Metabolomics--a new exciting field within the "omics" sciences.
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Environmental Health↗
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The "omics" haystack: defining sources of sample bias in expression profiling.
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Bias↗
Multi-omics-based study on the biological characteristics of kidney renal deficiency and blood stasis in ankylosing spondylitis.
OBJECIVE: To explore the objective biological evidence for the classification and diagnosis of Traditional Chinese Medicine (TCM) syndromes in ankylosing spondylitis (AS) using multiomics analysis. METHODS: Patients with AS were categorized into kidney deficiency and blood stasis syndrome (SX group) and damp-heat stasis syndrome (SR group). Transcriptomic sequencing and quantitative plasma proteomics were performed on patients with AS and healthy volunteers. Multiomics integration was used to characterize the biological basis of AS with renal deficiency and blood stasis syndrome. Specific proteins were validated by quantitative reverse transcription-polymerase chain reaction (RT-qPCR) and enzyme-linked immunosorbent assay (ELISA). RESULTS: Transcriptomic sequencing identified 31 significantly upregulated genes in patients with AS compared to healthy controls. These genes were primarily involved in tumor necrosis factor, interleukin-17, and nuclear factor kappa-B signaling pathways, as well as osteoblast differentiation and various viral infection pathways. Differentially expressed genes, including intercellular adhesion molecule 1 (ICAM1), 6-phosphofructo-2-kinase, cyclin-dependent kinase inhibitor 1A, interleukin 1 receptor antagonist, integrin alpha IIb, and myosin light chain 9 were more upregulated in the SX group than in the SR group. Quantitative proteomics identified 723 differential proteins associated with the disease and 788 differential proteins between the SX and SR groups. Notable proteins such as myeloperoxidase, cluster of differentiation 14, macrophage simulating 1 (MST1), and Ras homolog enriched in brain may serve as characteristic proteins of the SX group. By integrating transcriptomic and proteomic data, 45 associated differential molecules involved in platelet activation, pathogenic intestinal flora infection, glycolysis/gluconeogenesis, and T-cell receptor signaling pathways were identified in patients with AS compared to healthy controls. Additionally, ICAM1, MST1, C-X-C motif chemokine ligand 8 (CXCL8), suppressor of cytokine signaling 3 (SOCS3), and insulin-like growth factor binding protein 1 (IGFBP1) were detected in TCM syndromes by RT-qPCR and ELISA, showing upregulation in AS renal deficiency and blood stasis syndromes, which is consistent with the proteomic and transcriptomic results. CONCLUSIONS: ICAM1, MST1, CXCL8, SOCS3, and IGFBP1 were identified as biomarkers of renal deficiency and blood stasis syndrome in AS. This study provides a biological basis for the differential diagnosis of TCM syndromes in AS, offering new insights into Chinese medicine evidence and more precise Chinese medicine treatments for AS.
Humans↗
Biomedical literature mining: challenges and solutions in the 'omics' era.
It is now obvious that the rate-limiting step in high throughput experimentation is neither data acquisition nor analysis, but rather our ability to interpret data on a genome-wide scale. Indeed, the explosion of data sampling capacity combined with increasing publication rates greatly impairs our ability to find meaning in vast collections of data. In order to support data interpretation, bioinformatic tools are needed to identify critical information contained in large bodies of literature. However, extracting knowledge embedded in free text is an arduous task, compounded in the biomedical field by an inconsistent gene nomenclature, domain-specific language and restricted access to full text articles. This paper presents a selection of currently available biomedical literature mining software. These tools rely on statistic and, more recently, semantic analyses (Natural Language Processing) to automatically extract information from the literature. In addition, a literature mining strategy has been developed to explore patterns of term occurrences in abstracts. This method automatically identifies relevant keywords in collections of abstracts, and uses a pattern discovery algorithm to generate a visual interface for exploring functional associations among genes. Term occurrence heatmaps can also be combined with gene expression profiles to provide valuable functional annotations. Furthermore, as demonstrated with tumor cell line literature profiling results, this approach can be applied to a variety of themes beyond genomic data analysis. Altogether, these examples illustrate how literature analysis can be employed to support knowledge discovery in biomedical research.
Algorithms↗
Multi-omics Approaches to CCAAT/Enhancer-Binding Protein Beta in Oral Squamous Cell Carcinoma: Crosstalk Between Tumor Cells and Tumor-Associated Macrophages Driving Disease Progression.
BACKGROUND: CCAAT/Enhancer-Binding Protein Beta (CEBPB) is an important transcription factor that regulates tumor progression. However, the mechanism by which CEBPB regulates the progression of Oral Squamous Cell Carcinoma (OSCC) remains incompletely understood. Tumor progression depends on complex intercellular interactions within the tumor microenvironment. The purpose of this study was to investigate the role and epigenetic regulatory mechanisms of CEBPB in interactions between OSCC cells and tumor-infiltrating immune cells. METHODS: Bulk RNA-seq, ChIP-seq, and scRNA-seq data were obtained from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. The HOMER algorithm was employed to identify enhancers and predict the CEBPB-binding motif. Cell cluster analysis, functional enrichment, and intercellular interaction analysis were performed using the "Seurat" R package. H3K27ac enrichment at GAS6 enhancers was validated by ChIP-qPCR. Metastatic OSCC cells with CEBPB knockdown or GAS6 overexpression were established and co-cultured with THP-1 cells. IL-10 and IL-6 secretion from co-cultured THP-1 cells was detected via ELISA. Chemotaxis of OSCC cells toward THP-1 cells was assessed through a Transwell assay. RESULTS: CEBPB was upregulated in OSCC and correlated with poor prognosis. By integrating H3K27ac ChIP-seq and bulk RNA-seq data, 131 CEBPB-regulated enhancer-controlled genes were identified in lymph node metastatic OSCC cells. scRNA-seq analysis revealed eight major cell clusters in primary foci and lymph node metastases, including T/NK cells, malignant epithelial cells, B/plasma cells, macrophages, fibroblasts, dendritic cells, endothelial cells, and mast cells, with the malignant epithelial cells stratified into distinct sub-clusters. CEBPB expression was elevated in malignant epithelial cells of lymph node metastases compared to primary foci. Furthermore, 15 pairs of enhanced ligand-receptor interactions were identified in lymph node metastases relative to primary foci. GAS6 was a CEBPB-regulated enhancer-controlled gene, primarily mediating interactions between malignant cells and macrophages. CEBPB knockdown in metastatic OSCC cells significantly impaired their chemotaxis toward cocultured THP-1 cells, and downregulated IL-10/IL-6 secretion and CD206 expression in cocultured THP-1 cells. Conversely, GAS6 overexpression reversed these inhibitory effects. CONCLUSION: CEBPB activated GAS6 transcription in metastatic OSCC cells. The CEBPB/ GAS6 axis in metastatic OSCC cells enhanced their chemotaxis toward macrophages and promoted the M2 polarization of macrophages, thereby facilitating the establishment of an immunosuppressive microenvironment.
Humans↗
Integrative Multi-Omics Deciphering of Gu Shu Kang Granules: A Comprehensive Systems Biology Approach to Unraveling Molecular Mechanisms in Sarcopenia-Osteoporosis Intervention.
INTRODUCTION: Sarcopenia is a degenerative musculoskeletal disease affecting the elderly, significantly impairing patients' quality of life and challenging modern medicine. This study innovatively combines Traditional Chinese Medicine (TCM) theories with modern medical research to explore the mechanisms by which Gushukang granules address sarcopenia. METHODS: The research integrated multi-dimensional research methods, including network pharmacology, metabolomics, and animal experiments, to comprehensively investigate the scientific mechanisms of Gushukang granules' intervention in sarcopenia. RESULTS: Network pharmacology analysis identified multiple potential targets related to muscle growth and repair. UPLC-Q-TOF MS technology tracked metabolic pathways, while animal experiments verified that Gushukang granules precisely regulate muscle metabolic balance by modulating key signaling pathways involved in protein synthesis and degradation. DISCUSSION: The findings demonstrate the potential of integrating traditional and modern medical approaches in addressing age-related muscle degradation, providing scientific validation for TCM treatment of sarcopenia. CONCLUSION: This study establishes a model for modernizing TCM research, offering solid scientific evidence for comprehensive intervention of chronic diseases in the elderly and highlighting the TCM concept of "preventing disease before its onset" in modern medical translation.
Sarcopenia↗
PCSK9 as a Key Gene of Metastasis in Lung Adenocarcinoma: A Multi-omics and Experimental Validation Study.
BACKGROUND: Lung adenocarcinoma (LUAD) is the most common form of lung cancer. Proprotein convertase subtilisin/kexin type 9 (PCSK9) is abnormally expressed in various tumor tissues and is associated with malignant phenotypes. However, the clinical significance, function, and mechanism of LUAD invasion and metastasis remain unclear. METHODS: We retrospectively enrolled 100 patients with LUAD in this study. Initially, qRT-PCR was performed to detect PCSK9 levels in clinical tissues. Subsequently, bioinformatics analysis of scRNA-seq and The Cancer Genome Atlas Program (TCGA) datasets was performed to predict the role of PCSK9 in tumor cell malignancy and its potential downstream pathways. These predictions were validated experimentally using the CCK-8 assay, TUNEL staining, wound healing, transwell invasion assay, and an in vivo lung metastasis model. Finally, Western blotting and an AKT inhibitor (MK2206) were used to verify the underlying mechanism. RESULTS: PCSK9 was significantly upregulated in LUAD tissues compared to paracancerous tissues and was associated with poorer OS and DFS. Bioinformatics analysis of scRNA-seq data and TCGA analysis predicted that PCSK9 is highly enriched in tumor cells and is involved in EMT, and that the PI3K/AKT pathway plays a significant role in LUAD development. Experiments confirmed that PCSK9 markedly promoted LUAD cell proliferation, migration, and invasion in vitro and lung metastasis in vivo. PCSK9 overexpression significantly upregulated p-AKT, p-PI3K, and p-mTOR levels. Furthermore, the AKT inhibitor, MK2206, reversed the promoting effects of PCSK9. CONCLUSIONS: PCSK9 expression is associated with the prognosis and diagnosis of LUAD. This molecule activates the PI3K/AKT signaling pathway, thereby driving invasion, metastasis, and proliferation in LUAD.
Humans↗
Multi-omics identification and functional validation of signal regulatory protein gamma as a prognostic biomarker and immune regulator in head and neck squamous cell carcinoma.
BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) comprises biologically diverse tumors, and durable responses to immune-checkpoint blockade are achieved by only a subset of patients. There remains a need for markers that connect clinical outcome with malignant-cell phenotypes and tissue-level immune organization. METHODS: We integrated The Cancer Genome Atlas HNSCC cohort (TCGA-HNSC), five Gene Expression Omnibus (GEO) validation cohorts, single-cell RNA sequencing, Visium spatial transcriptomics, cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq)-informed protein-potential inference, pharmacogenomic screening, genetic-risk analysis and experimental validation. A reconstructed 296-pipeline survival modelling framework was used to prioritize prognostic hub genes across validation-cohort-specific analyses. RESULTS: SIRPG was repeatedly ranked among the top ten selected genes in all five validation cohorts. At single-cell resolution, SIRPG-high tumor cells showed stronger malignant-cell features, immune-inhibitory and metabolic programs, Scissor-positive risk association, CLCA2/P53-related perturbation signals and inferred SIRPG-CD47/signal regulatory protein (SIRP) communication. Spatial analyses placed this axis within an immune-checkpoint-coupled niche, supported by Maxspin/multiview intercellular spatial modelling (MISTy) spatial coupling, communication analysis by optimal transport (COMMOT)-inferred CD47-SIRPG communication and scProTrans-inferred CD47/SIRPG protein-potential overlap. Functionally, SIRPG knockdown reduced HNSCC cell viability and increased apoptosis, whereas re-expression of short hairpin RNA (shRNA)-resistant SIRPG restored the CLCA2-BAX/BCL2 protein response. CONCLUSION: Together, these findings identify SIRPG as an immune-related prognostic hub and context-dependent tumor-cell regulator associated with apoptosis, immune communication and spatial microenvironmental organization in HNSCC.
Humans↗
Integrated multi-omics analysis and functional experiments reveals PPAP2C as a potential prognostic biomarker and therapeutic target in breast cancer.
BACKGROUND: This study aims to systematically elucidate the clinical significance and biological function of the phospholipid phosphatase (PLPP) family member (PPAP2C) phosphatidic acid phosphatase type 2C in breast cancer, and to evaluate its potential as a prognostic biomarker and therapeutic target. METHODS: Gene expression data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Line Encyclopedia (CCLE) databases were integrated to characterize the expression profile of PLPP family members, focusing on PPAP2C in breast cancer. The prognostic value of PPAP2C, initially identified at the mRNA level (TCGA, (METABRIC) Molecular Taxonomy of Breast Cancer International Consortium, Gene Expression Omnibus (GEO)), was confirmed at the protein level by immunohistochemistry (IHC) on tissue microarrays (TMA). The oncogenic functions of PPAP2C were investigated in triple-negative breast cancer (TNBC) cells through CRISPR-Cas9-mediated knockout and ectopic overexpression, with assessment of key phenotypes including proliferation, colony formation, migration, and invasion. In vivo validation was subsequently performed using an MDA-MB-231 xenograft model. RESULTS: PPAP2C exhibits the most significant overexpression pattern across 33 cancer types (upregulated in 16 cancers, downregulated in only 3). Compared with normal tissues, PPAP2C showed specific overexpression in breast cancer tissues and was significantly associated with advanced clinical stages and aggressive subtypes (HER2+ and TNBC). Survival analysis demonstrated that high PPAP2C expression correlated with significantly shorter overall survival and disease-free survival, which was further validated in METABRIC and GEO cohorts. Tissue microarray analysis confirmed higher PPAP2C protein positivity in tumor tissues (94.7%) than in adjacent normal tissues (59.7%), with worse OS and RFS in high-expression groups. Multivariate analysis identified PPAP2C as an independent prognostic factor for OS. Functional experiments revealed that PPAP2C knockout (via 5-bp/1-bp frameshift mutations) suppressed TNBC cell proliferation, colony formation, migration, and invasion, while overexpression enhanced these phenotypes. In vivo studies further demonstrated complete tumor regression in MDA-MB-231 xenografts upon PPAP2C knockout. CONCLUSION: This study identifies PPAP2C as a key oncogenic driver and a robust independent prognostic biomarker in breast cancer. The findings provide compelling evidence that PPAP2C represents a promising therapeutic target, offering a new strategic avenue for precision therapy, particularly for aggressive breast cancer subtypes.
PLPP2↗
Circulating Tumor DNA in Bladder Cancer: Current Clinical Evidence and Emerging Multi-Omics Perspectives-A Narrative Review.
Background: Circulating tumor DNA (ctDNA) analysis has emerged as a promising tool for real-time disease monitoring in muscle-invasive bladder cancer (MIBC). This narrative review summarizes current clinical evidence regarding ctDNA across disease stages. Methods: We examine recent translational and clinical findings, incorporating key prospective data from practice-changing trials, as well as insights into minimal residual disease (MRD) detection, treatment escalation and de-escalation strategies, and systemic barriers to adoption. Results: Postoperative ctDNA positivity consistently identifies patients with molecular residual disease (MRD) who face a substantially higher risk of recurrence and mortality, frequently preceding radiographic relapse by several months. Prospective evidence now validates ctDNA as a predictive biomarker to guide adjuvant immunotherapy escalation, while sustained ctDNA negativity correlates with high long-term disease-free survival. Beyond plasma ctDNA, emerging multi-compartment liquid biopsies-integrating urinary tumor DNA (utDNA)-demonstrate enhanced sensitivity, particularly in bladder-sparing and local surveillance settings. Furthermore, integrating genomic ctDNA profiling with novel post-transcriptional layers like epitranscriptomics offers a functional framework to capture tumor adaptation under therapeutic pressure. However, clinical translation remains constrained by a lack of assay harmonization, variable analytical sensitivity, and the need for standardized intervention thresholds. Conclusions: Longitudinal liquid biopsies are rapidly shifting MIBC management from static, stage-based paradigms toward dynamic, molecularly informed precision oncology. While ctDNA-guided strategies show robust clinical utility, prospective interventional validation and technical standardization are required before widespread routine integration.
biomarkers↗
Multi-Omics Platforms Reveal Synergistic Intestinal Toxicity in Tilapia from Acute Co-Exposure to Polystyrene Microplastics, Sulfamethoxazole, and BDE153.
Polystyrene microplastic (MP) and its co-existing contaminants may exert different toxic effects on its surrounding aquatic organisms. In order to detect the intestinal harmful responses, tilapia were subjected to exposure with 75 nm of MPs, 100 ng·L-1 of sulfamethoxazole (SMZ), 5 ng·L-1 of BDE153, and combinations thereof over periods of 2, 4, and 8 days. Enzymatic assays, transcriptomics, proteomics, and metabolomics were employed to evaluate intestinal histopathological effects. Results showed that significant reductions were observed in ATP, ROS, SOD, EROD, lipid metabolism-related enzymes, pro-inflammatory cytokines (TNFα and IL-1β), and apoptosis marker caspase 3 across all groups at day 8. Histological evaluation revealed diminished goblet cell density, with distinct vacuole formation in the BDE153+MPs group. KEGG pathway analysis highlighted disruptions in endocytosis, MAPK signaling, phagosome formation, and actin cytoskeleton regulation. Proteomic findings indicated notable enrichment in endocytosis (decreased sorting nexin-2; increased Si:dkey-13a21.4), MAPK/PPAR signaling, protein processing in the endoplasmic reticulum (Sec61 subunit gamma), and cytoskeletal modulation (reduced fibronectin; elevated activation peptide fragment 1), with or without SMZ and BDE153. Metabolomic profiling showed significant alterations in ABC transporters, aminoacyl-tRNA biosynthesis, protein digestion and absorption, and linoleic acid metabolism. In summary, these findings suggest that BDE153 and MPs synergistically exacerbate intestinal damage and gene/protein expression over time, while SMZ appears to exert an antagonistic, mitigating effect.
Animals↗
Analysis of the dual role of amyloid-beta in Alzheimer's disease through multi-omics integration.
Accumulation of amyloid-beta is highly important in the development of Alzheimer's disease. Given the limitations of the amyloid cascade hypothesis and the repeated clinical failures of anti-amyloid-beta therapies, researchers are increasingly exploring the infection hypothesis. This review explores the dual behaviors of amyloid-beta in Alzheimer's disease, with a particular focus on its protective role against infection by microorganisms and its complicated connections with innate immune system. This new opinion holds that amyloid-beta can play an antimicrobial peptide role. During microbial invasion, its original role is to protect neural tissue, but prolonged accumulation leads to chronic deposition and involvement in pathological processes. Evidence from in vitro experiments, animal models, and clinical studies indicates that amyloid-beta may possess antiviral and antibacterial properties, particularly against infections such as herpes simplex virus, human immunodeficiency virus, and Porphyromonas gingivalis . However, excessive accumulation of amyloid beta triggers a neuroinflammatory cascade that impairs neuronal regeneration and cognitive function. Despite substantial research into Alzheimer's disease, current treatments have not yielded significant clinical benefits. Although monoclonal antibodies such as Aducanumab , Lecanemab , and Donanemab have been approved for marketing, their strict indications and high costs pose challenges for widespread promotion. The infection hypothesis of amyloid-beta has spurred clinical trials investigating vaccines targeting specific pathogens to assess their potential in preventing or treating Alzheimer's disease. This highlights the need for further exploring the multifaceted role of amyloid-beta in Alzheimer's disease. In addition, microbial infections can also trigger or regulate genetic and epigenetic factors, accelerating amyloid beta deposition. Among them, the apolipoprotein E epsilon 4 allele is the strongest genetic risk factor for Alzheimer's disease, as it exacerbates the accumulation of amyloid beta and promotes neuroinflammation. Strategies targeting epigenetic regulation may provide novel approaches to inhibit Alzheimer's disease pathology. This review also integrates various technologies such as genomics, proteomics, and metabolomics. This provides a broader system-level understanding of the risk gene loci, protein interaction networks, and metabolic changes associated with amyloid beta under the influence of microbial infections. Such techniques may lead to the identification of new molecular targets, the development of individualized treatment strategies, and the creation of early biomarkers for use in clinical research. In conclusion, this review suggests that amyloid-beta is not merely a pathological by-product but an environmentally responsive molecule with dual functions. A deeper understanding of the dynamic regulation of amyloid-beta, considering infection status and disease stage, can provide new directions for treatment strategies aimed at the prevention and treatment of Alzheimer's disease.
Herpesvirus 1↗
Characterizing the impact of plasma protein levels on human brain structure and disorders leveraging integrative multi-omics analysis.
With recent advances in high-throughput proteomic technologies, population-scale plasma proteomics datasets, often linked to extensive genetic and phenotypic information, have become increasingly accessible. Yet the relationships between circulating protein levels, brain imaging phenotypes, and risk for neurological and psychiatric disorders remain largely unexplored. Proteome-wide association studies offer a promising approach for elucidating biological mechanisms that connect genetic variation to complex brain-related traits and diseases. In this study, we integrated protein quantitative trait loci (pQTLs) from the two largest plasma proteomic resources (the UK Biobank Pharma Proteomics Project [UKB-PPP] and Ferkingstad et al. [deCODE]) with genome-wide association studies of brain imaging-derived phenotypes in UK Biobank using Mendelian randomization and colocalization analyses. We identified 120 cis and 20 trans associations between plasma proteins and imaging phenotypes and validated these findings using brain tissue-derived proteomic and transcriptomic datasets. Multivariable Mendelian randomization revealed eleven plasma proteins (coding genes APOE, ARL3, MICB, NSF, RHOC, RSPO3, ENPP2, BTN2A1, EIF2AK3, MRVI1, and OPLAH) with significant direct effects on the risk of Alzheimer's disease, Parkinson's disease, multiple sclerosis, bipolar disorder, and schizophrenia. Single-cell expression and pathway enrichment analyses further revealed cell-type-specific effects and distinct biological processes underlying these protein-disease associations. Together, these findings demonstrate robust links between plasma protein variation and brain structure, delineate protein-disease pathways, and highlight the cellular and molecular mechanisms that contribute to neurobiological diversity and pathology.
Journal Article↗
[The "omics" are coming--one gene is not enough anymore].
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Animals↗
Who tangos with GOA?-Use of Gene Ontology Annotation (GOA) for biological interpretation of '-omics' data and for validation of automatic annotation tools.
The number of large-scale experimental datasets generated from high-throughput technologies has grown rapidly. Biological knowledge resources such as the Gene Ontology Annotation (GOA) database, which provides high-quality functional annotation to proteins within the UniProt Knowledgebase, can play an important role in the analysis of such data. The integration of GOA with analytical tools has proved to aid the clustering, annotation and biological interpretation of such large expression datasets. GOA is also useful in the development and validation of automated annotation tools, in particular text-mining systems. The increasing interest in GOA highlights the great potential of this freely available resource to assist both the biological research and bioinformatics communities.
Animals↗
[Research of toxicological mechanism and "Omics"].
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Genomics↗
Mapping ovarian cellular and molecular landscape across the lifespan of women: a scoping review.
BACKGROUND: With growing interest in ART, fertility preservation, and postmenopausal health of women, reproductive medicine is increasingly focused on characterizing oocytes and ovarian tissue composition, as well as understanding the molecular mechanisms that guide ovarian function throughout its lifecycle. High-throughput omics technologies have enabled the characterization of different molecular layers, leading to substantial advances in our understanding of their complex dynamics. However, not all molecular aspects are studied equally, and studies examining the same modalities often show inconsistencies, underscoring the need for data standardization and highlighting the potential for using transformative artificial intelligence and machine-learning (AI/ML) methods for ovary studies. OBJECTIVE AND RATIONALE: This study aims to evaluate how multi-omic studies have advanced our understanding of the ovarian lifecycle from fetal development to postmenopause. We systematically reviewed published studies that have investigated molecular/omic layers, including the genome, methylome, transcriptome, and proteome throughout ovarian development and aging. Our analysis identified key molecular and cellular patterns, highlighted inconsistencies across studies and addressed gaps in data analysis, interpretation, and reproducibility to guide future research. SEARCH METHODS: We conducted a systematic literature search of Medline (PubMed), Embase (Ovid), and Web of Science Core Collection (Clarivate) using a combination of controlled and free text terms for human ovary, oogenesis, folliculogenesis, ovary development and (epi)genome, transcriptome, proteome, and multi-omic mechanisms to find relevant articles published before August 2025. To focus the scope of the current review, studies of domesticated and farm animals, rodents and other model organisms, non-human primates, as well as those examining various human ovarian pathologies were excluded. OUTCOMES: The search identified 23 546 studies for screening, of which 637 full-text studies were assessed for eligibility. Subsequently, we extracted data from 121 studies. Most studies analyzed the transcriptome of oocytes, granulosa cells, and ovarian tissue from reproductive-age individuals (n = 91), with fewer studies examining samples from individuals of advanced reproductive age (n = 45) and fetal (n = 16) samples. Transcriptome analyses were most common (n = 103, 85%), followed by proteome (n = 19, 16%) and epigenome (n = 14, 12%) studies. We found substantial variation in how studies defined and reported participants' groups as well as in their sequencing technologies and data analysis methods, with a lack of standardized reporting of background clinical information, data analysis methods, and pipeline details. The key findings underscore the prevailing consensus on genes defining major ovarian cell types and their roles throughout the ovarian lifespan, from prenatal development to postmenopausal transformation. This review highlighted the underrepresentation of certain patient groups, particularly prepubertal and peri-/postmenopausal individuals, among researched populations, due to obvious clinical and ethical reasons. WIDER IMPLICATIONS: This scoping review offers a comprehensive overview and benchmark of the current state of high-throughput omics-based research on ovarian cellular composition and molecular dynamics. To address these shortcomings, we propose general recommendations for multi-omics ovary studies and emphasize the necessity for more thorough multi-omic data integration by effectively applying novel AI/ML approaches. They can potentially improve the quality of multi-omics analyses at both single-cell and tissue levels despite limited sample sizes and enable integration of molecular profiling data with clinical and radiology datasets, enabling a more comprehensive understanding of ovarian biology. Such advancements can enhance reproducibility of research findings and guide future research to deepen our understanding of ovarian biology and ultimately support the development of medical technologies for better preserving fertility and alleviating infertility. REGISTRATION NUMBER: A protocol was published a priori on the Open Science Framework (https://osf.io/z38gb/).
Female↗