Analytical Chemistry

Native mass spectrometry (nMS) is a powerful tool for analyzing biomolecules and their complexes under near native conditions. The preservation of the native state depends strongly on the ionization methods used to transfer intact molecules from solution to gas phase. In this work, capillary vibrating sharp-edge spray ionization (cVSSI)- based nMS and in-droplet hydrogen deuterium exchange mass spectrometry (HDX-MS) were used to evaluate calcium-dependent interactions between calmodulin and calmidazolium (CDZ). We found that cVSSI produced a narrow charge-state-distribution (CSD) with low average charge states indicating that this method preserved the native-like state. cVSSI was also able to resolve stepwise Ca2+-binding containing one to four Ca2+-bound species of the protein. In absence of Ca2+, no detectable CDZ-binding was observed. However, CDZ-binding was observed when calmodulin was fully loaded with Ca2+. CDZ-binding to the protein caused marked redistribution of the CSD toward lower charge states, consistent with ligand-induced stabilization of the protein into a more compact conformation. The apparent dissociation constant (Kd) of the interaction was determined to be 261 {+/-} 29 nM and 126 {+/-} 17 nM from Langmuir and quadratic binding models, respectively. Complementary in-droplet HDX-MS showed an approximately 23% reduction in deuterium uptake upon ligand binding indicating reduced solvent accessibility and increased structural stabilization supporting nMS findings. Together, these results demonstrate that cVSSI-based nMS coupled with in-droplet HDX-MS provides an integrated platform for simultaneously resolving metal loading, ligand binding, binding affinity, and ligand-induced conformational changes. This approach complements traditional structural methods by enabling direct interrogation of dynamic, metal-dependent protein-ligand interactions in their native states.

Understanding proteoforms, i.e., the various molecular forms in which proteins can exist, is important for deciphering biological processes and diseases. While capillary zone electrophoresis (CZE) proved advantageous for proteoform separation, limited sample loading capabilities restrict its application. Here, we present a novel comprehensive two-dimensional nanoLCxCZE-MS platform for deep top-down proteomics (TDP). The 2D platform is highly automated, enabling robust performance and the possibility to perform proteoform quantitation as demonstrated by isobaric labeling experiments. The high orthogonality of reversed-phase LC and CZE leads to a peak capacity of 2200, leading to an increase in the number of identified proteoforms in a human Caucasian colon adenocarcinoma cell lysate sample by a factor of 3 compared to nanoLC-MS. Furthermore, CZE mobilities enable the attribution of many more proteoforms to a certain proteoform family on the MS1-level. Overall, the flexible platform enables highly efficient separation of intact proteoforms combined with sensitive MS-based TDP workflows, both for untargeted and targeted analysis of complex biological samples. Graphical AbstractWe report a robust and automated comprehensive nanoLCxCZE-MS platform for top-down proteomics. In addition to large volume sample injection and separation by hydrophobicity in the nanoLC, the orthogonal separation by CZE in the second dimension leads to a strong increase in peak capacity and, thus, in the number of identified proteoforms. CZE mobilities also enable the attribution of many more proteoforms to a proteoform family on the MS1-level. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=46 SRC="FIGDIR/small/725123v1_ufig1.gif" ALT="Figure 1"> View larger version (11K): org.highwire.dtl.DTLVardef@df07b6org.highwire.dtl.DTLVardef@736d5corg.highwire.dtl.DTLVardef@10cef1org.highwire.dtl.DTLVardef@1825b55_HPS_FORMAT_FIGEXP M_FIG C_FIG

Detection of low-level analytes in complex chromatographic-mass spectrometric data requires a criterion to discern apparent peaks from background. Conventional signal-to-noise criteria rely on simple, constant-variance noise models and overlook spurious peaks generated by chemical noise and co-eluting interferences. We introduce a wavelet-based Monte Carlo technique for determining the statistical significance of SRM LC-MS/MS peaks in the presence of structured chemical noise. The method empirically characterizes chemical-noise peaks in samples and builds a generative noise-only null model. Monte Carlo resampling of the noise model assigns p-values that are controlled for the family-wise type I error rate (FWER). We validated the method with SRMs from a dilution series of drug compounds in plasma with known ground-truth concentrations. Triplicate technical replicates were used, spanning concentrations from far above the limit of detection to far below it. Peaks with adjusted p < 0.05 matched the expectation for true positives above the detection limit. Peaks below the limit of detection matched matrix blanks as true negatives, and intermittent detection in the transition region was observed. An independent external validation using a clinical pain panel confirmed the method detects ketamine in confirmed positive samples with signal intensity below the lowest calibration standard while correctly classifying matrix blanks and biological negatives. As a demonstration, we applied our method to a recently published lipid mediator data set. By replacing subjective noise-region selection with a formal hypothesis test against an empirical null model, the method provides an objective and reproducible criterion for deciding whether peak integration is warranted.

Native mass spectrometry (nMS) is well established for measuring protein masses and stoichiometries using nano-electrospray ionization (nESI), yet salt adduction and source activation energies can limit routine measurements. In this study, we benchmark submicron quartz nanopipette nESI emitters (<50 nm internal diameter) across three mass spectrometry platforms (quadrupole-time-of-flight, quadrupole-Orbitrap, and tribrid-Orbitrap platforms) and a wide protein mass range (17-800 kDa). We analysed holo-myoglobin (17 kDa) over a range of concentrations (10 M-10 nM) and capillary voltages to determine limits of detection and define a gentle operating regime. We additionally observe reduced Na+ adduction and preservation of the Zn2+-bound metalloproteoform of carbonic anhydrase II (29 kDa). Proteins and protein complexes spanning the mid-to-high mass range including ovalbumin ([~]44 kDa), malate dehydrogenase ([~]70 kDa), glutamate dehydrogenase ([~]350 kDa), {beta}-galactosidase ([~]465 kDa), and GroEL ([~]800 kDa), were readily detected using nanopipette emitters. Compared with conventional 1-2 m internal diameter borosilicate emitters, quartz nanopipettes provided higher signal-to-noise ratios and fewer adducts. Finally, direct analysis of clarified bacterial lysate expressing -synuclein yielded a clear monomeric charge-state distribution, demonstrating compatibility with complex biological matrices. Collectively, these results establish quartz nanopipette nESI as an instrument-portable, salt-tolerant approach suitable for routine nMS analysis across a broad range of protein molecular weights and sample complexities.

Double-stranded RNA (dsRNA) is a potent immunogenic impurity and its detection is a critical quality attribute in characterizing mRNA therapeutics. Standard analytical methods (e.g., sandwich ELISA) are only able to resolve the bulk presence of dsRNA and cannot characterize the different sub-species that may be present within a mRNA sample.. In this study, we use mass photometry (MP) as a single-molecule analytical platform for the simultaneous detection and characterization of dsRNA impurities in mRNA samples. We demonstrate how ionic strength can interfere with the stability of the mAb/dsRNA complex and measure the binding affinity (1 nM) under a set of parameters for reproducible characterization of the complex. We then leverage the J2 antibody to identify antibody/dsRNA complexes that then resolve dsRNA-positive species within an mRNA sample based on discrete molecular weight profiles. Furthermore, we introduce a novel MP assay that harnesses the repulsive surface chemistry of uncoated glass to exclude the bulk mRNA analyte to enable the use of higher loading concentrations to sensitively profile trace dsRNA impurities as antibody-bound species. This work establishes MP as a valuable next generation mRNA analytical tool for analyzing dsRNA byproducts within mRNA samples.

Advances in Electrostatic Linear Ion Trap (ELIT) Charge Detection Mass Spectrometry (CDMS) over the past 10 years have revolutionized its use for analyzing very high-molecular-weight species such as protein complexes, viral vectors, vaccines, viruses, and amyloid fibrils. Nonetheless, ELIT-based CDMS has remained confined to a small number of specialized instrumentation groups, predominantly in academia, where large and complex home-built instruments are operated by highly skilled scientists in dedicated facilities. In this report, we discuss the primary challenges addressed in the design of a benchtop ELIT-based CDMS instrument. We highlight key design aspects of the hardware, acquisition modes, and control software, and we present important performance metrics (mass range, resolution and sensitivity) demonstrated using samples representative of the technology's key application areas.

The structural and functional diversity of RNAs is expanded by the post-transcriptional incorporation of nucleoside variants. Emblematic of this, tRNAs contain extensive modifications that ensure their function during protein synthesis. Mass spectrometry has long been the field standard for identifying specific sites of chemical modifications on RNA. Nonetheless, mass spectrometry-based mapping approaches are not widely implemented. This is partially due to technical challenges associated with current methodologies including the limited diversity of available RNases, complexity of RNA mixtures, and conventional use ion-pairing reagents that require dedicated instrumentation. Here, we present a bottom-up liquid chromatography-tandem mass spectrometry (LC-MS/MS) workflow employing hydrophilic interaction liquid chromatography (HILIC) without ion-pairing reagents to globally map E. coli tRNA modifications. We implement orthogonal digestions using RNase 4 and a folded digestion scheme with RNase T1 to generate uniquely mappable oligonucleotides compatible with HILIC-MS/MS analysis and achieve 75-100% sequence coverage for most tRNA isoacceptors. HILIC-MS/MS matches the performance of traditional ion-pairing reverse-phased LC-MS/MS. This level of coverage allowed us to discover a new site of methylation (Gm17) in tRNAGly, and confirm the presence of an s4U8 modification predicted in tRNAArg. Furthermore, by applying this method to E. coli lacking the m5U54 methyltransferase (trmA) we confirmed the established dependence of acp3U47 insertion on m5U54 in tRNAPhe. Our findings show that RNase 4 improves bottom-up tRNA sequencing, enabling high-quality E. coli tRNA analysis without ion-pairing reagents. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=72 SRC="FIGDIR/small/727428v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@1f483a3org.highwire.dtl.DTLVardef@1ee5e01org.highwire.dtl.DTLVardef@5db88borg.highwire.dtl.DTLVardef@fee855_HPS_FORMAT_FIGEXP M_FIG C_FIG

Human milk contains structurally diverse glycans with key roles in shaping infant development, yet analytical constraints limit characterisation from low-volume samples. Glycosaminoglycans (GAGs), including chondroitin sulphate (CS), are understudied due to existing protocols requiring sample volumes of at least 5 mL and lengthy extraction steps prior to instrumental analysis. This study establishes a workflow for quantifying CS disaccharides from 25 {micro}L of human milk, enabling analysis of samples previously inaccessible to GAG profiling, such as those collected as salvage samples from neonatal intensive care units. For CS quantification, the CS is first enzymatically depolymerised using chondroitinase ABC to release repeating disaccharide units. Matrix complexity is reduced via two rounds of acetonitrile-based protein and lipid precipitation. Disaccharides are separated by hydrophilic interaction liquid chromatography and detected using a Triple Quadrupole Mass Spectrometer, providing robust sensitivity for all CS disaccharides. Method development and validation were performed using pooled mature human milk from term infants. This workflow facilitates detection of all CS disaccharides, with low but reproducible recoveries for total CS. Low- and high-level spike recoveries were 41.3% (RSDr 7.5%, RSDiR 15.9%) and 43.7% (RSDr 24.4%, RSDiR 27.9%), respectively. Despite modest absolute accuracy, precision remained sufficient to make relative comparison of CS concentrations between samples. This method expands the analytical toolkit for human milk glycomics, enabling same day preparation and CS profiling from sample volumes that are 200 times smaller than prior work, supporting future investigations into GAG-mediated functions in early life. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/723732v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@176dffborg.highwire.dtl.DTLVardef@16ae4ccorg.highwire.dtl.DTLVardef@d333c2org.highwire.dtl.DTLVardef@1eb3216_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO Schematic of sample preparation protocol 25 L of human milk is combined with lyase enzymes and TRIS buffer containing the internal standard prior to incubation. Samples then undergo multiple rounds of centrifugation and refrigeration before analysis via LC-MS/MS. Made using BioRender.com. Glycan nomenclature following Varki et al., 2015. C_FIG

Untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics is an important technology for unbiased discovery of small molecules in biomedical (e.g., drug discovery to diagnostics), animal, plant, environmental, and microbial research. Over the past decade, ion mobility has added an additional dimension to the triplet of MS1, MS2, and retention time, helping resolve co-eluting or isomeric features in an LC-MS/MS that aid in compound identification. Here, we focused on evaluating the current trapped ion mobility spectrometry (TIMS)-amenable feature-finding tools (MZmine 4.9, MS-DIAL 5.5, and MetaboScape 2025 14.0.3) for pre-processing of metabolomics data generated using a popular ion mobility mass spectrometry (IM-MS) technique, TIMS. We leveraged ten public and three benchmark TIMS datasets to evaluate these tools for their strengths and weaknesses. Our results show that MZmine consistently identified the highest number of features and confidently annotated features; however, this performance was accompanied by an increased number of false positives, due to peak splitting, as well as reduced accuracy in collision cross section (CCS) measurements. In contrast, MetaboScape achieved the highest fraction of high-quality MS2 spectra, reflecting a more conservative feature detection strategy. MS-DIAL demonstrated balanced performance, identifying features that other tools missed. Finally, we publicly release the ground-truth datasets and code to support future developments in improving IMS data analysis.

Accurate quantitation of therapeutic bacteriophages (phages) remains a challenge for clinical development. Plaque-based enumeration is the current standard but is laborious, host-dependent, and variable, particularly when distinguishing individual phages in cocktails. Targeted mass spectrometry of virion structural proteins offers an orthogonal, structure-based approach amenable to reproducible and scalable phage quantitation. Here, we describe a targeted proteomic liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay for host-independent quantitation of the Pseudomonas aeruginosa podovirus LUZ19. Proteomic characterization was performed on an LTQ Orbitrap XL to assess sequence coverage and select surrogate peptide candidates based on specificity and sensitivity. High-resolution peptide mapping identified multiple structural proteins of LUZ19 and provided 55% sequence coverage for the major head protein (YP_001671977.1). Fifteen peptides were detected and evaluated, from which the tryptic peptide EVAELDGQELAR was selected based on abundance, stability, and chromatographic performance. Quantitative analysis was conducted on a QTRAP 7500+ using optimized multiple reaction monitoring transitions for targeted peptide detection. Back-calculated concentrations met accuracy criteria across a validated range of 0.008 to 80 pg/mL, with bias spanning -8.2 to 8.2%, intra-day precision ranging from 0.5 to 9.8%, and inter-day precision ranging from 6.3 to 9.7%. Peptide concentrations from digested lysate samples were related to phage concentrations determined by double layer agar assay, yielding an estimated three copies of the major head protein per virion. ImportanceBacteriophages are the most abundant biological entities on the planet and represent a promising therapeutic class for combating drug-resistant bacterial infections. Realizing the clinical potential of bacteriophage therapy requires analytical methods capable of meeting the standards of modern drug development. Targeted mass spectrometry offers unmatched specificity and resolution for precise quantitation of individual bacteriophages within complex biological samples, a capability that conventional enumeration methods cannot match. Only one prior study has applied mass spectrometry to bacteriophage quantitation, using a well-characterized model bacteriophage at a single concentration without calibration or a validated analytical range. Using Pseudomonas aeruginosa podovirus LUZ19, we present the first targeted mass spectrometry-based bacteriophage quantitation assay developed and validated following FDA bioanalytical guidance. This work establishes a rigorous analytical foundation that moves bacteriophage therapy closer to the standards required for informed dose selection, candidate evaluation, and clinical development.

In this study, we report a series of highly water-soluble fluorogenic probes for glycosidases useful for microdevice-based single-molecule enzyme activity profiling (SEAP). The assay was able to detect trace glycosidase activities in blood with high sensitivity and with proteoform resolution. This platform revealed the potential of -mannosidase as sensitive blood-based liquid-biopsy biomarkers for liver injury, highlighting the potential of this approach for activity-based diagnostics.

Monoclonal antibody (mAb) glycosylation is a critical quality attribute that is difficult to rationally engineer and rapidly assess during cell line development. Here, we investigate whether cell-surface glycosylation can serve as a predictive indicator of mAb product glycosylation following targeted glycogene engineering in CHO cells. Five key glycogenes (COSMC, FUT8, B4GALT1, ST3GAL4, ST6GAL1) were investigated in two mAb-producing CHO cell lines. Product glycan analysis revealed consistent, gene-specific effects across hosts, including loss of core fucosylation, and tuneable galactosylation and sialylation. Lectin-based surface profiling reliably reflected product outcomes for COSMC and FUT8 modifications but showed limited predictive power for galactosylation and 2,3-sialylation, highlighting glycosylation pathway redundancy and context dependence. This study provides the first systematic, cross-cell line evaluation of lectin-based cell-surface glycan profiling as a predictor of mAb product glycosylation, establishing its practical utility and inherent limitations for CHO glycoengineering workflows. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/724788v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@6d5cfborg.highwire.dtl.DTLVardef@1f38e0aorg.highwire.dtl.DTLVardef@f25fa2org.highwire.dtl.DTLVardef@64a0dc_HPS_FORMAT_FIGEXP M_FIG C_FIG

Antibody Fc glycosylation is modulated in a variety of disease and immune response contexts, altering downstream functional responses including antibody-dependent cellular cytotoxicity through modified immune cell Fc receptor binding. Accessible, high-throughput glycosylation assays such as enzyme-linked lectin assays (ELLAs) are essential to advance understanding of glycosylation regulation and function. However, current ELLA protocols lack standardization and optimization, and results are reported out in arbitrary absorbance units, limiting reproducibility and cross-study comparability. We developed an optimized multi-lectin parallel ELLA with three specific improvements: systematic optimization of incubation times and reagent concentrations; incorporation of Protein A for IgG specificity; and use of commercially available bovine fetuin B as a quantitative surrogate standard for cross-study reproducibility. Our panel of 8 lectins, SNA, RCA, LCA, PHA-E, PHA-L, MAL-I, WGA, and DSL, cover the major IgG glycoforms. We demonstrate that our ELLA panel can reveal biologically relevant cytokine-induced plasticity of IgG glycosylation profiles in immortalized B cells.

One critical step in any targeted mass spectrometry experiment is selecting, from each protein of interest, a small number of peptides that respond well in the mass spectrometer and can serve as reliable proxies for protein quantification. Existing methods select target peptides either by relying on prior empirical measurements, limiting their applicability to previously observed peptides, or using machine learning to predict peptide behavior from sequence alone. However, current machine learning tools suffer from various limitations, including using detectability as an indirect proxy for intensity, relying on small training sets, or ignoring the precursor charge state. In this study, we introduce Bromo, a transformer-based deep learning model that ranks peptide precursors from a given protein by their relative response, taking charge state into account. Trained on millions of annotated peptide pairs derived from large-scale, publicly available data-independent acquisition mass spectrometry data, Bromo consistently outperforms existing sequence-based methods across diverse, independent datasets. Furthermore, we show that fine-tuning Bromo on experiment-specific data can account for differences in sample preparation, sample matrix, and instrument platform, all of which influence which peptides serve as optimal targets. This adaptability makes Bromo a practical tool for selecting target peptides for selected reaction monitoring and parallel reaction monitoring assay development across a wide range of experimental conditions.

Fresh-frozen tissues are considered the gold standard for proteomic analyses due to superior preservation of protein integrity; however, their use is limited by the logistical and financial requirements of long-term storage. Formaldehyde-fixed paraffin-embedded (FFPE) tissues provide a practical alternative owing to their stability and widespread availability in clinical settings. A critical step in FFPE proteomics is deparaffinization, which traditionally relies on organic solvents such as xylene, along with efficient reversal of formaldehyde-induced crosslinks. In this study, we evaluated multiple FFPE protein extraction and digestion workflows including chaotropic, surfactant-based, and detergent-free approaches in combination with xylene-free deparaffinization strategies, using label-free data-independent acquisition (DIA) LC-MS/MS. Among the tested methods, a chaotropic-, reductant-, and surfactant-free in-solution digestion workflow demonstrated robust protein and peptide recovery. A modified version of this protocol further improved peptide coverage while maintaining comparable protein depth. The applicability of the optimized workflow was assessed using FFPE needle biopsy samples from control, hepatic steatosis, and liver fibrosis groups. Distinct proteomic patterns were observed across conditions, with hepatic steatosis associated with early activation of stress-response pathways, while fibrosis showed evidence suggesting altered lipid metabolism. Overall, this study presents a simple, xylene-free, and MS-compatible workflow for FFPE proteomics that is suitable for low-input clinical samples and may support broader application of archival tissues in proteomic research.

NanoHILIC/MS/MS provides high sensitivity for low-input peptide analysis, yet its use in bottom-up proteomics has been constrained by a persistent solvent mismatch: tryptic peptides exhibit poor solubility in the [&ge;]95% acetonitrile (ACN) required for nanoHILIC injection. Our previously reported two-step solubilization method [Anal Chem 2025, 97 (19), 10227-10235] alleviated this issue but required large dilution volumes, limiting the amount of sample that could be injected. Here, we introduce DiReCT (Dissolution from Reverse-Phase Chromatography Tips), a StageTip-based workflow that integrates peptide solubilization, desalting, and nanoHILIC-compatible elution into a single operation. During elution from RP-StageTips, residual water on the stationary phase is rapidly displaced by a small volume of high-ACN solvent, generating a transient mid-ACN environment that maximizes peptide solubility without drying. This mechanism enables high-recovery peptide concentration and allows direct injection of the entire eluate onto nanoHILIC/MS/MS. Using [~]0.25 ng of HeLa digest, DiReCT/nanoHILIC/MS/MS identified 1177 peptides and 410 proteins, representing 8.9- and 6.7-fold increases over nanoRPLC/MS/MS, respectively.

Multiplexed imaging approaches of various molecular modalities in tissues are becoming increasingly adopted in discovery and translational studies. For clinical implementation, novel instrumentation, complex analysis workflows and high costs per sample are bottlenecks that hinders broader introduction in the clinical setting. Here, we demonstrate a cost efficient integrated workflow that combines multiplexed immunofluorescence of a handful of protein markers, with in situ proximity ligation assay, to detect direct protein interactions between neighboring cells. As a proof of concept case of relevance for clinical adaptation, we target the major immunotherapy signalling axis of programmed death receptor 1 (PD-1) and its ligand PD-L1, to demonstrate the interaction between immune cells in germinal centers of tonsil tissue and in a tertiary lymphoid structure in bladder cancer tissue, respectively, from a patient treated with immunotherapy.

Informatics is essential in metabolomics to analyze and interpret complex data for the advancement of biological insights. However, many current data-processing tools are time-consuming, require careful parameter selection, and depend heavily on user expertise, making reproducibility a challenge. To address these challenges, we developed maxiM/Ze, a Python-based application that utilizes image recognition algorithms to process liquid chromatography-high resolution mass spectrometry (LC-HRMS) metabolomics data prior to statistical analysis. The software implements an automated sequential pipeline that includes mass detection, extracted ion chromatogram (EIC) generation, peak alignment, and data visualization. By converting extracted ion chromatograms into PNG images, maxiM/Ze applies image processing techniques from OpenCV, including Canny edge detection, watershed segmentation, and Pearson correlation-based clustering, to align peaks across samples with minimal user input. Validation against Compound Discoverer 3.4 and mzmine 4.8.30 using eight replicate pooled plasma samples demonstrated competitive feature detection (12,067 features), annotation (219 unique compounds), and reproducibility (median CV of 35.8%) across platforms. The application is prepared for release on both Mac OS and Windows platforms, with the goal of improving reproducibility in metabolomics data analysis.

Many proteomics protocols rely on enzymatic digestion of complex protein mixtures to generate peptides with predictable cleavage patterns for the mass spectrometry analysis. One of the most utilized enzymes, trypsin, is classically defined as a serine endopeptidase with high specificity for cleaving peptide bonds on the C-terminal side of internal lysine and arginine residues. Accordingly, trypsin is not expected to remove the N-terminal arginine, which may arise through posttranslational modification such as arginylation or by proteolysis exposing internal residues as the new N-termini. N-terminal arginine plays important biological roles, including functioning as an N-degron and modulating protein interactions/signaling through its positive charge. Curiously, prior mass spectrometry-based studies utilizing trypsin to identify proteins bearing N-terminal arginine have frequently reported low and inconsistent yields, suggesting potential systematic bias in current proteomic approaches. Here, we explored whether trypsin would affect the integrity of the N-terminal arginine. By using antibodies specifically recognizing N-terminal arginine of different peptides, and by using mass spectrometry peptide analysis, we show that trypsin can remove N-terminal arginine residues in an exopeptidase-like manner. This effect occurs across a range of digestion conditions consistent with standard proteomic workflows, on peptides or whole proteins, and depends on trypsin concentration, incubation time, and catalytic activity. In addition, we show that the alternative arginine-cleavage enzyme Arg-C can also affect N-terminal arginine in a sequence-dependent context. In contrast, Lys-C and LysargiNase do not exhibit such effects, providing suitable alternative digestion strategies. Together, these findings reveal an unappreciated enzymatic behavior of arginine-cleaving proteases and suggest that their widespread use may systematically compromise the detection of N-terminal arginine in proteomic studies.

Transfer RNA methyltransferase 1 (TRMT1) installs N2-methylguanosine and N2,N2-dimethylguanosine modifications at position 26 of mammalian tRNAs, supporting tRNA structure, translation, and cellular response to redox stress. However, the local environment and interactome of TRMT1 in the cell is poorly defined. Here, we use APEX2-based proximity labeling of the N- and C-terminus of TRMT1, coupled with label-free quantitative proteomics to map candidate TRMT1-proximal proteins in HEK293T cells. Mass spectrometry data was acquired using both data-independent acquisition (DIA) and data-dependent acquisition (DDA) methods, and it was found that DIA substantially increased proximity proteome coverage, reproducibility, and the number of significantly enriched candidate hits compared to the DDA method. N- and C-terminal APEX2-TRMT1 constructs captured largely overlapping proteomes, suggesting the dual-labeling strategy provides a robust map of proximal proteins. Analysis of the significant TRMT1-proximal proteins reveals enrichment in RNA processing and ribonucleoprotein-associated factors, in addition to hits connected to tRNA modification, tRNA biogenesis, and redox-associated biology. These data provide a proteome-scale view of TRMT1-associated cellular proteins and environments, and lay the groundwork for future validation of functional TRMT1 interaction networks. SignificanceO_LIFusing APEX2 enzyme to both N-terminal and C-terminal of the bait enhanced the sensitivity for identification of protein interactions. C_LIO_LICombining APEX2-based endogenous labeling with DIA mass spectrometry increases reproducibility and depth of proximity proteome. C_LIO_LIThe study provides a rich source of potential interacting or proximally close proteins to TRMT1, which warrants further validation studies. C_LI