Molecular Omics

Type 2 diabetes (T2D) is a prevalent metabolic disorder affecting millions worldwide, characterized by insulin resistance and impaired glucose homeostasis. While mammalian models are widely used, Drosophila melanogaster provides a powerful alternative due to its conserved insulin signaling pathways, genetic tractability, and suitability for high throughput studies. In addition to glucose dysregulation, lipid metabolism plays a crucial role in T2D pathophysiology, as alterations in lipid composition contribute to insulin resistance and metabolic dysfunction. Lipidomic studies have emerged as an essential approach to identify metabolic signatures and potential biomarkers for disease progression and therapeutic targeting. In this study, T2D like model was established by inducing insulin resistance through knockdown of the insulin receptor in brain insulin-producing cells using the dilp2-Gal4>UAS-InRRNAi system. This genetic manipulation resulted in significant metabolic dysregulation, including elevated glucose, trehalose, and triacylglyceride levels, along with increased oxidative stress indicators. Additionally, mRNA expression analysis of key insulin signaling components, including insulin receptor substrate 1, dilp2, dilp3, dilp5, and phosphorylated Akt, further validated the model. To further investigate metabolic alterations, Lipid profiling was performed using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) in non targeted LC-MS-based metabolomics approach to identify lipid biomarkers associated with T2D. Multivariate statistical analyses, including PCA and PLS-DA, revealed distinct lipid signatures between wild-type and T2D flies. Notably, specific phosphatidylglycerol species PG 34:0, PG 34:4, PA 38:3, PIP 38:1, PIP2 38:6, and LPS 24:0 demonstrated an area under the curve (AUC) of 1, indicating their strong reliability as lipid biomarkers for T2D diagnosis.

Gangliosides are glycosphingolipids implicated in the neurodegenerative pathology of Niemann-Pick disease type C2 (NPC2 disease), where accumulation of GM2 and GM3 is a hallmark of disrupted lipid trafficking and disease progression. Some gangliosides are structural isomers with identical molecular composition and mass, making them difficult to distinguish by mass spectrometry imaging (MSI) alone and leaving their spatial distributions unresolved. To address this challenge, we combined ion mobility spectrometry with mass spectrometry imaging for isomer-resolved ganglioside analysis. We assessed ganglioside complexity in wild-type (Npc2+/+) and NPC2-deficient mice with or without AAV-BR1-mediated NPC2 gene therapy (Npc2-/- AAV-BR1-NPC2 and Npc2-/- vehicle) using matrix-assisted laser desorption ionization (MALDI) MSI, and imaging-parallel reaction monitoring-parallel accumulation serial fragmentation (iPRM-PASEF-MS/MS) for in situ brain tissue analysis. Trapped ion mobility spectrometry (TIMS) enabled gas-phase separation of isomeric gangliosides, while iPRM-PASEF-MS/MS facilitated structural discrimination based on diagnostic fragment ions and identification of molecular modifications. Brain sections were analyzed by MALDI-MSI at 20m spatial resolution, with selected regions examined using post-ionization imaging by MALDI-2 at 5m. 59 mobility-resolved ganglioside features representing 23 putative annotations were identified, including separation and spatial mapping of GM1a/GM1b and GD1a/GD1b isomers. Notably, unreported modifications of gangliosides were identified, including GM1 fucosylation in Npc2-/- mice and GD1 O-acetylation exclusively detected in Npc2+/+ mice. Spatial ganglioside profiling by MSI demonstrated that gene therapy of Npc2-/- mice partially restored normal ganglioside localization in brain, indicating modulation of lipid storage. This study establishes isomer-resolved lipid imaging for investigating ganglioside alterations in disease models and the effects of therapeutic intervention.

Contextualized protein-protein interaction networks provide crucial insight into diseases and other biological processes, but for a profound understanding of such processes and their distinct effects on individuals, the protein-protein interactions within individual samples must be investigated. A straightforward approach to estimate the PPI network of a sample is to restrict a general network of known PPIs to the proteins that are found in the sample. Although proteomics methods are becoming more accessible and precise, large-scale and single-cell studies still mainly target characterizing the transcriptomics profile of the samples, which is then often used as an approximation of the protein activities. The correlation of gene expression and protein abundance has been addressed in the past, but information about the deviations of the different omics-based estimates of the PPI networks is still lacking. In this study, we performed a comparative analysis of transcriptomic-based and proteomic-based sample-specific PPI network estimates to fill this gap. We created a framework for a comprehensive and transparent comparison of the two omics levels in two independent datasets, with a special focus on time-related network dynamics. We found that the size-adjusted characteristics of the different omics-based networks are very similar; the overall trend of how they change with time is also often the same, but the rate of the changes typically differs. The characteristics of the nodes present in both types of networks also show high similarity and often different time-related rates of change, but this varies among metrics. These results shed light on the properties of PPI network estimations and advise caution in interpreting them appropriately.

Genetic variation impacts biological response to chemical and environmental exposures, contributing to differences in resistance or susceptibility. Forward genetic screens of genetically diverse cell populations can be used to identify the precise genetic variants that drive response variation. Cell lines from laboratory mouse genetic reference populations like the Diversity Outbred (DO) population are well powered for genetic mapping. Diverse stem cell panels are especially beneficial for studying early developmental effects of chemical exposures like triphenyl phosphate (TPHP), an organophosphate flame retardant linked to adverse developmental effects, including altered cell cycle in stem cells. Self-renewal and pluripotency are defining properties of stem cells during development; therefore, it is important to understand how chemicals like TPHP may alter these critical characteristics. To better understand the influence of genetic variation on TPHP exposure response, we utilized a genetically diverse panel of DO induced pluripotent stem cells (DO iPSCs). Our analyses characterize interline variation in gene expression through differential gene expression analysis and expression quantitative trait locus (eQTL) mapping. We identified genomic loci that contribute to variation in gene expression following TPHP exposure, including a regulatory hotspot on chromosome 15. Gene set enrichment highlighted several pathways affected by TPHP including lipid metabolism, steroid hormone signaling, and cell cycle. Our study shows that genetic variation modulates the effects of TPHP on gene expression and cell cycle in stem cells. Additionally, our work demonstrates that genetically diverse cell panels like the DO iPSC resource offer a tractable and scalable approach for identifying the genetic determinants of chemical toxicity.

Krabbe disease is a rare autosomal recessive lysosomal disease caused by deficiency of galactocerebrosidase (GALC), leading to accumulation of galactosylceramide and formation of the toxic metabolite galactosylsphingosine (psychosine). While psychosine accumulation is well-established as a primary pathogenic mechanism, the broader metabolic consequences of GALC deficiency remain incompletely understood. In this study, we used stable isotope tracing to comprehensively characterize metabolic perturbations in a human oligodendrocellular Krabbe disease model. This approach revealed elevated de novo ceramide synthesis in GALC knock-out cells, characterized by increased incorporation of glucose-derived serine into ceramide biosynthetic pathways. This enhanced ceramide production was amenable to pharmacological intervention by tezacaftor, an inhibitor of sphingolipid {Delta}4-desaturate (DEGS); tezacaftor administration also normalized psychosine levels, raising the possibility of its use as substrate reduction therapy. Additionally, we identified significant disruption of UDP-hexose metabolism, manifesting as an overabundance of truncated and hypogalactosylated glycans. These findings suggest impaired protein glycosylation as a previously unrecognized pathogenic mechanism in Krabbe disease. Our findings reveal novel metabolic dysregulation in Krabbe disease extending beyond established psychosine toxicity. The identification of enhanced de novo ceramide synthesis presents a new therapeutic target, while the discovery of galactose-deficient glycosylation defects supports galactose supplementation as a potential therapeutic intervention. These metabolic insights provide new mechanistic understanding and therapeutic opportunities for this devastating neurodegenerative disorder.

Hexose-6-phosphate dehydrogenase (H6PD) catalyzes the first two steps of an endoplasmic reticulum-specific pentose phosphate pathway, regenerating luminal NADPH levels in the process. Its function remains insufficiently well understood. Since expression of H6PD is notably high in the liver, we aimed to assess its role in hepatic metabolism. Considering the central role of the liver in lipid synthesis, breakdown and storage, we focused our efforts specifically on studying the effect of H6PD on hepatic lipid metabolism. An H6PD knockout mice strain was generated and characterized by liquid chromatography-high-resolution mass spectrometry (LC-HRMS)-based lipidomic and proteomic analyses of liver tissue. Lipidomics analysis revealed an overall increase in hepatic triglycerides and a specific increase in unsaturated long-chain triglycerides in H6PD knockout mice. Intracellular lipid accumulation was confirmed through Nile Red staining of liver sections. Functional enrichment analysis of proteomics data from the H6PD deficient mice identified a corresponding upregulation of multiple fatty acid metabolism-associated pathways. Additionally, an H6PD knockout AML12 cell line was generated through CRISPR/Cas9 and characterized by lipid staining and functional assays to assess metabolic outcomes. Loss of H6PD led to intracellular lipid accumulation, reduced mitochondrial {beta}-oxidation and increased sensitivity to lipotoxicity, even though fatty acids remained the cells' primary mitochondrial fuel. Ultimately, our results indicate that H6PD plays an as-of-yet undescribed role in hepatic lipid metabolism, implying a link between the availability of NADPH within the endoplasmic reticulum and fatty acid homeostasis.

BackgroundAcute myocardial infarction (AMI) and periodontitis (PD) have been epidemiologically linked, but the molecular mechanisms underlying this association remain elusive. We aimed to elucidate shared pathogenic signatures between AMI and PD using a comprehensive bioinformatics approach. MethodsWe integrated transcriptomic data from multiple Gene Expression Omnibus datasets, including an AMI cohort profiled from enriched circulating endothelial cells (GSE66360) with whole-blood validation (GSE48060), and PD gingival tissue cohorts (GSE16134 and GSE10334). Weighted gene co-expression network analysis, protein-protein interaction network analysis, and LASSO-based feature selection were applied. Functional enrichment, immune deconvolution, transcription factor-gene regulatory network analysis, and single-cell RNA sequencing analysis were performed to characterize shared molecular features. ResultsWe identified 95 shared differentially expressed genes (DEGs) between AMI and PD. By intersecting the shared upregulated DEGs with disease-associated WGCNA modules, we obtained 46 candidate shared genes. LASSO-based feature selection further highlighted FCN1 and LYN as overlapping candidates, which showed good discriminative performance in both training and external validation cohorts in ROC analyses. Enrichment analyses suggested that the shared signature was mainly related to myeloid cell migration, phagocytosis, and neutrophil-related inflammatory pathways (e.g., neutrophil extracellular trap formation). Immune deconvolution in PD gingival tissues suggested increased plasma cells and neutrophils and decreased resting memory CD4+ T cells; immune deconvolution results in the AMI cohort were interpreted cautiously due to the CEC-enriched sample source. Single-cell analysis revealed that FCN1 and LYN were predominantly expressed in macrophage/monocyte-derived populations. ConclusionsOur study suggests a shared inflammatory and immune-mediated transcriptomic program linking AMI and PD, and identifies FCN1 and LYN as candidate shared immune markers. These findings provide a molecular rationale for the oral-cardiovascular association and are hypothesis-generating, warranting future experimental and prospective validation.

Background: Chronic obstructive pulmonary disease (COPD) is a complex, heterogeneous disease influenced by genetic and environmental factors. However, the interactions between COPD risk genes and their collective role in COPD susceptibility remain largely elusive. We hypothesize that the protein-protein interaction (PPI) network of genes associated with COPD risk can provide molecular insight into the mechanisms of COPD pathogenesis. Methods: We used affinity purification mass spectrometry (AP-MS) to detect protein-protein interactions of six well-established COPD GWAS gene products (AGER, FAM13A, FBXO38, HHIP, IREB2, MFAP2) in two relevant lung cell lines (IMR90, 16HBE). These genes are located in significant COPD GWAS regions, and their physiological role in COPD has been previously confirmed by functional studies. We analyzed the impact of the newly identified interactions in contextual, cell type-specific PPI networks built from the combination of publicly available PPI data (HUBRIS) and cell type-specific gene expression data. Results: Using AP-MS, we identified 482 unique interactors across 701 newly identified protein interactions with the six GWAS gene products in at least one of the cell lines (AGER: 122, FAM13A: 83, FBXO38: 71, HHIP: 108, IREB2: 8, MFAP2: 309). Subsequent network analysis incorporating these new PPIs with public PPI databases revealed: (1) the new interactions significantly reduce the network distance between known COPD GWAS gene products; (2) 5.2% of the newly identified interactors are differentially abundant between COPD cases and controls in lung proteomic data from the Lung Tissue Research Consortium (LTRC); and (3) 41 (14.3%) of the newly identified interactors were directly connected to more than one of the six COPD GWAS genes. We identified a tightly connected core network of COPD GWAS gene products and protein biomarkers with intersecting signals; this network consists of 8 proteins, including 5 of the 6 GWAS gene products (AGER, FAM13A, HHIP, FBXO38, and MFAP2) as well as CAVIN1, TGM2, and HSPA1A. Conclusions: By combining AP-MS experimental data, multiple types of "omics" data, and network analysis, we have constructed a disease network module for COPD. This module can be a foundation for understanding the collective influence of COPD GWAS gene products in disease pathogenesis.

Phenylketonuria (PKU) is an autosomal recessive disorder caused by PAH gene defects and can cause irreversible neurological damage as early as the fetal period; however, non-invasive prenatal monitoring methods are lacking. In this study, Pah+/- female mice were mated with Pah+/-; male mice and wild-type male mice, respectively. Urine samples were collected longitudinally from pregnant mice at 10 time points during gestation from Day 1 to Day 19. Data-independent acquisition (DIA) quantitative proteomics combined with GO/KEGG analysis, WGCNA, and a dual-strategy LASSO-random forest approach was used to screen candidate biomarkers. The results showed that stable differentially expressed proteins between groups were detectable as early as Day 1, and the differences persisted throughout gestation and exhibited temporal dynamics. Permutation tests indicated that the probability of random generation of the differential protein combination was low (0.04-0.06), suggesting that it was not randomly generated. The intersection of the two strategies yielded five proteins, GINM1, PRG4, LY6A, NTM, and YIPF3, and after including GLO1, a total of six candidate biomarkers were obtained. The candidate proteins had interaction or co-expression associations with 14 proteins involved in PKU mechanisms, with GLO1 serving as a network hub. This study found that the maternal urine proteome can non-invasively reflect the systemic response induced by fetal-origin PKU, providing proof of concept and candidate biomarker resources for non-invasive early-pregnancy screening of PKU.

Microglial inflammatory activation is accompanied by extensive molecular remodeling, yet proteomic, lipidomic, and metabolomic responses are often analyzed independently. Here, we applied an integrated mass spectrometry-based multiomic workflow to characterize proteomic, lipidomic, and polar metabolomic remodeling from matched BV-2 biological samples following stimulation with interferon-{gamma} and lipopolysaccharide (IFN-{gamma} and LPS). Inflammatory activation was confirmed by increased nitrite accumulation, elevated TNF- and IL-6 secretion, and treatment-associated morphological changes. Discovery proteomics quantified 8,676 proteins and identified 562 significantly altered proteins, including 344 increased and 218 decreased proteins. Increased proteins were enriched for interferon-responsive, innate immune, inflammatory effector, and antigen-associated pathways, whereas decreased proteins were associated with cellular organization, protein biogenesis, vesicular trafficking, and metabolic regulation. Targeted lipidomics identified 237 significantly altered lipid features out of 356 measured lipids, including increased triacylglycerols and diacylglycerols and broad remodeling of glycerophospholipids, lysophospholipids, and sphingolipid-related species. Targeted polar metabolomics identified 75 significantly altered metabolites out of 98 measured metabolites, including changes in nucleotide/NAD-related metabolism, amino acid metabolism, methylation-associated metabolites, acylcarnitine abundance, phospholipid precursors, polyamine metabolism, arginine/nitric oxide-associated metabolism, and redox-associated metabolites. Process-level integration of significant features revealed coordinated remodeling of inflammatory protein programs with lipid storage, membrane remodeling, nucleotide metabolism, amino acid availability, phospholipid precursor abundance, nitric oxide-associated metabolism, and redox/osmolyte pathways. These findings demonstrate that IFN-{gamma} and LPS-induced activation of BV-2 cells involves integrated immune, lipid, and metabolic adaptation rather than isolated induction of canonical inflammatory mediators. This integrated multiomic framework provides a resource for investigating how lipid and metabolic remodeling regulate microglial inflammatory states. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=121 SRC="FIGDIR/small/741024v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@11dd431org.highwire.dtl.DTLVardef@155f107org.highwire.dtl.DTLVardef@1430e89org.highwire.dtl.DTLVardef@16f4a25_HPS_FORMAT_FIGEXP M_FIG C_FIG

Dried blood spot (DBS) sampling is becoming a popular alternative to traditional blood sampling approaches, offering advantages such as convenience of collection, transportation, and storage, as well as lower biohazard risk. N-glycosylation, a major post-translational modification of proteins associated with numerous biological and pathological functions, is one area of interest for DBS analysis. In this study, we utilize a protocol for N-glycosylation profiling of DBS by ultra-high-performance liquid chromatography based on hydrophilic interactions and fluorescence detection (HILIC-UHPLC-FLR). The protocol includes DBS cutting, protein extraction and enzymatic digestion, labeling with 2-aminobenzamide, followed by cleanup and HILIC-UHPLC-FLR measurement. We compare DBS with plasma and demonstrate the stability of DBS N-glycosylation profile when DBS are prepared from fresh blood, frozen whole blood, or a combination of separated frozen blood cells and corresponding frozen plasma. Additionally, we compared DBS N-glycans from pre- and diabetic subjects. Fucosylation, bisection, and galactosylation showed a statistically non-significant increasing trend in diabetes, whereas sialylation showed a statistically non-significant decreasing trend in diabetes. The main advantage of this method is the ability to repurpose samples, which were initially not intended for biomarker N-glycan analysis, such as frozen whole blood. Additionally, DBS N-glycan profiling is the easier, cheapest and the least invasive approach to conventional plasma in pre-diabetes and diabetes patients' diagnostics and monitoring.

Why was the work done?: Gluten-free (GF) beers typically exhibit poorer foam stability than conventional barley malt-based beers, yet the molecular basis underlying this difference remains poorly understood. This study aimed to investigate protein-level factors associated with foam stability in GF and barley beers. How was the work done?: Three commercially produced GF beers, comprising two lagers brewed from rice or sorghum, and one pale ale brewed from a millet-buckwheat-rice blend, were compared with two commercially produced barley-malt beers, comprising a lager and a pale ale. Soluble protein was isolated from freeze-dried beer powder using TCA-acetone. Protein profiles were examined using SDS-PAGE, and proteomes were characterised and compared using label-free quantitative LC-MS/MS. What are the main findings?: GF beers showed lower soluble protein concentrations and significantly lower foam stability than the barley beers. SDS-PAGE revealed fewer and weaker protein bands in GF beers compared with barley beers. Proteomic analysis showed that barley beers were enriched in known foam-active proteins, particularly lipid transfer protein 1 (LTP1) and Protein Z (serpin family members), whereas these proteins were not detected or were detected only at very low levels in GF beers.

Highly reactive aldehydes are generated during metabolic processes in the body, and their detoxification is essential for maintaining cellular homeostasis. Hexadecenal, a long-chain fatty aldehyde, is formed during the sphingolipid degradation pathway from the lipid mediator sphingosine-1-phosphate (S1P). However, the cytotoxicity resulting from dysregulation of hexadecenal metabolism is still unclear. To elucidate the effects of impaired hexadecenal metabolism, we analyzed the function of ALDH3A2, an aldehyde dehydrogenase in humans. Our results revealed that ALDH3A2 enzymatic activity is crucial for the suppression of DNA damage, particularly interstrand DNA crosslinks, upon S1P exposure. Furthermore, we demonstrated that hexadecenal accumulation promotes cell death accompanied by the activation of cellular stress responses and morphological abnormalities in the endoplasmic reticulum. These findings suggest that ALDH3A2 functions as a metabolic safeguard to suppress DNA damage and cell death in response to the enhanced metabolic flux of hexadecenal.

Cell confluency is a major determinant of cell-cell communication, protein interactions, access to nutrients, and signalling dynamics, thereby significantly impacting biological outcomes. Lung cancer cells like A549 are widely used as screening models for scientific studies wherein their growth in vitro progress from non-confluent to confluent growth. In this study, we investigated the transcriptomic adaptations associated with the transition of A549 cells from baseline non-confluent to confluent growth. Comparative transcriptomic analysis between confluent and cells at baseline identified 815 upregulated and 671 downregulated transcripts. Pathway enrichment analysis of deregulated transcripts in confluent cells revealed enhanced cholesterol and sterol biosynthetic pathways, along with suppression of chromosomal segregation and mitotic pathways. At confluency, an increased expression of glucose transporters (SLC2, SLC60, and SL37 families) and glycolytic pathways, and a decrease in amino acid transporters (SLC1, SLC7, SLC38, and SLC36) and amino acid metabolic pathways is observed. A reduced one-carbon metabolic signature (SHMT2, DHFR, and MTHFD2) and enhanced fatty acid precursor synthesis (HMGCLL1, ALDH6A1, and AASS) were also observed at confluency. 1H NMR profiling of culture media revealed higher glucose and glutamine utilisation with lactate accumulation during culture maturation. Collectively, the data suggest transcriptome-level rewiring in A549 cells with preferential biosynthesis of lipids and sterols at confluency and underscore the importance of considering culture maturity in cancer biology, metabolism, and therapeutic studies.

The limited understanding of transporter substrate spectra constrains our ability to interpret cell and membrane function, highlighting the need for methods that enable transporter deorphanization and characterization of promiscuous transport activities. Here, we present a Xenopus oocyte-based platform for unbiased transporter substrate discovery. Oocytes expressing heterologous solute carrier proteins (SLC) were incubated in human blood serum, a chemically complex metabolite library containing thousands of endogenous metabolites and xenobiotics, followed by paired untargeted LC-MS/MS profiling of intracellular extracts and surrounding medium to capture metabolite exchange events. Across the five human SLC transporters, viz. SLC10A2, SLC10A6, SLC13A2, SLC16A10, and SLC46A1, metabolite exchange signatures were detected, and automated feature annotation was refined by manual chromatographic peak inspection. The workflow recovered known substrates of SLC10A2 and SLC16A10 and identified additional transported metabolites with MS/MS confirmation. This method provides a scalable framework for transporter substrate profiling and prioritization of candidates for targeted validation.

The liver of laying hens plays a central role in metabolism and reproduction, supporting the synthesis of egg yolk precursors under strong hormonal regulation. Despite its physiological importance, a high-resolution cellular reference of the adult chicken liver is still lacking. Here, we generated a single-nucleus RNA sequencing atlas of the adult laying hen liver from eight individuals, providing a comprehensive view of its cellular composition and transcriptional landscape. Using this framework, we identified major hepatic cell populations, including hepatocytes, endothelial cells, cholangiocytes, hepatic stellate cells, and diverse immune cell types, revealing a broadly conserved vertebrate liver architecture. However, hepatocyte zonation, a key feature of mammalian liver organization, was not observed, consistent with the absence of hepatocyte zonation reported in birds. Importantly, we demonstrate that the use of an enriched genome annotation, incorporating additional protein-coding and long non-coding RNA models, substantially improves transcript detection and enhances cell-type resolution in single-nucleus datasets. This improved resolution allows more accurate marker-based assignment of hepatocyte subpopulations and refines the interpretation of hepatic cellular heterogeneity. Within hepatocytes, we uncovered transcriptionally distinct subpopulations associated with lipid metabolism and reproductive function, including estrogen-responsive programs involving cytochrome P450 genes such as CYP2C23A and CYP2C23B. In parallel, we characterized a complex immune compartment composed of resident macrophages and adaptive immune cells, highlighting the dual metabolic and immunological roles of the avian liver. Overall, this atlas provides a high-resolution reference for avian liver biology and demonstrates that improved genome annotation enhances the resolution and interpretation of cellular heterogeneity in single-cell transcriptomic studies.

Chromatin profiling methods, such as ChIP-seq (chromatin immunoprecipitation followed by sequencing), are used to characterize the genomic localization of DNA-associated proteins. While ChIP and ChIP-seq have been used for decades, an orthogonal approach, CUT&Tag (Cleavage Under Targets and Tagmentation), is gaining popularity as an efficient and cost-effective alternative. Although previous comparative studies note discrepancies in signal-to-noise ratios and detection bias at certain genomic regions, many differences between ChIP-seq and CUT&Tag results remain largely unreconciled. Here, we systematically investigate the disagreeing signals captured by these two methods across well annotated genomic regions. We assess multiple histone mark profiles generated by different groups in two cell lines (K562 and MCF-7). Overall, our analysis indicates that compared to ChIP-seq, CUT&Tag may have limited sensitivity in low-GC environments and, as previously observed, increased signal in hyper-accessible chromatin. Notably, within GC-poor regions of active gene bodies and Polycomb-repressed domains, CUT&Tag exhibits a loss of H3K36me3 and H3K27me3 signal, respectively, where occupancy of these histone marks is otherwise expected. Further, active promoters with discrepant H3K4me3 and H3K27ac signal between the two assays differ systematically in GC content and chromatin accessibility. Promoters differentially enriched for CUT&Tag signal relative to ChIP-seq generally show higher, broader GC-content profiles and higher DNase-seq and ATAC-seq signal, while promoters enriched for ChIP-seq signal harbor the opposite characteristics. A local bias for high GC content and/or chromatin accessibility in CUT&Tag may also explain its differing signal patterns at nucleosome-depleted regions (NDRs) compared to ChIP-seq and MNase-seq promoter profiles. Altogether, our results highlight that studies focused on profiling the intensity and structure of histone modification occupancy can be sensitive to potential biases of CUT&Tag to GC content and chromatin accessibility. These characteristics should be accounted for when selecting a chromatin profiling approach, analyzing and interpreting data as well as drawing biological conclusions.

Glaucoma is an irreversible blinding disease that affects millions of individuals worldwide. Elevated intraocular pressure (IOP), regulated by the trabecular meshwork (TM) in the anterior eye, is the only modifiable risk factor. Human TM cells can be cultured from donor eyes, providing a precious resource for studying factors that induce or prevent glaucoma. The goal of this study was to produce datasets that define the molecular profile of human non-glaucomatous TM cells. Using 18 human TM cell strains cultured from non-glaucomatous individuals deposited from seven laboratories in the USA and UK, this study used transcriptomic, proteomic, lipidomic, and metabolomic analyses to characterize the molecular content of TM cells. The data herein provides the most comprehensive multiomic analyses of human TM cells to date and will be a useful resource for researchers and clinicians in the TM and glaucoma fields.

Simultaneous quantification of monoglycosphingolipid stereoisomers is required to monitor changes in defective enzymatic pathways linked to diseases such as Gaucher Disease, Parkinson's Disease, and Krabbe Disease. Resolution of beta-glucosyl and beta-galactosyl epimers cannot be achieved by standard liquid chromatography, electrospray ionization, tandem mass spectrometry (LC-ESI-MS/MS). Separation becomes possible when field asymmetric ion mobility spectrometry (FAIMS), also known as differential mobility mass spectrometry (DMS), is added as an orthogonal separation technique to LC. FAIMS/DMS separates epimeric ion clusters in a high versus low electric field (separation voltage, SV) then redirects the target epimeric ions to the mass spectrometer through the application of a direct current (compensation voltage, CoV). Resolving SVs and CoVs must be manually determined for each lipid. Manual derivation is a labour-intensive process that requires pure synthetic standards, limiting the number of stereoisomers a user can include in an assay. To address this problem, we introduce here intelligent DMS (iDMS). iDMS is an in silico supervised neural network model that learns the ion mobility relationships between SV and CoV and the monoglycosphingolipid structural features of sugar headgroup, N-acyl chain length, and N-acyl degree of unsaturation. iDMS predicts the SV and CoV combinations capable of resolving any stereoisomer pair from a training dataset of composed of measured signal intensities across a range of SVs and CoVs of 12 lipids. This machine learning alternative to manual DMS optimization promises to accelerate the deployment of multiple-reaction-monitoring mode (MRM) RPLC-ESI-DMS-MS/MS assays for the routine and rapid quantification of biologically relevant monoglycosphingolipid stereoisomers.

Acetyl-CoA and other acyl-CoA thioesters are critical intermediates in the metabolic reactions that cells rely on to produce energy and carry out biosynthesis. Therefore, the analysis of acyl-CoA provides valuable information about the metabolic activity of cells, especially when combined with stable isotope tracing. Acyl-CoA species are routinely monitored by reversed-phase liquid chromatography coupled to tandem mass spectrometry (RPLC-MS/MS). However, drastic differences in the hydrophobicity of short-chain versus long-chain acyl-CoA species have been challenging to accommodate with a single set of RPLC conditions. Here, we describe a convenient method based on hydrophilic interaction liquid chromatography (HILIC-MS/MS) for the concurrent detection of both short-chain and long-chain acyl-CoA and their corresponding acyl-carnitine species. Using this strategy, we tracked the metabolism of isotope-labeled fatty acids in multiple cell lines, which revealed differences in their propensities for fatty acid oxidation and the extent to which isotope incorporation into acyl-CoA mirrored that of acyl-carnitine. We also applied the HILIC-MS/MS workflow to the analysis of NADH and ATP, making it a useful technique for gauging cellular bioenergetics as reflected by the acetyl-CoA/CoA, NADH/NAD+, and ATP/ADP ratios. Altogether, this HILIC-MS/MS platform enables a streamlined analysis of acyl-CoA species and other key intermediates in cell metabolism.