Molecular Omics

A multi-omic approach utilizing a single biospecimen is important to avoid intra-sample heterogeneity associated with testing multiple omic single-samples, and for more efficient use of small volumes of precious biopsies (<30 mg). This is especially true for the microanatomy of post-mortem human brain samples. Using post-mortem human brain biospecimens from the NIH NeuroBioBank, a penta-omic sequential extraction method is described, Simultaneous Metabolomic, Proteomic, Lipidomic - DNA, RNA Extraction (SiMPL-DREx). Each sequential omic extract was compared to those obtained by the gold standard single omic method. Preserving RIN is critical for brain and tissue banks, as it is a primary measure of tissue quality. For all five omic extracts, the tissue integrity numbers and omic profiles did not significantly differ from those obtained by the respective omic gold standard method. Unlike past multi-omic studies, this study quantified the relative solvent percentages and upstream losses for both the organic and aqueous phases, confirming an omics loss of under 5%.

Background: Fragile X-associated Tremor/Ataxia Syndrome (FXTAS) is a late-onset neurodegenerative disorder caused by FMR1 premutation CGG repeat expansions (55-200 repeats). The epigenetic landscape of the FXTAS brain remains uncharacterized. We performed genome-wide DNA methylation profiling of postmortem prefrontal cortex tissue to identify differentially methylated positions (DMPs) and candidate genes, and sought protein-level support for a neuroinflammatory signal. Methods: DNA methylation was profiled in postmortem prefrontal cortex (Brodmann area 9) from 27 male FXTAS cases and 29 male controls using the Illumina MethylationEPIC array (EPICv1 and EPICv2 platforms), merging 721,802 common probes. Surrogate variable analysis (SVA) controlled for confounders. DMPs were defined by p-value and FDR < 0.05; exploratory Reactome 2024 pathway analysis was performed on the DMP-associated gene list. Targeted proteomic profiling was performed in the same brain region using the Olink (proximity extension assay) Inflammation panel in 9 FXTAS cases and 12 controls, with SVA-adjusted differential abundance analysis, and concordance assessment against a prior mass spectrometry dataset. Results: We identified 108 significant cg-type DMPs mapping to 80 genes (50 hypermethylated, 58 hypomethylated in FXTAS). The strongest signal was CYP2E1 (7 concordant hypomethylated DMPs), an oxidative stress gene also implicated in Parkinsons disease. FTCD, a one-carbon cycle enzyme, carried 5 hypermethylated DMPs. A cluster of DMP-associated genes with established roles in innate immune and NF-kB signaling, TRAF3 (the single most significant DMP among the inflammation genes, hypermethylated), BATF, RCOR1, and MSI2; they pointed toward neuroinflammatory dysregulation. Additional genes included LINGO1 (myelination inhibitor), SYT3 (synaptic vesicle), and SLC39A4 (zinc transporter). Exploratory Reactome enrichment using the DMP-associated gene set nominated themes including neuroinflammation resolution, axonal growth inhibition, zinc homeostasis, and CYP2E1 metabolism at nominal significance (p<0.05); however, the gene-to-pathway mapping rate was low and no pathway survived correction for multiple testing. Olink proteomic analysis independently identified 60 significantly altered inflammation proteins (59 downregulated), including CXCL8, CXCL10, IL6, IL15, IL18, TLR3, IRAK1/4, and complement C1QA, which were directionally concordant with prior mass spectrometry data. Conclusions: This integrated study reveals a genome-wide epigenetic signature in the FXTAS prefrontal cortex implicating oxidative stress, myelination failure, zinc dysregulation, one-carbon cycle disruption, and most notably a coordinated set of epigenetically altered genes governing innate immune and NF-kB signaling. Convergence of TRAF3 hypermethylation with independent downregulation of TLR3 and NF-kB-pathway proteins at the protein level supports a coherent, cross-platform model of dysregulated neuroinflammatory signaling in FXTAS, identified here through individual gene- and protein-level convergence rather than formal pathway enrichment. FTCD hypermethylation proposes a self-reinforcing epigenetic loop via SAM depletion. These multi-omic findings establish FXTAS as a disorder of pervasive epigenetic reprogramming and nominate candidate genes for future mechanistic and therapeutic investigation.

BACKGROUND: Erythrophagocytosis constitutes a major pathogenic mechanism of metabolic dysfunction associated fatty liver disease (MAFLD). Our previous research established a quantitative thin-layer chromatography (TLC) technique for sphingomyelin, revealing reduced levels in the red blood cells (erythrocytes) of patients with metabolic dysfunction associated fatty liver disease (MAFLD). This reduction was accompanied by erythrocyte sphingosine accumulation, a driver of pro-inflammatory erythrophagocytosis, though sphingosine 1-phosphate release remained stable. To better understand erythrocyte sphingosine metabolism, we adapted our quantitative TLC method to analyze sphingosine within the erythrocyte-conditioned media (ECM) of MAFLD patients. Methodology Separation was performed on 10X10cm Silica gel 60 F254 plates using a mobile phase of chloroform, methanol, acetic acid, and water (60:50:1:4 v/v/v/v). The dynamic range, linearity, and range of linearity were assessed by analysing sphingosine levels from 0.1 to 10microg/spot. We validated the system precision and sensitivity by performing triplicate analyses of sphingosine standards (1.25, 2.5, and microg). The limits of detection and quantification were derived from the calibration curve slope and standard deviation (3.3 XSD/slope for LOD; 10 XSD/slope for LOQ). Accuracy was assessed via recovery tests at 100%, 200%, and 300% of a 2.5microg load. We confirmed specificity by evaluating the retention factors against other lipid species. This protocol was applied to Folch-extracted lipids from the ECM (5 X 107 cells/ml) of four MAFLD patients and four healthy controls, spiked with 5microg of sphingosine. Findings The calibration model, based on combined Green and Blue color intensities, followed the linear equation y = -11.171x + 353.25(R2 = 0.94). Interday precision values were 0.21%, 1.65%, and 0.44%, while recovery rates (accuracy) ranged from 94.5% to 98.7%. The measured LOD and LOQ were 0.75microg and 1.21microg, respectively. The sensitivity was calculated at 90ng. Statistical analysis showed no significant variance in sphingosine concentrations in erythrocyte-conditioned media between the MAFLD group and the control group. Summary The described thin layer chromatography is accurate, precise, sensitive, with good limits of detection and quantification, and most importantly is low-cost and time-efficient. Using this method, we show that while erythrocytes of MAFLD patients exhibit sphingosine accumulation, the utilisation of exogenous sphingosine from their erythrocytes is not affected. This suggests that the metabolic shift may be driven by increased sphingosine supply from the plasma.

Comprehensive detection of steroids, beyond the limited panels typically analyzed in clinical chemistry laboratories, has become increasingly important given their pivotal roles in diverse biological processes. However, steroid quantification poses several analytical challenges, including differences in ionization efficiency and structural similarities across the entire steroid metabolic network. To address these challenges, we developed a targeted ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) assay to analyze 21 steroids using reverse-phase chromatography combined with rapid polarity switching. Mass spectrometry (MS) analysis was performed in scheduled multiple reaction monitoring (sMRM) mode. Depending on the steroid and matrix, the validated lower limits of quantitation (LLOQ) ranged from 12.0 pM to 1216 pM in plasma and 41.1 pM to 384 pM in fecal sample homogenates. In adipose tissue, it was from 0.01 pmol/g to 9 pmol/g. Measured steroid concentrations obtained from the commercial control samples (MassTrak Steroid Serum QC Set 1 and the MassCheck Steroid Panel 1 Serum Control) showed close agreement with the reference values. As a proof of concept, the method was successfully applied to 469 plasma samples in several projects, 15 adipose tissue samples, and 332 fecal samples, demonstrating its applicability to large-scale studies. In conclusion, the method enables sensitive, derivatization-free quantification of an expanded steroid panel in plasma and complex biological matrices, including adipose tissue and fecal samples, representing a significant advancement in comprehensive steroid profiling.

Dried blood spots (DBS) are well-established microsamples used in clinical testing and newborn screening. However, their use in deep proteomics is hindered by highly abundant blood proteins and inefficient protein recovery from filter paper matrices. The non-targeted analysis of non-specifically DBS-absorbed proteins (NANDA) workflow partially overcomes the impact of abundant blood proteins and has enabled the identification of over 5,000 proteins from DBS samples. Nonetheless, residual abundant proteins, including hemoglobin and fibrinogen, constrain deep proteomic analysis. Therefore, this study aimed to evaluate the effects of the metal chelator ethylenediaminetetraacetic acid (EDTA) on the depth of DBS proteomic analysis. An optimized EDTA-enhanced NANDA protocol that incorporated a 100 mM EDTA wash step was compatible with standard DBS collection procedures and required no modification of current clinical workflows, markedly enhancing the depletion of abundant proteins and facilitating its potential use in clinical and translational settings. When combined with Orbitrap Astral data-independent acquisition mass spectrometry, this approach enabled the single-shot identification of more than 7,000 proteins from DBS samples; to the best of our knowledge, this represents the deepest proteome coverage reported to date, and the workflow further supported high-throughput and highly reproducible analyses. Additionally, its application to mouse disease models revealed disease-specific systemic immune signatures from minimal blood volumes. Collectively, these results establish EDTA-enhanced NANDA as a practical and scalable workflow that overcomes longstanding limitations of DBS proteomics, thereby enabling deep, high-throughput, minimally invasive proteomic profiling across diverse biological and experimental contexts.

Background/ObjectivesMetabolism-level interpretation of metabolomics datasets requires aggregation analyses across metabolites. One highlyused aggregation analysis is pathway enrichment analysis (PEA), which involves detecting pathways enriched with metabolites that are differential between experimental groups. Annotating metabolites with pathway associations is a prerequisite for PEA. While several knowledgebases define pathways and include metabolite-pathway annotations, these definitions are often partially or even grossly incomplete due to limitations in current metabolic knowledge and its curation, which greatly limits the effectiveness of PEA. MethodsIn this work, we used a novel multitask classification, graph convolutional-like neural network to generate high-quality metabolite-pathway annotations for pathways defined across KEGG, MetaCyc, and Reactome. We then included these predicted metabolite-pathway annotations when performing PEA on 990 datasets deposited in Metabolomics Workbench. ResultsWe demonstrate an 8-fold increase in the median number of enriched pathways detected across these datasets compared to using only knowledgebase-derived annotations. ConclusionsThe significant increase in enriched pathways substantially improves the biological and biomedical interpretability of metabolomics datasets.

Phenylketonuria (PKU) is a genetic metabolic disorder caused by the lack of functional phenylalanine hydroxylase (PAH). Elevated levels of phenylalanine (Phe) are known to be neurotoxic; however, the molecular mechanisms underlying Phe's effects remain elusive. This study investigates the impact of PKU on proteostasis, redox balance, and metabolism in BTBR PAHenu2 mice, a severe disease animal model. Combined proteomics and metabolomics revealed impaired redox homeostasis in the brain and disrupted mitochondrial energy metabolism (ATP and TCA intermediates). The dysregulation was further supported by decreased levels of ATP, reduced glutathione (GSH), cysteine, and reduced catalase activity. Western blot analyses revealed substantial remodeling of protein degradation systems: the 19S regulatory (Rpt1) subunit and 26S proteasome content and activity were significantly increased, and ubiquitinated protein levels were elevated, indicating protein turnover and activation of the ubiquitin-proteasome system. Autophagy was also activated, as evidenced by a reduced LC3-II/LC3-I ratio, decreased p62 levels, unchanged ATG5 levels, and increased HSPA8 protein expression. By contrast, UPR markers remained stable despite an increase in the oxidized-to-reduced PDI ratio, suggesting a localized shift without activation of a full ER stress response. In parallel, systemic alterations were assessed in whole blood. Indeed, GSH, cysteine, ATP and ADP were decreased in PKU, whereas NADPH increased. These changes were accompanied by reduced activities of GSH reductase and GSH peroxidase, thereby confirming metabolic and redox disruption. Collectively, these findings indicate that PKU is associated with activation of protein degradation pathways as an adaptive response to cellular stress combined with redox imbalance and energy dysregulation.

Abstract Background: Acute Lymphoblastic Leukemia (ALL) is a highly heterogeneous pediatric malignancy. Despite high survival rates, relapse and the involvement of central nervous system (CNS) remains a significant clinical challenge. Traditional clinical parameters often lack the precision required for early detection and risk stratification. This study utilizes high-throughput proteomics and machine learning to identify molecular signatures in cerebrospinal fluid (CSF) that characterize disease effect and treatment response. Methods: 82 CSF samples from 41 pediatric ALL patients at diagnosis (VD) and remission (VR) were analyzed. Proteomic profiling of 276 proteins was performed using Olink Proximity Extension Assay. Differentially abundant proteins were identified (q-value< 0.05, |Log_2FC| > 0.5) using the Wilcoxon rank-sum test. Three machine-learning algorithms - Random Forest, LASSO, and SVM-RFE - were integrated to select the differentially abundant proteins in VR and VD and between CNS involvement levels. To validate the data Pan-Cancer Atlas analysis was done using two different platforms. Results: In the remission phase, we observed significant alterations in the expression of key proteins compared to diagnosis, with ADGRG1 and KYNU showing a marked increase, while CCL17, CD5, CD27, CXCL9, CXCL11, FASLG, GZMA, and TNFRSF9 were significantly downregulated. Furthermore, our analysis identified distinct protein signatures associated with CNS involvement: CCL4, CTSC, CXCL10, CXCL9, and MMP7 were differentially abundant at the VD stage, whereas CAIX, CASP-8, HAGH, CXCL9, MMP7, MCP-2, and VWC2 at the VR stage. Conclusion: Integrating Olink proteomics with machine learning identified molecular signatures in ALL that have the potential to be further developed to a biomarker panel for monitoring treatment response and guiding personalized therapeutic strategies shifting the focus toward the Precision One Health approaches.

Capillary electrophoresis-mass spectrometry (CE-MS) offers unique analytical advantages for polar metabolite profiling but has remained underutilized in metabolomics relative to liquid chromatography-MS (LC-MS), in part due to challenges in managing migration time drift during data analysis. Here we introduce the use of indexed migration time (iMT) for easily managing this aspect of CE-MS data for metabolomics. Migration time indexing using a panel of stable isotope-labeled (SIL) amino acid reference standards, stored as an iRT database in Skyline, outperformed both uncorrected migration time and relative migration time (RMT) correction across three independent analytical batches spanning 90 samples from four biological matrices. The indexed migration time approach achieved sub-1% relative standard deviation (RSD) in migration index across batches, compared to up to [~]15% RSD for uncorrected migration times. Additionally, we evaluate the use of single-point external calibration in Skyline for the purposes of metabolite quantification from complex matrices in order to ease the burden of translational metabolite quantification from metabolomics using high-resolution mass spectrometry (HRMS). Single-point external calibration using a biological matrix-based calibrator was benchmarked against a 13-point linear calibration curve across a panel of amino acids; above 1 M, greater than 95% of back-calculated concentrations fell within {+/-}20% of multi-point calibration. Application of the complete workflow to plasma, serum, urine, and NIST Standard Reference Material (SRM)-1950 demonstrated low inter-batch variability by principal components analysis, broad metabolite coverage across 126 quantifiable analytes, and strong quantitative concordance (Deming slope = 0.862, pseudo-R2 = 0.994, n = 64 analytes) with an independent comprehensive reference dataset for NIST SRM-1950. Together, these results establish a practical mCE-HRMS metabolomics workflow that bridges targeted and discovery metabolomics paradigms and lays the groundwork for single-point external calibration as a powerful tool for translational metabolomics.

The chorioallantoic membrane (CAM) model represents a promising three-dimensional in vivo platform for preclinical drug testing in human tissues. In this study, we investigated whether the tissue penetration and distribution of benzbromarone, a known inhibitor of the Ca2+ activated chloride channel TMEM16A and potential therapeutic agent for autosomal dominant polycystic kidney disease (ADPKD), can be successfully visualized in human renal cyst tissue cultured on the CAM. To this end, desorption electrospray ionization mass spectrometry imaging (DESI-MSI) combined with an ultrahigh-resolution time-of-flight mass spectrometer was employed. We achieved spatially resolved molecular mapping of endogenous metabolites and lipids as well as the applied compound. MSI enabled clear differentiation between CAM and cystic tissue based on their distinct lipid profiles. Benzbromarone was reproducibly detected in the cyst specimens and exhibited selective accumulation along the cyst epithelium, which is considered the principal site of action. These observations were complemented by multivariate analyses including Uniform Manifold Approximation and Projection (UMAP), and sparse multinomial logistic zero-sum classification. The data-driven approach confirmed molecular differences between tissue types and allowed accurate classification of drug-treated and untreated regions. This study demonstrates that topically applied benzbromarone penetrates human renal cyst tissue in the CAM model and localizes to pharmacologically relevant tissue regions, notably the location of the Ca2+ activated chloride channel TMEM16A in the epithelial lining. The integration of high-resolution DESI-MSI with advanced statistical analysis provides a robust and label-free method to study drug distribution in human tissue grafts. Our findings contribute to the advancement of translational research in analytical chemistry and pharmacology.

Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide despite recent therapeutic advances, driven in part by its marked etiological and molecular heterogeneity and the lack of broadly effective therapeutic targets. Identifying conserved tumor dependencies shared across distinct etiological backgrounds may provide new opportunities for targeted therapy. Here, we developed an integrative computational framework to systematically integrate transcriptomic, functional genomics, and clinical datasets for the identification and prioritization of candidate tumor dependency genes in liver cancer. We reanalyzed transcriptomic data from murine models of liver cancer driven by genotoxic (DEN), oncogenic (c-Myc), and inflammatory (lymphotoxin) stimuli, identifying more than 380 genes consistently upregulated across all tumor models. Functional enrichment analysis revealed a strong overrepresentation of cell cycle-related pathways and liver cancer signatures. Integration with DepMap dependency datasets identified 26 genes with strong dependency scores. Candidate genes were further prioritized by comparing their expression across models of liver regeneration, chronic liver injury, and liver cancer. Analysis of the TCGA-LIHC cohort confirmed significant overexpression of all 26 genes in human HCC, with high expression associated with poor patient survival. Together, these findings establish an integrative framework for identifying conserved tumor dependencies, providing a prioritized set of proliferation-associated genes for functional evaluation as therapeutic targets in HCC.

Cross-reactive antibodies against dengue virus are known to cause antibody-dependent enhancement (ADE) of infection or disease severity under specific conditions. In our previous study, we showed that primary immunization with the COVID-19 vaccine induces induces cross-reactive IgG causing ADE against dengue. In the present study, we investigated the influence of IgG Fc-glycosylation (analyzed by LC-MS/MS) on ADE mediated by cross-reactive IgG against dengue from IgG against SARS-CoV-2. We found a clear correlation between anti-DENV2 E IgG2 galactosylation and the ADE capacity of cross-reactive IgG against dengue in individuals vaccinated against COVID-19. IgG2 sialylation increased over time; however, it was not correlated with ADE capacity. This phenomenon was restricted to IgG2, whereas anti-DENV2 E IgG1 Fc-glycosylation remained stable after COVID-19 vaccination.

Neurodegenerative diseases are complex disorders characterised by progressive neuronal loss and widespread transcriptomic dysregulation; however, the coordinated interactions among coding and non-coding RNAs that contribute to disease progression remain incompletely understood. In this study, RNA-seq datasets from disease-relevant neuronal populations and brain regions representing Alzheimer's disease (AD), Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS) were analysed using an integrative network-based framework. Differential expression analysis coupled with weighted gene co-expression network analysis identified modules significantly correlated with disease and prioritised highly connected hub genes. Integration of these hub genes with curated RNA interaction database enabled the construction of candidate lncRNA-miRNA-mRNA regulatory networks. Functional enrichment analysis revealed Gene Ontology biological processes associated with synaptic signalling, mitochondrial function, RNA metabolism and neuroinflammatory responses across neurodegenerative conditions. The inferred regulatory networks suggested both disease-specific and shared post-transcriptional regulatory modules involving key hub genes and non-coding RNAs. Additionally, putative sequence variants were identified within untranslated regions of selected hub genes, suggesting potential alterations in miRNA-mediated regulations. Therefore, this study provides a systems-level view of transcriptomic dysregulation across major neurodegenerative diseases and identifies candidate regulatory interactions and molecular targets for future functional investigation

It was recently reported that Methylobacterium extorquens AM1 produces the citrate-hydroxamate siderophore N-deoxyschizokinen A, identified by LC-HRMS. Multiple properties were inconsistent with the assignment: the feature eluted far later than the other schizokinen derivatives (17 min versus 6-8 min), a reversed-phase shift larger than a single-hydroxyl difference in a molecule can explain, further its accurate mass deviated from the calculated one by 28 ppm, well outside the error on the co-analyzed standards and its diagnostic m/z 105 and 77 fragments suggest a molecule with an aromatic moiety. A replicate comparison of identical samples in plastic versus glass autosampler vials was decisive: the m/z 387 feature was reproducibly present with plastic vials and absent with glass. We therefore conclude that the reported detection of N-deoxyschizokinen A in M. extorquens AM1 is an artifact, and recommend glass-vial and solvent-blank controls, an explicit accurate-mass threshold, and narrow MS/MS isolation when assigning trace siderophore-like features from complex extracts.

Plasma-derived cell-free small non-coding RNAs are promising non-invasive biomarkers for cancer detection and monitoring. However, variability in sequencing output limits standardization, and cross-platform performance for plasma small RNA profiling has not been systematically evaluated. Illumina short-read sequencing is the current standard, whereas the newcomer, Ultima-Genomics platform, has been less extensively studied for circulating small RNA in plasma. To directly compare platform performance, we sequenced plasma cell-free RNA from 39 patients with pancreatic cancer and 39 matched controls on both platforms. After filtering, Ultima-Genomics retained more mature microRNA reads, whereas Illumina achieved slightly higher enrichment efficiency and mapping rates. Despite these technical differences, both platforms produced concordant expression profiles, with strong cross-platform correlations for shared microRNAs and clear separation of cases and controls within each dataset. Differential expression analysis identified 14 significant microRNAs on both platforms with concordant directions of change, most of which are supported by pancreatic cancer databases. Pathway enrichment analysis highlighted signaling pathways implicated in pancreatic cancer, supporting the biological relevance of both shared and platform-specific signatures. These findings indicate that both Illumina and Ultima Genomics platforms are suitable for plasma small RNA profiling and capture biologically relevant signals in pancreatic cancer.

In many tropical countries, fermentation of palm sap into palm wine is an important fermented beverage contributing to local economies, tradition, and culture. Traditionally made in villages and families, palm sap is not inoculated with starter cultures and fermentation commences spontaneously. It is therefore possible that fermentation is influenced by multiple ecological factors, which affect microbial dynamics and thus flavour outcomes. Here, we studied microbial communities during fermentation of palm sap from three different palm tree species (palmyra, coconut, and sugar palm) on the island of Bali, Indonesia in both the wet and dry seasons. Our results suggest that season of collection has a strong influence on microbial dynamics and succession, and these changes positively correlate to metabolite concentration. The change of the season from the dry to wet season led to the loss of microbial diversity with lower richness in the dry season. The dominance of Saccharomyces cerevisiae was not affected by season and fermentation time and was dominant in all samples. Potential spoilage species, such as Candida tropicalis were negatively correlated to ester production and more abundant in the dry season. As microbial species varied in incidence and thus biochemical activity, the chemical groups of esters from their metabolism related to the change of season and fermentation time, while volatile compounds and small molecules were highly discriminated by season in the resultant wines. Ethyl octanoate was consistently different across all variables through comparison by three-way ANOVA and is proposed as a biomarker of seasonal variation in palm sap fermentation. These findings improve our understanding of microbial dynamics in palm sap fermentation, revealing flavour differentiation within season and suggests that strategies for microbial management, product development and quality assurance will elevate this traditional product into the future.

Despite advances in high-resolution mass spectrometry (HRMS), confident lipid annotation remains challenging due to the extensive chemical diversity of the lipidome and the prevalence of isomeric species. Ion mobility collision cross section (CCS) measurements provide structural information that complements HRMS; however, not all HRMS platforms can perform these measurements, necessitating a trade-off among mass resolution, accuracy, and robustness. Here, we introduce a method to infer lipid CCS values directly from liquid chromatography (LC)-Orbitrap MS experiments (Orbi). We show that Orbitrap mass analyzer pressure readings, and therefore CCS values, are influenced by the LC gradient solvent composition, requiring correction using isotopically labeled internal standards injected post-column. We also show that hundreds of lipid features can be assigned OrbiCCS values in a single LC run, with average precision better than 1% and an accuracy of 1-2% relative to reference DTCCS and TIMSCCS values. This excellent CCS accuracy not only enables more reliable annotation of lipid species in complex mixtures by matching OrbiCCS values to reference databases but also accelerates lipid structural elucidation based on the unknown's position in Orbi-retention time-m/z space.

Background. Post acute sequelae of COVID 19 (PASC) is clinically heterogeneous and mechanistically unresolved, and single-analyte studies have struggled to explain it. Methods. We profiled matched plasma proteomics, metabolomics and whole-blood transcriptomics at acute infection and convalescence (mean 86 days later) in a Belgian cohort, using linear mixed models, multiomic gene-set enrichment, and a degree-matched differential-correlation approach to quantify how each node's interactions were rewired between patients who developed PASC and those who recovered; seven axis proteins were additionally quantified by multiplex immunoassay as orthogonal validation. Findings. Single omic testing yielded few FDR significant features, yet multi-omic enrichment showed sustained complement cascade involvement from acute illness to follow-up in PASC. Correlation networks re-organised topologically toward C3 and lost the immunoglobulin V gene coexpression seen in recovery. The most rewired nodes, heparin cofactor II (SERPIND1), alpha 1 antitrypsin (SERPINA1), complement factor H related 5 (CFHR5), prothrombin/thrombin (F2) and immunoglobulin V gene transcripts (notably IGLV3 21), changed in their co-expression structure rather than in abundance. In multiplex validation, acute CRP was elevated in patients who developed PASC (FDR = 0.012), whereas the directly measured abundances of the network-nominated proteins were unchanged. Interpretation. These trajectory aware, cross omic networks nominate a thrombo inflammatory axis in which complement and coagulation regulation remain dysregulated in PASC at the level of wiring rather than abundance, providing a systems framework for validation and for exploring interventions at the complement coagulation platelet interface.

Microplastics are pervasive environmental pollutants increasingly implicated in adverse human health effects, with emerging evidence linking MPLs exposure to elevated cardiovascular risk, including atherosclerosis. However, their specific mechanisms of action remain unknown. Human aortic endothelial cells (HAECs), located in the innermost layer of blood vessels, play a crucial role in maintaining vascular homeostasis and the development of atherosclerosis. This study demonstrates that polystyrene microplastics (80 nm MPLs) can enter HAECs through multiple pathways, including macropinocytosis, clathrin-mediated endocytosis, and caveolin-mediated endocytosis, and co-localize with mitochondria and lysosomes. MPLs exposure resulted in coordinated transcriptional, epitranscriptomic, and metabolomic reprogramming in HAECs, characterized by disruption of mitochondrial genes and an inflammatory response with activation of TNF-a; and NF-kB signaling. Integrative analysis revealed remodeling of the epitranscriptomic profile, demonstrated by an increase in 1-methyladenosine (m1A) modification along with reciprocal regulation (TRMT61A upregulation and ALKBH3 suppression) of its transcriptomic machinery, alongside other enzymes associated with 3-methylcytidine (m3C), pseudouridine (Y), 5-methylcytidine (m5C), and 7-methylguanosine (m7G) pathways. By comparing transcriptomic data from MPLs-treated HAECs with those of human atherosclerotic plaques, several common dysregulated pathways were identified, particularly those related to vascular physiological regulation and cell signaling. Metabolomic profiling further revealed significant remodeling of lipid metabolic networks associated with oxidative stress and inflammatory signaling. In summary, this study reveals that HAECs can internalize MPLs, leading to multiple disturbances in the transcriptome, epigenome, and metabolic networks, suggesting that MPLs exposure may pose a potential hazard to human cardiovascular health.

Targeted long read sequencing (LRS) of native genomic DNA (gDNA) using Oxford Nanopore Technologies (ONT) is an economically and computationally accessible method for sequencing selected genomic regions without the limitations associated with amplification-based approaches. At present, efficiency, multiplexing, and scalability remain key challenges for existing targeted LRS. We have developed Cas12a-Targeted Multiplexed Nanopore Sequencing (CTM-nSeq), which combines Cas12a-targeting, DNA fragment enrichment, and optimized adapter ligation using T7 DNA ligase. Unlike previously established protocols, CTM-nSeq is compatible with the latest ONT flow cell chemistry. Performing CTM-nSeq on a single sample with an R10.4 MinION flow cell routinely yields hundreds of on-target reads. Furthermore, CTM-nSeq enables targeting of multiple loci and is the first targeted ONT sequencing method, allowing reliable, barcode-assisted multiplexing. CTM-nSeq is an efficient and accessible method for sequencing native gDNA and analysing DNA methylation, repeat expansions, and sequence integrity. As such, CTM-nSeq has a wide range of analytical and diagnostic applications.