Journal of Proteomics

Plasma proteomics is used for a variety of applications including biomarker discovery, disease monitoring, and drug development. Data-independent acquisition (DIA) has vastly improved the breadth of proteins that are identified from samples; however, given challenges in reproducibility and translation, it is critical that the quantitative performance of these methods is reliable. Analysis of global proteomics data typically incorporates information from all detected peptides. However, some peptides do not reflect their parent protein amount, due to irreproducible digestion, modification, analytical interferences or instability. We hypothesise that including these peptides impacts protein relative quantification, and thus, a refined spectral library containing only quantitatively representative peptides provides superior protein quantification. By analysing a defined multi-species spike-in model, we show that refining a plasma spectral library by removing precursors that fail to meet quality control metrics (25.4% of all identified precursors) reduces noise and variability, improving precision, accuracy and differential abundance analysis by up to [~]11%, with minimal identification losses and substantial reduction in computational demand. This demonstrates proof-of-concept that refining spectral libraries produces results that prioritize quantification quality over quantity. This approach could enable development of universal tissue-specific refined spectral libraries able to improve quantification quality with easy implementation and minimal processing time. Significance of the StudyAs DIA mass spectrometry proteome depth increases, the quality of the associated protein quantifications must be considered alongside identification breadth, particularly in complex matrices such as plasma, which presents additional technical challenges. The spectral library used for protein identification and quantification is a critical determinant of DIA performance, and its composition requires considerable consideration. This work illustrates an initial step toward improving protein quantification starting at the spectral library level by filtering precursors which are poor quantitative representatives of their parent proteins. In doing so, the resulting data is more reliable for downstream and biological interpretation, with fewer false differential abundance assignments and reduced quantitative noise. As such, this work represents a broader shift away from the habitual focus of MS workflows on maximising the number of protein and differential abundance identifications and instead prioritises the quality of quantification over quantity. These initial findings lay the groundwork for further development of spectral library refinement strategies, with the potential to continue improving the accuracy and precision of protein quantification in DIA-based proteomics.

Sonication is a fundamental technique in proteome sample preparation, primarily used for protein solubilization and shearing of genomic DNA. Although the mechanical shearing of DNA is well-characterized, its unintended impact on protein structural integrity remains a significant "blind spot" in high-throughput analytical workflows. In this study, we systematically investigated sonication-induced protein fragmentation by combining gel-based fractionation (PEPPI-MS) with sequence-level compositional analysis and bioinformatic mapping. Our results demonstrate that sonication does not significantly alter overall proteome identification or the recovery of membrane proteins; however, it induces extensive and non-random protein fragmentation. Sonication caused an approximately three-fold increase in the abundance of >45 kDa protein-derived fragments migrating into the <40 kDa fraction, and 1,620 high-molecular-weight (MW) proteins were uniquely detected in the lower-MW fraction upon sonication, an eight-fold increase over non-sonicated controls. Peptide-level amino acid composition analysis revealed subtle but directional shifts in the sonication-derived fragments. This residue-level signature is reinforced by two orthogonal structural analyses (MobiDB peptide-level mapping and protein-level profiling using metapredict V3 software), which show that sonication-susceptible proteins harbor more than twice the disordered content of length-matched controls (median 40% vs. 18%). This study identifies a previously unrecognized "structural bias" whereby intrinsically disordered region (IDR)-rich proteins are selectively compromised during sample preparation. Because these fragments are indistinguishable from enzymatic digestion products in conventional bottom-up proteomics, the underlying structural damage is effectively masked in global quantitative datasets, potentially distorting biological interpretations related to protein size, isoforms, and stability, particularly for IDR-rich classes, such as transcription factors and signaling molecules. We propose that optimizing and standardizing sonication parameters is essential for ensuring the accuracy and reproducibility of quantitative proteomic analyses.

Peptide spectrum annotation is critical for the assignment of peptides and the localisation of modifications. While many existing tools provide spectrum annotation capacities, they often lack the flexibility required to allow bespoke spectral annotation of peptides containing multiple labile modifications or the accurate assignment of peptides in which fragmentation deviates from canonical patterns. In these cases, user-guided annotation is widely used to improve assignment completeness, however it typically does not integrate peptide scoring, making it challenging to assess the empirical improvement of the associated annotation and its impact on downstream false-discovery rate estimations. Here, we introduce an interactive annotation environment, the 'MassSpectrum Analyzer', which aims to streamline the exploration and analysis of modified peptides by enabling user-defined customisation with peptide scoring. Using (2-Aminoethyl)trimethylammonium carboxyl-derivatised peptides and glycopeptides as case studies we demonstrate the capacity of the MassSpectrum Analyzer to rapidly explore and allow the assessment of modified peptide datasets. By enabling direct assessment of the impact of user-guided choices on peptide scoring, we show how the detection of highly modified peptides can be improved through post-search integration of modification fragmentation information in a statistically robust manner. Similarly, by permitting comparisons of peptide ion intensities across spectra, we show that global fragmentation patterns can be quantified allowing the interrogation of trends that only become clear when spectra are assessed en masse. Combined, the MassSpectrum Analyzer streamlines the generation of publication-ready spectra and provides a means to assess how the inclusion of annotated features influences assignment scores.

BackgroundThe commercialization of Nigerias single-line pod borer-resistant (PBR) cowpea (IT97KT), the first transgenic cowpea variety in the world expressing Cry1Ab gene, has raised questions about potential unintended molecular changes and their ecological implications. This study employed integrated proteomic and metabolomic profiling to compare the transgenic line with its non-transgenic isoline (IT97KN) and assess molecular indicators associated with ecosystem resilience. MethodsProteomic analyses were conducted using LC-MS/MS following filter-assisted sample preparation, while metabolomic profiling employed GC-MS and UHPLC-MS/MS platforms. Differential protein and metabolite abundance were assessed using label-free quantification, volcano plot analysis, principal component analysis (PCA), hierarchical clustering, and Gene Ontology (GO) enrichment analyses. ResultsProteomic profiling revealed substantial overlap between IT97KT and IT97KN, with only a limited subset of proteins exhibiting significant differential abundance. Upregulated proteins in IT97KT were primarily associated with seed storage, redox regulation, oxidative stress mitigation, and defense-related functions, including Late Embryogenesis Abundant Protein 1 (LEA1), vicilins, thioredoxin, and iron superoxide dismutase. Among 37 proteins linked to ecological adaptation, only LEA1, CPRD22, and Bg7S showed significant differences. Similarly, only carbonic anhydrase II displayed differential abundance among proteins associated with potential ecological risk. PCA and clustering analyses demonstrated high proteomic similarity between genotypes. Metabolomic analyses identified sixteen major metabolites, predominantly fatty acids, with no statistically significant differences in abundance or composition between transgenic and non-transgenic lines ConclusionsThe transgenic PBR cowpea exhibited minimal unintended proteomic and metabolomic alterations relative to its non-transgenic isoline. These findings indicate that Cry1Ab insertion did not substantially disrupt molecular pathways associated with ecological adaptation, environmental risk, or metabolic homeostasis, providing molecular evidence supporting the environmental and biosafety equivalence of PBR cowpea.

Proteins are dynamically regulated by a myriad of post-translational modifications (PTMs) that control their stability, conformation, activity, subcellular localization, and local interactions. Capturing the precise composition of these various modification states, or proteoforms, is a principal objective of top-down proteomics (TDP). By ionizing intact proteoforms and combining measurements of precursor ion and fragment ion masses, the position, stoichiometry, and combination of PTMs can be determined. Despite the highly valuable measurements that TDP can provide, it is typically less sensitive than corresponding peptide-level analysis with many reports utilizing input material in the microgram to milligram range. Contributing to this lack of sensitivity is the risk of sample loss due to non-specific binding to surfaces during sample preparation. The most widely employed sample preparation approaches for TDP either require high sample input (e.g. precipitation and ultra-filtration) or fail to effectively remove surfactants (e.g. solid-phase extraction). These limitations have hindered advancement of targeted TDP applications involving immunoprecipitation and other enrichment strategies. Bead-assisted protein aggregation, also referred to as single-pot, solid-phase-enhanced sample preparation (SP3), has emerged as a popular sample preparation strategy for bottom-up proteomic workflows, but has only been used in TDP with secondary ion exchange chromatography cleanup. We envisioned a magnetic bead based protein cleanup approach that proceeds directly to MS analysis with judicious choice of bead surface chemistry and elution conditions. Here we report a sample preparation method using hydroxyl-functionalized magnetic beads for top-down proteomics applications.

Class imbalance remains a challenge in metabolomics research, where biological and technical variability can affect statistical inference and machine learning (ML) performance. Class-balancing algorithms address this issue by either increasing minority-class observations or reducing the number of majority-class samples. This study evaluated the impact of oversampling and undersampling algorithms on targeted and untargeted metabolomics datasets derived from LC-MS and GC-MS analyses of plasma samples from patients with glioblastoma, meningioma, and controls. Synthetic Minority Oversampling Technique (SMOTE) and Random Undersampling (RUS) were applied to balance the datasets, and their effects on data distribution, inter-feature correlations, and machine learning model performance were compared. RUS preserved the original feature distributions but reduced representativeness by removing the majority-class samples. In contrast, SMOTE introduced synthetic samples that altered covariance structures, increasing the risk of overfitting, particularly in small datasets (n=10). These effects diminished with larger groups (n=30), partially restoring correlations between metabolites. Model performance varied across the class-balancing algorithms. Random Forest classifiers benefited from both balancing methods, with undersampling often yielding higher F1 scores, whereas Support Vector Machine models showed reduced classification performance. These findings highlight the importance of selecting class-balancing strategies based on dataset size, analytical platform, and ML algorithm in metabolomics studies.

Metabolites from Plantago lanceolata (plantain) biomass have been linked with biological nitrification inhibition (BNI) in soil. After grazing, leaf metabolite chemistry is altered via digestion, and a suite of secondary metabolites are then delivered onto soil via dung and urine. The purpose of this study was to establish if urine from sheep grazed on plantain had BNI activity when added to pasture soil, and to identify the metabolite profile(s) that most likely contribute to the BNI effects observed. Groups of sheep (n=5) were grazed on one of nine different plantain cultivars in autumn and spring with analysis of leaf material, urine, soil incubation and BNI bioassay data used to identify potential metabolite candidates implicated with BNI. The urinary nitrogen and metabolite composition of sheep fed plantain varied significantly between cultivars and season. After 28 days of incubation, all soil microcosms treated with plantain-derived urine had up to 35% less nitrate than comparative ryegrass urine controls in both seasons, except one in autumn. The key phytochemistry associated with lower soil nitrate concentrations was phenylethanoid and iridoid glycosides resulting in a higher output of glucuronidated, methylated and sulfated secondary metabolites in the urine. Among 19 secondary metabolites identified in the urine, hydroxytyrosol-related metabolites as well as catechol glucuronide, 2-methoxyphenyl sulfate and guaiacol-{beta}-D-glucuronide appear to be the most likely target compounds with respect to the BNI effects observed. Variation in metabolites from different plantain cultivars affected the ratio of metabolite derivatives in urine, which ultimately affected soil nitrification rates. Cultivar phytochemistry is therefore an important consideration with respect to BNI under urine patches. HighlightsO_LISheep grazing different plantain cultivars had different urine compositions C_LIO_LIUrines elicited biological nitrification inhibition (BNI) in soil and in vitro C_LIO_LIDifferent BNI response was related to differential expression of urine metabolites C_LIO_LIKey urine metabolites associated with BNI are derived from glycosidic compounds C_LI

With the rapid development of mass spectrometry-based proteomics, the volume of phosphoproteomic data has increased substantially. However, accurate localization of phosphorylation sites and standardized statistical validation remain critical analytical bottlenecks. To address the lack of standardized cross-algorithm evaluation, we introduce onsite, a unified and open-source Python framework. onsite integrates an alanine-decoy strategy to estimate the false localization rate (FLR) across three algorithms: AScore, PhosphoRS, and pyLucXor. This modular architecture efficiently processes large-scale datasets and enables global FLR calculation. Benchmarking on the standard synthetic phosphopeptide dataset PXD000138 highlighted distinct inter-algorithmic variations. Using the same 5% global FLR threshold, pyLucXor localized the most target sites (28,353). It also reached a high accuracy (91.22%) against the known ground truth, resulting in the largest number of correctly localized sites (25,865). Reanalysis of the highly fractionated, large-scale PXD012255 dataset further demonstrated that native integration of onsite into the quantms pipeline enables scalable processing and provides a standardized framework for FLR control in large-scale phosphoproteomics. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=64 SRC="FIGDIR/small/737157v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@e4c85dorg.highwire.dtl.DTLVardef@1e8464org.highwire.dtl.DTLVardef@185cea1org.highwire.dtl.DTLVardef@1c0d1bc_HPS_FORMAT_FIGEXP M_FIG C_FIG

Retinopathy of prematurity (ROP) is caused by hyperoxic exposure of prematurely born infants. The mouse model of oxygen-induced retinopathy (OIR) recapitulates pathological features of both phase I and phase II ROP. We here looked at the retinal proteins that change in response to hyperoxia in phase I of the mouse model of OIR. Using tandem mass tag labeled proteomics, we found several differentially expressed proteins (DEPs) in phase I of OIR. Of all the DEPs, we investigated the role of previously unknown protein NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 4-like 2 (NDUFA4L2). NDUFA4L2 protein and its paralog NDUFA4 are both mitochondrial complex I proteins; however, here we demonstrate that NDUFA4L2 changes in both phases of OIR, with no changes in its paralog NDUFA4, implying its unique function in pathophysiology of the disease. We demonstrate that NDUFA4L2 is an oxygen-sensitive protein and regulates retinal endothelial cell migration by rescuing isocitrate dehydrogenase flux impaired by hyperoxia in phase I of OIR.

N-glycolylneuraminic acid (Neu5Gc) has been repeatedly associated with human cancer, but reliable detection has remained elusive, generating controversy regarding its presence in human samples. To address this, matrix-matched calibration curves, which have been pioneered in proteomics and metabolomics for assessing changes in complex mixtures, were measured of released N-glycans at four orders of magnitude dynamic range in defined mixtures, systematically benchmarking Neu5Gc-containing N-glycan detection across multiple LC-MS platforms and sites. Orthogonally, the gold-standard analytical method, consisting of fluorescence detection of labelled monosaccharides separated by LC, was applied to the same samples, yielding absolute concentrations of Neu5Gc. LC-MS demonstrated an extended detection range of three or more orders of magnitude while retaining intact N-glycan measurement, improving assay specificity and enabling detection of the variety of Neu5Gc-bearing N-glycans. By combining orthogonal dimensions of evidence, including chromatographic separation, isotopic distribution matching, and composition-confirming MS/MS, LC-MS confidently resolved Neu5Gc signals from noise, even at low abundance. In comparison, DMB-LC-FLR was limited to two orders of magnitude dynamic range, insufficient for detection of Neu5Gc in commercially available pooled human sera. These findings strongly support that DMB-LC-FLR assay specificity and sensitivity are insufficient for Neu5Gc detection in human samples due to noise overwhelming the Neu5Gc signal. By establishing a reusable benchmarking framework for future glycomic studies, we aim to use LC-MS to improve the measurement of Neu5Gc in clinical samples.

BackgroundDysregulated cell migration leading to metastasis remains the primary cause of cancer-related mortality. It has been challenging to understand how cells regulate migration. We have previously created the first selective inhibitor of cell migration, KBU2046. Here, we use it as a probe to identify regulatory processes. MethodsMetastatic and primary human prostate cancer cells were treated for different times and at different concentrations with KBU2046. Immunofluorescent microscopy examined protein localization in cells. Label-free mass spectrometry (MS) was performed on total cell proteins, Tandem Mass Tag (TMT) labeling MS was used on membrane fractions, and temporal phosphoproteomic profiling. Results were analyzed with a suite of bioinformatic tools. ResultsKBU2046-induced migrastasis is associated with the accumulation of activated integrin {beta}1 into focal adhesions. Whole-cell proteomics demonstrated suppression of processes that mediate intracellular protein trafficking and increases in mitochondrial energy-generation signatures. Evaluation of the membrane fraction identified increases in membrane repair and maintenance processes and decreases in those that drive motility. Temporal- and concentration-dependent phosphoproteomic profiling revealed that KBU2046 initiates a dynamic, cascading sequence of transient signaling waves rather than a static block. ConclusionsKBU2046-induced migrastasis appears to operate through spatial decoupling rather than structural degradation. By restricting the intracellular trafficking machinery required for receptor recycling, KBU2046 limits focal adhesion turnover, providing a correlative framework to inhibit metastatic dissemination independent of direct cytotoxicity. O_FIG O_LINKSMALLFIG WIDTH=122 HEIGHT=200 SRC="FIGDIR/small/736165v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@1cc69d3org.highwire.dtl.DTLVardef@137b843org.highwire.dtl.DTLVardef@1225e50org.highwire.dtl.DTLVardef@15dd8d2_HPS_FORMAT_FIGEXP M_FIG Graphic Abstract C_FIG

Bread wheat (Triticum aestivum L.) possesses a large and highly repetitive allohexaploid genome and annotation requires extensive protein-level validation. We developed a genome-based wheat proteogenomics workflow integrating large-scale MS/MS reanalysis, GFF3-based peptide coordinate reconstruction, thorough validation, and genome browser-compatible peptide deployment against the IWGSC RefSeq v2.1 reference genome. Public wheat proteomics datasets comprising 577 raw mass spectrometry files ([~]1.0 TB) from 32 tissues were reprocessed using FragPipe/MSFragger, generating 2,226,779 non-redundant peptides and 1,648,740 unique protein accessions. Peptide-to-genome projections using GFF3 annotation files produced 8,291,056 genomic peptide projected rows, of which 98.14% passed validation procedures. Overall, peptide evidence supported 103,095 high-confidence (HC) and 135,495 low-confidence (LC) wheat gene models, corresponding to 96.4% and 84.7% of all parsed HC and LC annotations, respectively. In total, 238,590 wheat gene models (89.4% of all parsed annotations) received protein-level support. Apollo/JBrowse-compatible BED tracks enabled exon-resolved visualisation of peptide evidence across wheat chromosomes. Together, this study establishes a scalable GFF3-based proteogenomics framework for complex polyploid plant genomes and provides an extensive community resource for wheat genome annotation refinement and visual exploration (https://bread-wheat-um.genome.edu.au/apollo/49826/jbrowse/index.html). Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=63 SRC="FIGDIR/small/733048v2_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@6e797org.highwire.dtl.DTLVardef@14ea4fdorg.highwire.dtl.DTLVardef@31f027org.highwire.dtl.DTLVardef@8d908a_HPS_FORMAT_FIGEXP M_FIG C_FIG

Heat stroke is characterized by profound central nervous system dysfunction and vascular abnormalities. Previous studies have demonstrated the marked vulnerability of the CNS to thermal stress, resulting in neuronal injury and glial activation. However, the metabolic mechanisms linking acute injury to chronic neurological long-term effects remain understood. The neuropathological changes are closely associated with neuroinflammatory and metabolic disturbances, including dysregulation of the kynurenine pathway, whose metabolites modulate neurotoxicity, neuroprotection, and immune responses. Here, we present the first comprehensive characterization of kynurenine pathway metabolomic profile across both plasma and brain tissue in a mouse model of heat stroke. Using a validated and sensitive LC-MS/MS method, we simultaneously measured and quantified 13 analytes (kynurenine, kynurenic acid, quinolinic acid, nicotinic acid, picolinic acid, xanthurenic acid, anthranilic acid, 3-hydroxykynurenine, 3-hydroxyanthranilic acid, indole-3-acetic acid, indole-3-lactic acid, 5-hydroxyindoleacetic acid and neopterin). The findings reveal a biphasic metabolic response, characterized by an acute serotonergic disruption and reduced neuroprotective capacity, followed by chronic activation of the kynurenine pathway, depletion of central serotonin metabolites, and metabolic signatures consistent with gut microbiota dysbiosis. The acute phase is marked by a transient imbalance favoring neurotoxic kynurenine pathway metabolites, whereas the chronic phase reflects sustained pathway activation. Notably, the plasma-brain dissociation of 5-hydroxyindoleacetic acid emerged as the most prominent cross-compartment finding, suggesting a potential biomarker of central serotonergic depletion and a mechanistic link between peripheral and central metabolic changes, with implications for therapeutic targeting during the subacute recovery phase.

Background: Early detection of pancreatic ductal adenocarcinoma (PDAC) is critical. While faecal elastase-1 (FE-1) is a standard clinical marker for pancreatic function, its diagnostic accuracy for malignancy is limited. We sought to identify plasma metabolites that enhance FE-1 performance in symptomatic "at-risk" patients. Methods: Using the DEPEND cohort (CRUK C45617/A29908), plasma metabolomics was performed on patients with resectable PDAC (n=23) and healthy volunteers (n=24). Predictive modelling included feature selection and cross-validation, with further validation in an independent external cohort. Results: Citrulline was identified as significantly depleted in PDAC patients across discovery and validation cohorts. In isolation, Citrulline achieved an AUC of 0.86 (internal) and 0.88 (external validation). Standalone FE-1 demonstrated an AUC of 0.67. However, combining Citrulline and FE-1 significantly improved diagnostic performance, achieving a combined AUC of 0.96. Stratification revealed distinct metabolomic signatures associated with poorly differentiated tumours, suggesting a link to histological grade. Conclusions: Integrating Citrulline with FE-1 testing substantially improves PDAC detection in symptomatic patients. This non-invasive panel offers high diagnostic potential, though prospective validation is required to establish clinical cut-offs for routine practice.

Background The ischemic penumbra, a metabolically compromised yet potentially salvageable region surrounding the ischemic core, is a prime target for acute stroke intervention. Yet an objective molecular definition of the penumbra, particularly during the earliest stages of ischemia, remains lacking. Methods and Results We applied principal component analysis (PCA) followed by k-means clustering to high-resolution mass spectrometry imaging data covering multiple metabolic pathways to identify a metabolically defined penumbra in a mouse model of hyperacute stroke (45 min middle cerebral artery occlusion, MCAO). Targeted spatial metabolomic profiling by matrix-assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI) reveals a distinct penumbral metabolic profile, marked by relative preservation of high-energy phosphates, comparable lactate accumulation, and reduced succinate accumulation relative to the core. Spatial transcriptomics revealed selective induction of immediate-early genes, including Npas4, Fos and Junb, within the penumbra. Consistently, imaging mass cytometry shows enrichment of phospho-histone H3 (pHH3) within the penumbra, suggesting a chromatin-associated response potentially linked to immediate-early gene activation. Conclusion Together, these findings provide a multimodal molecular atlas of the hyperacute metabolically defined penumbra and reveal molecular features that facilitates its identification and inform future therapeutic strategies.

The TETRATRICOPEPTIDE THIOREDOXIN-LIKE (TTL) proteins are a plant-specific family proposed to function as peripheral membrane proteins that contribute to abiotic stress tolerance in Arabidopsis, likely by maintaining cell wall integrity through brassinosteroid signaling. Previously, we identified a TTL gene that was differentially regulated at the translational level in nitrogen-fixing soybean plants under water deficit (WD) conditions. This finding prompted the characterization of the soybean TTL gene family. Using the Glycine max v4.0 proteome, we identified ten TTL homologs (GmTTL1-GmTTL10), which are unevenly distributed across five chromosomes. Phylogenetic and structural analyses grouped these genes into three clades and revealed a highly conserved exon-intron organization. Likewise, GmTTL proteins display a conserved number and arrangement of TPR and TRXL motifs. To gain insights into their potential biological functions, we integrated co-expression and differential expression analyses. This approach identified a co-expression module enriched for translationally downregulated genes related to the Gene Ontology terms "cellular anatomical entity", "membrane", "cell periphery", "cell wall modification", "nitrate assimilation", and "cell wall organization or biogenesis". Protein-protein interaction network analysis of this specific subset of genes uncovered a novel GmTTL connection with two nitrate reductase enzymes in nitrogen-fixing plants subjected to WD, potentially linking the TTL gene family to new functions or roles. This study provides a framework for future functional studies of GmTTL proteins and their contribution to abiotic stress adaptation in soybean. Key MessageThis work presents the first functional characterization of TTLs proteins in legume species and highlights key processes that may link the TTL gene family to new functions or roles.

The heterotrimeric G-protein transducin (Gt) is among the key proteins mediating phototransduction in rod and cone cells of the vertebrate retina. Even though this protein has been extensively characterized in mammals, little is known about its expression patterns in migratory songbirds. Here we characterised Gt expression in the European robin, a night-migratory songbird known for its light-dependent magnetoreception. The mechanism underlying magnetoreception is not fully understood, but one well-supported hypothesis involves a radical-pair formation in the blue light receptor cryptochrome type 4a. The - and {gamma}-subunits of cone specific transducin have been identified as possible interaction partners of cryptochrome 4a. Therefore, we analysed the expression patterns of various G-protein subunits in bird photoreceptors. Specifically, we combined single cell RNA sequencing and immunohistochemistry, and tested for protein interaction by pulldown, co-immunoprecipitation, and NanoBiT luminescence assays. We show that genes for G-protein subunits GNB1 and GNB3 (coding for Gt{beta}1 and Gt{beta}3, respectively) are predominantly expressed in rods and cones. Among {gamma}-subunits, GNGT2 (coding for Gt{gamma}T2) was the principal isoform in cones, whereas GNG11 (coding for Gt{gamma}11) was associated with rods. In contrast, we did not detect GNG10 (coding for Gt{gamma}10) expression in either photoreceptor type. Interaction assays demonstrated that all three {beta}{gamma} combinations; {beta}{gamma}T2, {beta}{gamma}10, and {beta}{gamma}11, can associate in vitro. These findings indicate that {beta}{gamma} dimer formation in vivo is likely constrained by the photoreceptor-specific expression of the respective subunits. Furthermore, the absence of GNG10 expression in rods and cones does not support a role of this {gamma}-subunit in photoreceptor-based magnetoreception.

Background: Single cell RNA sequencing provides a wealth of information to explore the complexities of the tumour microenvironment, but crucially the spatial topology of the tumour is lost and studying cellular interactions is limited. Spatial transcriptomics aims to address this however the technique remains cost prohibitive for the generation of data from meaningfully-sized clinical cohorts. In contrast, spatial proteomic profiling with multiplex immunofluorescence, preserves spatial interactions, is relatively cost accessible, and is scalable for large clinical cohorts to address powerful translational questions. Whilst multiplex approaches have advanced in recent years, we note that cancer-associated fibroblasts (CAFs) have been explored in less detail, potentially due to difficulties associated with CAF heterogeneity and the diversity of markers used to define them. Methods: We designed, optimised, and validated a multiplex immunofluorescence panel that combines four frequently used CAF markers; alpha smooth muscle actin (aSMA), fibroblast activation protein (FAP), podoplanin (PDPN) and platelet-derived growth factor receptor alpha (PDGFRa) with CD8 and pan-cytokeratin. Here we share our methodology and the practical considerations taken to inform the final panel design. We also highlight the benefits of robust optimisation experiments.

O_LIThe control of amount of nuclear proteins is fundamental in regulating plant gene expression, but the mechanisms of quantitative dynamics of the nuclear proteome are largely unstudied during adaptive responses to pathogens. C_LIO_LIHighly specific labeling, enrichment and measurement of the nuclear proteome was performed using TurboID LC-MS of Arabidopsis thaliana leaves treated with the pathogen-associated molecular pattern (PAMP), flg22, and/or cycloheximide. The chosen experimental approach allowed discrimination of the effects of translation, nuclear protein import, trafficking of preexisting proteins, derepression, and nuclear protein turn-over upon elicitation of basal immunity. C_LIO_LIThe highly specific, deep coverage of proteins in the nucleus makes this study a resource for anyone interested in plant nuclear proteome dynamics and defense. C_LIO_LIAround 2,000 nuclear proteins were repeatedly quantified, including more than 300 transcription factors or other proteins related to transcription. Several proteins with documented activity in endosomes were newly synthesized and imported into nuclei upon PAMP challenge, suggesting alternative nuclear functions in PAMP-triggered immunity (PTI). Circadian clock components, including the transcription factor, CIRCADIAN CLOCK ASSOCIATED 1 (CCA1)-HIKING EXPEDITION (CHE), were depleted upon PAMP challenge, suggesting a safeguard against untimely induction of systemic acquired resistance (SAR). C_LIO_LIBased on proteomic patterns, proteins moonlighting in the nucleus as well as trafficking and turn-over regulation of the proteome are common elements during plant immunity. C_LI

High-throughput data-independent acquisition (DIA) workflows paired with short chromatographic separations are increasingly adopted for systems biology and clinical proteomics. However, narrower peak widths from rapid separations demand faster mass spectrometer cycle times to maintain quantitative depth and reproducibility. The synchro-PASEF acquisition mode on timsTOF mass spectrometers diagonally scans across ion mobility and m/z space, enabling efficient sampling of the precursor ion cloud with shortened cycle times. While synchro-PASEF has demonstrated competitive identification depth for global protein abundance samples compared to conventional dia-PASEF, its performance for phosphoproteomics - where the precursor ion cloud is characteristically broader and bimodally distributed - has not been evaluated. Here, we systematically optimized synchro-PASEF methods for phosphoproteomics and benchmarked performance against two dia-PASEF methods across three sub-hour separations. We found that synchro-PASEF performance depends critically on balancing diagonal window number, total isolation width, and gradient length, with longer gradients favoring more windows for selectivity and shorter gradients favoring fewer windows to preserve sampling frequency. An optimized configuration quantified over 19,000 localized phosphosites using a 23-minute separation. Retention time summation (RTsum) with a factor of 2 increased phosphopeptide identifications by 5-20% and reduced phosphosite-level coefficients of variation by up to 30% across all dia-PASEF and synchro-PASEF methods tested. Using {beta}2-adrenergic receptor (B2AR) activation as a signaling model, we demonstrate that label-free DIA phosphoproteomics can be used to model phosphoproteomics dose-response relationships, showing that synchro-PASEF and dia-PASEF produce highly concordant phosphoproteomic responses, with comparable numbers of responding phosphosites, similar effect sizes, and nearly identical predicted protein kinase A (PKA) substrates downstream of the activated B2AR. While synchro-PASEF did not surpass optimized dia-PASEF in identification depth, its comparable biological performance and amenability to post-acquisition optimization through RTsum support its utility for high-throughput phosphoproteomics. This work provides a transferable framework for synchro-PASEF method optimization and demonstrates the broad utility of retention time summation for PASEF-based phosphoproteomics workflows.