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

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

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.

IntroductionReliable generation of hepatocyte-like cells (HLCs) from pluripotent stem cells remains limited by heterogeneity and incomplete maturation of the cells. Derivation of induced pluripotent- and embryonic stem cells into hepatocytes typically relies on complex, and costly reagent-intensive protocols, with inconsistent reporting of differentiation efficiencies and functional maturation criteria. Variability in protocol designs highlights the need for optimisation, particularly in mouse embryonic stem cells (mESCs) systems that can be more comparable with mouse models for underpinning translational and toxicological studies. Here, we developed and evaluated two cytokine-based strategies: an advanced hepatic-inducing cocktail (A-HIC) and a simplified hepatic-inducing cocktail (HIC), both designed to reduce complexity while increasing functional maturation. MethodsHepatic differentiation and maturation were assessed by morphology, immunofluorescence, flow cytometry, and qRT-PCR. Functional competence was evaluated via urea production, glutathione synthesis, indocyanine green handling, cytochrome P450 inducibility, and impedance-based cell layer integrity monitoring. ResultsMorphological, molecular and phenotypic analyses confirmed that both protocols supported hepatic lineage progression, generating heterogeneous populations of hepatoblast-like and more mature HLCs. Gene expression confirmed the loss of pluripotency, transient endoderm induction, and subsequent hepatic specification. Functionally, cells exhibited glycogen storage, inducible urea production, glutathione depletion, and active ICG uptake and clearance, with stable monolayer formation by day 21. A-HIC-derived HLCs demonstrated enhanced maturation, with higher ASGR1 expression and stronger Cyp1a1 induction. DiscussionThese findings suggest that both protocols generate functional HLCs; however, A-HIC yields a higher proportion of functionally mature cells with reduced variability. This approach enables a simple, cost-effective, and time-efficient generation of HLCs, supported by improved functional characterisation with potential applicability to more complex pluripotent systems, including human iPSC-based models for disease modelling and toxicology.

1Sex steroid hormones are not exclusively localised in the circulation and can be found in numerous extragonadal tissues, in concentrations unrelated to the circulating fraction. Existing methodology to measure intramuscular steroid hormone concentrations includes both immune-based assays and liquid chromatography-mass spectrometry (LC-MS), the gold standard for hormone measurements. To date, no LC-MS based methods validation has been published on the measurement of intramuscular sex steroid hormones, despite clear biological relevance. Here, we describe the development and validation of a simple, high-throughput LC-MS Orbitrap method for the measurement of 10 intramuscular sex steroid hormones, including pregnenolone, progesterone, dehydroepiandrosterone, androstenedione, testosterone, epitestosterone, dihydrotestosterone, oestrone, oestradiol, and oestriol. In brief, isotope labelled standards were added to 5-6 milligrams of lyophilised muscle tissue, homogenised and extracted with ethyl acetate. The extracts were dried down and sequentially derivatised with 1-methylimidazole-2-sulfonyl chloride and hydroxylamine hydrochloride to target both the phenolic hydroxyl groups and ketone groups. The limit of detection was 1.0 {+/-} 1.0 pg/mg (range 0.36 - 3.26 pg/mg), with a R2 > 0.99 for all analytes. Matrix effects were 90-110% for all analytes except for dihydrotestosterone (143.6%), and precision was <10 CV% for all analytes in the presence of a muscle matrix. Our method allows for 20-40 samples to be prepared in [~]4 h, with a sample data acquisition time of 13 minutes. Moreover, our method provides the opportunity for specific analysis of steroid hormone concentrations in skeletal muscle, allowing target tissue specificity instead of relying on proxy measures from the circulation.

Colletotrichum sublineola (Cs), the hemibiotrophic fungus that causes sorghum anthracnose, impacts sorghum grain and biomass crop production worldwide. Although nutrient availability is known to influence development in filamentous fungi, including Colletotrichum species, how in vitro nutrient limitation reprograms the Cs cellular state remains unclear. We cultured Cs on full-strength, half-strength, and one-tenth-strength potato dextrose agar (PDA) to define responses across a nutrient gradient. Nutrient limitation induced a pronounced high-sporulation phenotype, with one-tenth-strength PDA producing the strongest conidiation response, followed by half-strength PDA. To study the underlying molecular programs in each condition, we employed a multiplexed metabolite, protein, and lipid extraction (MPLEx) protocol for global proteomics and metabolomics. Global proteomics resulted in 4,590 protein identifications, including 204 unique to one-tenth-strength PDA. Among them are proteins linked to sporulation, vesicular transport, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, and common in fungal extracellular membrane (CFEM)-domain proteins. Differential abundance and pathway analyses revealed a broad reduction of central carbon and energy metabolism, including glycolysis/gluconeogenesis, pentose phosphate, pyruvate metabolism, and glyoxylate pathways, together with increased ribosome-related processes, cAMP signaling, and cell-surface remodeling in one-tenth-strength PDA conditions. In addition, correlative metabolomics supported selective metabolic depletion and resource reallocation toward stress adaptation, membrane remodeling, and conidiation, supporting proteomics findings. Together, these data support a starvation-adapted Cs developmental state associated with enhanced sporulation, cellular pathway reprogramming, and potential virulence linked preparedness under nutrient-limited growth conditions in vitro. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/724728v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@f6ceb2org.highwire.dtl.DTLVardef@17c4836org.highwire.dtl.DTLVardef@68e995org.highwire.dtl.DTLVardef@1bf3983_HPS_FORMAT_FIGEXP M_FIG C_FIG

Indica rice, being a tropical crop, is highly sensitive to cold temperature. Cold stress affects vegetative growth, photosynthetic efficiency, along with reproductive features. Genetic resource screening in diverse landraces is an approach for identifying cold-tolerant traits. Here, we have characterised a boro germplasm, CB1, with an efficient germination rate and growth vigour when treated at chilling temperatures. CB1 seedlings show a higher survival rate compared to IR36 when subjected to prolonged chilling stress. Biochemical analyses indicated efficient ROS modulation, higher chlorophyll content, enhanced photosystem II efficiency and unique stomatal traits, leading to higher relative water content in CB1 plants during stress and recovery. Transcriptome analysis supported upregulation of chlorophyll biosynthesis, photosystem, & light harvesting complex and ROS scavenger genes in CB1 seedlings. Interestingly, high D1 protein turnover in CB1 promotes damage-repair of PSII for efficient photosynthesis. Furthermore, key transcription factors for stomatal development and expression of photosynthetic genes were upregulated in CB1 during stress recovery. Notably, higher expression of OsGLK1 and enrichment of GLK1 targets were observed in CB1 plants during chilling stress and recovery. Taken together, our results suggested that CB1 plants exhibit cold tolerance by modulating photosynthesis efficiency and stomatal behavior for better adaptability and survival against chilling temperature. HIGHLIGHTSThe efficient photosynthetic recovery, active ROS scavenging system and maintenance of water content through regulating stomatal traits, enhance the survival of indica germplasm CB1 against chilling stress.

Vitamin D signaling has recognized roles in female reproductive physiology, but its effects at the chromatin level in endometrial stromal cells are still unclear. Here, we investigated how the active form of vitamin D, 1,25-dihydroxyvitamin D3, or calcitriol, influences the accessible chromatin landscape of human endometrial stromal cells. Assay for transposase-accessible chromatin using sequencing (ATAC-seq) was performed on T-HESCs treated with either a vehicle or 1,25(OH)2D3. Ligand treatment increased overall chromatin accessibility, shown by higher ATAC-seq signal intensity, while causing only minor changes in the total number of called peaks. Peak annotation revealed that accessible regions were spread across both promoter-proximal and distal genomic areas. Integrating this data with CUT&RUN and RNA sequencing showed that most vitamin D-responsive cistromic modifications and transcripts were linked to nearby open chromatin, though fewer were associated with regions that were significantly differentially accessible. These results suggest that 1,25(OH)2D3-dependent transcription mainly occurs within a permissive, pre-accessible chromatin environment. This study offers new evidence that active vitamin D influences the epigenomic landscape of human endometrial stromal cells, establishing the chromatin-based molecular response to a chemically-defined VDR ligand, 1,25(OH)2D3, relevant to stromal differentiation and preparation for decidualization. HighlightsO_LIFirst evidence suggesting the direct impact of active vitamin D, 1,25-dihydroxyvitamin D3, 1,25(OH)2D3, enhanced the signal intensity of chromatin accessibility in human endometrial stromal cells C_LIO_LIMost accessible chromatin regions were shared between vehicle and ligand-treated human endometrial stromal cells C_LIO_LI1,25(OH)2D3-responsive transcription occurs largely within pre-accessible chromatin in human endometrial stromal cells C_LIO_LIAssay for transposase-accessible chromatin sequencing (ATAC-seq) defines a chromatin-level pharmacologic response to a chemically defined VDR ligand in human endometrial stromal cells C_LI

Insect herbivory triggers cytosolic proteome reprogramming by activating defense pathways and modulating key metabolic processes. We found that simulated herbivory in pigeon pea (Cajanus cajan) induced reactive oxygen species (ROS) production and molecular alterations within 12 hours (h) of post treatment. We compared the leaf proteome profiles of two cultivated genotypes, ICPL 332 (moderately resistant) and ICPL 87 (susceptible), using two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) coupled with mass spectrometry (MS). More than 220 protein spots were detected in ICPL 332 and over 200 in ICPL 87. Comparative analysis revealed 75 differentially accumulated proteins (DAPs), of which 40 were consistently reproducible across biological replicates. These included 11 unique to ICPL 87, 9 unique to ICPL 332, and 10 common to both genotypes. Among the shared DAPs, ICPL 332 showed five upregulated and five downregulated, whereas ICPL 87 exhibited only two upregulated and eight downregulated. Functional categorization grouped DAPs into primary metabolism, stress response, and growth and development. Proteins related to primary metabolism were largely downregulated in both genotypes, while stress-associated proteins exhibited substantial downregulation in ICPL 87 compared to ICPL 332. Overall, the results demonstrate proteomic adjustments underlying defense responses in pigeon pea genotypes.

Human milk contains structurally diverse glycans with key roles in shaping infant development, yet analytical constraints limit characterisation from low-volume samples. Glycosaminoglycans (GAGs), including chondroitin sulphate (CS), are understudied due to existing protocols requiring sample volumes of at least 5 mL and lengthy extraction steps prior to instrumental analysis. This study establishes a workflow for quantifying CS disaccharides from 25 {micro}L of human milk, enabling analysis of samples previously inaccessible to GAG profiling, such as those collected as salvage samples from neonatal intensive care units. For CS quantification, the CS is first enzymatically depolymerised using chondroitinase ABC to release repeating disaccharide units. Matrix complexity is reduced via two rounds of acetonitrile-based protein and lipid precipitation. Disaccharides are separated by hydrophilic interaction liquid chromatography and detected using a Triple Quadrupole Mass Spectrometer, providing robust sensitivity for all CS disaccharides. Method development and validation were performed using pooled mature human milk from term infants. This workflow facilitates detection of all CS disaccharides, with low but reproducible recoveries for total CS. Low- and high-level spike recoveries were 41.3% (RSDr 7.5%, RSDiR 15.9%) and 43.7% (RSDr 24.4%, RSDiR 27.9%), respectively. Despite modest absolute accuracy, precision remained sufficient to make relative comparison of CS concentrations between samples. This method expands the analytical toolkit for human milk glycomics, enabling same day preparation and CS profiling from sample volumes that are 200 times smaller than prior work, supporting future investigations into GAG-mediated functions in early life. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/723732v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@176dffborg.highwire.dtl.DTLVardef@16ae4ccorg.highwire.dtl.DTLVardef@d333c2org.highwire.dtl.DTLVardef@1eb3216_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO Schematic of sample preparation protocol 25 L of human milk is combined with lyase enzymes and TRIS buffer containing the internal standard prior to incubation. Samples then undergo multiple rounds of centrifugation and refrigeration before analysis via LC-MS/MS. Made using BioRender.com. Glycan nomenclature following Varki et al., 2015. C_FIG

Cancer is a major global health issue with rising incidence and mortality. Early detection, tumor characterization, and disease surveillance are crucial for timely and effective treatment, ultimately reducing mortality rates. Liquid biopsy (LB) has emerged as a valuable detection tool offering a non-invasive method to determine tumor-derived biomarkers in body fluids with demonstrated translational potential. To increase biomarker sensitivity, high-throughput sequencing platforms deliver massive volumes of data. Artificial Intelligence (AI) is pivotal in enabling huge and complex data integration. This contribution aims to assess the current state of integrative AI-based research in the LB field and provide methodological guidance. First, we conducted a PubMed search and found that the literature is sparse in studies integrating LB features, particularly by applying AI. When adopting the latter approach, defining the study objectives is crucial to guide the subsequent methodological aspects, including study design, patient selection criteria, sample size, nature of the LB features, and metadata to collect. Specifically, we propose strategies and tools for data preprocessing, including normalization and batch correction, as well as handling outliers and missing data. Furthermore, we recommend various Machine/Deep Learning approaches for feature selection techniques to ensure model robustness, and we highlight the importance of undergoing rigorous internal and external validations of the selected models. Assessing clinical utility and interpretability is often overlooked but fundamental for real-world implementation. In conclusion, we provide the LB scientific community with an AI-based methodological guidance to bridge the two fields and enhance the integrative analysis of LB features. Graphical abstractWorkchart for multiomics integrative studies in the liquid biopsy field. Note: CTCs, circulating tumor cells; ctDNA, circulating tumor-DNA; TEPs, tumor-educated platelets; miRNA, microRNA; cfRNAs, cell-free RNAs. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=159 SRC="FIGDIR/small/724535v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@1f250b2org.highwire.dtl.DTLVardef@18fe36corg.highwire.dtl.DTLVardef@19c02b9org.highwire.dtl.DTLVardef@176f6e0_HPS_FORMAT_FIGEXP M_FIG C_FIG

Polycomb Repressive Complex 2 (PRC2) maintains repression of genes specific for other cell differentiation states. PRC2 binds RNA in vitro with a preference for G-rich sequences. UV-based crosslinking coupled with immunoprecipitation (CLIP) experiments have shown that PRC2 also binds RNA in cells. Recently, Guo et al reported that a stringent denaturing variant of CLIP called CLAP did not detect PRC2 RNA binding in cells. We present a reanalysis of CLAP data that supports direct interaction of PRC2 with RNA in cells. CLAP for Halo-tagged PRC2 subunits from mixed populations of human and mouse cells specifically enriched for RNA from the species in which the proteins were tagged. The lack of apparent PRC2 RNA binding in Guo and colleagues analysis stems from a scaling step that deflates enrichment scores for low-complexity CLAP samples. Our findings pave the way for studies seeking to determine the physiological roles of PRC2 RNA binding activity.

Public data reuse is a key driver of progress in omics sciences, including increasingly metabolomics data. In this study, we present a validated analysis of confirmed reuse of datasets from the MetaboLights data repository, one of the leading resources in the field. Candidate publications were collected via dataset identifiers (MTBLS#) using a Python-based retrieval pipeline across major publisher databases. They were next manually validated to distinguish active reuse from citation-only mentions. Overall, 272 unique publications were confirmed to have reused at least one MetaboLights dataset. Reuse is dominated by Method/Tool Development, with smaller contributions from Secondary Biological Analysis and Data Integration/Meta-analysis. LC-MS datasets account for the majority of reuse, whereas NMR and GC-MS also contribute but at a lower level. Data reuse has increased over time, with a noticeable acceleration in the most recent years. At the dataset level, reuse follows a long-tail distribution, where a small subset of datasets accounts for repeated reuse, mainly as community benchmarks. These results provide a conservative estimate of public metabolomics data reuse and show that public datasets are predominantly used for methodological and computational applications. They also indicate that reuse is under-detected when dataset identifiers are not consistently reported, highlighting the need for standardised dataset citation to improve traceability and recognition of reuse. Statement of significance of the studyThe impact of public metabolomics repositories has been difficult to assess due to the lack of reliable evidence distinguishing true data reuse from simple literature citations. This study addresses that gap by providing a conservative, manually validated baseline for confirmed reuse of datasets from the MetaboLights data repository. The analysis clarifies how MetaboLights datasets are used in practice, showing that reuse is concentrated to a limited number of datasets and is dominated by computational and methodological applications.

Aging is accompanied by complex, tissue-specific molecular changes across multiple biological layers, yet integrative analysis of multi-omics datasets remains challenging for many experimental researchers due to technical and computational barriers. Here, I present Shiny Aging Murine Multi-Omic Analyzer (Shiny AMMOA), a graphical user interface (GUI)-based, user-friendly analytical platform that enables interactive exploration of murine aging-associated bulk transcriptomic, proteomic, and metabolomic datasets. Shiny AMMOA integrates publicly available multi-omics resources within a unified R Shiny framework and supports end-to-end analyses, including differential expression testing, pathway enrichment analysis, and pathway-level visualization across individual and multiple omics layers. Using representative use cases, I demonstrate that Shiny AMMOA recapitulates key findings from original source studies and facilitates intuitive discovery of tissue-, pathway-, and modality-specific aging signatures, including age-associated alterations in unfolded protein response, extracellular matrix organization, and metabolic pathways across specific tissues and omics layers. The platform further enables integrated visualization of molecular changes across omics layers on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway diagrams, supporting hypothesis generation at the systems level. By democratizing access to integrative multi-omics analysis while preserving analytical rigor, Shiny AMMOA provides an extensible resource for experimental biologists and aging researchers to interrogate large-scale public datasets, prioritize biological pathways, and accelerate translation of multi-omics insights into testable experimental hypotheses. Shiny AMMOA is available at https://github.com/M-Ninomiya-Kanda/Shiny_AMMOA_local, and a lightweight web-based demonstration version with limited functionality is available at https://m-ninomiya-kanda.shinyapps.io/shiny_ammoa_web/.

Assessing reproducibility across different molecular profiling studies is a persistent methodological challenge (Zhang et al., 2009; Sweeney et al., 2017; Ioannidis, 2005). Differences in platform technology, cohort composition, analytical pipelines, and feature definitions often make it difficult to interpret cross-study comparisons based solely on gene-identity overlap. In this study, we conducted a retrospective computational analysis of seven publicly available analytical datasets (including alternative analytical pipelines applied to the same cohort) derived from five biologically independent peripheral blood transcriptomic and DNA methylation cohorts, comprising 3,487 samples (1,824 Parkinsons disease cases and 1,663 controls). Reproducibility was evaluated using gene-identity overlap, enrichment-based comparisons, and a permutation-based framework to assess directional consistency of effect estimates across datasets. We also tested the robustness of results by varying false discovery rate thresholds and applying alternative probe-to-gene collapsing strategies. All analyses were performed using reproducible workflows implemented in R and Python with fixed random seeds. Across independent cohorts, gene-identity overlap was generally limited, with enrichment ratios close to one, especially when datasets were generated using different platforms. In several datasets, limited numbers of statistically significant features further constrained overlap-based comparisons. In contrast, directional consistency showed greater stability. High levels of directional consistency were observed across independent cohort comparisons when restricted to overlapping statistically significant features and remained stable across statistical thresholds (90.0% at FDR < 0.05 and 82.8% at FDR < 0.10). When evaluated across the full shared gene universe without conditioning on statistical significance, directional consistency was substantially lower ([~]30 to 32%) but remained significantly above permutation-based null expectations. Permutation testing confirmed that the observed directional consistency exceeded what would be expected by chance. A combined analysis including methodological replicates (n [&ge;] 3 datasets) showed 98.3% directional consistency; however, this estimate includes non-independent analytical pipelines applied to the same cohort and reflects analytical stability rather than independent biological replication. Rather than introducing a new statistical method, this study examines how commonly used reproducibility metrics behave under crossstudy heterogeneity and identifies their practical limitations and appropriate use boundaries.

Advances in transcriptome profiling have revealed transcriptomic differences across different cellular states. However, functional interpretation requires precise perturbation tools and experimental frameworks. This study benchmarked two widely used modalities: CRISPR interference (CRISPRi) and Cas13d/CasRx. A standardized workflow was established to generate human pluripotent stem cells (PSCs) with inducible ZIM3-dCas9 or CasRx expression. The cell lines were subjected to flow cytometry, copy number, and immunocytochemical analyses. The knockdown performance was validated via robust OCT4 suppression and the expected downstream effects on pluripotency genes. Time-course measurements indicated that CRISPRi produced faster and stronger repression but slower recovery after inducer withdrawal. In contrast, CasRx yielded slower and typically weaker knockdown with rapid reversibility. Furthermore, a key limitation of CRISPRi was demonstrated using the ATF5-NUP62 locus, wherein CRISPRi could co-repress genes with overlapping promoter regions. In contrast, CasRx avoids these limitations and supports isoform-resolved targeting of circular and alternatively spliced transcripts, albeit with variable efficiency. These results provide practical guidance for selecting complementary knockdown tools to improve the interpretability of transcriptomic function studies. MOTIVATIONAdvances in transcriptome profiling have enabled the detection of subtle cell type-specific differences. However, mechanistic interpretation still depends on perturbation tools that can modulate transcripts with high precision and efficiency. Recent CRISPR-based modalities, CRISPRi and Cas13/CasRx, function as robust and orthogonal methods to achieve the knockdown of specific gene targets. However, a standardized approach for cell line preparation and comparative studies on their relative performances and limitations remains unclear. Consequently, this study presents a standardized workflow for generating cell lines that support high-efficiency knockdown using CRISPRi and CasRx. Moreover, it compares the trade-offs in potency, reversibility, and isoform resolution, along with a practical overview of method-specific pitfalls to guide tool selection and data interpretation in future studies. HIGHLIGHTSO_LIDoxycycline-inducible AAVS1 knock-in human PSC platforms for CRISPRi (ZIM3-dCas9) and CasRx (RfxCas13d) were generated to enable standardized RNA perturbation experiments. C_LIO_LIThe prepared cell lines demonstrated strong OCT4 knockdown, with expected downstream effects on the expression of another pluripotency gene, NANOG. C_LIO_LIA comparison of knockdown characteristics and their reversibility revealed rapid and sustained repression with CRISPRi, whereas slow but rapid recovery was observed with CasRx. C_LIO_LIA CRISPRi-specific off-target effect arising from TSS proximity/overlap (ATF5-NUP62) was identified, whereas CasRx achieved ATF5 knockdown without collateral repression of the neighboring NUP62 gene. C_LIO_LICasRx enables isoform-resolved knockdown of structural isoforms (circHIPK3 vs. linear HIPK3 mRNA) and splice isoforms (RAB6A-iso1 vs. RAB6A-iso2). C_LI

The yeast secreted proteome plays critical biological roles and influences product and production parameters in industrial fermentation. Systematic profiling of the response of the yeast secretome to intrinsic and extrinsic factors is therefore essential for understanding these functions and for optimizing manufacturing processes. Here, we characterized the yeast secretome under diverse proteosynthetic stress conditions, including glycosylation deficiency, oxidative, reductive, and thermal stresses. The secretome was predominantly composed of conventionally secreted proteins, while a subset of proteins appeared to be secreted via unconventional pathways. Distinct secretome profiles were observed in response to different stressors, driven by a combination of altered intracellular proteomes, altered canonical secretion, and altered cell lysis and unconventional protein secretion, while reflecting the underlying metabolic state of the cells. Heat stress did not impact protein glycosylation but did cause similar protein misfolding stress to N-glycosylation deficiency. Intriguingly, canonically intracellular chaperone BiP was abundant in the secretome in particular stress conditions where its activity would be beneficial. BiP interacted with probable extracellular client proteins in vitro, consistent with it acting as a functional extracellular chaperone/holdase in conditions such as reductive stress in which client proteins could be misfolded outside the cell.

Metabolomics offers a sophisticated analytical framework for characterising the molecular phenotype of biological organisms and complex living systems at a high resolution. As the functional endpoint of the omics cascade, the metabolome serves as a close reflection of cellular activity. It integrates genetic, transcriptomic and proteomic variations with external environmental influences. However, the inherent complexity of metabolomic datasets, characterised by high-dimensional chemical diversity, wide dynamic ranges, and significant matrix effects, necessitates a rigorous suite of chemometric and bioinformatic workflows. For researchers uninitiated in computational biology, the multi-stage requirement for raw data pre-processing, signal deconvolution, and multivariate statistical modelling (such as PCA or PLS-DA) presents a substantial barrier to entry. Navigating these convoluted data architectures remains a primary challenge in deriving biological meaning from the global metabolic profile. Here, we present a workflow to use Python Dash Apps to create a user-friendly interface for simplifying data processing and statistical calculations. Users can select their desired samples to initiate calculations for various statistical tests, generating interactive and publication-quality figures to explore their results. These apps were deployed on an Apache server via cPanel, allowing individuals to share their findings with collaborators and for research facilities to share metabolomics results with their users.