Molecular Omics

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.

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

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

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a prevalent condition that progresses from simple steatosis to advanced fibrosis, significantly affecting liver function and systemic health. Despite its widespread impact, therapeutic options are limited, highlighting the urgent need for comprehensive exploration to identify potential therapeutic targets. In this study, we created an analysis pipeline anchored on liver gene expression, integrating differential meta-analysis of transcriptomic data across three MASLD stages, transcriptome-wide Mendelian randomization (MR), and transcriptome-wide association studies (TWAS), to identify 39 candidate genes potentially involved in MASLD progression. Furthermore, we prioritized these genes using a scoring system that incorporated gene expression-clinical phenotype correlation meta-analysis, proteome-wide association studies (PWAS), and external genetic data from the GWAS Catalog and ExPheWAS. Single-nucleus RNA sequencing (snRNA-seq) analysis of liver cells from healthy to cirrhotic stages revealed stage- and cell-type-specific expression patterns of these prioritized genes. Through experimental validation in a lipid overload hepatocyte model, we confirmed the role of MLIP in lipid metabolism. These findings, available through an interactive web portal (masldportal.net), provide valuable insights into MASLD mechanisms and offer an easy-accessible resource for the research community. Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/706502v2_ufig1.gif" ALT="Figure 1"> View larger version (58K): org.highwire.dtl.DTLVardef@1cec9f8org.highwire.dtl.DTLVardef@12df220org.highwire.dtl.DTLVardef@1734cecorg.highwire.dtl.DTLVardef@bf5b52_HPS_FORMAT_FIGEXP M_FIG C_FIG

FSFC is an emerging technology that can greatly enhance our understanding of the single-cell proteomic landscape. However, its application to cells derived from solid tissues has been hampered by their complex autofluorescence signatures and lack of optimized tools for non-immune cells. Here, we present a protocol and discuss key controls that minimize the impact of unmixing errors enabling us to resolve multiple EC subpopulations isolated from different tissues in models of chronic tissue injury. Research Topic(s)Vascular biology, cell heterogeneity, full spectrum flow cytometry Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/695385v2_ufig1.gif" ALT="Figure 1000"> View larger version (43K): org.highwire.dtl.DTLVardef@1745181org.highwire.dtl.DTLVardef@1930db9org.highwire.dtl.DTLVardef@16a0b3dorg.highwire.dtl.DTLVardef@107ec29_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsOptimisation of a FSFC panel to enable in-depth phenotyping of tissue- and model-specific endothelial subpopulations from solid tissues. Discussion of appropriate controls to minimize the impact of tissue autofluorescence and enhance the signal-to-noise ratio for cell phenotyping in complex models of inflammation and fibrosis. Trajectory analysis to track cellular plasticity over time. Application of full spectrum cell sorting to isolate rare endothelial subpopulations with complex phenotypes.

Extracellular vesicles (EVs) are promising biomarkers, yet their proteomic analysis from plasma is hampered by low abundance and co-purification of contaminants (e.g., lipoproteins, platelets) and technical variability, particularly in small-volume animal models. We developed and validated a modular protocol integrating Size Exclusion Chromatography (SEC) with Strong Anion Exchange (SEC-SAX) specifically tailored for quantitative LC-MS proteomics from small starting volumes (150 l of plasma). SEC alone successfully removed 99% of Albumin, and the SAX step significantly enriched EVs over contaminating lipoproteins. Downstream single pot solid phase enhanced (SP3) sample prep and STAGE tip solid phase extraction ensured maximum proteome depth. Critical confounding factors were objectively assessed: Platelet Factor 4 (PF4) was confirmed as a highly sensitive platelet marker, confirming the necessity of meticulous plasma preparation. Sample hemolysis impacted the plasma EV proteome data. As such, an objective measure (nanodrop spectrophotometer) of hemolysis and exclusion of hemolysed samples (heme >0.3 mg/ml) is recommended. The protocol is applicable to both human and mouse plasma as demonstrated by EV enrichment and quantification of biomarker proteins associated with neurodegenerative diseases from eight individual mouse plasma samples. Manuscript HighlightsO_LIDevelopmental workflow for a quantitative SEC-SAX protocol for EV proteomics from small plasma volumes (150 l). C_LIO_LIA range of variables tested including SAX beads amount, digestion buffer, digestion time, STAGE tip solid phase extraction, SAX elution buffer and sample filtration. C_LIO_LIThe SAX step significantly enhances EV proteome depth by increasing EV purity in relation to ApoB lipoproteins. C_LIO_LIShows the impact of the major confounding factors of sample hemolysis and platelet contamination on the EV proteome. C_LIO_LIPlatelet contamination increases the number and abundance of proteins detected including known disease biomarkers and sample hemolysis is associated with proteins derived from platelet and red blood cell derived EVs. C_LIO_LIPlatelet Factor 4 (PF4) is identified and confirmed as a sensitive marker for platelet contamination. C_LIO_LIApplicable to both human and mouse plasma. C_LI

BackgroundDengue virus (DENV) appears to manipulate several cellular metabolic pathways to permit its replication and immune evasion in the host. Here, we employed high-resolution mass spectrometry (HR-MS) to investigate the serum metabolomic landscape of clinical DENV infection. MethodsSerum specimens from primary dengue (n=11), secondary dengue (n=9) samples, and healthy controls (n=10) were used for untargeted and targeted metabolomic quantification on a Waters Xevo G2-XS QTof Mass Spectrometer. The binding potential of selected ligands against DENV NS1, NS3, and NS5 was evaluated. Crystal structures were retrieved from Protein Data Bank and prepared using the Schrodingers protein preparation wizard. Based on findings from untargeted metabolomics, we validated certain bioactive lipid metabolites using commercial enzyme immunoassays. ResultsSerum metabolomic profiling revealed multiple distinct patterns for primary and secondary dengue versus controls. A consistent peak was observed at 2.06 mins across all samples. Certain bioactive lipid metabolites, such as, lysophospholipids, phosphatidylcholines, phosphatidylserines, and phosphatidylinositols, were detected alongside carnitine fragments, ceramides, diacylglycerols (DAGs), and bile acid conjugates in dengue. Molecular docking showed that DAG consistently exhibited strong binding to all the DENV proteins. Notably, LPC 22:6 showed a selectively strong affinity for NS5. Enzyme validation showed that in the secondary dengue cohort, LPC was significantly elevated than primary and healthy controls (p<0.05). ConclusionsOur investigations of the metabolomic landscaping, unveiled certain characteristic anabolic shift revealing metabolic vulnerabilities in clinical DENV infection, warranting investigations for use as potential biomarkers of inflammation in disease diagnosis and prognosis. Author summaryDengue is a mosquito-borne tropical viral infection that can range in severity from asymptomatic to life-threatening manifestations. Dengue virus (DENV) hijacks cellular machinery to sustain its survival in the host. Using high-resolution mass spectrometry (HR-MS), we studied the serum metabolomic imprints of dengue infection. The binding ability of selected metabolomic ligands against DENV NS1, NS3, and NS5 was studied. We found several distinct retention patterns for the dengue cases, with a consistent peak at 2.06 min across all samples. Further, several bioactive lipid metabolites were detected in the dengue infected cohort. Our molecular docking studies showed that diacylglycerol, a lipid metabolite exhibited strong binding with all the DENV proteins. We concluded that certain unique lipid metabolomic imprints exist in clinical DENV infection. The identified metabolomic signatures reveal significant potential for metabolomics to elucidate host-virus interactions, contributing to the advancement of antiviral and symptomatic treatments, along with prognostic or diagnostic biomarkers of dengue disease.

The use of decellularized diseased livers in regenerative medicine is a promising approach for eliminating organ shortages. Bioengineering studies have shown that ECM can impact cell physiology, inducing cell activation, function, and ECM deposition, which suggests that the ECM has a "memory" that is involved in the outcome after recellularization. However, the effect of diseased ECM memory on new cells in vitro and in vivo has not been thoroughly investigated. Since it has been increasingly recognized that liver ECM changes due to different factors, it is comprehensively that diseased ECM obtained from discarded organs will ensure a distinct environment and impact cell survival and physiology. Thus, we aimed at investigating the impact of the memory of diseased ECM obtained from metabolic dysfunction-associated steatohepatitis (MASH)-derived organs on steatohepatitis establishment. To address this aim, we explored decellularized ECM obtained from rats and humans with MASH in different contexts. First, MASH ECM was characterized and then submitted to transplantation to investigate whether a MASH-derived ECM could be used as a scaffold for transplantation and to promote steatohepatitis features in control animals. Histological analysis revealed that the MASH-ECM was completely recellularized after transplantation in both control and MASH recipient rats. However, steatosis and fibrosis were observed in MASH ECM after transplantation in both groups. Molecular analysis showed that MASH ECM stimulates de novo lipogenesis and fibrosis 30 days after transplantation. Untargeted metabolomic analysis revealed that cells grown on MASH ECM had a similar metabolic profile, even when transplanted into healthy or MASH recipient rats. In addition, we observed that MASH ECM promoted impaired lipid oxidation and mitochondrial dysfunction when transplanted into healthy recipients. Altered lipid turnover and inflammatory signaling were observed in MASH ECM transplanted in MASH recipients. In vitro analysis revealed that MASH ECM induced lipid accumulation in HepG2 cells after 10 days of culture. Calcium signalling experiments obtained from HepG2 cells cultured in MASH ECM showed a lower response to ATP, a reduced calcium signalling amplitude, and a distinct response profile than that observed in healthy ECM. On the other hand, a diseased human-derived ECM could still provide an environment that allows cell development. Taken together, our data showed that MASH ECM impacts cell metabolism, promoting steatohepatitis maintenance. In conclusion, our data confirm that diseased ECM memory can impact cell physiology contributing to disease progression.

Macrophages are innate immune cells present in most tissues of the body, whose molecular programs are determined by their ontogeny and environment. From the earliest stages of embryonic development, macrophages are recruited into developing tissues where they support organogenesis with trophic factors such as WNT, VEGF and PDGF. While macrophage subsets have been described in different tissues at single cell resolution, little is known about transcript isoforms and proteoforms that underpin their differentiation and function. Here we assessed enhancer, promoter, transcript and proteomic variation as pluripotent stem cells differentiate to macrophages, identifying over 200 previously uncharacterised genes and over 20,000 new mRNA isoforms, updating our current understanding of the human genome, its regulation and potential output. Newly discovered myeloid-expressed transcripts and proteins were enriched for motifs associated with secreted proteins, and these included previously uncharacterised isoforms of growth factors, in which we predict N-terminal changes impact on their location and function. Activation of primary adult monocytes and monocyte-derived macrophages was also characterised by the expression of diverse transcript isoforms, largely arising from alternate transcription initiation sites and predicted to impact on the acute response to bacterial or fungal stimuli. Understanding the full spectrum of gene products expressed by these cells further extends our understanding of the phenotypic plasticity and trophic potential of macrophages in human development and may lead to the discovery of new clinical targets for tissue engineering or immune-related studies. Graphical AbstractIn this manuscript, Berrocal-Rubio and colleagues examined the differentiation of human macrophages using an iPSC model of tissue macrophage biology. Combining long-read sequencing technology with promoter profiling identified over 17, 700 genes implicated in pluripotency-myeloid specification. 7% of transcripts profiled from previously characterised genes were predicted to encode new proteins, and a further 3% of transcripts were derived from genes newly discovered in this project. They confirmed that a high proportion of these alternate transcripts were detectable in primary monocytes but also discovered that activation of primary monocytes led to further alternate promoter usage, with the potential to further diversify the innate immune responses to a broad set of pathogens. The newly described macrophage genes and transcripts encoded proteins enriched for motifs associated with secreted peptides. These data suggest that alternate transcription of macrophage genes leads to new effectors of innate immune function, that include a substantially expanded number of growth factors or secreted proteins. Created in BioRender. Berrocal, M. (2025) https://BioRender.com/d0n47le O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=193 SRC="FIGDIR/small/703142v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@454cf0org.highwire.dtl.DTLVardef@1be28e8org.highwire.dtl.DTLVardef@16fb2e8org.highwire.dtl.DTLVardef@4aca30_HPS_FORMAT_FIGEXP M_FIG C_FIG

Primordial germ cells (PGCs) are the population of cells that, in the human embryo, specify day 12 post-fertilization, and form the precursor cells for the future egg or sperm cells. Although in vitro differentiation of PGCs from human stem cells has been achieved, these primordial germ cell-like cells (hPGCLCs) fail to further mature. The reason for this is unclear. Previous studies in mice revealed that several specific metabolic changes occur during the maturation of these cells, which are essential for their developmental progress. However, very little is known about the metabolic profile of human primordial germ cells. In the severe scarcity of human PGCs, hPGCLCs serve as a research model to study PGC formation. To investigate this, we differentiated hPGCLCs using induced-pluripotent stem cells and performed a mass spectrometry analysis to establish their metabolome and proteome. These cells revealed distinct metabolic profile, with changes particularly at the proteome level. This included a shift between canonical and non-canonical citric acid cycle in hPGCLC, downregulation of late-stage glycolysis and reduction of nucleotide de novo synthesis. By providing an integrative map of these metabolic networks, we aim to provide insight on the influence of metabolism on human PGC development that could help improve methods for in vitro differentiation and maturation hPGCLCs.

DNA methylation plays a central role in the regulation of gene expression. In plants, methylation occurs in the CG, CHG and CHH contexts, via distinct DNA methyltransferases including MET1, CMT3 and the RNA-directed DNA Methylation (RdDM) pathway via DRM2. In interspecific hybrids, these epigenetic mechanisms are confronted to a mixed small RNA population and two subgenomes harbouring specific methylation patterns, therefore generating unique expression profiles. The aim of this work was to understand these regulations by analysing gene expression, DNA methylation and small RNAs in a Citrus hybrid resulting from the cross between C. reticulata (mandarin) and C. australasica (finger lime). Haplotype-resolved subgenomes assembly identified hundreds of allele-specifically expressed genes. Asymmetric reprogramming of methylation was observed, in particular an increase in CHH in C. australasica haplotype. Surprisingly, CHH methylation, usually associated with gene silencing, was correlated here with increased expression, but also 24nt small RNA populations at their promoter regions. Similar analyses of the parental lines and other citrus species suggest the correlation between CHH methylation-enriched promoter and high expression level is not due to the hybridization, but seem to be generally true for all citrus. These observations suggest that, in citrus fruit, RdDM could activate transcription. This work also provides a full pipeline to analyse the expression profiles and DNA methylation in complex hybrids, which could be crucial for anticipating varieties resistant to diseases and the current threats affecting citriculture such as the Huanglongbing disease.

Saliva offers a noninvasive, lowcost, and patientfriendly matrix for biomarker discovery. Affinitybased proteomic technologies such as the Proximity Extension Assay (PEA) are increasingly being adopted for largescale biomarker studies, yet they remain underexplored in saliva. This study applied the Olink Explore HighThroughput (HT) PEA platform to profile approximately 5,400 proteins in saliva samples collected from donors representing periodontal health, gingivitis, advanced periodontitis (baseline and 3months posttreatment), and edentulism. Saliva from 68 donors was analysed, and all samples passed Olinks qualitycontrol procedures, with only 17 of 5,416 assays failing. Fortyone percent of proteins were detected above the limit of detection, demonstrating substantial assay sensitivity in this biofluid. Principal component analysis revealed clear compositional differences between clinical groups, with posttreatment periodontitis samples clustering more closely with health than baseline disease. Pairwise group comparisons identified hundreds of differentially abundant proteins, with consistently more proteins increased than decreased relative to health. This study demonstrates, for the first time, that Olink HT can robustly measure thousands of proteins in saliva with high data quality and biologically meaningful discrimination between periodontal states. The platforms minimal samplevolume requirements and scalability present strong potential for future salivabased biomarker discovery and translational research.

Extracellular vesicles (EVs) hold significant promise as biomarkers, but their clinical translation is constrained by variability in pre-analytical handling and isolation. EV isolation methods directly shape which EV populations are captured and characterized, yet systematic method comparisons across multiple analytical dimensions are limited. We comprehensively evaluated eleven EV isolation methods to define their performance and applications. EVs were quantified by NanoFCM, profiled for tetraspanins (CD9, CD63, CD81) via MSD assays, and further characterized by LC-MS/MS proteomics. We show that different EV isolation methods recover different EV populations. Our data provide guidance on method selection based on downstream application needs and serve as a look-up tool if a protein of interest is detected. EV isolation methods broadened proteome coverage but showed divergent performance and recover different EV populations. While all methods captured EVs in the 50-150nm range, centrifugation and ultracentrifugation identified the broadest proteomes (up to 1093 proteins) driven by higher plasma protein carryover. Conversely, ExoEasy and qEV 70 isolated larger EVs and achieved stronger depletion of abundant plasma proteins but showed lower proteome coverage. A total of 117 proteins were detected across all isolation methods. Pre-clearing samples removed contaminants but at the cost of protein identifications. We demonstrate that method selection must align with the specific analytical goal: centrifugation for comprehensive proteome profiling, affinity/size-exclusion methods for contaminant-sensitive assays, and precipitation for high-throughput applications. This systematic characterization provides an evidence-based framework and look-up resource for matching isolation strategies to downstream applications and research questions. Graphical Abstract for Table of Contents O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/710675v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@12ad967org.highwire.dtl.DTLVardef@270e4eorg.highwire.dtl.DTLVardef@1c41bcorg.highwire.dtl.DTLVardef@11fb236_HPS_FORMAT_FIGEXP M_FIG C_FIG This study evaluated 11 extracellular vesicle (EV) isolation methods which enriched distinct EV subpopulations with varying degrees of contaminants. No single approach optimized purity or proteome coverage; in this paper we present an Evidence-Based Framework to select plasma EV isolation methods based on downstream application needs.

Harbour porpoises (Phocoena phocoena) in the North and Baltic Seas are increasingly impacted by anthropogenic pressures, including underwater noise, fisheries and pollution. These pressures correlate with declining population health, particularly affecting the respiratory system. Growing pathological lesions, partly resulting from high prevalence of parasitic infestations and subsequent diseases, can impair tissue function and oxygen supply to distant end-organs. In this study, we applied an integrative MultiOmics approach (proteomics, metabolomics, lipidomics) to analyse the lungs and muscles of 12 wild harbour porpoises with compromised respiratory health. Our aim was to identify dysregulated biological pathways across omics layers to advance insights into adaptive physiological responses and to define disease-associated molecular signatures that could assist health assessments. Our analysis revealed pronounced immune system and antioxidative responses in the lungs and muscles, indicated by enhanced immunoglobulins, plasmalogens and glutathione-related proteins. In the lungs, high cardiolipin levels and reduced collagen suggest impaired tissue structure and function, while tissue maintenance processes were elevated in the muscle. Both tissues exhibited metabolic alterations suggestive of energetic imbalance, including increased purine metabolism in the lung and decreased lipid metabolism in the muscle. Several dysregulated molecules were shared across tissues, pointing to pathophysiological effects. The proposed disease-associated molecular signatures included the protein SLC25A4, the metabolite O-phosphoethanolamine and the lipid TG O-16:0_16:0_20:4 for the lung, and the protein SPEG, the metabolite pipecolic acid, and the lipid BMP 18:1_22:6 in the muscle. Our findings elucidate the complexity of molecular mechanisms linking anthropogenic and environmental stressors with vulnerability and resilience in a marine sentinel species. Furthermore, this study highlights the potential of integrative omics to define disease-related marker panels, thereby supporting ongoing and future health monitoring and conservation efforts.

Membrane lipid composition changes concomitantly with human colonocyte differentiation, a tightly regulated process occurring along the colon crypt. This process is heavily disrupted in colon cancer. Nonetheless, the regulatory mechanisms driving these changes, especially the replacement of arachidonic acid phosphatidylinositol species with monounsaturated fatty acid species, and how they are altered in cancer, remain unknown. To establish the transcriptional networks underlying this remodeling, we integrated transcriptomic and lipidomic profiles of isolated healthy and tumor human colonocytes using system biology approaches; identifying key gene regulatory networks involved in arachidonic acid and eicosanoid metabolism and phosphatidylinositol cycle as significant regulators during differentiation. Consistently, a distinct impact was found on organoid differentiation depending on colonocyte subtype and specific prostaglandin. Remarkably, the shift and associated transcriptomic programs were lost in tumor that heightened phosphoinositide metabolism. Altogether, these results underscore the importance of lipid remodeling in colonocyte stemness maintenance and proper onset of differentiation programs.

Accurate, simultaneous, and efficient quantification of chemically diverse phytohormone species is a critical task towards understanding the complex system of phytohormone signaling pathways. Quantification of phytohormones with the commonly used technique liquid chromatography coupled to tandem mass spectrometry is susceptible to the influence of non-phytohormone components present in the sample, a phenomenon referred to as matrix effect. To reduce matrix effect, some phytohormone quantification methods include additional steps of cleanup of crude extracts. However, to what extent additional purification steps provide increased accuracy compared to simpler, less laborious methods is seldomly evaluated. We evaluated three previously described phytohormone extraction methods, two of which include solid-phase extraction and one that does not, in their ability to minimize matrix effect and generate accurate estimates of phytohormone species spanning six classifications, from fruit and leaf tissue of Solanum lycopersicum cv. Micro-Tom (tomato). Our results show that, while the methods that included solid phase extraction occasionally outperformed each other regarding matrix effect and/or recovery efficiency for broad range of phytohormones, they rarely outperformed the simpler single-phase extraction method. Short AbstractAccurate, simultaneous quantification of chemically diverse phytohormones by LC-MS/MS is frequently confounded by matrix effects, leading to the incorporation of additional purification steps. We systematically compared three published extraction protocols with or without solid-phase extraction in tomato tissues across six hormone classes. Solid-phase methods occasionally improved matrix suppression or recovery, but did not consistently outperform the single-phase approach, questioning the added value of extra cleanup steps, particularly when high-throughput is desired, as in the case of systems biology interrogations.

Oxidative DNA damage caused by endogenous reactive oxygen species (ROS) is a key driver of mutagenesis, cellular dysfunction, and aging, contributing to diseases like cancer, neurodegeneration, rheumatoid arthritis, cardiovascular disorders, and diabetes. Although more than 20 oxidative base lesions have been identified, ROS-induced DNA-protein crosslinks (DPCs) are poorly characterized. ROS-DPCs are unusually bulky and highly toxic lesions that accumulate in metabolically active tissues with age, but their identities, biological consequences, and repair in living cells have remained elusive. In the present work, we characterized ROS-DPCs in human fibrosarcoma (HT1080) cells treated with hydrogen peroxide (H2O2) and elucidated the mechanisms of their removal. Mass spectrometry-based proteomics has identified over 100 cellular proteins that participated in DPC formation, most of which are involved in DNA metabolism. Our data further reveal that DNA replication and transcription facilitate DPC detection and identify a critical role of the ubiquitin-proteasomal system (UPS), replication-coupled activity of SPRTN metalloprotease, and nucleotide excision repair (NER) in removing ROS-induced DPCs. ROS-DPC formation was blocked by pretreatment with metabolically stable and cell-permeable glutathione (GSH) analog ({Psi}-GSH), suggesting a possible therapeutic strategy for preventing diseases associated with increased ROS levels. KEY POINTSMass spectrometry-based proteomics identified over 100 proteins participating in DNA-protein cross-links in human cells treated with ROS Our work reveals the mechanisms through which living cells recognize and remove ROS-DPCs Our study demonstrates the potential of a glutathione analog to prevent ROS-DPC formation GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/704426v2_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@15d9c33org.highwire.dtl.DTLVardef@ba0307org.highwire.dtl.DTLVardef@1cd46dorg.highwire.dtl.DTLVardef@be80ca_HPS_FORMAT_FIGEXP M_FIG C_FIG

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.

Spatiotemporal organisation of biological molecules is a key driver of cellular processes, including many post-transcriptional epigenetic processes. The germline-specific germ granules are biomolecular condensates that act as hubs for mRNA and small RNA processing and are core regulators of germline gene expression programming. Germ granules have been studied extensively in C. elegans, and recent developments have led to many subdivisions of the germ granule into specialised compartments. Rapid advancements in microscopy and protein-protein interaction (PPI) screening techniques have produced a large amount of data towards characterising the localisation of proteins to specific granules. However, common methods used to probe PPIs are limited in their ability to robustly detect valid interactions, especially the multivalent and sometimes transient ones observed in granule environments. Here we perform a meta-analysis of granule protein interaction screens. While these experiments generally enrich for proteins matching the profile of granule-associated proteins, we find that when considering screens individually, reproducibility is surprisingly low, highlighting not only the variability inherent in these methods but also the dynamic nature of the PPI networks present in granules. We developed an algorithm to provide a measure of each proteins association with specific granules across various experiments. By further clustering and investigation of the resulting score matrix, we demonstrate the power of this holistic approach to provide deeper insights into germ granule organisation and highlight novel can provide a resource to better inform future investigations into granules and their constituent proteins.

Tissue-nonspecific alkaline phosphatase (TNAP) is a ubiquitous enzyme whose substrates are various phosphorylated extracellular molecules including pyridoxal phosphate (vitamin B6) and adenine nucleotides. Dysfunctions of TNAP result in hypophosphatasia, a rare disease characterized by defective bone mineralization and impaired brain functions. In the brain, TNAP expression peaks during development and is associated with various steps of neurogenesis. However, the influence of TNAP activity on neurogenesis remains poorly understood in its cellular and molecular aspects. Here we used the SK-N-SH D human neuroblastoma cell line as a cell culture model to further investigate the involvement of TNAP in neuronal precursor proliferation and neuronal differentiation. We also used 1H-NMR-based metabolomics to investigate the molecular correlates of TNAP action on SK-N-SH D cell proliferation and differentiation. We first observed an increase in alkaline phosphatase (AP) activity when the cells were placed in differentiation medium. We next found that inhibiting TNAP with a specific inhibitor (MLS-0038949) impeded neuroblastoma cell proliferation. TNAP inhibition also hindered neuronal differentiation, as evidenced by a decrease in the number of neurite-bearing cells. In contrast, neurite length was not affected by TNAP inhibition, suggesting that TNAP controls neurite sprouting, but not neurite outgrowth per se. The metabolomic results indicate that proliferation and differentiation are associated with a decrease in the amounts of proteinogenic amino acids as well as that of compounds potentially involved in lipid production. This analysis also revealed that proliferation and differentiation are associated with increased glutathione levels and decreased amounts of hypotaurine and taurine, supporting proposals that organosulfur compounds play an important role in these processes. Since pyridoxine was present in the culture media, these results suggest that TNAP is involved in neurogenesis through mechanisms in addition to its role in vitamin B6 metabolism and may instead involve the ectonucleotidase activity (or an unidentified activity) of TNAP.