Clinical Proteomics

ObjectiveA catheter-free, non-radiolabeled method that permits in vivo measurement of tissue-specific glucose uptake does not exist. To address this gap, we sought to develop and validate a new, higher throughput mass spectrometry (MS)-based method that combines an injection of insulin with a non-radiolabeled glucose tracer, 2-fluoro-2-deoxyglucose (2FDG), to determine insulin-stimulated tissue-specific glucose clearance in conscious, unrestrained mice. MethodsInjections of saline or insulin with 2FDG were coupled with LC-Q Exactive Hybrid Quadrupole-Orbitrap (LC) MS-based measures of plasma 2FDG and tissue (2-fluoro-2-deoxyglucose-6-phosphate) 2FDGP to determine glucose clearance in mice under several different conditions. ResultsThe newly developed method was first applied to a dose response experiment in mice. Next, the ability of this method to quantify changes in glucose clearance in response to an insulin stimulus was assessed, and glucose clearance was compared between chow and high fat fed mice. Results from these studies showed that insulin-stimulated skeletal muscle and heart glucose clearance can be estimated following a bolus injection of tracer, and these fluxes are blunted in diet-induced obese mice. The broad applicability of this approach was then demonstrated by assessing glucose clearance in a mouse model with anticipated changes in insulin-stimulated skeletal muscle glucose metabolism. ConclusionsThe results validated a new LC-MS method to quantify insulin-stimulated tissue-specific glucose clearance in vivo without the use of catheters or radiolabeled tracers. The method offers great potential because it is designed for application to pre-clinical studies seeking high throughput tests and/or assays that can be coupled with discovery technologies such as genomics, proteomics and metabolomics. HIGHLIGHTSO_LIIn vivo glucose clearance can be estimated by a new non-radiolabeled method. C_LIO_LIThe plasma tracer to tracee ratio is required to determine tissue tracer phosphorylation. C_LIO_LIMeasures of plasma glucose and tracer kinetics are critical for data interpretation. C_LIO_LIThe new method can be combined with omics technologies such as metabolomics. C_LI

Laser capture microdissection (LCM) - based spatial mass spectrometry proteomics is a rapidly emerging technique with strong potential for use in formalin-fixed, paraffin-embedded (FFPE) tissues. Several sample-preparation methods have been developed to decrosslink FFPE proteins for spatial proteomics; however, residual crosslinks often remain, and depth can remain impaired relative to fresh frozen tissue samples. To increase proteome coverage in spatially resolved LCM-FFPE samples, we investigated a panel of chemical compounds with the potential to catalyze the decrosslinking of nucleophilic functional groups on proteins. Systematic screening and optimization of temperature, incubation time, and reagent concentration led to the identification of 3,4-diaminobenzoic acid as an effective agent for improving proteome coverage in FFPE pancreatic tissue. This compound could boost precursor identifications by more than 10% at both reduced (70 {degrees}C) and high (90 {degrees}C) temperatures. Application of this chemical-decrosslinking strategy to a pancreatic ductal adenocarcinoma tissue section enabled the identification of numerous cell-type-enriched proteins with clinical and therapeutic relevance. Taken together, our findings show that chemical decrosslinking can increase proteome coverage in FFPE tissues, thereby advancing our understanding of tissue microenvironments in physiological and pathological contexts.

1.BackgroundHuman infertility affects approximately 17.5% of the global population, with male factors accounting for nearly half of all cases. The identification of reliable molecular biomarkers is crucial for improving the diagnosis and assessment of male fertility. In this study, we developed and optimized an untargeted high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (HPLC-ESI-MS/MS) workflow for comprehensive lipidomic and metabolomic profiling of human spermatozoa using only 1.25 million cells per sample. ResultsCompared to previous reports, our optimized method achieved unprecedented analytical depth, identifying 473 lipid species and 955 structurally annotated metabolites, corresponding to nearly 7.600-fold improvements in detection efficiency per cell over published approaches. Lipidomic analysis revealed cholesterol, fatty acids, phosphatidylcholines, and phosphatidylethanolamine plasmalogens as the most abundant lipid classes, consistent with the structural complexity of the sperm plasma membrane. Metabolomic profiling showed strong enrichment of lipid-related and steroidogenic pathways, including phospholipid biosynthesis, glycerolipid metabolism and androgen and estrogen metabolism. The integration of lipidomic and metabolomic data highlighted functionally interconnected pathways related to membrane dynamics, energy metabolism, and hormone biosynthesis. ConclusionsOverall, this work establishes a robust, sensitive, and scalable analytical framework enabling high-coverage molecular characterization of spermatozoa from limited sample material, laying the groundwork for future biomarker discovery and clinical applications in male infertility research. One Sentence SummaryDevelopment of a highly sensitive untargeted HPLC-ESI-MS/MS lipidomic and metabolomic workflow that achieves unprecedented molecular coverage from only 1.25 million human spermatozoa, revealing interconnected lipid and metabolic pathways and providing a robust foundation for biomarker discovery in male infertility. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/703749v1_ufig1.gif" ALT="Figure 1"> View larger version (74K): org.highwire.dtl.DTLVardef@10b3132org.highwire.dtl.DTLVardef@1caf850org.highwire.dtl.DTLVardef@746adborg.highwire.dtl.DTLVardef@1135539_HPS_FORMAT_FIGEXP M_FIG C_FIG

Malignant peripheral nerve sheath tumors (MPNSTs) are aggressive soft tissue sarcomas and the most common cause of disease-associated death for Neurofibromatosis Type 1 (NF1) patients. In the context of NF1, MPSNTs develop from benign premalignant precursors. The transition to malignancy is usually accompanied by loss of the polycomb repressive complex 2 (PRC2), leading to aberrant upregulation of many genes. The specific mechanisms disrupted by PRC2 loss remain incompletely understood. There is a significant gap in our knowledge of which cell-surface targets become derepressed and therapeutically actionable following PRC2 loss, contributing to the current lack of effective targeted therapies for MPNSTs. This study aims to address this gap by using cell-surface capture technology with mass spectrometry to profile MPNST models. In doing so, we define PRC2-dependent effects on the cell surface proteome, including specific biological pathways that are enhanced or suppressed at the cell surface protein level. We also create an MPNST cell-surface protein compendium comprised of proteins that are highly expressed across a variety of well-defined MPNST models. We prioritized proteins that are preferentially expressed in MPNST or other cancers and for which FDA-approved therapies already exist. Specific proteins from this compendium were molecularly targeted with antibody-drug conjugates in these models to surmise their therapeutic efficacy. Results reveal PTK7 as a novel and promising target for MPNST. In total, these efforts represent a step toward addressing the knowledge gap in MPNST genesis and identifying new therapeutic targets for further testing. Additionally, this data serves as a resource for other researchers wishing to characterize specific molecular targets. KEY POINTSPRC2 modulates key MPNST signaling pathways through the cell surface proteome Cell surface proteomics identifies a plethora of therapeutic targets for MPNST targeted therapy Antibody-drug conjugates targeting PTK7 show enhanced efficacy in reducing MPNST viability IMPORTANCE OF THE STUDYThis study utilizes advances in biochemistry to profile the surface proteome of malignant peripheral nerve sheath tumors. In doing so, it identifies many proteins whose presence is abundant on the cell surface of MPNST cells. Pre-clinical drug testing shows that use of antibody-drug conjugates may be effective in killing MPNST cells when targeted to epitopes identified in our MPNST cell surface proteome compendium. This study is a departure from more commonly used transcriptomic methods to identify cell surface proteins by using direct surface capture and mass spectrometry, providing a more direct measurement of cell surface protein abundance. Additionally, it identifies a handful of proteins which can be directly targeted pharmaceutically and one in particular, PTK7, whose targeting is highly effective in killing MPNST cells.

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.

Plasma extracellular vesicles (EVs) are considered excellent sources for biomarker discovery since they carry signatures of their cellular origin and disease processes. In this paper, we evaluate the potential of plasma EV proteomics analysis for identifying predictive biomarkers of developing type 1 diabetes (T1D), which results from autoimmune destruction of insulin-producing {beta} cells in the islet. We used strong anion exchange beads (Mag-Net) to capture plasma EVs from 19 donors with islet autoimmunity (diagnosed by circulating autoantibodies against islet proteins - AAB+) vs. 17 control individuals and analyzed their protein cargo by mass spectrometry. The analysis identified and quantified 5,480 proteins, a 3.2-fold increase in proteome coverage compared to our previous T1D biomarker proteomics study that used whole plasma depleted of the 14 most abundant proteins. The Mag-Net approach also detected 1,306 out of the 1,717 proteins (76%) that we previously verified as EV proteins. Statistical tests revealed 448 proteins to be differentially abundant in AAB+ vs control volunteers, including 69 previously verified EV proteins. A functional-enrichment analysis resulted in overrepresentation of 25 pathways among the differentially abundant proteins, including pathways related to autoimmune response and lipid metabolism. The capacity of this data to predict AAB+ was tested with a machine learning analysis using a random forest model, resulting in a receiver operating characteristic-area under the curve of 0.81. Overall, our study indicates that plasma EV proteomics analysis can be an exciting approach for studying biomarkers for developing T1D. Significance of the studyType 1 diabetes (T1D) is a disease characterized by the bodys inability to produce insulin and consequently, to control blood glucose levels. Despite the initial trigger being unclear, the disease development process involves an autoimmune response to the islets of Langerhans, resulting in the death of insulin-producing {beta} cells. There is no cure for the disease, and treatment relies on exogenous administration of insulin. Therefore, preventive therapies that block the autoimmune process are attractive for treating T1D. In fact, anti-CD3 antibody (Teplizumab) delays the onset of T1D by 2 years by targeting T cells. Predictive biomarkers for developing T1D are needed to aid the development and implementation of new therapies and to identify the initial trigger and mechanisms of the islet autoimmune process. In this paper, we assess the potential of plasma extracellular vesicle (EV) proteomics analysis for identifying predictive biomarkers of T1D. Our results show excellent potential of the approach, opening opportunities to perform broader studies to identify biomarkers for developing T1D.

Lynch syndrome, historically known as hereditary nonpolyposis colorectal cancer, is caused by germline mutations in the DNA mismatch repair (MMR) genes, MLH1, MSH2 (EPCAM), MSH6, and PMS2. While the genetic changes associated with Lynch Syndrome have previously been characterized, there have not been studies of the associated proteomic alterations, in part because of the limited availability of primary samples and the absence of in vitro model systems. In this study, the first large-scale tissue proteomic assessment of Lynch Syndrome samples as well as three other subtypes of colorectal cancer was completed with specimens from the Ohio Colorectal Cancer Prevention Initiative. The cohort contained three groups of microsatellite unstable (MSI-high) CRC patients (Lynch syndrome, double somatic MMR mutation, and MLH1 hypermethylation) and a group of microsatellite stable (MSS) CRC patients. A total of 122 tumor and complimentary normal mucosa samples from 61 patients were evaluated using label-free bottom-up proteomic analysis. Hierarchical clustering analysis of the global proteome showed that the MSS group was significantly different than the three MSI-high groups. Of the 1,084 proteins found to be dysregulated across all four colorectal cancer subtypes, there were age at diagnosis associated shifts in proteins correlated with tumor proliferation and immune regulation for the Lynch syndrome and Double Somatic samples. The proteins TPD52, GMDS, and DSP showed increased protein abundance correlated with older age at diagnosis. In addition, the Lynch syndrome samples showed substantial sex-based differences in immune and inflammatory pathways, for example, downregulation of ZG16, DIS3, and WDR43. This study fills a critical gap as the first proteomic characterization of Lynch syndrome samples to date. Data are available via ProteomeXchange with identifier PXD073693. TeaserThis is the first study of the global proteomic differences between Lynch Syndrome and other forms of colorectal cancer.

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

BackgroundChromosome 8q (chr8q) copy number gain is associated with high-grade transformation in malignant peripheral nerve sheath tumors (MPNST), an aggressive soft tissue tumor with poor outcomes in the high-risk and metastatic settings. Although chr8q gain is associated with inferior overall survival in patients with MPNST, standard of care therapies do not currently consider stratification by genomic features, including chr8q status. MethodsWe employed a proteogenomic approach to characterize proteomic and transcriptional programs associated with chr8q and nominate drug targets for potential treatment stratification based on chr8q status. We leveraged our growing library of fully characterized MPNST patient-derived xenografts (PDX) and collected LC-MS/MS global and phospho-proteomics measurements for six of these samples. We then integrated these data with transcriptomics and copy number data to identify molecular changes that are correlated with chr8q copy number. We nominated pathways, transcription factors, and kinases that were differentially active in chr8q gain samples and posited that these samples would respond differently to drugs compared to chr8q wildtype samples. We then tested this hypothesis in vitro. ResultsOur results suggest that the chr8q gene MYC may be a key driver of downstream effects that can be targetable with inhibitors of PLK1. Conversely, EGFR inhibition may be more effective in MYC-diploid MPNSTs than those with MYC gain. These results nominate candidate pathways and drug classes to target tumor heterogeneity in MPNST through the proteogenomic integration and drug sensitivity prediction in distinct tumor subpopulations. ConclusionsWe show that integration of multiomics data can identify specific drug therapies to selectively target tumor cells based on chr8q copy number. This not only provides novel avenues for drug nomination going forward but also may be important for stratifying treatment and mitigating resistance in heterogeneous tumors.

Here we present a novel data independent acquisition (DIA) mass spectrometry (MS) operating mode termed parallel accumulation-mobility aligned fragmentation (PAMAF) that offers enhanced speed and sensitivity of ion fragmentation analysis for nontargeted discovery workflows such as bottom-up proteomics. This mode of operation leverages high-resolution ion mobility (HRIM) separation capabilities of the structures for lossless ion manipulation (SLIM) technology to achieve HRIM-based precursor isolation in place of traditional quadrupole filtering approaches. This PAMAF mode of operation increases the number of features that can be identified per MS1/MS2 acquisition cycle by employing mobility-based time alignment to associate fragment ions with their corresponding precursor ions. By using a high-speed, lossless separation technique for precursor isolation instead of the comparatively slow and wasteful quadrupole filtering method, we can avoid ion losses up to 99% while simultaneously increasing the rate at which precursor ions are sequentially fragmented and detected. Additionally, by storing ions in a trapping region while the previous packet of ions is being analyzed, the PAMAF mode achieves [~]100% ion utilization efficiency. Benchmarking results of LC-PAMAF-MS analysis of a whole cell protein digest showed approximately 6x more protein group identifications compared to a standard data-dependent acquisition (DDA) analysis without HRIM on the same QTOF instrument, and to over 100x improvement for low-load workflows. Quantitative evaluations demonstrated that PAMAF mode could quantify low abundance peptides, including those undetectable by DDA. Additionally, since precursor isolation in PAMAF mode is size-based rather than m/z-based, many coeluting isobars and isomers can be resolved prior to fragmentation to eliminate chimeric spectra that compromise identification accuracy. In this work we also explored the benefits of combining HRIM and quadrupole isolation to achieve improved specificity. This approach, known as DIA-PAMAF mode, further reduces the frequency of chimeric fragmentation spectra, and enabled the detection of over 8,000 protein groups from a HeLa digest analysis. PAMAF mode brings a powerful new technique to the field of proteomics that has the potential to improve the sensitivity and selectivity of mass spectrometry-based proteomics. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/704896v1_ufig1.gif" ALT="Figure 1"> View larger version (114K): org.highwire.dtl.DTLVardef@7984c3org.highwire.dtl.DTLVardef@1fb2fe3org.highwire.dtl.DTLVardef@50d35org.highwire.dtl.DTLVardef@1a62926_HPS_FORMAT_FIGEXP M_FIG C_FIG

Transmission electron microscopy (TEM) is the gold standard for assessing subcellular glycogen localization in skeletal muscle fibres, but conventional manual analysis is extremely time-consuming and limits large-scale studies. Here, we developed and validated a deep learning-based semantic segmentation approach to automate quantification of glycogen particles across defined subcellular compartments in human skeletal muscle. Skeletal muscle biopsies were obtained from seven healthy men under conditions of normal, depleted, and supercompensated glycogen content. TEM images were acquired from myofibrillar and subsarcolemmal regions and manually annotated to train two complementary attention U-Net models: a region model identifying subcellular structures (intermyofibrillar space, intramyofibrillar regions including A-band, I-band and Z-disc, and mitochondria) and a glycogen model detecting individual glycogen particles. Combining the two models enabled estimation of compartment-specific glycogen areal densities. Model performance was evaluated against manual point-counting. At the fibre level, estimates based on 10-12 images per region achieved biases below 15% and coefficient of variation below 26% for all compartments. Importantly, model-derived total glycogen volume density showed strong concordance with biochemically determined muscle glycogen content across biopsies. In conclusion, this validated semantic segmentation workflow provides a robust, objective, and highly time-efficient tool for quantifying subcellular glycogen distribution in skeletal muscle. The model substantially reduces analysis time and enables high-throughput investigations of compartmentalized glycogen metabolism, with model weights and code made openly available.

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

Mantle cell lymphoma (MCL) is one of the deadliest forms of Non-Hodgkins B-cell lymphoma. Typically, patients present with both overexpression of CyclinD1 and secondary mutations identified by genomic sequencing. Although MCL patients may initially respond to treatment, they eventually relapse and succumb to disease, highlighting the essential need to identify new targets for treatment. Here we performed proteomic profiling of healthy B cells and three different forms of B-cell malignancies, including MCL, to define the proteomic signature of MCL. We compared the proteome of each to MCL and identified 10 proteins that are specifically upregulated in MCL. Of these 10 proteins, seven of them show no transcriptional changes and have been overlooked by conventional RNA expression analysis. Further analysis of the proteomic signature reveals potential avenues for dual targeting in CAR T-cell therapy and provides guidance for personalized therapeutics based on protein expression. STATEMENT OF SIGNIFICANCEWe present a resource defining the protein landscape of MCL, CLL, and FL as compared to healthy b cells identified utilizing quantitative proteomics from primary patient samples. Applied to MCL, our results identify 10 proteins specifically upregulated in MCL that may prove to be therapeutic targets to treat the disease.

Metabolic syndrome (MetS), characterized by abdominal obesity, insulin resistance, dyslipidemia, and hypertension, affects a substantial proportion of the global population and increases the risk for cardiovascular disease, diabetes, and metabolic dysfunction-associated steatotic liver disease (MASLD). Despite its prevalence, there are currently no effective pharmacological therapies targeting MetS, highlighting the need to identify novel etiological mechanisms, particularly within the gastrointestinal (GI) tract. Using a mouse model of MetS and healthy lean controls, we assessed the colonic microenvironment through metabolomic, transcriptomic, and microbiome analyses. Colonic organoids were cultured to further explore epithelial alterations. Additionally, human MetS fecal metabolomics data were cross-compared with the mouse model to validate translational relevance. MetS mice exhibited upregulation of colonic anabolic pathways, including glycolysis, the pentose phosphate pathway, and the tryptophan/kynurenine pathway, without evidence of intestinal inflammation. Microbiome analysis revealed an increased abundance of the genus Lactobacillus in MS NASH mice. Colonic organoids from MetS mice showed altered goblet cell differentiation. Comparative analysis with human MetS fecal metabolomics demonstrated similar dysregulated pathways, underscoring the translational relevance of these findings. Our study reveals significant metabolic and microbial alterations in the colon of MS NASH mice, implicating a dysfunctional GI tract as a potential etiological factor in MetS. These findings highlight specific metabolic pathways and microbial signatures that could serve as future therapeutic targets for MetS. NEW & NOTEWORTHYThis study identifies the colon as a metabolically active tissue affected in metabolic syndrome. Despite the absence of intestinal inflammation, MS NASH mice displayed altered colonic metabolism and microbiota composition, with conserved metabolite changes matching those seen in humans with metabolic syndrome. These findings highlight colonic metabolic dysfunction as a potential driver of gut dysbiosis and disease progression in metabolic syndrome and MASLD. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/716131v1_ufig1.gif" ALT="Figure 1"> View larger version (77K): org.highwire.dtl.DTLVardef@1b7c685org.highwire.dtl.DTLVardef@4a832aorg.highwire.dtl.DTLVardef@1e95c66org.highwire.dtl.DTLVardef@1b14209_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundSkeletal muscle in wasting conditions often exhibits a common set of phenotypes that include atrophy, mitochondrial respiratory dysfunction, and fragmentation of the acetylcholine receptor (AChR) cluster at the endplate. Mitochondria are frequently implicated in driving muscle pathology in these conditions, although which aspects of mitochondrial function are most relevant is poorly understood. MethodsTo address this gap, we focused on mitochondrial permeability transition (mPT), a well-established pathological mechanism in ischemia-reperfusion injury and neurodegeneration but poorly studied in skeletal muscle. We performed a broad assessment of the consequences of mPT in skeletal muscle, focusing on features that are common in wasting conditions. We then tested whether tumor-host factors could promote mPT and compared differentially expressed genes (DEGs) with mPT and a mouse model of pancreatic cancer cachexia. ResultsInducing mPT in mouse skeletal muscle bundles in a Ca2+ retention capacity assay progressively altered mitochondrial morphology, beginning with cristae swirling and condensation, progressing to mitochondrial cristae displacement, and culminating in breach of the outer mitochondrial membrane; features that are common in wasting conditions. Inducing mPT with Bz423 in single mouse muscle fibers increased mROS and Caspase 3 (Casp3) activity and was prevented by inhibitors of mPT, mROS or Casp3. Incubating single muscle fibers with Bz423 for 24 h reduced fiber diameter by [~]20% which was prevented by inhibiting mPT, mROS, or Casp3. Inducing mPT caused a complex I-specific mitochondrial respiratory impairment and increased co-localization of lysosomes with mitochondria. Inducing mPT also fragmented the AChR cluster at the muscle endplate and was prevented by inhibiting mPT or Casp3. The Ca2+ threshold for mPT and mitochondrial calcein colocalization were reduced by pancreatic tumor-conditioned media in skeletal muscle or C2C12 myoblasts, respectively, and these effects were counteracted by mPT inhibition or cyclophilin D knockout. Finally, there was significant overlap between the transcriptome of mPT and that seen in diaphragm muscle in a mouse model of pancreatic cancer cachexia, particularly during the muscle wasting phase. ConclusionsWe conclude that inducing mPT in skeletal muscle recapitulates muscle phenotypes common with muscle wasting conditions like cachexia. Furthermore, mPT is engaged by tumor-host factors and had significant overlap with DEGs seen during the muscle wasting phase in a mouse model of pancreatic cancer cachexia, warranting further investigation of mPT as a therapeutic target.

Dogma suggests protein quantification is a pre-requisite to LC-MS/MS based proteomics studies. Such quantification allows a standardized ratio of sample to digestion enzyme and enables physical normalization of protein digest loaded onto the mass spectrometer for analysis. Most proteomics studies include these steps. However, there are significant costs in time, money and experimental complexity, associated with performing protein quantification and physical normalization for every sample, especially for larger studies. Proteomics data analysis pipelines typically include computational normalization strategies to compensate for unavoidable systematic biases. These strategies also have the potential to compensate for avoidable variation such as omitting sample amount normalization. Here we investigate the effects of either physically normalizing the amount of protein for each individual sample or leaving it unnormalized. Our results show the relationship between increased protein amount variation in sample input, and the variance of quantified relative abundances of peptides and proteins output after data analysis. The experiments presented here suggest that protein quantification and physical normalization steps can be omitted from some quantitative proteomic experiments without incurring an unacceptable increase in measurement variability after computational normalization has been applied. This work will enable important time and cost saving optimizations to be made to many proteomics workflows.

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

Laser microdissection (LMD) enables enrichment of defined cellular populations from heterogeneous tissues, providing histologically-resolved molecular profiles with spatial resolution. However, the absence of standardized methods for generating optimized regions of interest (ROIs) limits reproducibility and scalability for spatial multi-omic workflows. To address this gap, we developed ShapeUpLMD, an open-source software tool and integrated workflow that automates the optimization of spatially defined ROIs for LMD, directly from digital pathology annotations. Fresh-frozen uterine serous carcinoma (USC) tumors (n = 11) were sectioned onto polyethylene naphthalate slides, scanned and subsequently annotated by an expert pathologist. Tumor and non-tumor ROIs were used to train tumor-specific classifiers in HALO (Indica Labs). Classified tumor or unbiased whole tissue ROIs were refined and optimized by ShapeUpLMD prior to automated collection on a Leica LMD7 microscope. Across the cohort, predicted tumor ROIs increased effective tumor purity by 86 {+/-} 6% relative to whole tissue while maintaining high spatial concordance between predicted and collected ROIs (accuracy = 0.9 {+/-} 0.05). Data-independent acquisition mass spectrometry quantified upwards of [~]6,500 proteins across spatially resolved ROIs, revealing regionally coherent clustering of adjacent tumor regions and abundance patterns consistent with tumor and non-tumor cell admixture in an unbiased spatial proteomic application. ShapeUpLMD provides an automated, reproducible, and scalable framework that bridges digital pathology with LMD enabling high-fidelity spatial enrichment for multi-omic analyses. This workflow increases throughput, reduces inter-operator variability, and supports standardized, regionally resolved tissue collection for spatial systems biology applications. The software is available at https://github.com/GYNCOE/ShapeUpLMD.

Capillary zone electrophoresis (CZE)-mass spectrometry (MS) has been proposed as a powerful analytical tool for bottom-up, top-down, and native proteomics (multi-level proteomics) decades ago to analyze complex biological samples at the levels of peptides (bottom-up), proteoforms (top-down), and complexoforms (native). However, its broad adoption has been impeded by the limited robustness and reproducibility. Here, we present multi-level proteomics data from nearly 170 CZE-MS runs ([~]170 hours of instrument time), demonstrating qualitatively (i.e., the number of identified peptides and proteoforms, the number of detected complexoforms, and their migration time) and quantitatively (i.e., peptide, proteoform, and complexoform intensity) reproducible measurement of complex samples with varying levels of complexity, i.e., Escherichia coli cells, HeLa cells, and human plasma. CZE-MS-based native proteomics enabled the detection of hundreds of complexoforms up to 800 kDa from the complex systems via consuming only nanograms of protein material. The results indicate that CZE-MS is sensitive and reproducible enough for broad adoption for multi-level proteomics-based biomedical research.

BackgroundSmall extracellular vesicles (EVs) are nanosized, endosome-derived particles which transfer RNA, proteins, and bioactive molecules to mediate intercellular communication. While EV signaling has been observed in many organ systems, it remains unclear whether glomerular endothelial cell (GEC)-derived small EVs directly interact with podocytes in vivo or how they traverse the glomerular basement membrane (GBM). MethodsGEC-derived small EVs were characterized by nanoparticle tracking analysis, electron microscopy, RAMAN spectroscopy and flow cytometry. Cargo composition was analyzed by proteomics, and microRNA (miR) profiling. Functional and structural features were examined using protease, collagenase, adhesion, and multimodal imaging assays. GEC-derived small EV uptake and downstream transcriptional effects were studied in cultured podocytes, while in vivo trafficking was assessed by injection of labeled small EVs into transgenic zebrafish larvae under baseline conditions, puromycin-induced damage, and cd2ap-knockdown. ResultsGECs released bona fide exosome-like small EVs carrying a highly cell type-specific miR cargo. Small EV transfer to podocytes induced a defined transcriptional response consistent with miR-mediated repression of target genes involved in extracellular matrix organization, cell cycle regulation, and cellular stress responses. Proteomic analyses revealed enrichment of surface proteases and integrin-associated proteins that conferred sustained proteolytic activity and enabled GEC-derived small EV migration through extracellular matrix surrogates. In vivo, circulating small EVs traversed the GBM and localized selectively to podocytes in healthy glomeruli, whereas glomerular injury permitted small EV entry into the tubular compartment. ConclusionThese findings provide first in vivo evidence that GEC-derived small EVs can cross the GBM and impact on podocytes. By identifying integrin- and protease-dependent mechanisms which facilitate vesicle passage, this study redefines the GBM as a dynamic interface of heterocellular, vesicle-mediated communication.