Authors
Shabnam Salimi, Wentao Zhao, Maria Partida-Aguilar, Elle Harwood, Hayley Purcell, Daniel Raftery

Institutions
University of Washington, Department of Anesthesiology and Pain Medicine, North West Metabolomic Research Center, Mitochondrial Metabolism Center

Abstract
Doxorubicin (DOX) is an effective chemotherapeutic agent whose anti-tumor effects are mediated through DNA damage and apoptosis induction in cancer cells. However, DOX also exerts substantial off-target toxicity that contributes to accelerated aging phenotypes, mitochondrial dysfunction, metabolic dysregulation, and reduced healthspan. We hypothesized that DOX induces both short- and long-term metabolomic alterations in skeletal muscle myoblasts and that spermidine or rapamycin may mitigate these metabolic perturbations. Mouse skeletal muscle myoblasts were treated with 0.2 µM doxorubicin, followed by evaluation of metabolomic changes at short-term (3 days) and long-term (10 days) time points using untargeted Q-TOF metabolomics with SERRF normalization and machine learning–based analyses. We additionally evaluated the effects of spermidine and rapamycin treatment on cell viability and metabolomic remodeling following DOX exposure. DOX treatment induced marked alterations in pathways related to NAD+ metabolism, glutamate metabolism, coenzyme A metabolism, oxidative stress, inflammation, vitamin B6 metabolism, pyrimidine and purine metabolism, and mitochondrial function. Short-term exposure demonstrated depletion of NAD+ metabolites, perturbations in nitrogen metabolism, and reductions in vitamin E–related metabolites and biopterin-associated pathways. Long-term exposure further revealed inflammatory prostaglandin signaling and persistent mitochondrial metabolic dysfunction. Spermidine and rapamycin partially reversed several DOX-associated metabolic abnormalities. Both interventions altered pathways related to glutathione metabolism, folate metabolism, amino acid metabolism, mitochondrial energetics, and inflammatory signaling. Methionine isotope tracing experiments demonstrated increased methionine cycle activity, elevated S-adenosylmethionine (SAM), and increased decarboxylated SAM (dc-SAM) following spermidine and rapamycin treatment, suggesting potential epigenetic regulation through altered DNA methyltransferase activity. These findings demonstrate that DOX induces profound metabolic remodeling associated with aging-related hallmarks, including mitochondrial dysfunction, oxidative stress, and altered one-carbon metabolism. Spermidine and rapamycin may represent promising strategies to mitigate chemotherapy-induced metabolic and epigenetic dysfunction. Future studies integrating DNA methylation analyses will further define the molecular mechanisms underlying these protective effects.

Authors
Emma Timmins-Schiffman, Karen Junge, Katarina Abrahamsson, Michael Riffle, Ruben Shtrestha, Brook Nunn

Institutions
University of Washington (ETS, KJ, MR, and BN); University of Gothenburg (KA); Bruker (RS)

Abstract
Despite its austere landscape, Antarctica is home to abundant life, some of which is found in the seasonal sea ice that doubles the size of the continent during the austral winter months. Marine microbes are caught in this massive ocean freezing event and metabolically active communities develop in sea ice brine channels. These sea ice microbes have the genomic potential to participate in a variety of ecologically important nutrient cycling and biogeochemical processes, including the bromocarbon cycle. Bromocarbons are highly concentrated in polar sea ice and some forms, when volatilized, can chemically deplete ozone. However, studies of these microbes are hindered by problems such as field site access, the impermanence of the habitat, a lack of available sequencing resources, the low abundance of microbes in this system, and a lack of knowledge of the variability of species and metabolic processes over space and time. We have developed a method to leverage de novo sequencing of high coverage Bruker timsTOF Ultra2 metaproteomics data from Antarctic sea ice, snow and seawater, combined with a limited sequenced metagenome. Our pipeline allowed for the identification of over 41,000 peptides across a diverse assemblage of ice- and snow-bound microbes, revealing metabolic processes associated with important environmental variables, such as temperature, salinity, and bromocarbon concentration. These discoveries reveal the roles of microbes in an extreme environment that is under threat from global ocean warming.

Authors
Claire E. Elbon1, Gabrielle Chebli2, Emma TImmins-Schiffman1, Micheal Riffle1, Bo Wen1, William Noble1, Brook L. Nunn1

Institutions
1. University of Washington, Seattle, WA. 2. Georgia Institute of Technology, Atlanta, GA.

Abstract
Harmful algal blooms threaten coastal ecosystems, fisheries, shellfish harvests, and public health across Washington State, yet current monitoring approaches largely detect blooms after biomass or toxin accumulation has already occurred. Currently, blooms are primarily defined in “initiation” and “termination” phases, which are determined by rate changes in chlorophyll in the water column. A major unresolved challenge is determining the full suite of bloom phases; including why, when, and how they initiate, terminate, and the duration of a bloom. Understanding molecular signals at the protein-level of the co-associated bacterial and viral community provides a unique opportunity to define bloom phase transitions, particularly whether bloom decline reflects viral lysis, algal physiological collapse, dormancy, or bacterial restructuring of bloom-derived organic matter. Here, I use a high-frequency multi-omic time series collected every four hours over 22 days in East Sound, Washington, to connect microbial community state changes with active protein-level function during harmful algal bloom succession. Initial analysis of 133 bacterial and viral (1-0.22 micron filter) metagenomic time points revealed that the microbial community organizes into four distinct ecological states across the bloom time series. These states do not simply mirror chlorophyll fluorescence, suggesting that microbial and viral succession captures underlying bloom-phase structure that is not visible from biomass alone. I use these metagenome-defined ecological states as the framework for interpreting high-resolution DIA metaproteomics across the full time series. Metaproteomic samples were generated using a novel high-throughput, low biomass S-Trap 96 well plate preparation method and run using 20-minute gradients on an Orbitrap Astral Mass Spectrometer optimized for our complex environmental samples. For each metaproteomic sample, I will search DIA spectra against a time-matched metagenome through a collaborative workflow that integrates metagenomic and metaproteomic analyses. The resulting data are processed using Carafe3 (a prototype developed by Bo Wen) which reduces metagenome-derived protein databases by retaining only nonredundant proteotypic peptides, enabling deep peptide detection and protein-level interpretation across microbial and viral communities with minimal desktop GPU computing. By aligning peptide abundance patterns with the four metagenomically defined ecological states, I will identify the functions that distinguish bloom initiation, maintenance, decline, and post-bloom restructuring. Specifically, I will evaluate viral infection through structural and replication proteins, stress and mortality through oxidative-stress, proteolytic signatures, dormancy or persistence through cell-wall remodeling and stress-tolerance proteins, and processing of bloom-derived biomass through carbohydrate degradation, peptide degradation, respiration, and nutrient-scavenging pathways. This phase-guided metaproteomic framework will test whether bloom transitions are marked by reproducible peptide-level signatures and whether specific molecular processes precede visible bloom decline. Together, this work moves harmful algal bloom monitoring beyond reactionary water sampling toward a mechanistic, protein-level understanding of microbial ecosystem transitions. By using metagenomic ecological states to guide interpretation of high-resolution DIA metaproteomics, this project establishes metaproteomics as a powerful tool for resolving active microbial and viral controls on coastal bloom dynamics.

Authors
Julien Margaret A. Dagan, Alesi R. Escobedo, Anran Yu, Miklos Guttman

Institutions
University of Washington

Abstract
Hydrogen Deuterium Exchange-Mass Spectrometry (HDX-MS) is routinely employed for probing protein structures and conformational dynamics. However, its reproducibility is limited by back-exchange after following labeling, which complicates exchange rate measurements and data interpretation. While most back-exchange occurs in solution or during ionization, some studies have also observed deuterium loss post-ionization within the MS. We hypothesize that ambient water is entering the MS and contributing to deuterium loss. Here, we quantitatively track deuterium loss using EDTA and P1 peptide to systematically evaluate water vapor-driven back exchange in both negative and positive modes across five widely used MS platforms: Thermo Orbitrap Tribrid (Ascend and Eclipse), Waters Synapt G2-Si, Thermo Finnegan LTQ, and Bruker timsTOF fleX. This study aims to define the magnitude of water vapor effects and highlight platform-dependent differences.

Authors
Anais Gentilhomme1,  Chris Hsu1, Bo Wen1, Justin A. Sanders1, William S. Noble1, Laura Zelter2, Michael Riffle1, Susan Q. Lang3, William J. Brazelton4, and Brook L. Nunn1

Institutions
1University of Washington (Department of Genome Sciences) 2Florida State University (Department of Chemistry & Biochemistry) 3Woods Hold Oceanographic Institution (Department of Geol. and Geophys. and Marine Chem. and Geochem.) 4Blue Marble Space Institute of Science (Seattle, WA)

Abstract
We present current efforts in an ongoing investigation to develop and evaluate low-input metaproteomic workflows tailored to Lost City Hydrothermal Field (LCHF) chimneys, aiming to identify expressed microbial functions. LCHF is an ultramafic, serpentinizing submarine hydrothermal vent system characterized by high pH (9-11), temperatures of ~100℃ in venting fluids, and elevated concentrations of formate, methane, and sulfide that can support C1-metabolizing organisms (Kelley et al. 2005, Brazelton et al., 2006). These characteristics make LCHF an ideal study site and analog for serpentinizing systems predicted to be on icy ocean worlds such as Europa and Enceladus. Metaproteomics allow us to both detect and measure microbial functional responses to environmental conditions. Metagenomic datasets for LCHF chimneys and venting fluids are available (Alian et al., 2025, Brazelton et al., 2022), but no metaproteomic studies have been reported. The goal of this study is in two phases, to first identify chemosynthetic-associated peptides within these chimneys and the second is to then apply a targeted proteomics approach on 1.5 km of LCHF uncharacterized rock core to detect peptides that are relevant for life detection in the subsurface of ocean worlds with serpentinizing hydrothermal systems. Samples from five different active LCHF chimneys and three carbonate veins were sampled during the 2018 ROV Jason Lost City expedition (i.e. Brazelton et al., 2022, Moore et al., 2021). These samples are not only composed of a complex matrix but also have low cell counts ( < 2E76 cells/g of chimney rock) and therefore have inherently low protein concentration (20-100ng total protein). For phase one metaproteomic investigation, one of the chimneys with the highest amount of predicted biomass, Sombrero, was selected for methods and search development and prepared in technical triplicate. The protein fraction was isolated, and the cellular biomass solubilized by a combination of mechanical and chemical disruption using an in-house EDTA/SDS solution. Proteins were purified using High Recovery S-trap to isolate and wash the proteins embedded in the complex matrix, followed by trypsin digestion to peptides. Then digested peptides were analyzed using an Orbitrap Astral mass spectrometer coupled to a Vanquish Neo UHPLC system and the data were acquired using a data dependent acquisition (DDA). It was searched against metagenome-derived protein database that was chimney-specific and contained the top 100k most abundant contigs using Comet and Percolator (Peptide Spectral Match (PSM) False Discovery Rate < 0.01) where results were visualized and analyzed in limelight and R (4.3.2) (Chambers et al., 2012, Eng et al., 2013, Käll et al., 2007, Riffle et al., 2025a,b). Across these technical replicates, peptide-spectrum matches were detected under strict false discovery control of 0.01, though peptide recovery was low, which is consistent with the low biomass of this system. Despite these challenges, we detected about ~1400 LCFH- associated unique peptide sequences across samples. In addition, we are currently comparing our metagenomic based proteomic identification findings with an agnostic de novo approach to identifying peptides without relying on a reference database. This approach is especially relevant for life detection applications as it presents the evaluation of peptide level biomarkers in environments where genomic context may be absent.

Authors
Reta Birhanu Kitata1*, Zhangyang Xu1, Nadia Bayou2, Rui Zhao3, William B Christler1, Matthew J Gaffrey1, Karl K Weitz1, Mara Serena Serafini2, Elisabetta Molteni2, Vladislav A Petyuk1, Massimo Cristofanilli2, Tao Liu1, Carolina Reduzzi2, Tujin Shi1*

Institutions
1Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA 2Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, NY 10021, USA 3Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, USA

Abstract
With advanced mass spectrometry (MS)-based proteomics, deep proteome coverage can be achieved from bulk cells. However, such bulk measurement obscures cell to cell heterogeneity, precluding proteome profiling of single cells and small numbers of cells. Recent advances in MS-based single-cell proteomics (SCP) have revolutionized the SCP field for comprehensive characterization of cellular heterogeneity. We and others have recently developed multiple convenient SCP methods (e.g., surfactant-assisted one-pot single-cell sample preparation termed SOP) based on standard 96-, 384-PCR well plates or low-bind PCR tube.

However, they have different types of shortcomings. Herein we report an easily adoptable improved Surfactant-assisted One-Pot (iSOP) method enabling robust low-volume single-cell processing on 384-well plate for high throughput SCP.

iSOP-MS capitalizes on processing of single cells at an easily manageable volume of ~3 µL, which can be fully automated for single cells. With a commonly accessible LC-MS platform, iSOP-MS quantified ~1,200–1,800 protein groups from single HeLa or MCF7 cells. Application of iSOP-MS to two neuroblastoma cell lines has enabled reliable identification of an average of ~1,700 and ~2,050 protein groups from single BE2-C and SK-N-SH cells, respectively, and precise characterization of cellular heterogeneity. Overall, iSOP-MS provides a robust and convenient platform for routine, cost-effective, quantitative SCP analysis.

Authors
Tim S. Veth1, Emmajay Sutherland1, David Bergen2, Rafael D. Melani2, Christopher Mullen2, and Nicholas M. Riley1*

Institutions
1Department of Chemistry, University of Washington, Seattle, WA, USA 2Thermo Fisher Scientific, San Jose, CA, USA

Abstract
Glycan heterogeneity is a fundamental property of glycoproteins, but the high degree of glycosite-level heterogeneity leads to technical challenges in measuring glycoproteoforms. A holistic understanding of glycan modification states is critical to translating glycoproteome regulation to biological function, but bottom-up glycoproteomics cannot recapitulate the full ensemble of glycoproteoforms from glycopeptide measurements alone. Promising efforts to profile masses of intact glycoproteins have recently explored data-independent acquisition (DIA) coupled with proton-transfer charge reduction (PTCR) or electron-capture-induced charge reduction mass spectrometry (MS). While valuable for generating broad glycoproteoform mass distributions, these approaches have remained limited in their ability to generate discrete glycoproteoform mass measurements, largely because they rely on low-resolving power measurements and deconvolution that does not account for isotopic information. To this end, we developed a DIA-PTCR workflow that couples high-resolving power (Rp ~240,000 at m/z 200) tandem mass spectra with an open-source processing suite to define glycoproteoform populations within 20 ppm mass accuracy thresholds. We demonstrate the glycoproteoform characterization capabilities of this platform using a collection of glycoproteins with well-described translational interests (EpCAM, TIGIT, CD40, PDL1, and CD24). We showcase how intact glycoproteoform masses acquired using our high-resolving power DIA-PTCR (hRp-DIA-PTCR) approach can be integrated with bottom-up intact glycoproteomics and Direct-Mass Technology (i.e., Orbitrap-based charge-detection MS) acquisitions to inform structural and biological insights. Altogether, our hRp-DIA-PTCR method extends the current capabilities of intact glycoprotein analyses by enabling robust characterization of isotopically resolved proteoforms and facilitating deep biological interpretation of glycosylation heterogeneity. Our open-source informatics platform includes a GUI-based tool called PTsliCR to clean PTCR scans directly from DIA-PTCR raw files and a deconvolution R package called IsoTrac, both of which are freely available on GitHub at https://github.com/riley-research.

Authors
Matthew D. Berg, Alexis Chang, Kyle Hess, Ricard A. Rodriguez-Mias, Judit Villén

Institutions
Department of Genome Sciences, University of Washington, Seattle, WA, USA

Abstract
DNA sequencing has identified millions of natural genetic variants that alter protein sequence. However, determining the functional impact of these variants remains challenging. Traditional mutagenesis approaches are not scalable for millions of variants and high-throughput approaches such as deep mutational scanning are limited to investigating one protein per experiment. To address this, our lab has developed Miro – a high-throughput proteomic approach to functionally annotate the impact of missense mutations across entire proteomes. In this approach, stochastic errors in protein synthesis are induced to create amino acid substitutions throughout all expressed proteins within a cell. Biochemical selections that probe general protein properties like solubility, thermal stability, ligand binding, protein-protein interactions and post-translational modifications are then applied to the collection of protein variants. After selection, variants are quantified by mass spectrometry to determine the functional impact of each mutation on the measured property. Here, we harness a collection of tRNAs that we engineered to mis-incorporate alanine at non-alanine codons and determine the impact of over 50,000 alanine substitutions on the thermal stability of more than 2000 yeast proteins. We find impactful substitutions are enriched at protein-protein interfaces and uncover both known and novel residues that facilitate these interactions. Our work establishes proteome-scale alanine scanning as a powerful approach for mapping mutational sensitivity and for uncovering the structural and functional determinants of protein thermal stability.

Authors
Lauren Fields1, Bo Wen1, Chris Hsu1, Deanna L. Plubell2, Philip M. Remes2, Lilian R. Heil2, Michael J. MacCoss1

Institutions
1Department of Genome Sciences, University of Washington, Seattle, Washington, 2ThermoFisher Scientific, San Jose, CA, United States

Abstract
Introduction Despite the widespread adoption of data-independent acquisition (DIA), linear ion trap (LIT) instruments have been largely overlooked in favor of high resolution accurate mass (HRAM) instruments. LITs have several strengths, including fast scan speed and sensitivity, alongside practical benefits of being cost effective. While challenges remain in adapting unit-resolution instrumentation to DIA proteomics, we show that the acquisition speed of the Thermo Stellar MS, combined with emerging computational strategies, can minimize limitations of instrument mass accuracy. We demonstrate a stepwise approach to improve DIA performance by constraining search space, correcting local space charge effects, and refining retention time (RT) predictions, achieving improved precursor and protein detections. These results highlight the potential of unit-resolution platforms for DIA proteomics. Methods Tryptic peptides from HeLa cell lysates were analyzed by the Thermo Scientific Orbitrap Astral and Stellar MS using identical LC and analytical column setups to compare analyzer-specific performance. Gas-phase–fractionated DIA acquisitions spanning sequential 100 m/z windows were collected to generate spectrum libraries. A machine learning strategy was developed to correct local space charge effects and improve mass measurement accuracy of Stellar data. Performance was benchmarked across DIA-NN (v.1.8.0; 2.2.0), XCorrDIA, and EncycopeDIA.

DIA-NN v.2.2.0 was optimized for subsequent analyses using MS/MS-only searching with a filtered FASTA database at 1% FDR. Fragment ion intensity predictions were refined through Carafe (v.2) to account for Stellar-specific fragmentation patterns and run-specific RT. Preliminary Data or Plenary Speakers Abstract To adapt DIA frameworks originally designed around HRAM to unit-resolution instrumentation, we implemented a peptide-centric analytical strategy. We benchmarked multiple analysis engines (e.g., DIA-NN, EncyclopeDIA, XCorrDIA), across Stellar datasets, evaluating their reliance on mass accuracy versus alternative measured peptide properties. DIA-NN demonstrated robustness to reduced mass accuracy, leveraging other features (e.g., improved retention time from Carafe) to drive peptide identification. Performance was further improved by restricting analysis to MS/MS-only, eliminating unnecessary dependence on MS1 information, inherently limited on LIT platforms, producing a 74% increase in precursors detected relative to analyses performed with MS1 data. We hypothesized that a high-resolution reference proteome could bridge the performance gap between unit-resolution and HRAM platforms by constraining the peptide search space to experimentally observable candidates. By limiting the query space to peptides detected on the Orbitrap Astral, we reduced the search space while retaining sensitivity for observed peptides. This search space reduction proved impactful, increasing precursor detections by an additional 33% on the Stellar. To improve the mass measurement accuracy, we implemented a lightweight machine learning model designed to correct local space-charge effects and other systematic errors. This model improved the root mean square error by 27%. We addressed fragmentation and RT model mismatches by fine tuning the AlphaPeptDeep-based prediction model using Carafe. This enabled fragment ion predictions that more accurately reflect the behavior of the Stellar MS. Additionally, the RT was improved specific to the column and HPLC setup. These computational refinements yielded an additional 22% increase in precursor identifications, altogether lending an over 3-fold improvement in precursors compared to starting conditions, expanding detections from 18,755 to 59,201 precursors. Ongoing work extends this framework toward quantitative benchmarking, comparing beam-type and resonance CID fragmentation. Collectively, these results demonstrate a practical and scalable pathway for high-throughput DIA proteomics on a nominal linear ion trap instrument. Novel Aspect This work democratizes DIA proteomics, achieving results via a nominal mass linear ion trap, minimizing the gap with HRAM instruments.

Authors
Doudou Yu1,10, Kristine A. Tsantilas1,2,10, Michael Riffle1, Christine C. Wu1, Bo Wen1, Caitlin S. Latimer3, Gary A. Churchill4, Stephanie McGrath5,6,7, Julie A. Moreno5,7,8, William S. Noble1,9*, Michael J. MacCoss1*

Institutions
1Department of Genome Sciences, University of Washington, Seattle, WA, USA. 2Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA 3Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA 4The Jackson Laboratory, Bar Harbor, ME, USA 5Brain Research Center, Colorado State University, Fort Collins, CO, USA 6Department of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO, USA 7Center for Healthy Aging, Colorado State University, Fort Collins, CO, USA 8Department of Environmental & Radiological Health Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO, USA 9Department of Computer Science, University of Washington, Seattle, WA, USA 10These authors contributed equally

Abstract
Aging is a major risk factor for disease across species. Blood plasma, a minimally invasive biopsy of the whole body, offers a window into age-associated conditions such as neurodegeneration. Yet whether and how plasma proteomic signatures of aging are conserved across species remains understudied, for example when comparing inbred laboratory mice with genetically diverse companion dogs that share the environments and health-care systems of their human owners. Here we quantified the plasma extracellular vesicle (EV) proteome across the lifespan in 86 mice and 200 dogs, using Mag-Net, a species-agnostic EV-enrichment method coupled with data-independent acquisition mass spectrometry. Overcoming the dynamic-range challenge of the plasma proteome, Mag-Net measured 5,675 proteins from 63,125 precursors in mice and 6,848 proteins from 87,020 precursors in dogs. Aging clocks built from these plasma EV features accurately predicted chronological age within each species (correlation R = 0.92 in mice and R = 0.83 in dogs) and dementia in dogs, with top features implicating the inflammatory response. Notably, 38 protein features used to predict age in dogs were conserved across both species and have been repeatedly implicated in the aging human plasma proteome. Furthermore, we built a combined dog-mouse clock from shared protein features that predicted age in both species. Together, these results demonstrate that easily collected plasma EV proteomics can identify interpretable, conserved protein signatures of aging, including brain-aging signatures detectable in plasma that predict dementia in dogs. These conserved signatures may serve as cross-species biomarkers for evaluating aging interventions, streamlining a translational pipeline from laboratory mice to companion dogs and, ultimately, to humans.

Authors
Kayla A. Markuson, Chinmayee Deshpande, Nicholas M. Riley

Institutions
University of Washington

Abstract
Mass-spectrometry based glycoproteomics relies heavily on enrichment methods to isolate modified peptides from complex samples. However, no single enrichment strategy has been universally accepted as a standard in the field. The use of diverse chemistries, such as strong anion exchange and hydrophobic interaction liquid chromatography, as well as different enrichment platforms (e.g., solid-phase extraction), directly impacts reproducibility and the reliability of downstream data. Introducing automation to glycopeptide enrichment has the potential to improve sensitivity, selectivity, and reproducibility in glycoproteomics workflows used across the field. Additionally, automation decreases analyst hands-on time, allowing for high-throughput studies. Here we demonstrate an automated glycopeptide enrichment approach using strong-anion exchange magnetic beads. Experiments used cell lysates from human immortalized myelogenous leukemia K562 cells. Proteins were digested with trypsin and desalted with Strata-X SPE cartridges (Phenomenex). Glycopeptides were enriched with Oasis-MAX SPE cartridges (Waters) and strong-anion exchange magnetic beads (ReSyn BioSciences) and. Data were collected on an Orbitrap Ascend Tribrid MS (Thermo Fisher Scientific) coupled to a Vanquish Neo UHPLC system. Samples were separated online with a 25 cm, 75 µm ID Aurora Series Gen3 LC column packed with 1.6 µm C18 particles (IonOpticks).

Data acquisition used Autonomous Dissociation-type Selection utilizing a real-time library search to perform stepped HCD MS/MS for N-glycopeptides and EThcD for O-glycopeptides. Automated experiments were performed on the KingFisher Flex (Thermo Scientific). Data were processed using MSFragger-Glyco and GlyCounter.

Standard glycopeptide enrichment has historically involved solid-phase extraction (SPE) cartridges. However, the analyst time required for these workflows limits scalability, and the SPE format potentially limits sensitivity and selectivity. Current solid phase extraction methods, including SAX and HILIC, require upwards of 90 minutes to enrich up to twelve samples. In contrast, an automated platform using magnetic beads instead of SPE packing material can process 96-well plates and require ~20 minutes of analyst time for set up. Using strong anion exchange bead chemistry coupled to magnetic beads (ReSyn Bioscienes), we have successfully enriched glycopeptides from standard protein tryptic-based digests. The flexibility of an automated workflow enables simultaneous analysis of multiple variables, including bead chemistry, sample type, and load, wash, and elution conditions. Under optimized conditions, we observe an increase in the percentage of MS/MS spectra containing glycan-specific ions from 75 to 83%. By comparison, SPE methods designed to reduce analyst time, such as Oasis MAX, yielded a lower proportion of glycan-specific MS/MS spectra (71%). Our preliminary data also shows that this automated strategy maintains, and even improves upon, reproducibility achieved in slower SPE-based methods that are state-of-the-art for the field.

Additionally, bead-based enrichment eliminates additional SPE preparation steps, including cartridge activation and conditioning, thereby reducing workflow complexity and solvent usage. Enrichment performance was evaluated across a broad range of starting material (25–500 µg), confirming robust performance at higher peptide loads. The automated platform also accommodates bead volumes ranging from 5 µL to 100 µL per well without unintended bead loss during processing. When combined with our previously published automated dissociation-type selection mass spectrometry method optimized for glycopeptide analysis, this workflow yields a higher proportion of spectra containing glyco-associated ions. Overall, automated digestion and enrichment using platforms such as the KingFisher Flex System enable high-throughput, precise, and reproducible sample handling.

Authors
Christopher J. Weir, Anna B. Lin, Trung Nguyen, Matthew F. Bush

Institutions
University of Washington

Abstract
Introduction Temperature is often used as a way to modulate protein stability and control rates of reactions. Previous methods for incorporating temperature control into ESI have required long equilibration times and unwieldy hardware. Programmed-temperature electrospray ionization (ptESI) is a technique that modulates sample temperature as a function of time and analyzes the sample in real time using native MS. We are using ptESI to monitor processes including protein folding, the effects of ligands on protein stability, and to One example is the ability to monitor disulfide reduction in real time. To take these experiments to the next level, we have developed (1) a new ptESI source that takes advantage of 3D-printing and off the shelf hardware and (2) software to generate and visualize XML-based method files, control and track experiment progress in real time, and create SQLite files that encode the data and metadata for downstream integration with MS data.

Methods The ptESI source has a low thermal mass and modulates the temperature of liquid samples as a function of time according to user-defined temperature profiles. The source was positioned in front of a Waters Cyclic IMS system to enable the real-time characterization of ions using a variety of MS-based experiments. Ribonuclease A was chosen as a test case due to its four native disulfide bonds and strong thermal stability. Samples of ribonuclease A were prepared in aqueous 200 mM ammonium acetate at pH 7 with 5 mM dithiothreitol (DTT).

Control of the ptESI source is accomplished using a 3D-printed box that provides all necessary power, communication, and water cooling. It uses consumer-grade computer components and other commonly available hardware for a relatively low-cost solution. A codebase and GUI have been developed with AI assistance to provide easy methods for creating temperature profiles with XML sequences and gathering relevant experiment metadata and storing it in a stable SQLite format.

Results The ptESI source was characterized and it was found that liquid samples could be heated or cooled with high fidelity at rates exceeding ±60 °C⸱min–1. Additional experiments have shown that a wide variety of temperature profiles can be used for protein stability measurements.

Samples of ribonuclease A containing DTT were run through a temperature profile that went from 30 to 90 °C at 30 °C⸱min–1, during which time the sample was continuously analyzed by nESI-MS. The initial mass spectrum was consistent with the expected mass of the disulfide-intact protein; that spectrum persisted until ~90 °C and then rapidly began to change. The sudden change suggests that all four native disulfide bonds were reduced and the rate of that reaction may be strongly linked to protein structure and the accessibility of the reducing agent to the disulfide bonds. This process may be especially cooperative, i.e., the loss of the first disulfide bond preferentially favors the unfolded state and further increases the accessibility of the remaining disulfide bonds. Future work will refine the source and design and continue preliminary experiments applying real-time disulfide reduction to therapeutic antibodies of interest.

Authors
Kiran Iyer, Rosalynn Molden, Erin Weisenhorn, Hannah Townsend, Luis Fernandez-Ruiz, Shannon Hayes

Institutions
Just Evotec Biologics, Protein Metrics LLC

Abstract
Glycans can significantly influence the stability, activity, and immunogenicity of biologics, making their analysis crucial for assessing the quality of antibody therapeutics, especially biosimilars. Presented here is a tool to streamline mass spectrometry-based glycan analysis automated glycan grouping with software. From released glycans to peptide-level multi-attribute method (MAM) workflows, automated glycan grouping and intuitive visualizations can transform complex datasets into actionable insights. Work presented here shows the utility of glycan grouping in bridging data from multiple analytical methods such as MAM and released glycan analysis. Additionally, glycan grouping was used to compare day of culture samples from different bioreactors using mass spectrometry (MS)

Authors
Anna B. Lin, Christopher J. Weir, Trung Nguyen, Matthew F. Bush

Institutions
University of Washington

Abstract
Introduction: The measurement and characterization of protein stability is critical to many aspects of biopharmaceutics. In the context of quality control, understanding protein stability is essential for ensuring the consistency and integrity of manufactured therapeutics. In biopharmaceutical research, this same understanding is critical to probing many life-threatening diseases associated with protein misfolding. One such example is transthyretin (TTR), a homotetrameric protein implicated in the development of familial amyloid polyneuropathy (FAP) and senile systemic amyloidosis (SSA). Transthyretin can dissociate into individual monomers that are prone to misfolding and aggregation, leading to the formation of fibrils associated with these diseases. To date, more than 150 TTR mutations have been identified each with their own stability profile. Among these, the V30M variant is one of the most recognized, exhibiting accelerated amyloid formation relative to the wild-type protein. A promising strategy to combat this process involves the use of pharmacological chaperones, which work by shifting the thermodynamic landscape of protein folding while also providing kinetic facilitation of folding the desired structure. However, pharmacological chaperones have only been successfully developed for a handful of diseases, including tafamidis for TTR amyloidosis. The small number of approved pharmacological chaperones across all diseases emphasizes how challenging it is to identify molecules with these properties, highlighting the need for novel technology capable of enabling measurements that can identify viable candidates across many proteins. To meet this need, the Bush Lab has developed programmed-temperature electrospray ionization (ptESI), a method that modulates the temperature of liquid samples across varying gradients while enabling real-time mass spectrometry analysis. In this work, ptESI is used to characterize the kinetic stability of transthyretin, and tafamidis is applied to provide preliminary validation of ptESI as a ligand screening tool, with the broader goal of extending this approach toward identifying pharmacological chaperones for other protein misfolding diseases.

Methods: Wild-type and V30M transthyretin (3 uM tetramer) solutions are prepared in a blend of 200 mM ammonium acetate and 200 mM acetic acid for a final pH of 4. Samples are buffer exchanged and loaded into the ptESI source using nano-ESI borosilicate capillaries. Temperature programs spanning 40 °C to 75 °C are used to modulate the temperatures of solutions while MS data is continuously acquired using a Waters Cyclic IM-MS system.

Preliminary Data: Under acidic conditions (pH 4) and thermal stress, wild-type TTR exhibits partial tetramer dissociation at ~75 °C, reaching a plateau over a 10 minute hold at that temperature. Under identical conditions, V30M undergoes complete and significantly faster dissociation into its monomer subunits. In preliminary analysis, an exponential decay is fitted to each replicate for both species. Individual decay rate constants are used to determine a mean apparent decay rate constant and direct comparisons of the decay rates between the two species confirm that the mutation significantly destabilizes the tetramer and complete dissociation of V30M into subsequent monomer subunits is observed prior to the onset of WT-TTR dissociation. However, all samples appear to undergo subtle behavioral changes as a function of time after sample preparation, strongly suggesting that the samples are not at equilibrium. For both WT and V30M, this is reflected in differences in the initial delay period preceding dissociation as well as a shift in decay rates during dissociation. Despite the variance that this contributes to the data there is a significant difference between the stability of WT and V30M.

To validate ptESI as a tool for pharmacological chaperone screening, tafamidis is introduced to evaluate its effect on TTR thermal dissociation. When the tafamidis bound complex is subjected to identical conditions as before, a significant stabilizing effect is observed as expected. Tafamidis bound WT-TTR remains intact until a point at which dissociation begins at a significantly reduced rate relative to apo WT-TTR. Preliminary data has shown a similar stabilizing effect of tafamidis on V30M transthyretin as well. Ongoing work aims to continue characterizing this temperature dependent behavior and begin a screening of a small library of ligands.

Authors
Gina M Many 1 , Hasmik Keshishian 2 , Gregory Smith 3 , Natalie M Clark 2 , Gayatri Iyer 4 , Patrick Hart 2 , Malene E Lindholm 5 , Samuel Montalvo 5 , Zidong Zhang 3 , Christopher Jin 6 , James A Sanford 1 , Steven A Carr 2 , Joshua N Adkins 1 7 , D R Mani 2 , Sue C Bodine 8 , Scott Trappe 9 , Joseph A Houmard 10 , Nicolas Musi 11 , Kim M Huffman 12 , William E Kraus 12 , Lauren M Sparks 13 , Anna E Thalacker-Mercer 14 , Stuart C Sealfon 3 , Ashley Y Xia 15 , Daniel H Katz 5 , Christopher B Newgard 12 , Charles F Burant 4 , Paul M Coen 13 , Bret H Goodpaster 13 ; MoTrPAC Study Group

Institutions
1 Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA. 2 Proteomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA. 3 Icahn School of Medicine at Mount Sinai, New York, NY. 4 Department of Internal Medicine, University of Michigan, Ann Arbor, MI. 5 Department of Medicine, Stanford University, Stanford, CA. 6 Department of Genetics, Stanford University, Stanford, CA. 7 Oregon Health and Science University, Portland, OR. 8 Oklahoma Medical Research Foundation, Oklahoma, University of Oklahoma, Oklahoma City, OK. 9 Human Performance Laboratory, Ball State University, Muncie, IN. 10 Department of Kinesiology, Human Performance Laboratory, East Carolina University, Greenville, NC. 11 Cedars Sinai Medical Center, Los Angeles, CA. 12 Duke University School of Medicine, Durham, NC. 13 AdventHealth Orlando, Translational Research Institute, Orlando, FL. 14 The University of Alabama at Birmingham, Birmingham, AL. 15 National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institutes of Health.

Abstract
The Molecular Transducers of Physical Activity Consortium (MoTrPAC) was established to systematically characterize the molecular basis of the health benefits of exercise. Here, we present the integrative, multi-omics response of human skeletal muscle to acute endurance (EE) and resistance (RE) exercise. Distinct temporal responses were observed, with changes in ATAC-seq, phosphoproteome, and metabolome occurring before changes in the transcriptome and proteome. These distinct temporal multi-omic dynamics were used to identify transcriptional regulatory hubs converging around MEF2A and NFIC regulation of autophagy, angiogenesis and metabolism. Further, early RE-specific phosphoproteome signatures counteracted epigenetic modifications and downregulated transcripts involved in protein turnover. Additional findings include suppression of HIPK2/3 kinase signatures linked to the acute exercise regulation of sarcomeric proteins TTN, NEB, ANKRD2 and LMOD2. Our data demonstrate distinct temporal regulation across the multi-omic landscape of human skeletal muscle, with EE and RE eliciting common and unique molecular signatures.

Authors
Andrea I. Gutierrez, Lillian T. Tatka, Gaelle Mercenne, Julia Robbins, Daniele Canzani, Anastasiya Prymolenna, Sebastian J. Paez, William E. Fondrie, Lindsay K. Pino, Alexander J. Federation

Institutions
Talus Bioscience

Abstract
Introduction (120 words) STAT6 is a transcription factor (TF) central to immune regulation and implicated in cancer progression, making it a highly attractive therapeutic target. Upon activation, STAT6 translocates to the nucleus and binds chromatin to regulate genes involved in immune signaling, tumor microenvironment modulation, and disease progression. Despite its biological relevance, STAT6 remains a challenging drug target, as transcription factors generally lack conventional druggable binding sites, and limited successful small molecule approaches have succeeded. To address the challenges associated with drugging STAT6, we developed a high-throughput screening approach leveraging subcellular fractionation, quantitative chromatin proteomics, and machine learning-powered in silico compound screening to identify small molecule STAT6 inhibitors.

Methods (120 words) We adapted regulome profiling, our semi-automated high-throughput screening platform, to quantify changes in the chromatin-bound proteome, including STAT6, following compound treatment. Briefly, cells are first stimulated with IL-4 for 30 minutes to induce STAT6 nuclear translocation and chromatin binding. Compound treatment and stimulation is performed using an Echo acoustic dispenser, followed by subcellular fractionation to enrich DNA-bound proteins found in the chromatin fraction. Chromatin proteins are digested via SP3, and resulting peptides are quantified using DIA mass spectrometry on a timsTOF Ultra II. Data analysis is performed using DIA-NN version 1.8.1 on the quantms Nextflow pipeline.

Preliminary data (300 words) We first qualified the STAT6 target on our mass spectrometry-based regulome profiling platform. Specifically, we evaluated cell models, stimulation conditions, and treatment durations that maximize quantitative accuracy and reproducibility for chromatin-associated STAT6. We found that stimulating cells for 30 minutes with IL-4 was required to achieve abundant, reproducible, and quantifiable detection of STAT6, a critical optimization step required for reliable screening.

Second, we trained a machine learning algorithm on millions of protein-compound interactions and leveraged it to prioritize a set of candidate inhibitors from a library of 12,000 covalent compounds, reducing the experimental space of the primary screen. We empirically screened the selected small molecules in the STAT6 cell-based system and have thus far identified 25 preliminary hits that significantly inhibit the function of STAT6 chromatin-binding. Traditional drug screening efforts yield 0.1 to 1% hit rates on average, where our platform has achieved a 5% hit rate for this difficult target. Our target-agnostic screening platform captures compound-induced changes across the broader chromatin-associated proteome, providing insight into selectivity, potential off-target effects, and pathway-level consequences of STAT6 modulation. This systems-level view enables early assessment of both efficacy and specificity, critical considerations for advancing compounds toward therapeutic development. The identified hits are currently undergoing validation through orthogonal cell-based assays to confirm STAT6 target engagement and assess downstream functional effects on immune and cancer-related signaling pathways. The scalability and throughput of regulome profiling positions this approach as a powerful strategy for screening against difficult TF targets implicated in cancer and immune dysregulation. Ongoing validation efforts will prioritize lead compounds for advancement into a STAT6-focused drug discovery campaign.

Novel aspect (20 words) A high-throughput chromatin proteomics platform combining machine learning compound prediction and DIA-LCMS to drug challenging targets such as STAT6.

Authors
Michal Maes, Casey Propst, Alex Zelter, Winnie Wen, Michael MacCoss, Nina Isoherranen

Institutions
Department of Pharmaceutics, Department of Genome Sciences, University of Washington

Abstract
Identification of the enzymes involved in the metabolism of a drug candidate is critical in drug development to predict drug-drug interactions, interindividual variability and impact of disease states and pharmacogenetics on drug disposition. Despite their prevalence in drug metabolism, the enzymes carrying out hydrolysis reactions remain poorly characterized. While many drugs are known to undergo hydrolysis in vivo, the specific enzymes that contribute to amide and ester hydrolysis remain poorly characterized. This project focuses on characterizing in vitro hydrolase activity with the ultimate goal of identifying major drug hydrolase enzymes in human tissues and determining the relative importance of these enzymes in drug clearance. As the liver is one of the main metabolic organs, we prepared subcellular fractions from individual liver donors from the UW human liver bank. These were tested for hydrolase activity with compounds known to undergo hydrolytic metabolism in vivo. As proof of concept, we show that hydrolysis of Z-Arg-Arg-AMC, which is expected to be lysosomal, happens exclusively in the fractions containing cytosolic proteins. Interestingly, formation of the inactive metabolite of clopidogrel, clopidogrel carboxylic acid, was observed in both the cytosolic and the microsomal fractions although CES1, the enzyme suggested to be responsible for this reaction, is localized to the ER lumen. The abundance of CES1 was quantified using DIA-based proteomic analysis. CES1 abundance showed a significant correlation with clopidogrel hydrolysis activity in microsomes. In contrast, clopidogrel hydrolysis activity in the cytosolic fraction did not correlate with CES1 abundance. These findings suggest that, in addition to CES1, another as-yet-unidentified cytosolic enzyme is also involved in clopidogrel hydrolysis. As hydrolysis pathways play essential roles in numerous biochemical processes across diverse tissues, we investigated the proteomic expression of hydrolase enzymes in a variety of human tissues. As adipose tissue showed a high abundance of CES1, we tested whether clopidogrel hydrolysis activity was present in human omental and subcutaneous adipose tissue. The hydrolysis activity appeared different between the omental and subcutaneous adipose depots, but the data suggest additional enzyme contributions beyond CES1 in adipose tissue as well. Further analysis of correlations between enzymatic activity assays and the full proteome data in individual donors and different tissues may allow identification of unknown hydrolase enzymes contributing to drug metabolism. These findings highlight the power of combining DIA-derived protein abundance with activity assays to identify drug metabolizing hydrolase enzymes.

Authors
Conor P Herlihy, Elijah Biletch, Lidan Li, Olivia Weissenfels, Keriann M. Backus, Brian J Beliveau, Devin K Schweppe

Institutions
University of Washington, University of California at Los Angeles

Abstract
Regulating nuclear structure is paramount to carrying out fundamental cellular processes and preventing onset of disease. Much of that maintenance is mediated through regulating the diverse protein, DNA, and RNA composition of nuclear compartments. In spite of this critical function, the core molecular composition of many nuclear compartments are still not fully understood. Deciphering de novo multiomic environments of specific proteins and nucleic acid loci in an unbiased and comprehensive manner is challenging with the currently available approaches. To overcome this challenge, we developed a proximity labeling platform using photosensitizer-dependent oxidation and capture by amine (POCA). Our approach enables target specific proteome identification using photosensitizer conjugated oligonucleotides or antibodies and proximity labeling followed by affinity purification and mass spectrometry. Thus, developing the first oligo-directed photocatalytic labeling approach. To better understand the molecular composition of nuclear speckles, nucleoli, and constitutive heterochromatin, we leveraged POCA targeting their defining molecular components. Employing complementary approaches has revealed the diverse molecular composition of these compartments. For example, targeting both SON and MALAT1 at nuclear speckles shows significant enrichment of 24 proteins exclusive to Malat1, 226 proteins exclusive to SON, and 56 shared proteins. Additionally, targeting both NPM1 and ITS1 has elucidated local proteomes unique to the periphery and the core of the nucleolus. Together these findings establish the flexibility of our method for quantitative proximity labeling proteomics targeting both nucleic acids and proteins to define sub-compartment composition and better understand nuclear structure.

Authors
Jinyu Liu, Alex Zelter, Aprajita Yadav, Michael Riffle, Lindsay C. Czuba, Yue Wen, Michael MacCoss, Katya Rubinow, Nina Isoherranen

Institutions
University of Washington

Abstract
Adipose tissue serves as an energy reservoir and as an active endocrine organ, playing a central role in metabolic homeostasis. Subcutaneous (SC) and omental (OM) adipose depots are functionally and histologically distinct, with OM adipose tissue more strongly associated with insulin resistance and metabolic disease. Retinoids, including retinol and its bioactive metabolite, all-trans retinoic acid (atRA), are essential signaling molecules that regulate lipid metabolism, immune function, and cellular differentiation. Animal models have suggested that retinoid homeostasis is altered in obesity. Yet retinoid concentrations in human adipose tissue depots have not been previously measured, and the relationship between tissue retinoids and the local proteome remains uncharacterized. We hypothesized that adipose tissue retinoid homeostasis is regulated locally in an autocrine manner, and that depot-specific retinoid concentrations correlate with both the tissue proteome and systemic metabolic markers.

Paired SC and OM adipose biopsies were collected from 31 non-diabetic participants (20 female, 11 male; age 25–65 years; BMI 21–56 kg/m²) during elective surgeries. Tissue concentrations of retinol, all-trans retinoic acid, and 13-cis retinoic acid were quantified by LC-MS/MS. Proteomic profiles were generated using LC-MS/MS-based data-independent acquisition (DIA). Depot-specific differences in retinoid concentrations and proteomic signatures were identified, and correlations between retinoid concentrations and proteomic and clinical variables (BMI, insulin, adiponectin, and leptin) were tested.

Retinoid concentrations differed significantly between SC and OM depots, and the relationship between tissue and circulating retinoid levels was depot dependent. Consistent with these differences, ALDH1A2, a key retinoic acid-synthesizing enzyme, was also differentially expressed between depots. Tissue or serum retinoid levels were significantly correlated with BMI, leptin, and adiponectin. Proteomic analyses identified retinoid-metabolizing enzymes, including members of the ADH, ALDH, and AOX families, as significantly correlated with tissue retinoid concentrations across donors. Notably, ADH7 was associated with multiple retinoid species in both depots. The retinoid-correlated protein networks were distinct across depots and retinoid species, indicating depot-specific regulatory patterns. Finally, a predictive model integrating retinoid metabolism-related proteins and circulating retinoid levels was developed for estimating tissue retinoid concentrations.

This study provides the first characterization of depot-specific retinoid profiles in human adipose tissue, linking them to the local proteome and identifying the enzymatic basis for inter-depot differences. Correlations between tissue retinoid levels, adipokines, and metabolic markers further support a functional connection between retinoid signaling and lipid metabolism in adipose tissue.

Authors
Kayleigh Voos1*, Gayatri Iyer2*, Anne Marie Weitzel3, Gina Many1, Laurie Goodyear4, Sue Bodine5, Karyn Esser6, Joshua Adkins1, Charles Burant3 and the MoTrPAC Study Group

Institutions
1 Pacific Northwest National Laboratory, Richland, WA. 2 Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI. 3 Department of Internal Medicine , University of Michigan, Ann Arbor, MI. 4 Joslin Diabetes Center, Harvard Medical School, Boston, MA. 5 Oklahoma Medical Research Foundation, Oklahoma City, OK. 6 University of Florida, Gainesville, FL.

Abstract
Skeletal muscle plays a central role in generating force for movement and mediating adaptive responses to exercise. In this study, we examined the temporal molecular response of rat skeletal muscle to a single bout of acute endurance exercise across multiple molecular layers, from immediately post-exercise to 48 hours post-exercise. We observed that the phosphoproteome and epigenome exhibited the most robust early responses to exercise. Notably, several molecular links were identified between phosphoproteomic changes and epigenomic regulation, suggesting a coordinated interplay between these layers. Additionally, peak-to-gene correlation analyses revealed associations between epigenetic changes and corresponding alterations in transcriptional programs related to protein degradation and folding, transcription factor activity, and aerobic respiration. Multi-omics factor analysis (MOFA) across molecular layers further revealed a subset of exercise-responsive and sex-dependent modules with clear temporal dynamics. These included proteins and genes associated with mitochondrial respiration, cytoskeletal remodeling, sarcomere assembly, RNA metabolism, and inflammatory responses. Furthermore, temporal clustering of metabolomic, phosphoproteomic, and transcriptomic data revealed early, middle, and late molecular programs responsive to an acute exercise bout. Together, these data provide a time-resolved molecular characterization of the skeletal muscle response to acute exercise in untrained female and male rats. These findings contextualize early molecular responses that precede muscle adaptation to training and enhance our understanding of muscle biology on the minute-to-hour timescale following a single bout of endurance exercise.

Authors
Gayatri Mishra1*, Anna Berim1, David Roger Gang

Institutions
1Institute of Biological Chemistry, The Washington State University, Pullman, WA 99164, USA

Abstract
Popping beans are derived by crossing indigenous South American nuña beans with temperate-adapted common bush beans. Popping beans are a unique group of Phaseolus vulgaris L. that expand upon heating, similar to popcorn. This emerging crop is attracting interest because of its unique popping trait, making it well-suited for nutritious, protein- and fibre-rich snack foods. Despite their culinary and nutritional potential, the biochemical basis of this distinctive popping behaviour remains poorly understood. Here, we present the first integrated proteomic and metabolomic analysis of diverse popping bean accessions and compare them with commercial common bean accessions to identify protein and metabolite signatures associated with the popping trait. Whole seeds from six popping bean and six commercial bean accessions were powdered and analysed using liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS) with electrospray ionisation (ESI). A total of 1,878 proteins were identified across all accessions. Comparative proteomic analyses revealed distinct protein signatures between popping and commercial beans. KEGG pathway enrichment analysis identified glycan degradation, valine/leucine/isoleucine biosynthesis, pantothenate and CoA biosynthesis, and porphyrin metabolism as major enriched pathways in popping beans, whereas commercial bean accessions were characterised by enrichment of proteins associated with endoplasmic reticulum protein processing, including molecular chaperones and protein-folding proteins. Metabolomic profiling further differentiated the two bean groups. Popping beans exhibited higher abundances of raffinose, stachyose, flavonoids (quercetin, myricetin, epicatechin, and astragalin), and organic acids. In contrast, commercial bean accessions were enriched in amino acid-related metabolites, including histidine, ornithine, serine, and urocanic acid, together with several phenolic acid derivatives. Integration of proteomic and metabolomic datasets revealed distinct biochemical signatures that differentiate popping and commercial bean seeds.

This work provides new insights into the biochemical basis of the popping trait and identifies distinct protein and metabolite signatures associated with popping beans. These findings enhance our understanding of the nutritional composition of popping beans and support their potential development as a novel, nutritious food crop and source of value-added food products.

Keywords: Emerging crop; LC–ESI–MS/MS; Metabolomics; Nutritional composition; Popping trait; Proteomics; Seed proteins; Storage proteins.

Authors
Kristine A. Tsantilas, Jeffrey R. Whiteaker, Myung Sik Jeong, Rose C. Pletcher, Jeremy Hoog, Lei Zhao, Hong Wang, Max Short, Ian Hagemann, Regine M. Schoenherr, Uliana J. Voytovich, Richard G. Ivey, ChenWei Lin, Foluso Ademuyiwa, Jingqin Luo, Cynthia X. Ma, Amanda G. Paulovich

Institutions
1. Fred Hutchinson Cancer Center, Seattle, WA 2. Washington University in St. Louis, St. Louis, MO

Abstract
Antibody-drug conjugates (ADCs) are monoclonal or bispecific antibody-based therapeutics with an increasing variety of cellular antigen targets that can deliver a cytotoxic “payload” to tumors. As the number of available ADC antigens has increased, the need to match patients with potential therapies has grown. Classically, this required quantifying target proteins separately with immunohistochemistry. We sought to remove this bottleneck by applying a multiplexed immuno-PRM assay (“ADC-PRM panel”) in patient selection by measuring 7 ADC target antigens. Furthermore, we expanded the panel to include characterization of a DNA-damage payload target and the pharmacodynamic response associated with successful payload delivery.

The ADC-PRM assay quantifies 128 peptides derived from 64 proteins. The workflow entails isotope dilution with cleavable isotopic standards coupled with peptide immunoaffinity enrichment of endogenous and internal standard peptides with 106 antibodies. LC-MS analysis of 256 precursors in a single 32.5-minute gradient was performed using an Evosep ONE (Whisper Zoom SPD40) coupled to a PRM method on an Orbitrap Astral mass spectrometer (Thermo). Basic performance metrics of the ADC-PRM panel were characterized in our CLIA-CAP environment in two matrices: LCL57 cell line exposed to 10GY of radiation (LCL57-10GY) and pooled frozen breast tumor lysate from two individuals. The assay was applied to a retrospective cohort of frozen breast cancer patient biopsies treated with HER2-directed antibody therapy.

The panel quantifies 7 tumor antigens currently targeted using monoclonal or bispecific ADCs (B7H3, B7H4, EGFR, FOLR1, HER2, HER3, PDL-1) including 2 that are clinically approved (HER2, FOLR1). Downstream, we measure a common DNA-damage agent payload target (TOP1), the pharmacodynamic response (DNA-damage response, “DDR”), and additional relevant downstream pathways including RAS signaling, RTK response, and proteins involved in the regulation of cell cycle, cell death, survival, and proliferation.

In preliminary studies, we utilized LCL57-10GY and frozen breast tumor lysate to evaluate linearity and protein input requirements of the assay. In a peptide response curve, 72% of the analytes the panel measures have an estimated lower limit of quantification (LLOQ) of 100 fmol or lower. We could detect endogenous signal from 104/128 peptides (77%) in 250 μg of frozen breast tumor. We found most of the detected analytes were robust down to 25 μg of input where 98/128 peptides were detected - including 10/35 phosphopeptides. This is crucial for sample-limited patient biopsies.

We applied this ADC-PRM assay to a cohort of 47 retrospective samples from the PALTAN trial – a human clinical trial of breast cancer patients on a neoadjuvant treatment including trastuzumab, palbociclib, and letrozole. This included samples collected from patients at baseline prior to treatment, during treatment Cycle 1 on Day 15, and at surgery after completing 4 treatment cycles where each treatment cycle was 28 days. Preliminary results showed that we can quantify proteins associated with the 3 therapeutics in the study. We measured the trastuzumab antigen (HER2) in all the samples and observed a reduction in the estrogen receptor (ESR1) after treatment with letrozole. We quantified proteins downstream of CDK4/6 (CDKN1A, CCNE1, and RB1) and known DDR targets (H2AX, NBN, NUMA1) that are induced by DNA damage caused by palbociclib.

Authors
Samuel H Payne

Institutions
Brigham Young University

Abstract
As single cell proteomics transitions from a specialized technology to a widely adopted approach, the data volume created by our community has the potential to broadly characterize cellular activity and responses to both genetic and environmental perturbations. As a repository of single cell data, an Atlas is designed for intense data re-use - specifically as a quantitative reference dataset to build machine learning models. We anticipate that a single-cell proteomics Atlas could be leveraged into GPT-like AI frameworks where researchers can interactively explore potential experimental designs and likely results. With the recent improvements in proteome coverage and throughput, single cell proteomics has the potential to begin creating Atlas-scale datasets, quantifying thousands of proteins from hundreds of thousands of cells. However, creating this community resource requires significant planning to achieve the desired use-cases. Here, we discuss how data can be generated, aggregated and analyzed to create an Atlas capable of modeling cell biology.