Molecular & Cellular Proteomics

Peptide-spectrum match (PSM) rescoring has become standard in proteomics workflows, improving peptide identification accuracy across diverse search engines. Despite the availability of multiple rescoring strategies, systematic comparisons spanning several search engines, datasets, and database configurations remain limited. Here, we benchmarked seven publicly available search engines, evaluating standard target-decoy-based false discovery rate (FDR) estimation alongside Percolator, MS2Rescore, and Oktoberfest across four datasets acquired on different mass spectrometry platforms and searched against protein databases of varying size and composition. Rescoring substantially increased identification consensus and reduced variability between search engines, with prediction-based approaches yielding the largest gains. While database size had limited impact for human datasets, it significantly affected identification rates on a metaproteomic dataset. Entrapment-based evaluation indicated generally adequate FDR control across methods, although prediction-based rescoring exhibited a slightly higher tendency toward FDR underestimation in specific configurations. Overall, advanced rescoring strategies harmonize peptide identification outcomes across search engines, thereby enhancing robustness and comparability in proteomics analyses. However, careful feature selection and appropriate database choice remain essential to ensure reliable FDR control and optimal performance across diverse experimental settings.

Single-cell proteomics (SCP) offers direct insight into functional protein states that drive cellular heterogeneity, complementing genomic and transcriptomic analyses. Although recent reports have demonstrated improved proteome coverage, their reliance on specialized instrumentation limits broader adoption. Additionally, current evaluation practices remain largely centered on protein and peptide identification counts, which alone do not fully reflect data quality or biological interpretability. Here, we describe an accessible, label-free SCP workflow which implements easily accessible laboratory equipment: a single-cell dispenser, conventional multiwell plates, and an incubator with water-bath-based humidity control. Using trapped ion mobility spectrometry-time-of-flight mass spectrometry (timsTOF), we systematically optimize key sample preparation variables, including trypsin concentration, incubation time, reduction/alkylation, digestion conditions, and plate types, which together maximize data quality and reproducibility. We further introduce a data-quality framework that moves beyond identification counts, emphasizing quantitative consistency and biological interpretability via individual protein coverage completeness across cells, coefficients of variation across technical replicates, peptide-to-protein ratios, and single-cell-to-bulk correlations. Collectively, our approach lowers technical barriers to accessing SCP while enabling more rigorous, interpretable, and scalable SCP analysis across diverse research contexts.

Clusterin is an extracellular chaperone protein involved in maintaining proteostasis by binding to unfolded or misfolded proteins to prevent their aggregation. Its chaperone activity plays a significant role in human health, as seen in Alzheimers disease, where clusterin co-localises with amyloid-{beta} plaques to facilitate their clearance. However, its strong tendency to "cluster" with a wide range of client proteins and its low abundance in biological matrices complicate its characterisation. To enable comprehensive analysis of clusterin in human plasma, we established two affinity-based purification workflows, one for targeted clusterin purification and a second for interactome analysis, combining affinity enrichment with bottom-up proteomics. Systematic optimisation of the purification workflow revealed that disrupting ionic and hydrophobic protein-protein interactions was essential to improve clusterin enrichment while minimising co-purification. In contrast, preserving native protein-protein interactions enabled clusterin interactome analysis using affinity purification-mass spectrometry (AP-MS). Bottom-up proteomics profiling identified proteins within the clusterin interactome involved in immune response, hemostasis, and high-density lipoprotein (HDL)-related pathways. Together, these complementary workflows enable targeted clusterin purification and systematic characterisation of its interactome, offering new insights into clusterins biological roles in human plasma.

1Single-cell proteomics (SCP) enables direct measurement of cellular heterogeneity during dynamic biological processes. Here, we applied an SCP workflow to investigate proteome diversity during nerve growth factor (NGF)-induced differentiation of PC12 cells. Differentiated PC12 cells are highly adherent and prone to aggregation, complicating single-cell sample preparation. To address this challenge, sample handling was optimized using gentle dissociation, anti-adhesive conditions, and rapid processing immediately prior to cell isolation. Individual cells were deposited using a refined thermal inkjet (TIJ) dispensing system, enabling accurate single-cell placement with minimal sample loss. Inclusion of the mild nonionic surfactant n-dodecyl-{beta}-D-maltoside (DDM) improved recovery of membrane-associated and other low-solubility proteins. Coupled with high-sensitivity liquid chromatography-ion mobility-mass spectrometry, this workflow consistently quantified approximately 2,000-3,000 proteins per cell across differentiation stages. Single-cell proteomic profiles acquired over the differentiation time course revealed clear separation between undifferentiated and NGF-treated cells by Day 6. At later stages (Days 4-6), cells further partitioned into two distinct subpopulations with protein expression patterns not evident in bulk measurements. Dimensionality reduction and non-negative matrix factorization identified multiple proteomic states coexisting within the same differentiation stages, characterized by coordinated differences in pathways related to intracellular trafficking, protein translation, and neuronal structural organization. Together, these results show that while global proteome remodeling during PC12 differentiation is captured in both bulk and single-cell data, single-cell proteomics uniquely resolves functionally distinct cellular subpopulations that are masked in population-averaged analyses.

Recent advances in liquid chromatography-mass spectrometry (LC-MS) have accelerated the adoption of high-throughput workflows that deliver deep proteome coverage using minimal sample amounts. This trend is largely driven by clinical and single-cell proteomics, where sensitivity and reproducibility are essential. Here, we extend our previous benchmark dataset (PXD028735) using next-generation LC-MS platforms optimized for rapid proteome analysis. We generated an extensive DDA/DIA dataset using a human-yeast-E. coli hybrid proteome. The proteome sample was distributed across multiple laboratories together with standardized analytical protocols specifying two short LC gradients (5 and 15 min) and low sample input amounts. This dataset includes data acquired on four different platforms, and features new scanning quadrupole-based implementations, extending coverage across different instruments and acquisition strategies. Our comprehensive evaluation highlights how technological advances and reduced LC gradients may affect proteome depth, quantitative precision, and cross-instrument consistency. The release of this benchmark dataset via ProteomeXchange (PXD070049 and PXD071205), allows for the acceleration of cross-platform algorithm development, enhance data mining strategies, and supports standardization of short-gradient, high-throughput LC-MS-based proteomics.

The unfolded protein response (UPR) helps reinstate cellular proteostasis upon an accumulation of misfolded proteins in the endoplasmic reticulum (ER), in part through ER-associated degradation (ERAD). Ube2j2 is an ER-localized E2 ubiquitin-conjugating enzyme that participates in ERAD. We used mass spectrometry analysis of cultured U2OS cells to investigate how the loss of Ube2j2 affects the cellular proteome in response to tunicamycin-induced ER stress. We constructed a network of twelve statistically distinct modules of protein abundance profiles across conditions. We describe the Gene Ontology annotations for each module along with the "hub gene" proteins whose abundance levels most closely adhere to each modules protein abundance profile. Our analysis identifies known Ube2j2-associated pathways (e.g., the UPR and ERAD) and cellular functions that were previously unassociated with Ube2j2 (e.g., RNA metabolism, ER-Golgi transport, and cell-cycle progression). These data are available via ProteomeXchange with identifier PXD076153 and provide avenues for further investigation into the cellular functions of Ube2j2 under basal and ER-stressed conditions.

Quantitative evaluation of protein turnover in human neurons is crucial for understanding neuron homeostasis and guiding drug development for neurological diseases. However, measuring protein turnover in postmitotic neurons remains challenging due to the high dynamic range of protein half-lives and limited proteome coverage in SILAC (Stable Isotope Labeling by Amino acids in Cell culture) experiments. Despite broad applications of dynamic SILAC proteomics to measure protein turnover in rodent tissues and primary neurons, few studies have measured protein half-lives in human neurons with limited proteome coverage. Here, we established a comprehensive platform to quantify protein half-lives in human induced pluripotent stem cell (iPSC)-derived neurons. By integrating optimized dynamic SILAC labeling in human neuron cultures, extensive peptide fractionation, optimized data-dependent and data-independent LC-MS/MS acquisition methods, and a streamlined computational pipeline, we achieved deep and accurate measurement of 10,792 protein half-lives from 162,854 unique peptides. We then compared the protein turnover and abundances in iPSC-derived glutamatergic cortical neurons and spinal motor neurons, revealing globally conserved proteome dynamics alongside subtype-specific differences consistent with specialized neuronal functions. To enable broad community access, we created NeuronProfile (www.neuronprofile.com), an interactive web platform for exploring protein turnover, abundance, and subcellular location in human neurons. Together, this work provides a comprehensive analytical platform to assess human neuronal proteostasis and a foundational resource for neurological disease research and therapeutic development.

Understanding allelic variability is crucial for elucidating intrinsic bacterial mechanisms and distinguishing phenotypic profiles. However, such variability poses a major challenge for the reliable identification of proteins in data-independent acquisition (DIA) proteomics. To address this, we developed an analytical workflow that integrates protein sequence variability to enhance proteome coverage. Fifteen Legionella pneumophila isolates were analyzed using DIA-NN, with spectral libraries generated either from a reference proteome or incorporating allelic variability. Our workflow includes protein clustering and subsequent protein inference from these clusters, allowing the accurate assignment of shared and variant-specific peptides. Integration of variability enabled the identification of a comparable number of proteins as the reference proteome while capturing between 28 and 77 % of variant-specific sequences in each isolate, all while maintaining a low false positive rate. These findings demonstrate that accounting for allelic variability substantially improves proteomic coverage and identification confidence, providing a more comprehensive view of the proteome. This approach facilitates a deeper understanding of biological mechanisms and enables precise bacterial proteotyping of Legionella pneumophila isolates.

Single-cell proteomic (scProteomic) measurements of peripheral blood mononuclear cells (PBMCs) are of considerable value in human health, given their involvement in the maintenance of healthy and diseased states. However, the high heterogeneity and relatively small size of immune cell types demand maximal throughput and sensitivity in proteomic measurements that have yet to be fully realized. Here, we describe an approach that addresses sensitivity and throughput through the implementation of Real-Time spectral Library Searching (RTLS), TMTpro 32-plex labelling, an updated nested-nanodroplet processing in One pot for Trace Samples (N2), and a dual-column liquid chromatography system. By prioritizing tandem mass spectrometry (MS2) features with high similarity to library spectra, RTLS enables greater identification depth and feature reproducibility than a standard shotgun MS2 approach in low-input and single-cell samples. The platform permitted 660 single PBMCs to be measured per day, with an average of 750 protein identifications per cell and 1,648 proteins in total, achieving the necessary throughput and depth to characterize immune cell populations. Application of this scProteomic method and a new cell typing informatics approach to 2,130 PBMCs enabled the identification of both major and low-frequency cell types ([~]1-2%), as well as associated proteomic markers.

Although protein arginine methylation regulates diverse biological processes, it remains understudied relative to other post-translational modifications. Here, we analyzed C. elegans prmt-1 and prmt-5 null mutants using LC-MS proteomics to map PRMT methylation substrates and to quantify the effects of PRMT knockout on global protein abundance. High-pH strong cation exchange fractionation was used to enrich methylated peptides, and parallel analysis of whole cell lysates was used to measure global protein abundance. Quantitative methyl-proteomics identified 31 PRMT-1-dependent methyl-arginine peptides from 15 proteins with several arginine residues demonstrating dramatic decrease in both monomethyl- and asymmetric dimethyl-arginine abundance. Whole-proteome profiling revealed that prmt-1 knockout caused broad remodeling of the worm proteome with changes linked to DNA replication/cell-cycle programs, protein folding, and amino acid metabolism. Although prmt-5 knockout affected similar biological pathways to prmt-1 knockout, the effects on the C. elegans proteome were more modest. Together, these data connect PRMT-dependent methylation changes to proteome remodeling in a whole-animal model, support previous work suggesting that PRMT-1 is the dominant Type I PRMT in C. elegans, and provide a resource for studying how PRMT-1 and PRMT-5 shape protein regulation in vivo. All raw data have been deposited in the PRIDE database with accession number PXD074042.

A recent clinical study tested the effects of two different monoclonal antibodies (mAbs) (siltuximab, anti-IL6; tocilizumab, anti-IL6R) on the fate and function of T-cells in people with type 1 diabetes. While both mAbs affect the response of T-cells to stimulation, they have very different, sometimes opposing mechanisms. Here, we use mass-spectrometry based proteomics to analyze longitudinal serum samples (baseline and two weeks post-treatment) from 20 clinical trial participants to examine the effects of siltuximab and tocilizumab on extracellular vesicles. To accomplish this, serum samples were enriched for extracellular vesicles with Mag-Net and analyzed by LC-MS/MS to identify significantly differentially abundant protein groups and pathways. Proteome analysis confirmed highly reproducible measurements across multiple draw dates. In total, we quantified >3300 protein groups of which 46 protein groups had significantly altered abundance after mAb treatment. Tocilizumab altered pathways associated with proteostasis (neddylation) and pre-notch transcription and translation. Siltuximab altered FCGR activation pathway members. In addition, quantitation of the monoclonal antibody therapies themselves enabled the measurement of the correlation between drug amounts and impacted proteins. Taken together, this work demonstrates the utility of the Mag-Net method to evaluate the impacts of therapeutic interventions on serum extracellular vesicles.

Chemoproteomics is a popular approach for the identification of small molecule-protein interactions in biological systems. Several chemoproteomics workflows leverage functionalized chemical probes and mass spectrometry to measure protein engagement through direct protein enrichment or competition using a range of small molecule concentrations. Statistical methods for analysis of such dose-response chemoproteomics datasets are limited. For example, existing methods rely on fixed curve shapes and are sensitive to experimental variation, particularly when the number of doses or replicates is limited. Here, we present MSstatsResponse, a semi-parametric statistical framework for analyzing chemoproteomic dose-response experiments that uses isotonic regression that does not require a fixed curve shape. This approach improves the accuracy and robustness of curve fitting, target identification, and half-response estimation across diverse experimental designs. We evaluate MSstatsResponse by generating a benchmark chemoproteomic dataset that profiled the competition between the kinase-binding probe XO44 and the drug Dasatinib using three mass spectrometry acquisition strategies: data-independent acquisition, tandem mass tag-based data-dependent acquisition, and selected reaction monitoring. We further evaluate the method on simulated datasets that vary the number of doses, number of replicates, and levels of noise, and demonstrate that MSstatsResponse consistently improves sensitivity, specificity, and reproducibility compared to existing methods, particularly in low-replicate and low-dose settings. MSstatsResponse is implemented as an open-source R/Bioconductor package that integrates with the MSstats ecosystem for quantitative proteomics. It provides a unified workflow for preprocessing, curve fitting, target identification, and experimental design, enabling researchers to select the number of doses and replicates appropriate to their experimental goals. The software and documentation are freely available at https://bioconductor.org/packages/MSstatsResponse.

Single-cell proteomics (SCP) is uniquely suited for cell-type characterization, trajectory-based inference, and microenvironment mapping. Evaluating biological hypotheses in these experiments requires labeled cells. Without a pre-measurement label, machine learning is used to identify features that characterize the cell types and classify unlabeled samples. Current implementations of annotation methods come with several statistical and computational disadvantages. First, machine-learning methods require complete data, leading to large amounts of missing-value imputation in SCP. Additionally, some machine-learning methods select features and classify samples via cross-sample comparisons, nullifying downstream cross-sample comparisons, like differential expression, through double dipping. Finally, measurements from different proteomic experiments are not directly comparable due to batch effects, significantly limiting the accuracy of classifiers trained on external data. Here we present NIFty, a top-scoring pairs based feature selection method, implemented in a full classification pipeline, that does not require pre-imputed data as input or employ circular analysis techniques, and overcomes batch effects without batch correction. When tested on imputed vs unimputed data, data with large batch effects, and multiclass data, NIFty successfully overcame the targeted classification challenges and comparably, or more accurately, classified the samples in the varied datasets.

Post-translational modification with chains of the 76-amino-acid protein ubiquitin ( poly-ubiquitylation) confers diverse fates to the targeted protein and non-protein substrates, including degradation, intracellular trafficking, and signal transduction. Despite being one of the most frequent modifications, the complexity of poly-ubiquitin chains adds methodological challenges to their characterization. Trypsin-resistant tandem-ubiquitin-binding entities (trTUBEs), engineered from natural ubiquitin-binding domains, can capture intact poly-ubiquitylated proteins with high cumulative avidity. However, such approaches have suffered from considerable co-eluting contaminants in proteomics applications. Here, we introduce an optimized trTUBE-based method for poly-ubiquitylated proteome purification, drastically depleting non-ubiquitylated protein contaminants and mono-ubiquitylated proteins. The method, termed Ubiquitylomics by Stringent, Cleavable, Affinity-based Proteome Extraction (Ubi-SCAPE), offers a streamlined and reproducible (median R2 > 0.98; CV < 8%) means of characterizing the poly-ubiquitylome. Over 7,800 poly-ubiquitylated proteins and 8,500 ubiquitin-modified peptides (diGly) were quantified with Ubi-SCAPE at a throughput of 40 samples per day, as well as over 6,000 from an equivalent of 33 g unstressed cell lysate. Upon acute stress by heat-shock, we identified 2,700 proteins and 8,000 diGly peptides with increased poly-ubiquitylation--offering similar biological insight as with far more material-, labor-, and cost-intensive peptide-based ubiquitin enrichment methods. Ubi-SCAPE therefore provides a simple and effective means of comprehensively quantifying a selective-enriched poly-ubiquitylome. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/703496v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@a28120org.highwire.dtl.DTLVardef@cb801aorg.highwire.dtl.DTLVardef@47577aorg.highwire.dtl.DTLVardef@1c030af_HPS_FORMAT_FIGEXP M_FIG C_FIG

Combination therapies are widely used in acute myeloid leukemia (AML), but systematic datasets capturing proteome-wide responses to multi-drug perturbations remain limited. Here we present CoPISA (Combinatorial Proteome Integral Solubility/Stability Alteration), a quantitative proteomics assay designed to profile protein solubility and stability responses to single and combined drug treatments. The dataset includes two AML drug pairs (LY3009120-sapanisertib and ruxolitinib-ulixertinib) applied to four AML cell lines (MOLM-13, MOLM-16, SKM-1, and NOMO-1) under control, single-agent, and combination conditions in both lysate and intact-cell formats. Thermal solubility profiling coupled with TMT-based multiplexed LC-MS/MS generated 16 TMT16-plex experiments comprising 192 LC-MS/MS raw files, providing deep proteome coverage across treatments and biological contexts. The resource includes raw and processed proteomics data, detailed experimental metadata in Sample and Data Relationship Format (SDRF), and reproducible analysis scripts for reporter normalization, protein-level aggregation, statistical modeling, and classification of combinatorial response patterns. The experimental design enables identification of proteins responding uniquely to combination treatments as well as overlapping single-agent effects. Technical validation demonstrates reproducible quantification across multiplex experiments and assay formats. All data are publicly available through the PRIDE repository (PXD066812) together with analysis code, enabling independent reanalysis and method development. This dataset provides a benchmark resource for studying proteome responses to drug combinations, comparing lysate and intact-cell perturbation profiles, developing computational approaches for combinatorial target inference, and supporting training in computational proteomics.

Data-independent acquisition (DIA) proteomics enables reproducible, large-scale protein identification and quantification but remains challenging to analyze due to highly complex MS/MS spectra and chromatographic interference, particularly in low signal-to-noise single-cell proteomics. Here, we introduce ChatDIA, a zero-shot large language model (LLM)-based workflow for targeted DIA analysis that operates through an explicit reasoning-based decision framework. ChatDIA performs automated peptide identification and supports natural-language interaction with DIA data. Unlike purpose-built DIA software that relies on domain-specific models, ChatDIA employs general-purpose LLMs in a zero-shot setting to reason directly over extracted ion chromatograms and generate human-interpretable rationales for each decision. On an expert-annotated Streptococcus pyogenes DIA benchmark dataset, ChatDIA achieved 96.9% accuracy, matching the domain-specific state-of-the-art software DIA-NN (95.5%). In a challenging single-cell HEK-293T DIA proteomics dataset, ChatDIA further demonstrated excellent performance, achieving a lower risk-coverage area under the curve than DIA-NN (0.06 vs. 0.12) and identifying 17.5% and 45.25% of library peptides at 1% and 5% false discovery rate, respectively, compared with 16.25% and 48% for DIA-NN. Together, these results demonstrate that zero-shot LLM reasoning can competitively automate core targeted DIA decision-making while providing transparent, inspectable rationales that enable conversational, interactive validation and data exploration in noisy proteomics applications.

Proximity-based proteomics using TurboID has enabled cell-type-specific profiling without the need for cell purification, although major bottlenecks in sample lysis, biotinylated protein enrichment, digestion, and mass spectrometry (MS) parameters have limited depth of proteome coverage. Here, we systematically optimized these variables using TurboID-based labeling of BV2 microglia in vitro and brain astrocytes in vivo to define conditions that maximize proteome coverage. In microglia, the optimized protocol using 8 M urea lysis with on-bead S-Trap digestion and data-independent acquisition MS (DIA-MS) identified 4,016 proteins, double the depth of prior studies, and revealed metabolic, ribosomal, lipid-processing, autophagy, and trafficking signatures. Brain astrocyte proteomes were best recovered using SDS lysis with S-Trap digestion and DIA-MS, yielding a proteome of over 3,600 highly enriched proteins, twice the depth of prior astrocyte-TurboID studies. The expanded astrocyte proteomes captured canonical astrocyte markers as well as membrane-associated, vesicular trafficking, and presynaptic protein signatures, consistent with labeling of astrocyte-neuron interface regions, including proteins involved in receptor signaling, lipid metabolism, and plasticity at tripartite synapses, and several AD risk proteins. The increased peptide recovery following S-Trap digestion allowed the reduction of starting material to 20 {micro}g protein for DIA-MS, and enabled multiplexed tandem mass tag (TMT-MS) proteomics using even smaller samples. When applied to synaptosomes enriched from mouse brains with neuronal TurboID labeling, our pipeline identified a synapse-specific proteome of 2,529 proteins, revealing synaptic, mitochondrial and disease-relevant signatures not detectable in prior studies. By tackling critical bottlenecks from tissue processing to MS, our optimized pipelines enable cell-type and compartment-specific proximity-labeling proteomics to obtain comprehensive biological and disease-relevant insights across various biological fields.

Parallel Accumulation with Mobility-Aligned Fragmentation (PAMAF) achieves near-complete ion utilization and high spectral specificity by fragmenting all mobility-separated precursors without quadrupole isolation. Leveraging the ultrahigh mobility resolution of SLIM, this quadrupole-free strategy maximizes ion utilization efficiency and offers a promising approach in mass spectrometry-based proteomics, particularly for low-abundance peptides or low-input samples. However, the unique data structure of PAMAF where precursor-fragment relationships are encoded along the mobility dimension renders it incompatible with existing peptide identification tools. Here, we present xTracer, the first untargeted peptide identification algorithm developed specifically for PAMAF data. xTracer integrates correlations across both chromatographic and mobility dimensions to associate precursor and fragment ions, reconstruct pseudo-spectra, and enable database searching using well-established DDA search engines. Applied to datasets with varying sample loads and acquisition throughputs, xTracer consistently achieved robust and reproducible peptide identifications, outperforming single-domain correlation strategies. Overall, xTracer provides a versatile and high-efficiency computational framework for reconstructing pseudo-spectra from quadrupole-free, mobility-aligned fragmentation data, enhancing the analytical power of high-resolution ion mobility (HRIM)-based proteomics.

Phosphoproteomics by liquid chromatography/tandem mass spectrometry requires efficient phosphopeptide enrichment, but conventional workflows are often time-consuming and prone to sample loss, particularly at low input. Here, we present Rapid Hydroxy Acid-Modified Metal Oxide Chromatography (Rapid HAMMOC), a streamlined, TiO2-based enrichment workflow that features three key improvements. First, we optimized the TiO2 column loading conditions and found that alternative pH-buffering agents and organic solvents, such as sodium bicarbonate combined with ethyl acetate, outperformed the commonly used Tris-based buffer with isopropanol. Second, to minimize sample loss and manual handling in desalting, phosphopeptides eluted under basic conditions were directly loaded onto a dual-membrane StageTip composed of stacked strong anion exchange (SAX) and reversed-phase styrene-divinylbenzene (SDB) membranes (SAX-SDB StageTip). Third, the addition of lauryl maltose neopentyl glycol (LMNG), which is readily removed during desalting, suppressed nonspecific adsorption. Rapid HAMMOC provided markedly improved sensitivity, identifying approximately 5,000 class I phosphosites from 5 g of K562 cell digests, with a median 7.9-fold increase in intensity compared to the original workflow. Rapid HAMMOC also identified, on average, approximately 8,000 class I phosphosites from as little as 0.5 g input from HeLa, A549, and HCT116 cells. Furthermore, coupling Rapid HAMMOC with anti-puromycin immunoprecipitation enabled single-day profiling of nascent polypeptides from ultra-low input samples, yielding 2,310 high-confidence co-translational phosphosites. Beyond providing a practical enrichment workflow, this study offers broadly applicable insights that can be extended to other TiO2-based phosphoproteomic methods.

Tandem mass tags (TMT) allow highly multiplexed and thus high-throughput, precisely quantitative proteomic analysis. Incorporation of additional deuterated reporter channels has near-doubled the multiplexation achieved with Thermo Scientific TMTpro reagents from 18 to 35-plex but requires extremely high [~]100k analyzer resolving power at m/z 128 to differentiate and quantify reporter ion channels, far beyond any single reflection time-of-flight analyzer, and exceeding the multi-reflection Thermo Scientific Astral analyzer in its standard operation. A multi-pass mode of Astral operation has been developed for the Thermo Scientific Orbitrap Astral Zoom mass spectrometer that triples the ion path to 90 m, more than doubling resolving power for a narrow m/z range. This "TMT HR mode" has been integrated into a new method of TMT proteomic analysis that splits regular MS2 analysis of labeled peptides into paired measurements comprising wide mass range scans for peptide identification, and TMT HR mode scans for reporter ion quantification. The method has been shown to accurately quantify 32-plex labeled HeLa protein lysate and provide far greater depth of analysis as state-of-the-art Orbitrap-only methods, while analysis of 11-plex labeled yeast showed no analytical depth sacrificed vs regular Orbitrap Astral TMT analysis. Further comparative measurements of a 2-cell line 35-plex sample demonstrated greater analytical depth, and similar quantitative precision, to "gold standard" Orbitrap MS3 methods.