Table 8), as the current information is quite technical and verbose and only\nprovided at the PSM level. Providing a more concise and cleaner table containing the sites\n(i.e. UniProt ID, gene name, citrulline position within the UniProt sequence, modified peptide,\noptionally a sequence window centered around the citrulline) would increase the resource\nvalue of the manuscript by making this information more accessible to a broader audience.\nDone. We have added a “combined_site” sheet to Supplementary Tables 6 and 9, and new\nSupplementary Tables 10 and 11.\nSame as above, but for Supplementary Table 6.\nDone.\nUnless I am mistaken, the methods section does not detail how the mouse RAW data was\nsearched, nor where the library originated from.\nThank you for flagging this omission. We have added a detailed description in the Methods\n(page 21-22). The search parameters were consistent with those used for the HeLa dilution\nseries.\ngradient, or the time where peptides are eluting. The actual gradient length (based on\ninvestigating RAW files) is 27 min, so the figure should be amended to either state “22 min\nactive gradient” or “27 min”.\nCorrect. We have now revised Figure 4 and Methods (page 21, line 1) to state “22 min active\ngradient”.\nReviewer #2 (Remarks to the Author):\nIn this manuscript, Gonzalez et al. describes the development of a high-throughput chemical\nworkflow for global citrullinome profiling, which demonstrates a >10-fold increase in the\ndetection of citrullinated peptides compared to other methods. This workflow increases\ncitrullinome coverage in the analysis of tissues and, when applied to the study of NETs, reveals\nnew dynamics of protein citrullination involved in remodeling the neutrophil structure during\nNETs formation. The manuscript is technically rigorous and the data and conclusions are not\noverstated. While the manuscript is better suited to a technical report in a biochemical journal,\nthe study of citrullination dynamics during NET formation makes it highly innovative.\nNevertheless, the study of NETs is also a major caveat of the manuscript.\nWe thank the reviewer for recognizing the technical rigor and innovation of our study. We\nrespond below to the specific points raised.\n1. In the manuscript, NETs were induced with ionomycin, a calcium ionophore that\nhyperactivates PADs, resulting in global hypercitrullination. Ionomycin is obtained from\nStreptomyces conglobatus, which is not pathogenic to humans. Importantly, among the large\nvariety of mechanisms associated with the induction of NETs in humans, none has been linked\nto calcium ionophores. The problem is that hypercitrullination induced by ionomycin targets\nalmost everything, including proteins and citrullination sites that are unlikely to be important for\nthe formation of NETs. Under these conditions, it is impossible to distinguish between\ncitrullinated proteins and citrullination sites that are directly involved in the process of NETs\nfrom those that are citrullinated bystanders. Thus, all ontology pathways and proteins linked to\ncitrullination-induced NETs formation described in the manuscript could be artifacts due to the\nprominent effect of ionomycin. For instance, despite the general dogma, there is rigorous data\nfrom the Zychlinsky lab showing that citrullination is not required for the induction of NETs\n(PMID: 28574339). Moreover, MS analysis of NETs induced with RNP immune complexes (PMID:\n28649674) only detected few citrullinated proteins (no more than 10) compared to the\nhundreds described in this manuscript. Certainly, the quality of the MS analysis in PMID:\n28649674 is terrible, but not even citrullinated histones, which are easily detected by WB,\nwhere found in the NETs citrullinome. Thus, there is concern that the citrullination dynamics\ndescribed in the manuscript may not be biologically relevant for NETs.\nWe fully agree that not all NET pathways involve calcium /PAD activation, nor are all\ncitrullinated proteins detected under PAD stimulation functionally relevant. Identifying\nbiologically meaningful sites is a key goal of citrullinome profiling.\nIonomycin was selected as it is a well-established reagent for strictly modeling PAD-dependent\nNETosis. While not pathogen-derived, it induces a distinct, calcium-driven pathway separate\n8\nfrom oxidative mechanisms, offering controlled and reproducible PAD activation in primary\nneutrophils (Kenny et al., 2017, PMID: 28574339; Wang et al., 2009, PMID: 19153223).\nTo ensure biological relevance beyond broad hypercitrullination, we designed a dose–response\nexperiment that:\n1. Quantifies up-regulated citrullination events during dose-dependent ionomycin-induced\nNET formation instead of single high dose (Fig. 4),\n2. Dissects basal vs. PAD4-mediated sites through inhibition with GSK484 (new Extended Data\nFig. 5),\n3. Enables pEC50 estimation of down-regulated sites and defines a core citrullinome (384\nactivation- and 190 inhibition-responsive sites; Fig 4 and new Extended Data Fig. 5).\nThese results revealed that only a subset of detected citrullination sites is strongly dose-\nresponsive and PAD4-mediated, suggesting their functional relevance. The remaining sites\nlikely represent baseline modifications, background reactivity, or are mediated by PAD2\nisozyme. Furthermore, regulated sites were enriched in solvent-exposed and disordered\nregions, consistent with known PAD substrate preferences, and showed motif features\nassociated with PAD activity.\nFinally, gene ontology (GO) analysis was restricted to regulated citrullination sites, not the full\nlist of detected modifications. The enriched pathways—chromatin remodeling, cytoskeletal\nreorganization, and organelle structure—are highly consistent with known cellular changes\nduring NET formation and PAD activation with more site-specific resolution.\nThus, we believe our ionomycin-based dose-dependent profiling provides biologically\ninformative and selective insight into PAD-driven citrullination events relevant to NETosis.\n2. Detecting citrullinated proteins in ionomycin activated neutrophils is not a challenge. It can\nbe done using any MS approach. The true test for the workflow described in the manuscript is to\ndetermine the citrullination dynamics during biologically significant NET formation. Over the\nlast 20 years, the number of conditions linked to NETs has grown exponentially. Some include\nIL-8, LPS, Candida albicans, Group B Streptococcus, toxin-free S. aureus, RNP immune\ncomplexes (PMID: 28574339, 28649674, 15001782, 17210947) and many others. There are\nnumerous options to consider. If the authors want to use the study of NETs to demonstrate the\nimportance of their novel approach, the manuscript must include at least two examples of\ncitrullination dynamics in NETs induced by any physiological or pathologic stimuli relevant to\nhumans. Importantly, some bacteria secrete pore-forming toxins (e.g., S. aureus) that can\ninduce necrosis and citrullination (PMID: 17210947), which could be confused with NETs.\nContamination with pore-forming toxins should avoided.\nWe agree and have addressed this by profiling primary neutrophils stimulated with heat-killed\nCandida albicans (HKCA), a clinically relevant pathogen shown to induce PAD4-dependent\nNETosis (PMID: 28574339, 25622091). This experiment is now detailed in new Figure 5 (below)\nand described in the revised Results (page 11, lines 3-30). Briefly, we found that:\n1. In 4 biological replicates, we identified 268 citrullination sites in HKCA-stimulated\nneutrophils, vs. ~40 in serum-treated controls.\n2. ~200 sites were consistently observed in ≥2 replicates, and ~87% overlapped with\nionomycin-regulated sites (Fig. 5c).\n9\n3. GO analysis revealed strong enrichment in chromatin organization, cytoskeletal\nremodeling, and organelle structure—highly consistent with the ionomycin-regulated\ncitrullinome (Fig. 5e).\n4. Shared targets included key NET components like histones, lamin B1, and cytoskeletal\nproteins, defining a reproducible “core citrullinome” conserved across NET stimuli (Fig. 5f).\nWe also attempted stimulation with RF-immune complexes (PMID: 28574339), but were unable\nto identify robust and reproducible activation conditions. Nonetheless, the substantial overlap\nand biological concordance between HKCA- and ionomycin-induced citrullination profiles\nconfirm that our workflow captures a physiologically meaningful citrullination signature\nrelevant to NET formation.\nReviewer #3 (Remarks to the Author):\nLee and coworkers reported a streamlined chemical proteomics workflow to study protein\ncitrullination, an important protein post-translational modification that remains underexplored\ndue to the lack of good methodology for proteome-wide profiling. The authors utilized\ncommercially available 4-azido-phenyl glyoxal as a probe to derivatize citrulline in biological\nsamples. This installed azido group onto citrullinated proteins, allowing click reaction to\nincorporate a cleavable biotin group for enrichment and subsequent release for MS analysis.\nThey thoroughly investigated the MS results, including the fragmentation pattern of the\nderivatized peptides as well as the sensitivity and reproducibility of the detection of\ncitrullinated sites and proteins. They successfully utilized their workflow to study protein\ncitrullination in peptides, cell lysates, brain tissues and primary neutrophil cells, and identified\npreviously uncharacterized citrullination sites on proteins such as on linker histone H1 and\nlamin B1.\nThe manuscript is well-written and is a pleasure to read. All the data have been well analyzed.\nAlthough the reagents used in their chemical proteomics workflow have been reported and the\n10\nchemistry itself is not new, this is the first time protein citrullination has been profiled using\nboth a clickable probe and a cleavable linker. This “clickable-cleavable” platform enabled a\nsignificant increase in the number of citrullinated peptide detection as compared to the\nreported workflow (Anal. Chem. 2022, 94, 17895). The authors also demonstrated the\napplicability of their workflow for studying protein citrullination in a variety of biological\nsamples. This work should be of great interest to a broad readership, including those working\non protein post-translational modifications, cell biology, chemical proteomics and mass\nspectrometry.\nWe thank the reviewer for their thoughtful and encouraging assessment of our work.\nYet, there are some concerns that should be addressed prior to publication:\n1. Selectivity of the probe derivatization of citrullination site\nThe peptides were reacted with a high concentration of APG probe (400 mM; from the Method\nsection) in 50% TFA at 50 oC for 3 hours. The reaction condition is quite harsh. The authors\nshould perform an experiment using control and probe-treated biological samples such as cell\nlysates, and then analyze probe selectivity and any undesired off-target modifications by\nopen search.\nTo address this concern, we performed an open search comparing untreated and probe‑treated\nHeLa lysates. As shown below, the vast majority of identified peptides carried no modification.\nIn the probe‑treated samples, we observed ~90 PSMs with delta masses in the 200–250 Da\nrange. These correspond primarily to endogenous citrullinated proteins and a minor +212 Da\narginine side‑reaction (discussed in detail in Comment #3), rather than broad off‑target\nderivatization.\nImportantly, we did not observe any other recurring or enriched mass shifts outside this\nexpected window. Together, these data indicate that undesired side reactions in complex lysate\nbackgrounds are limited and that APG derivatization is largely selective for citrulline under our\nacidic labeling conditions.\n2. Any changes of other protein PTMs under the strongly acidic conditions used for\nderivatization of citrullination site\nThe harsh reaction condition for probe derivatization may also remove/introduce other protein\nmodifications. The authors discussed the potential PTM crosstalk between citrullination and\nhistone methylation/acetylation (line 242). While it is interesting, the authors should perform\nexperiments (e.g. proteomics for phosphorylation, acetylation and methylation) on control and\nprobe-treated biological samples to study any changes in these PTMs. This can provide\nimportant information to the readers about the possibility of using their workflow together with\nproteomics platforms for other PTMs.\noptimized for citrulline reactivity, but may destabilize acid-labile PTMs such as phosphorylation\nand some forms of acetylation.\nWe did not pursue co-enrichment of multiple PTMs in this workflow. As noted in the revised\nDiscussion (page 14, lines 19-24), we now recommend performing e.g.\nacetylation/methylation/phosphorylation analyses in parallel workflows rather than on\nderivatized samples when PTM crosstalk is a key question.\n3. Open search to confirm selectivity of probe modification in the brain tissues and neutrophil\nsamples\nGlyoxal compounds are also known to react with arginine (but under basic condition. e.g. ACS\nOmega 2018, 3, 14229-14235). The authors should perform an open search on their MS data\nfrom more complex biological samples (brain tissues and neutrophils) to confirm that there are\nno modifications on arginine.\nWe performed open searches on mouse brain and neutrophil datasets to assess the selectivity\nof APG derivatization in complex biological samples. A subset of peptides with a +212.0700 Da\nmodification on arginine residues was identified (see below). This likely reflects glyoxal-based\nreactivity toward arginine under basic pH, potentially during SCX cleanup, rather than a direct\nresult of the acidic APG labeling step.\nTo further assess the quantitative impact of these events, we re-searched our in vitro HeLa\ndilution series with both variable modifications of +213.0538 Da (Cit-derivatized) and\n+212.0700 Da (Arg-derivatized). In these data (see below and Extended Fig. 8):\n1. +213.0538 Da (Cit-derivatized) peptides showed a clear dose-dependent increase in\nidentification count and intensity.\n2. +212.0700 Da (Arg-derivatized) peptides remained constant or slightly decreased with\nincreasing citrullination levels.\n3. The majority of +212 Da PSMs were C-terminal arginine peptides, likely reflecting highly\nabundant tryptic peptides that are consistently present across samples.\n12\n4. Non-C-terminal +212 Da sites showed stable intensity across dilutions, consistent with a\nbackground signal rather than biologically meaningful citrullination.\nThese observations support the conclusion that +212 Da events represent a non-regulated\nbackground artifact. Crucially, the mass difference between +212 and +213 Da is readily\nresolved by high-resolution MS, allowing clear distinction in our analyses.\nTo ensure analytical rigor, our data processing pipeline includes explicit filtering of +212 Da\nevents by:\n1. Restricting site localization to +213.0538 Da for citrulline.\n2. Excluding C-terminally modified peptides.\n3. Validating consistent replicate or dose-dependent behavior.\nWe have now included this clarification in the revised Discussion section detailing these\nobservations, so readers can apply similar precautions (page 14, lines 5-18).\n4. Profiling downregulated citrullination using their workflow\nAlthough “profiling protein citrullination dynamics” is on the paper title, the authors have not\ninvestigated real biological samples with downregulation of citrullination. It is understandable\nthat protein citrullination levels are normally low, so more protein input may be required for\nexperiments on samples with downregulated citrullination. Yet, this is important to\ndemonstrate the feasibility of their workflow to detect protein citrullination below the basal level\nand disclose the experimental details (particularly the protein input). The authors should\nperform experiments on samples treated with PAD4 inhibitors or with PAD4 knockdown.\nWe agree that demonstrating the ability to quantify down-regulated citrullination is critical for\nvalidating dynamic profiling. Because basal citrullination is minimal in unstimulated cells,\ninhibiting an unstimulated system would not be informative. Thus, we assessed down-\nregulation in the context of PAD4 inhibition following ionomycin activation in primary human\nneutrophils.\nNeutrophils were pre-treated with a dose range of the PAD4-selective inhibitor GSK484 (0.3–\n30 µM) and then activated with ionomycin. This experiment revealed (see Extended Data Fig. 5\nand Results section page 10, lines 14-33):\n1. Quantification of 1,568 citrullination sites across all conditions,\n2. A dose-dependent reduction in total citrullinated peptide abundance,\n13\n3. Identification of 190 significantly down-regulated sites, of which 103 overlapped with\nionomycin-up-regulated targets—thereby defining a PAD4-responsive core citrullinome,\n4. EC values for inhibition were consistent across all regulated sites.\ncitrullination, providing site-specific resolution of PAD4 activity in physiologically relevant\nsettings.\nMinor:\n- Extended Data Fig.1: The chemical structure of dde-PEG-biotin alkyne is not correct\nCorrected. Thank you for catching this error.\n14"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have performed a very thorough set of follow-up experiments and validations, and they have addressed all my\nquestions and comments. I highly recommend their manuscript for publication.", "reviewer_2": "The comments were properly addressed with rigorous data. I only have few minor comments.\n1. Page 11, lane 25: The authors should consider the alternative that citrullination of ACTB, KRT1 and RPS6 is linked to C.\nalbicans but not to ionomycin. If these proteins are constitutively modified, as mentioned by the authors, they should also be\nfound in ionomycin-activated cells. The interaction and pathways activated during phagocytosis of C. albicans by\nneutrophils are very different to the simple activation of neutrophils by ionomycin. One possibility is that C. albicans induces\nchanges in the neutrophil, not induced by ionomycin, that exposes ACTB, KRT1 and RPS6 to citrullination. Indeed, it is\nexpected that different stimuli will generate different citrullinomes in neutrophils. Whether citrullination of ACTB, KRT1 and\nRPS6 is important for C. albicans-induced NETs or for some other function is unclear but should not be underestimated just\nbecause these are absent in neutrophils activated with ionomycin, which is an artificial stimulus.\n2. Fig. 5c and d. I am not sure that the term “up-regulated” is adequate to describe proteins citrullinated by ionomycin. The\nassay addresses citrullination, not protein expression. Please consider using: ionomycin-induced citrullinome, citrullinated\nproteins induced by ionomycin, or any other alternative.\n3. It is important to mention that RF-immune complexes failed to induce NETs. This information can be included anywhere,\nin results section or in methods. Indeed, it is consistent with previous work by Zychlinsky that failed to induce NETs using\nRNP-immune complexes (Elife. 2022 Oct 25;11:e68283. doi: 10.7554/eLife.68283. PMID: 36282064).", "reviewer_3": "The authors have convincingly addressed my comments and incorporated new data to investigate their probe labeling of\ncitrullination sites vs arginine. They also successfully demonstrate the profiling of downregulated citrullination in primary\nhuman neutrophils treated with a PAD4 inhibitor.\nI believe this revised manuscript is now ready for publication."}, "author_response": "Point-by-point response\nReviewer comments\nReviewer #2 (Remarks to the Author):\nThe comments were properly addressed with rigorous data. I only have few minor comments.\n1. Page 11, lane 25: The authors should consider the alternative that citrullination of ACTB,\nKRT1 and RPS6 is linked to C. albicans but not to ionomycin. If these proteins are constitutively\nmodified, as mentioned by the authors, they should also be found in ionomycin-activated cells.\nThe interaction and pathways activated during phagocytosis of C. albicans by neutrophils are\nvery different to the simple activation of neutrophils by ionomycin. One possibility is that C.\nalbicans induces changes in the neutrophil, not induced by ionomycin, that exposes ACTB,\nKRT1 and RPS6 to citrullination. Indeed, it is expected that different stimuli will generate\ndifferent citrullinomes in neutrophils. Whether citrullination of ACTB, KRT1 and RPS6 is\nimportant for C. albicans-induced NETs or for some other function is unclear but should not be\nunderestimated just because these are absent in neutrophils activated with ionomycin, which\nis an artificial stimulus.\nWe thank the reviewer for this comment. We have re-verified our dataset and confirm that\nACTB, KRT1, and RPS6 are indeed detected in the ionomycin-stimulated dataset. However, they\ndo not show statistically significant up-regulation upon stimulation. This lack of regulation in\nthe ionomycin model supports our interpretation that they likely represent constitutively\nmodified basal proteins rather than NET-specific signaling events. We have revised the text to\nexplicitly state that these proteins were detected but unregulated (see page.11, lines 25-26)\nRevised text: \"Interestingly, several highly citrullinated proteins in the HKCA condition—such as\nACTB, KRT1, and RPS6—while detected in the ionomycin dataset, were not significantly\nregulated. These likely represent constitutively modified proteins present in basal neutrophils,\nrather than NET-specific citrullination events.\"\n(Fig 5f: HKCA) (Same sets of proteins in Ionomycin dataset)\n1\n2. Fig. 5c and d. I am not sure that the term “up-regulated” is adequate to describe proteins\ncitrullinated by ionomycin. The assay addresses citrullination, not protein expression. Please\nconsider using: ionomycin-induced citrullinome, citrullinated proteins induced by ionomycin,\nor any other alternative.\nWe understand the reviewer’s concern regarding the distinction between protein expression\nand PTM abundance. In this context, we used \"up-regulated\" to strictly denote statistically\nsignificant increases in citrullinated peptide abundance. To avoid ambiguity, we have revised\nthe figure (Fig 5c/d) to refer to these as \"significantly induced by ionomycin”\nFigure 5c-d\n3. It is important to mention that RF-immune complexes failed to induce NETs. This information\ncan be included anywhere, in results section or in methods. Indeed, it is consistent with\nprevious work by Zychlinsky that failed to induce NETs using RNP-immune complexes (Elife.\nWe agree. We have added a statement to the Discussion explicitly noting that RF-immune\ncomplexes failed to induce robust NETosis in our hands, consistent with Zychlinsky et al. (eLife\n2022). Please see page 13, lines 4-7.\nRevised text: “Beyond calcium ionophore stimulation, we validated the physiological relevance\nof our findings using the fungal pathogen Candida albicans. We also attempted to induce\nNETosis using Rheumatoid Factor (RF)-immune complexes; however, consistent with\nprevious reports59, this stimulus failed to trigger robust NET formation in our primary\nneutrophil model. Importantly, while the total number of induced citrullination sites from C.\nalbicans was lower than in the ionomycin-stimulated condition, we observed a high degree of\noverlap (~87%) between the ionomycin- and C. albicans-induced citrullinomes.\n2"}]

Decision letter