Journal of the American Society for Mass Spectrometry

IntroductionRetinoids are bioactive vitamin A derivatives that regulate cellular differentiation and gene expression, yet their reliable quantification remains challenging due to low abundance, structural isomerism, and sensitivity to ionization conditions while handling. ObjectivesIn this study, we performed a systematic optimization of liquid chromatography-mass spectrometry (LC-MS)-based detection of retinoids across tissues and biofluids. MethodsChromatographic separation, adduct formation, ionization parameters, fragmentation behavior, and extraction procedures were evaluated in an integrated workflow. ResultsChromatographic conditions influenced not only retention time but also the ionic species detected, affecting precursor selection for MS{superscript 2} analysis. Retinoids exhibited compound-dependent responses to electrospray ionization and collision energy, requiring tailored acquisition parameters. Extraction experiments demonstrated differential recovery among retinoid classes and revealed matrix-dependent behavior, indicating that protocols used for tissues cannot be directly transferred to low-abundance biofluids. Using optimized conditions, retinoids were detected in mouse cerebrospinal fluid (CSF) at concentrations approaching the analytical detection limit, where MS{superscript 2} confirmation was necessary for reliable identification. ConclusionTogether, our results provide a framework for reproducible retinoid profiling across biological matrices and enables comparative studies of retinoid biology in low-volume and low-abundance biofluids.

Open modification search (OMS) strategies have gained popularity in mass spectrometry-based proteomics for identification of peptides carrying unknown or unexpected post-translational modifications. However, most OMS search engines report only the overall mass difference between the precursor and the matched peptide and do not explicitly identify or score combinations of multiple modifications at the peptide-spectrum match (PSM) level, leaving the interpretation of mass shifts up to the end user and to using downstream analysis tools. Here, we introduce MS Andrea, a novel OMS search engine developed to directly identify and score combinations of up to four variable modifications per peptide without having to predefine them. MS Andrea uses a sequence tag-based strategy to efficiently filter candidate peptides prior to scoring. Remaining candidates are evaluated using the MS Amanda scoring function, first considering fixed modifications only, followed by a second scoring stage in which combinations of modifications from the Unimod database are considered based on the observed mass difference and matched to the spectrum. We evaluated MS Andrea using phosphopeptide datasets from HeLa cells and Arabidopsis thaliana and compared its performance with the widely used OMS engines MSFragger and Sage. Across datasets, MS Andrea identified the highest number of PSMs at 1% false discovery rate while achieving comparable peptide-level identifications. Importantly, MS Andrea directly reports modification identities and sites at the PSM level and enables the identification of peptides having up to four variable modifications. Together, these results demonstrate that MS Andrea facilitates more detailed and interpretable characterization of peptide modifications while maintaining competitive identification performance in OMS-based proteomic analyses. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=132 SRC="FIGDIR/small/714851v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@52f65forg.highwire.dtl.DTLVardef@acf4e3org.highwire.dtl.DTLVardef@10171caorg.highwire.dtl.DTLVardef@1d594ad_HPS_FORMAT_FIGEXP M_FIG C_FIG

Peptide-level analyses are becoming increasingly popular in mass spectrometry-based proteomics and are being applied, for example, in immunopeptidomics, structural proteomics, and analyses of post-translational modifications. In such analyses, peptides that are not biologically meaningful but instead arise as artifacts prior to mass spectrometry analysis pose the risk of data misinterpretation. Here, we describe an approach based on retention time analysis and precise chromatographic peak matching to identify peptides generated by in-source fragmentation (ISF), which occurs between chromatographic separation of peptide mixtures and the first mass filter of a tandem mass spectrometer (MS). To understand the prevalence and properties of ISF, we generated 13 proteomics datasets and analyzed them along with additional 25 previously published datasets spanning a broad range of sample types, MS, and proteomics approaches including classical bottom-up proteomics, immunopeptidomics, structural proteomics, and phosphoproteomics. We found that, in typical trypsin-digested samples on average 1 % of fully-tryptic peptides and 22 % of semi-tryptic peptides originated from ISF. However, we observed large variations between datasets, and in-source fragments exceeded, in some cases, a third of the total peptide identifications. The extent of ISF was dependent on the peptide sequence, the instrument, method parameters, and sample complexity. Although ISF did not impair relative quantification across samples, it generated peptides that could be misinterpreted qualitatively, inflated peptide identifications, and comprised up to 37 percent of peptides shorter than 9 amino acids in immunopeptidomics datasets. We propose that, for peptide-centric applications, our open-source ISF detection approach be used to re-annotate peptides generated by ISF and remove them to avoid misinterpretation of data. ISF is an increasing concern with improving mass spectrometers, as they enable detection of an ever-increasing number of m/z features, including low abundance features like ISF products. Our work thus addresses a growing issue in proteomics and presents solutions to mitigate the impact of in-source fragment peptides. In the future, improved feature detection algorithms may enable elucidation of new ISF patterns affecting side chains that have been missed so far, which could contribute to explaining the vast space of as-yet unannotated proteomics data.

High-resolution binding site mapping is important for in-depth activity assessment of new therapeutics including AI-designed antibodies. However, complex protein targets such as glycosylated antigens are challenging for many methods including crystallography. PD1 is a highly glycosylated antigen, and with the traditional HDX-MS method, only 51% sequence coverage could be obtained with multiple epitope residues undetected for Pembrolizumab. By implementing glyco-peptide detection, subzero temperature LC-MS and electron based MSMS fragmentation, the new HDX FineMapping methodology enabled 100% sequence coverage and complete epitope characterization for the Pembrolizumab-PD1 system, with amino acid level resolution. Furthermore, HDX FineMapping detects binding epitopes directly in solution, without any mutation or modification to either the antigen or the antibody. The amino acid level resolution combined with low cost, minimal sample consumption, fast turnaround time, and no need of mutant library or crystallization makes it a competitive methodology for binding mode validation of AI-designed therapeutics.

Data-independent acquisition (DIA) proteomics enables reproducible and systematic peptide detection and quantification, and trapped ion mobility spectrometry (TIMS) on the timsTOF platform further improves DIA by synchronizing ion mobility separation with quadrupole precursor sampling. Analyzing the highly multiplexed spectra generated by DIA typically relies on spectral libraries, and fully leveraging the additional ion mobility dimension requires these libraries to include accurate retention time, fragment ion intensity, and ion mobility annotations. Existing in silico spectral library generation tools either lack ion mobility support entirely or rely on models trained on data-dependent acquisition (DDA) data, that can introduce a mismatch that may not capture unique experiment-specific biases when applied to each respective timsTOF dataset. Carafe is a software tool that uses deep learning models to generate high-quality, experiment-specific in silico libraries by training directly on DIA data. In this study, we extend Carafe to generate libraries for timsTOF DIA data, which involves fine-tuning retention time (RT), fragment ion intensity, and ion mobility prediction models using timsTOF DIA data. Carafe2 operates directly on native timsTOF raw data (Bruker .d directories) without the need for data conversion. We demonstrate the performance of Carafe2 across a wide range of DIA applications, including global proteome, phosphoproteome, and plasma proteome datasets. Comparing Carafe2 fine-tuned RT, fragment ion intensity, and ion mobility prediction models with pretrained DDA models, we find that Carafe2 models outperform pretrained models on a variety of DIA datasets. We then demonstrate the utility of in silico libraries generated by Carafe2 for peptide detection on several different types of timsTOF DIA datasets by comparing with the libraries generated with DDA-trained AlphaPeptDeep models, DIA-NN built-in models, and empirical spectral libraries generated from DDA experiments.

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.

Carbapenemases are major drivers of carbapenem resistance in Gram-negative bacteria and pose a critical threat to last-line antibiotic therapy. Rapid identification of carbapenemase classes is essential for appropriate treatment and epidemiological surveillance; however, current functional methods lack class-level resolution and may yield false-negative results for OXA-48-like enzymes. In this study, we developed and validated an assay based on liquid chromatography-mass spectrometry with trapped ion mobility spectrometry-time-of-flight [LC-MS (timsTOF)] for simultaneous detection and class-level differentiation of five clinically relevant carbapenemases (KPC, NDM, VIM, IMP, and OXA-48-like). The method employs three carbapenem substrates (meropenem, imipenem, and ertapenem). A total of 55 clinical isolates were analyzed using a standardized 2-hour incubation protocol, with a total analysis time of 7 min per sample. Ion mobility enabled unambiguous identification of the OXA-48-specific meropenem-derived {beta}-lactone based on its distinct collisional cross-section (185 [A]{superscript 2} vs. 191 [A]{superscript 2} for intact meropenem), despite identical mass and nearly identical retention time. This marker was detected in all OXA-48-like producers and was absent in all other groups. In contrast, imipenem and ertapenem did not provide comparable discrimination, highlighting the central role of meropenem. Distinct hydrolysis profiles enabled class-level differentiation supported by multivariate analysis. LC-MS (timsTOF) thus enables rapid, sensitive, and specific functional detection of carbapenemases within a single workflow. The ion mobility dimension is critical for accurate identification of OXA-48-like enzymes and supports the potential implementation of this approach in routine clinical microbiology laboratories. ImportanceThis study introduces an ion mobility-enabled LC-MS (timsTOF) approach for functional detection and class-level differentiation of clinically relevant carbapenemases within a single analytical workflow. By leveraging collisional cross-section measurements, the method enables reliable identification of OXA-48-like carbapenemase through detection of a meropenem-derived {beta}-lactone that is indistinguishable by mass alone. This directly addresses a major diagnostic limitation of conventional activity-based assays, which may yield false-negative results for OXA-48-like enzymes. The approach further demonstrates the potential of integrating ion mobility into routine clinical mass spectrometry to enhance specificity beyond traditional mass and retention time measurements. These findings support the development of next-generation diagnostic strategies capable of detecting both known and emerging resistance mechanisms without reliance on predefined targets.

Cardiac transthyretin amyloidosis (ATTR-CA) is caused by myocardial deposition of misfolded transthyretin, leading to progressive heart failure. Disease pathology, however, extends beyond passive amyloid deposition and also involves active processes such as extracellular matrix (ECM) remodeling and immune activation. Mass spectrometry (MS) is the gold standard for amyloid typing in diagnostics. Here, we applied quantitative MS-driven proteomics on formalin-fixed paraffin-embedded whole cardiac tissue sections from six ATTR-CA cases, ten unaffected controls and four AL-CA controls to investigate protein expression changes. In addition to transthyretin, over 500 proteins were upregulated in ATTR-CA biopsies, including complement and coagulation factors as well as extracellular matrix (ECM) remodeling proteins. Among these, members of the A Disintegrin and Metalloproteinase with Thrombospondin Motifs (ADAMTS) family, metalloproteinases (MMPs), and Tissue Inhibitor of Metalloproteinases (TIMP3) showed significant upregulation. These proteins are key regulators of ECM turnover and structural integrity. Immunohistochemistry confirmed ADAMTS4 enrichment in amyloid deposits, while TIMP3 showed strong expression in cardiomyocytes and weaker staining within amyloid deposits. Together, these findings indicate that ECM remodeling, alongside complement and coagulation activation, represents a reproducible feature of cardiac ATTR amyloidosis. Whole-tissue proteomics provides biological insights that extend beyond amyloid typing, with potential implications for biomarker discovery and therapeutic targeting in ATTR-CA.

Ubiquitin-like protein 3 (UBL3) is a post-translational modifier that sorts proteins into small extracellular vesicles and regulates the trafficking of disease-associated proteins such as -synuclein. The structural and dynamic features of the UBL domain that underlie these functions, however, remain poorly understood. Here we performed in silico structural dynamics analysis of the UBL3 UBL domain using an NMR structure ensemble combined with anisotropic network modeling (ANM) and perturbation response scanning (PRS). Principal component analysis and residue-wise fluctuation analysis consistently revealed high flexibility in the C-terminal region of UBL3. Comparative ANM analysis across 20 ubiquitin-like proteins (UBLs) further showed that C-terminal flexibility is a conserved yet variable property within the UBL family. PRS analysis demonstrated that residues forming the central -helix of the {beta}-grasp fold exert greater dynamic control over collective motions than {beta}-sheet residues. Notably, UBL3 displayed the highest helix/sheet PRS effectiveness ratio among all UBLs analyzed, highlighting the prominent dynamic contribution of helix residues in this domain. Together, these results provide a structural basis for understanding UBL3-dependent protein interactions and disease-related mechanisms, and suggest that helix-centered dynamic control in the UBL domain may represent a potential target for modulating UBL3 function.

Limited proteolysis coupled to mass spectrometry (LiP-MS) is a peptide-centric conformational proteomics approach during which a brief incubation with a non-specific protease (e.g., proteinase K) under native conditions generates structural fingerprints that report on treatment-induced conformational changes, which is followed by a tryptic digest under denaturing conditions allowing to read out these fingerprints 1. In contrast, the recently introduced peptide-centric local stability assay (PELSA) uses a high trypsin-to-substrate ratio under native conditions to release fully tryptic peptides that reflect structural stability upon ligand binding 2. In their paper, Li et al. compared PELSA and LiP-MS across several benchmarks and reported that PELSA exhibited quantitative sensitivity comparable to or exceeding LiP-MS. Notably, PELSA quantified a 21-fold greater rapamycin-induced change for FKBP1A compared to LiP-MS. Because such claims influence method selection for conformational proteomics, we reanalyzed the publicly deposited datasets underlying these comparisons and assessed the experimental and analytical choices that contributed to the reported effect sizes. Our evaluation indicates that the reported 21-fold difference arises from non-matched experimental conditions and undisclosed data imputation, and that conclusions regarding quantitative superiority or biological interpretability should therefore be treated with caution.

Age-accompanied chronic, low-grade systemic inflammation (inflammaging) drives the onset and progression of neurodegenerative disorders like Parkinsons disease (PD). Currently, no disease-modifying therapies are available for PD. Exposure to environmental toxicants, including paraquat (PQ), rotenone, and neurotoxic metals, increases disease risk. Conversely, sustained consumption of dietary soft electrophiles, such as flavonoids, carotenoids, vitamin E vitamers, and essential fatty acids, has been associated with increased lifespan and delayed age-related neurological decline. Omega-3 and select omega-6 fatty acids also serve as precursors of lipid-derived specialized pro-resolving mediators (SPMs), which exert potent anti-inflammatory and inflammation-resolving activities. Here, we report the development of a robust analytical method to quantify pro-resolving oxylipins in a PQ-induced Drosophila melanogaster model of PD, enabling investigation of how dietary phytochemicals modulate anti-inflammatory and pro-resolving lipid metabolism in vivo. We hypothesized that plant-derived soft electrophiles promote active resolution of neuroinflammation by enhancing the production of pro-resolving oxylipins derived from essential fatty acids, and that their neuroprotective effects are linked to their soft electrophilic properties. Our results demonstrate that specific lipophilic plant-derived soft electrophiles significantly upregulate pro-resolving oxylipins in Drosophila heads following PQ exposure. We identify a subset of flavones and structurally related phytochemicals that selectively enhance SPM biosynthesis and show that this response involves the NF-{kappa}B orthologue relish. Additionally, feeding modality and sex-specific dimorphisms were found to influence oxylipin production. Collectively, these findings indicate that structurally related dietary soft electrophiles enhance endogenous pro-resolving lipid pathways, promote resolution of toxin-induced neuroinflammation, and have potential preventive and therapeutic relevance for neuroinflammation-associated neurodegenerative diseases. HighlightsO_LIQuantification of pro-resolving lipids in a Drosophila Parkinsons model. C_LIO_LISpecific structural features of phytochemicals contribute to in vivo bioactivity. C_LIO_LILipophilic soft electrophiles show therapeutic potential against neuroinflammation. C_LIO_LIFeeding modality and sexual dimorphism also regulate oxylipin production. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/714080v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@2088cforg.highwire.dtl.DTLVardef@1f5d026org.highwire.dtl.DTLVardef@134aa44org.highwire.dtl.DTLVardef@965e28_HPS_FORMAT_FIGEXP M_FIG C_FIG

Amyloid beta (A{beta}), one of the hallmark proteins of Alzheimers Disease (AD), aggregates into plaques that are strongly linked to cognitive decline and neuronal death. Reducing its aggregation propensity may provide a strategy to slow the progression of AD. While chirality modulation has emerged as an innovative approach to disrupt this process, research has primarily focused on alterations at the C position, often overlooking the impact of the second chiral center, such as the C{beta} atom of Threonine. Furthermore, the underlying mechanisms governing these chiral effects remain elusive. Given the intrinsically disordered nature of the A{beta} peptide, we employed temperature-replica exchange molecular dynamics (T-REMD) simulations to explore its rugged conformational landscape. We considered sequence mutations (A2T, A2V), N-terminal chirality inversion of the first six residues (A2V1-6D and WT1-6D), and alteration of the second chiral center (C{beta}) of Threonine (A2TC{beta}). By analyzing the effect size and population change induced by these mutations and chiral modulation, we concluded that the modulation at the N-termini is not confined locally but also exerts specific effects on the central hydrophobic core (CHC) region. Inspection of their free energy landscape and representative structures reveals that the protective or pathogenic effects of these variants correlate with their similarity to the wild type (WT) ensemble. Beyond these static thermodynamics analyses, a direct connection to phase transitions was made by estimating heat capacity as a function of temperature. Both analyses predict that A2TC{beta} may exert a pathogenic effect, in contrast to the protective nature of A2T. These findings offer a deeper understanding of the effects of site-specific mutations and chirality and shed light on the development of advanced therapeutic strategies for AD.

Human immunodeficiency virus (HIV) infection promotes considerable bioenergetic, spatially heterogenous strain to the brain that is incompletely ameliorated through viral suppression afforded by antiretroviral therapy (ART). Disrupted homeostasis of brain lipids after HIV in humans or simian immunodeficiency virus (SIV) infection in rhesus macaques occurs due to elevated energetic demands, neuroinflammation, reactive oxygen species, and barrier leakiness. Brain lipids are particularly vulnerable to HIV-associated dysregulation due to their high abundance, unique composition, and specialized functional roles. Using rhesus macaques exposed to SIV and ART (tenofovir disoproxil fumarate (TDF), emtricitabine (FTC), and dolutegravir (DTG), we investigated the spatial distribution and abundance of lipids across brain regions and metabolically relevant peripheral tissues using mass spectrometry imaging. When comparing lipid abundance, individual lipids representing a multitude of species were more varied across tissues than by treatment condition. Further, we discerned either solely SIV infection or ART outweighed one another in altering phospholipids in different tissues Presence of ART had a greater influence on phospholipid homeostasis in the temporal cortex and hippocampus than in the midbrain, possibly due to differences in penetrance and turnover of ART across brain regions. Overall, these data demonstrate ART robustly increased phospholipids across brain regions while SIV infection had a varied impact depending on the brain region. These findings inform the need to further evaluate the neurologic consequences that may result in the brain due to disrupted lipid homeostasis across ART regimens.

Carnitines are a structurally diverse class of metabolites formed by conjugation of L-carnitine with fatty acids, amino acids, xenobiotics, and microbial metabolites. They play roles in transport, mitochondrial and peroxisomal metabolism, detoxification, and systemic signaling, yet their chemical diversity remains incompletely defined. We applied a pan-repository data mining strategy of LC-MS/MS data across GNPS/MassIVE, MetaboLights, and Metabolomics Workbench using MassQL diagnostic fragment ion filtering to systematically extract acylcarnitine spectra. This yielded a library of 34,222 unique MS/MS spectra representing 2,857 atomic compositions, corresponding to 3,872,050 detections. These datasets provide an MS/MS library for annotation, discovery, and contextualization of acylcarnitines, enabling identification of previously unknown carnitines, such as dihydroferulic acid conjugated carnitines and supporting future exploration of this metabolite class across host metabolism, diet, microbial activity, pharmacological exposures, and metabolic dysregulation.

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.

The core biopolymers (DNA, RNA and proteins) are assembled from their monomers under conditions that avoid water. RNA is crucial for the Origin of Life. When cleaved from its polymerized state, RNA first transitions to nucleoside 2,3-cyclic phosphates. In the reverse direction, RNA polymerizes from 2,3-cyclic monomers in dry states, creating short oligomers that then can ligate on a template under aqueous, alkaline conditions. We studied the role of the counterions in polymerization of 2,3-cyclic nucleotides under geologically plausible settings. Through experiments and simulations, we demonstrate that the presence of ammonium and alkylammonium counterions greatly improves RNA polymerization. The otherwise less reactive cytidine containing monomers formed polyC sequences of up to heptamers; copolymers of AU, GC, or GCAU were detected up to hexamers. Our findings suggest three reasons for this: (1) (Alkyl)ammonium cations form hydrogen bonds with phosphates, (2) their alkaline pKa value can trigger general base catalysis, and (3) (alkyl)ammonium salts naturally form dry, anhydrous materials. The findings indicate that pyrolyzed organic tars creating ammonia-rich gas pockets in subsurface rocks could have enhanced the early evolution of RNA. TOC image O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/713775v1_ufig1.gif" ALT="Figure 1"> View larger version (112K): org.highwire.dtl.DTLVardef@1adc431org.highwire.dtl.DTLVardef@12b8da0org.highwire.dtl.DTLVardef@5f187dorg.highwire.dtl.DTLVardef@140ed1a_HPS_FORMAT_FIGEXP M_FIG C_FIG

Cultivated meat production requires robust and validated analytical methods for comprehensive characterization. While transcriptomics-based approaches establish the foundational profile of molecular analysis, proteomics provides additional resolution that further enhances scientific certainty in both product development and safety characterization. However, the industry adoption of proteomics is currently hindered by technical complexity and a critical lack of analytical standardization, which leads to significant workflow-dependent variations in proteome coverage. To address this gap, we investigated the influence of key workflow steps (digestion, cleanup, LC-MS conditions) on the proteome profile of cultivated duck biomass. We compared five bottom-up sample preparation protocols - two traditional in-solution options (urea and SDC-based protocols), two device-based approaches (PreOmics iST and EasyPep kits), and an innovative protocol (SPEED), and demonstrated that device-based protocols offered the highest peptide yield and proteome coverage. However, optimization allowed cost-effective in-solution methods to achieve comparable performance. Specifically, an optimal digestion time of 3 hours at 37{degrees}C and the use of polymer-based desalting columns significantly enhanced protein identification ([~]4500 - 5000 IDs). Moreover, data independent acquisition (DIA) provided deeper proteome coverage than data dependent acquisition (DDA) with higher precision ([~]6500 vs 5000 IDs). The validated Standard Operating Procedures presented here establish a standardized framework for bulk bottom-up proteomics in cultivated meat, facilitating the generation of reliable and comparable data required for robust multi-omics characterization. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/713501v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@5b61b8org.highwire.dtl.DTLVardef@16c7e65org.highwire.dtl.DTLVardef@1de21d2org.highwire.dtl.DTLVardef@7e984a_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIComplexity and non-standardization limit MS-proteomics use in cultivated meat (CM). C_LIO_LICM protein profile varies with sample prep, LC-MS, and data processing pipeline. C_LIO_LIDevice-based and optimized cost-effective protocols offer a high proteome coverage. C_LIO_LIProteomics can complement transcriptomics for a comprehensive CM characterization. C_LIO_LIProposed standardized methods ensure reliable data for future regulatory submissions. C_LI

Pseudomonas putida strain KT2440 is a crucial model organism for synthetic biology and bioengineering applications, yet there currently exists no comprehensive metabolomics database comparable to those available for other model organisms. This gap hinders the use of untargeted metabolomics for exploratory analyses in this system. We developed the P. putida metabolome reference database (PPMDB v1) to address this limitation by consolidating metabolite information from multiple sources and expanding coverage through computational predictions. The database was constructed by curating metabolites from BioCyc, BiGG, and other literature sources, then computationally expanding this collection using BioTransformer environmental transformation predictions to generate additional predicted metabolites. We enhanced the databases utility for molecular annotation in metabolomics studies by incorporating analytical properties including collision cross-sections, tandem mass spectra, and gas-phase infrared spectra. These analytical properties were gathered from existing measurement data or predicted using computational tools. We further augmented the database through inclusion of reaction information and pathway annotations, facilitating biological interpretation of metabolomics data. This publicly available resource fills a critical gap in P. putida research infrastructure, supporting metabolite annotation and biological interpretation in untargeted metabolomics studies and enabling in-depth exploratory analyses of this important synthetic biology platform at the molecular level. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/713193v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@c8828forg.highwire.dtl.DTLVardef@1f3a5c5org.highwire.dtl.DTLVardef@1084535org.highwire.dtl.DTLVardef@1f7ca4a_HPS_FORMAT_FIGEXP M_FIG C_FIG

Background: Mitochondrial dysfunction is an emerging metabolic hallmark of age-related diseases, yet tools to directly profile mitochondrial pathways and test metabolic interventions in the living human eye remain limited. Multi-omics ocular liquid biopsy enables real-time proteomic and metabolomic profiling of the intraocular microenvironment, complementing systemic biomarkers and imaging surrogates. Here, we used this approach to define mitochondrial and tricarboxylic acid (TCA) cycle dysregulation in geographic atrophy (GA) and to assess whether oral -ketoglutarate (-KG) supplementation can modulate mitochondrial metabolites within the eye. Methods: Mitochondrial and TCA cycle-related proteins were profiled in aqueous humor (AH) samples from patients with GA using DNA-aptamer-based proteomics. In a phase 0 study, a second cohort undergoing sequential cataract surgery provided paired AH samples collected at first-eye surgery and at second-eye surgery after interim -KG supplementation. These samples underwent targeted metabolomic profiling using hydrophilic interaction liquid chromatography coupled with mass spectrometry. Results: In GA, 64 mitochondrial proteins were differentially expressed, including coordinated TCA-cycle deficiencies marked by reduced expression of enzymes regulating TCA entry and flux, including PDHB and DLST. In the phase 0 cohort, oral -KG supplementation significantly increased intraocular -KG levels and the -KG-to-succinate ratio (P < 0.05), with coordinated shifts across TCA intermediates consistent with enhanced TCA cycle flux. Conclusions: AH proteomics demonstrated mitochondrial pathway depletion in GA, consistent with reduced oxidative bioenergetic capacity. AH metabolomics provided first-in-human in vivo evidence that systemic -KG supplementation can modify intraocular metabolites and may enhance intraocular energy metabolism. These findings support ocular liquid biopsy as a precision-health framework for per-patient biomarker-guided metabolic trials in GA.

The contribution of soil chemistry to plant growth and resilience, including presence of phytohormones, is increasingly recognized. However, comprehensive characterization of soil phytohormones remains limited by chemical complexity of soil matrices, diversity and low- abundance of metabolites. To enable further discoveries we developed and validated performance of a liquid chromatography-mass spectrometry method with solid phase extraction, integrating targeted and untargeted hormonomic approaches for comprehensive soil phytohormone profiling. Method performance was evaluated for sixteen plant growth-regulating compounds and precursors, including abscisic acid, auxins, cytokinins, gibberellic acid, jasmonic acid, salicylic acid, karrikins, melatonin, serotonin, and tryptophan. The method demonstrated strong linearity (R{superscript 2} = 0.989-0.999), high sensitivity (limits of detection and quantification 0.1-50.2 and 1.4-167.3 pg on-column, respectively), and acceptable precision (1.3-9.6% intraday; 3.4-34.8% interday). Soil composition had a significant effect on recovery, with recovery being poor in some soils such as clay-rich soils; however, recovery for most phytohormones were within 20% of the matrix- adjusted spiked value. Validation results confirm that the method is suitable for use and was then used to quantify analytes in representative soil types. Integration of untargeted analysis expanded coverage to 250 additional putative phytohormones and hormone-related metabolites, revealing chemical signatures potentially associated with plant community composition. The method is robust across these soils spanning sandy, peat-rich, and clay-rich textures. This approach provides a versatile framework for investigating belowground phytohormone dynamics and their roles in plant physiology, resilience, and soil-plant feedbacks.