Molecular Omics

Adipogenesis is the process of adipose-derived stem cells (ADSCs) responding to extracellular signals from the stem cell niche to differentiate into adipocytes (fat cells) and may be studied in vitro using a cocktail of chemicals that promote adipogenic differentiation to produce differentiated ADSCs (dADSCs). The global membrane N- and O-glycosylation changes of this process have been previously analysed and compared to native adipocytes as a benchmark for a true adipocyte profile, and revealed that bisecting GlcNAc type N-glycans are characteristic of adipogenesis. As stem cell differentiation has been widely reported to result in cellular protein changes, the same cells (ADSCs, dADSCs and mature adipocytes) were characterised for their membrane proteome here using label-free quantitative shotgun proteomics analysis. The membrane proteome displayed more differences in protein numbers between the cell types compared to the previously reported N-glycome which had shown high identical glycomes between stem cells and in vitro dADSCs, suggesting that the proteome is more dynamic during in vitro adipogenesis. Following the global shotgun proteomics analysis, a more targeted approach of carrying out proteomic analysis of de-N-glycosylated peptides of gel-separated proteins unearthed new glycoproteins not detected in the shotgun proteomic analysis. This approach identified the adipogenic marker, CD36, to be under-represented in the shotgun proteome analysis, but as the dominant (glyco)protein in the adipocyte membrane proteome that was also up-regulated at the mRNA transcript level in both the in vitro differentiated ADSCs (7.1-fold increase) and mature adipocytes (102.9-fold increase). A comparison of CD36 sequence coverage in the global shotgun analysis with the de-N-glycosylated CD36 revealed a 41% increase when N-glycans were removed prior to trypsin digestion, explaining its observed increased abundance and highlights the crucial need for de-N-glycosylation of proteins in proteomics experiments for increased identification of glycoproteins. The systems glycobiology approach by the integration of previously reported glycomics data and the proteomics and transcriptomics analyses in this work extended the investigation of membrane protein glycosylation changes in adipose-derived stem cell differentiation. The work provides a framework for future glycoproteomics-based investigations into the differentiation of stem cells into adipocytes, and will allow their related pathologies and potential therapeutic applications to be discovered. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=121 SRC="FIGDIR/small/722121v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@189a786org.highwire.dtl.DTLVardef@5563b8org.highwire.dtl.DTLVardef@5cb5borg.highwire.dtl.DTLVardef@69e11f_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundHepatic stellate cells (HSC) are Vitamin A storing non-parenchymal cells of the liver. During injury and inflammation, HSCs are the major contributors of excessive extracellular matrix (ECM) leading to Liver Fibrosis (LF). Emerging evidence suggests a fibrosis-independent role of these cells as key regulators of liver homeostasis and liver regeneration, emphasising on the dual role of HSCs in liver. HSCs are known to secrete several growth factors through which they largely execute their functions. However, the role of secretome (exosomes) from early activated or undifferentiated HSCs in a fibrotic milieu nor its composition are completely understood. MethodsLX-2 cells were cultured in low to no serum conditions and their isolated exosomes were transplanted into fibrotic severe combined immune deficient (SCID) mice livers, followed by post-transplantation analysis of the liver tissue and compared to the untreated controls. Total proteomic profiling of cell and exosomal cargo was performed using mass spectrometry and the data analysed and compared with the total HSC cell proteome. ResultsSignificant reduction in collagen in the transplanted mice livers compared to untreated fibrotic controls was observed with both the cells and exosomes transplantation. Comparative analysis revealed distinct enrichment of proteins and signaling pathways associated with extracellular matrix regulation, cellular communication, and metabolism in exosomes. Notably, these pathways are prominently represented in the exosomal fraction, suggesting a selective packaging of functional mediators. ConclusionThis study suggests the potential role of HSCs in regulating the complex liver homeostasis via exosomal network of proteins that contribute significantly to liver repair by ECM remodelling and growth factor-mediated signalling to regulate metabolism, fibrosis and liver regeneration. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=126 SRC="FIGDIR/small/721862v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@99bbf4org.highwire.dtl.DTLVardef@1029dd0org.highwire.dtl.DTLVardef@c6f578org.highwire.dtl.DTLVardef@1dba81_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

Background & AimsGastric epithelial cells maintain homeostasis through dynamic self-renewal mechanisms involving stem and progenitor cells; however, identifying them has been challenging. This study aims to identify stem cells of healthy gastric epithelium and cell type-specific regulators defining gastric epithelial homeostasis via single-nucleus multiome analysis. MethodsTen unique gastric samples were collected from 8-12 week old wildtype mice. Isolated nuclei were subjected to simultaneous profiling of gene expression and chromatin accessibility. After quality control, 31,598 cells were analyzed with Seurat and Signac using weighted-nearest neighbors analysis for joint RNA and ATAC clustering. Furthermore, SCENIC+, MultiVelo, EpiCHAOS and Cell plasticity score were used to uncover gene regulatory networks, cell state dynamics and lineage trajectories. ResultsOur analyses were validated by the identification of known regulators of stem-cell differentiation into mature cell types. More importantly, it revealed previously uncharacterized regulatory networks comprising novel transcription factor combinations that define cell identities, including Ppara, Pparg, Arid5b and Sox5 as candidate regulators of parietal, foveolar, chief and neck cells, respectively. Further, our data support the identity of isthmus cells as stem-like cells of healthy gastric epithelium, as evidenced by epigenetic plasticity that simultaneously contains open chromatin states of all differentiated cell types in the absence of transcriptional reprogramming. ConclusionConsistent with Waddingtons epigenetic landscape hypothesis, gastric epithelial homeostasis is controlled by orchestrated epigenetic and transcriptional programs. Contrary to the prevailing hypothesis, stem cells can be defined not by a separate epigenetic state but by epigenetic superposition of differentiated cell states. Future work is needed to define the universality of these results.

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

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.

Inflammation and oxidative stress (OS) are key to Parkinsons disease (PD). We performed a cross-dataset integrative transcriptomic analysis to identify OS- and inflammation-related hub genes persistently dysregulated in PD and to evaluate their response to nutrigenomic interventions using publicly available datasets. Four GEO datasets (GSE7621, GSE20141, GSE20146, GSE49036) were analysed to identify differentially expressed genes (DEGs), which were intersected with GeneCards OS-inflammation gene sets. Functional enrichment analyses, including gene ontology (GO), pathway over-representation analysis (ORA), and protein-protein interaction (PPI) analysis, were used to identify key pathways and hub genes. Gene-food bioactive compound (FBC) association was explored by integrating PD signatures with nutrigenomic profiles from NutriGenomeDB. We identified 183 DEGs in PD, enriched in synaptic, dopaminergic, OS, and inflammatory pathways. Intersection analysis yielded 26 OS-inflammation-related genes and 10 central regulators, including TH, DDC, SNCA, LRRK2, HSPB1, and HSPA1B. revealed opposing transcriptional patterns, with several FBCs suppressing stress-related genes and upregulating dopaminergic markers such as TH, GCH1, and DDC. Overall, this integrative analysis highlights OS-inflammation gene networks in PD and identifies candidate diet-gene interactions that warrant further experimental validation

Efficient transport of membrane proteins through the endosomal network is essential for brain development and function, with perturbation implicated in disease. Deficiencies in Retromer, a key regulator of endosomal transport, have been linked to aging-related neurodegenerative disorders including Alzheimers and Parkinsons disease. To better define the neuroprotective role of Retromer, we have applied cell surface restricted proteomics to identify those integral membrane proteins whose recycling to the plasma membrane is mediated by Retromer and associated cargo adaptors, sorting nexin 3 (SNX3), its paralogue sorting nexin 12 (SNX12), and sorting nexin 27 (SNX27) (data available via ProteomeXchange: PXD078277). By comparing primary rat cortical neurons and astrocytes we have identified several cargoes that require either SNX3/SNX12- or SNX27-Retromer complexes for endosomal recycling, including proteins involved in synapse organisation, synaptic signalling and Alzheimers disease pathology. We highlight that perturbed Retromer function leads to endosomal enlargement, and we establish a key role of SNX27-Retromer in modulating transport of glutamate across both neuronal and astrocytic membranes via recycling of glutamate transporters EAAT3 (SLC1A1) and EAAT1 (SLC1A3) respectively. Our study provides further mechanistic insight into the consequences of Retromer deficiency for neuronal and astrocytic function, offering new avenues of research in the treatment of neurodegenerative disease. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=194 SRC="FIGDIR/small/724903v1_ufig1.gif" ALT="Figure 1"> View larger version (59K): org.highwire.dtl.DTLVardef@98277forg.highwire.dtl.DTLVardef@1490534org.highwire.dtl.DTLVardef@f4a9feorg.highwire.dtl.DTLVardef@c48402_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO Suppression of Retromer and the sorting nexins (SNX27, SNX3/SNX12) leads to a significant change in the surface proteome of rat cortical neurons and astrocytes. Focusing on the glutamate transporters, SLC1A1 and SLC1A3, we have validated that SNX27-Retromer is required for their trafficking, with SNX27-Retromer suppression in astrocytes leading to a loss of glutamate uptake. 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.

Bone is a highly durable biological tissue widely used in forensic, archaeological, and anthropological investigations; however, efficient protein recovery and understanding of protein stability over time remain major challenges in skeletal proteomics. Here, we systematically evaluated three bone protein extraction workflows and integrated them with data-independent acquisition (DIA) mass spectrometry to assess proteome coverage, reproducibility, and temporal protein dynamics under environmentally exposed conditions. Comparative analysis demonstrated that extraction strategy is a primary determinant of detectable proteome composition. EDTA-based demineralization followed by SDS extraction provided the deepest proteome coverage and highest reproducibility, whereas guanidine hydrochloride extraction preferentially enriched collagen and extracellular matrix proteins. In contrast, acid-based extraction yielded limited protein recovery. Temporal profiling of bone samples collected at 10 and 45 days post-exposure revealed two distinct protein classes. A temporally stable module, enriched in collagens and extracellular matrix proteins including COL1A2, COL5A2, BGN, SPARCL1, and NID2, exhibited minimal abundance change, indicating resistance to environmental degradation. In contrast, temporally dynamic proteins, enriched in mitochondrial, metabolic, and intracellular pathways such as ACO2, OGDH, PDHA1, ATP5PO, and PFKM, showed marked decline over time. These findings support a two-compartment model of bone protein preservation in which matrix-embedded proteins are preferentially retained while exposed intracellular proteins undergo progressive degradation. Collectively, this study establishes an integrated framework linking extraction methodology with temporal proteome stability and identifies candidate markers for skeletal preservation assessment and temporal biomarker development in forensic and archaeological applications.

The development of minimally invasive multi-organ aging clocks, established through the deconvolution of plasma proteomics, has provided a convenient tool to assess the organ heterogeneity of aging. However, prior studies relied on bulk transcriptomic data for organ marker identification, which may lead to the potential misidentification of protein markers, and their research scope was largely confined to a few common diseases. To address these limitations, this study integrated multi-dimensional data to refine organ-enriched marker panels by incorporating organ-specific proteome information, and developed Proteome-Aware Organ Proxy Proteome Aging Clock (PAOPAC). PAOPAC exhibited decelerated biological age corresponding to improved physiological phenotypes across two independent external datasets, demonstrating its generalizability. We then leveraged PAOPAC to generate a comprehensive disease-aging landscape and to investigate the process of chronological and biological aging. Our analyses revealed that the majority of diseases are associated with an accelerated aging phenotype.

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.

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.

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.

Alzheimers disease (AD) is a multifactorial neurodegenerative disorder characterized by coordinated dysregulation of multiple genes, requiring system-level approaches to elucidate underlying molecular mechanisms. While existing computational studies largely focus on differential expression analysis or machine-learning-based feature selection, they often overlook inter-gene relationships and network topology, limiting biological interpretability. In this study, we present a network-based framework for prioritizing candidate genes in Alzheimers disease by integrating gene co-expression network analysis with multiple centrality measures. Transcriptomic data comprising approximately 39,000 genes across 324 Alzheimers and control samples were preprocessed using log-transformation, variance filtering and Z-score normalization, followed by LASSO-based feature selection to retain phenotype-associated genes. A weighted gene co-expression network was constructed using Pearson correlation to capture coordinated transcriptional activity. Network topology was analyzed using degree, betweenness and eigenvector centrality to identify genes that are highly connected, act as information brokers or occupy influential positions within the network. A consensus ranking was derived by merging these centrality measures, enabling robust prioritization of candidate genes. The results highlight a subset of highly central genes, including several small nucleolar RNAs and regulatory transcripts implicated in RNA processing, synaptic function and neurodegenerative pathways. By jointly leveraging co-expression structure and complementary centrality metrics, the proposed framework provides an interpretable and reproducible strategy for identifying biologically meaningful gene candidates, offering potential utility for biomarker discovery and therapeutic target prioritization in Alzheimers disease.