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Over the past 5 years, protein-chip technology has emerged as a useful tool for the study of many kinds of protein interactions and biochemical activities. The construction of Saccharomyces cerevisiae whole-proteome arrays has enabled further studies of such interactions in a proteome-wide context. Here, we explore some of the recent advances that have been made at the '-omic' level using protein microarrays.
Raman spectroscopy of single bacteria provides an OMIC-like view of the chemical status of individual cells, reporting on metabolism, cell stress and growth, and is likely to become a significant tool in environmental and medical microbiology. We advocate the early development of integrated data models and informatics frameworks, in parallel with the development of Raman hardware and experimental protocols, in order to maximize the benefits of this emerging OMIC technology to the research community.
Livestock multi-omics integration is key to unraveling complex trait regulation, yet systematic, livestock-specific strategies remain scarce. This review traces the progression from single-omics accumulation to multi-dimensional integration, highlighting how large-scale genomic, epigenomic, and transcriptomic projects lay the foundation for functional dissection. We identify core impediments: extreme species diversity, marked data heterogeneity, limited sample sizes, and a pervasive reduction of multi-omics data to simplistic differential screens, resulting in low translational efficiency. We critically appraise four common pitfalls-overinterpreting correlation as causation, relegating proteomics to corroborating transcriptomics, incomplete microbiome-host integration lacking environmental context, and systematic neglect of metabolic fluxomics-and show how exposomics and fluxomics add necessary causal and dynamic dimensions. To address these, we propose a livestock-adapted three-tier analytical framework: (1) statistical association of cross-omics covariation patterns; (2) machine learning-driven feature mining and integrative modeling; and (3) causal interpretation encompassing Mendelian randomization, prior-knowledge-guided network inference, and physical causal evidence via fluxomics and metabolic control analysis. We further discuss how multimodal sequencing (single-cell, spatial, temporal) and generative AI can fundamentally mitigate heterogeneity and strengthen causal evidence. Finally, we outline future priorities in database standardization, livestock-specific benchmarking, and translational pipelines, charting a path from correlation-centric reporting to mechanistic causality and precision breeding.
BACKGROUND: Cuproptosis is a recently discovered type of cell death, but the role and behavior of cuproptosis-related genes (CuRGs) in cancers remain unclear. This paper aims to address these issues by analyzing the multi-omics characteristics of cancer-related genes (CuRGs) across various types of cancer. METHOD: To investigate the impact of somatic copy number alterations (SCNA) and DNA methylation on CRG expression, we will analyze the correlation between these factors. We developed a cuproptosis index (CPI) model to measure the level of cuproptosis and investigate its functional roles. Using this model, we assessed the clinical prognosis of colorectal cancer patients and analyzed genetic changes and immune infiltration features in different CPI levels. RESULTS: The study's findings indicate that the majority of cancer-related genes (CuRGs) were suppressed in tumors and had a positive correlation with somatic copy number alterations (SCNA), while having a negative correlation with DNA methylation. This suggests that both SCNA and DNA methylation have an impact on the expression of CuRGs. The CPI model is a reliable predictor of survival outcomes in patients with colorectal cancer and can serve as an independent prognostic factor. Patients with a higher CPI have a worse prognosis. We conducted a deeper analysis of the genetic alterations and immune infiltration patterns in both CPI positive and negative groups. Our findings revealed significant differences, indicating that CuRGs may play a crucial role in tumor immunity mechanisms. Additionally, we have noticed a positive correlation between CuRGs and various crucial pathways that are linked to the occurrence, progression, and metastasis of tumors. CONCLUSIONS: Overall, our study systematically analyzes cuproptosis and its regulatory genes, emphasizing the potential of using cuproptosis as a basis for cancer therapy.
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The field of pathology has experienced several transformative changes in recent years with the advent of digital pathology and spatial multi-omics. These technologies have enhanced every aspect of pathology practice, from streamlining daily workflows to generating high-fidelity multi-omics data that provide pathologists with novel tools to refine disease profiling and clinical diagnosis. Each layer of multimodal data (genomic, metabolomic, proteomic, or transcriptomic) has uncovered a distinct facet of disease pathologies, and combined with machine learning/artificial intelligence-based data analysis and pattern recognition models, has provided holistic understanding of regulatory mechanisms underpinning them. However, high-dimensional data have far exceeded the volume, scale, and complexity of immunostaining methods implemented by pathologists and, thus, have generated significant challenges related to deconvolution, interpretation, and clinical translation. Furthermore, these multimodal studies have predominantly relied on computational methods to process data and extract disease-relevant insights, thus raising questions around relevance or role of a pathologist in this new era of multi-omics. This review will provide a perspective on the evolving fields of molecular histopathology and spatial -omics, leveraging them to approach disease profiling, and redefining the role of a pathologist during this process.
MOTIVATION: Batch effects (BEs) are a predominant source of noise in omics data and often mask real biological signals. BEs remain common in existing datasets. Current methods for BE correction mostly rely on specific assumptions or complex models, and may not detect and adjust BEs adequately, impacting downstream analysis and discovery power. To address these challenges we developed NPM, a nearest-neighbor matching-based method that adjusts BEs and may outperform other methods in a wide range of datasets. RESULTS: We assessed distinct metrics and graphical readouts, and compared our method to commonly used BE correction methods. NPM demonstrates the ability in correcting for BEs, while preserving biological differences. It may outperform other methods based on multiple metrics. Altogether, NPM proves to be a valuable BE correction approach to maximize discovery in biomedical research, with applicability in clinical research where latent BEs are often dominant. AVAILABILITY AND IMPLEMENTATION: NPM is freely available on GitHub (https://github.com/bigomics/NPM) and on Omics Playground (https://bigomics.ch/omics-playground). Computer codes for analyses are available at (https://github.com/bigomics/NPM). The datasets underlying this article are the following: GSE120099, GSE82177, GSE162760, GSE171343, GSE153380, GSE163214, GSE182440, GSE163857, GSE117970, GSE173078, and GSE10846. All these datasets are publicly available and can be freely accessed on the Gene Expression Omnibus repository.
PURPOSE: Genomewide association studies (GWAS) have identified numerous primary open angle glaucoma (POAG) risk loci, yet most reside in non-coding regions with unclear function. Mapping these loci to effector genes can elucidate disease mechanisms, identify functionally conserved variants, improve cross-ancestry risk prediction by reducing population-specific noise, and uncover shared therapeutic targets. METHODS: Here, we integrate European POAG GWAS with six types of multi-omics molecular Quantitative Trait Locis (xQTLs) using multi-trait colocalization to identify candidate effector variants and evaluate their cross-population relevance using genetic risk score (GRS) analysis, and their therapeutic potential through drug target prioritization. RESULTS: We identified 25 POAG effector variants colocalized with at least one xQTLs. In non-European populations, effector variants showed stronger effect size correlations with Europeans than non-colocalized variants (Pearson r2 = African 0.85 vs. 0.71; East Asian 0.81 vs. 0.69; and Latin American 0.91 vs. 0.75). Effector variants also had smaller allele frequency variations across populations (average interquartile range [IQR] = 0.15 vs. 0.20). The genetic risk score based on effector variants performed comparably to the genome-wide significant single-nucleotide polymorphism (SNP)-based GRS in non-European populations. Drug prioritization identified zinc, copper, sunitinib, probucol, and astemizole as potential common therapeutic agents for POAG and its subtypes. CONCLUSIONS: Our findings offer deeper insight into the molecular mechanisms underlying glaucoma and effector variants for developing more robust GRS models and broadly effective therapeutic strategies for POAG.
BACKGROUND: Declining sequencing costs coupled with the increasing availability of easy-to-use kits for the isolation of DNA and RNA transcripts from single cells have driven a rapid proliferation of studies centered around genomic and transcriptomic data. Simultaneously, a wealth of new techniques have been developed that utilize single cell technologies to interrogate a broad range of cell-biological processes. One recently developed technique, transposase-accessible chromatin with sequencing (ATAC) with select antigen profiling by sequencing (ASAPseq), provides a combination of chromatin accessibility assessments with measurements of cell-surface marker expression levels. While software exists for the characterization of these datasets, there currently exists no tool explicitly designed to reformat ASAP surface marker FASTQ data into a count matrix which can then be used for these downstream analyses. RESULTS: To address this lack of a dedicated tool for ASAPseq data processing, we created CountASAP, an easy-to-use Python package purposefully designed to transform FASTQ files from ASAP experiments into count matrices compatible with commonly-used downstream bioinformatic analysis packages. CountASAP takes advantage of the independence of the relevant data structures to perform fully parallelized matches of each sequenced read to user-supplied input ASAP oligos and unique cell-identifier sequences. We directly compare the performance and user-friendliness of CountASAP to existing tools using similarly-structured data from a more common sequencing experiment: cellular indexing of transcriptomes and epitopes by sequencing (CITEseq). Further benchmarking against existing tools helps to identify proper defaults for CountASAP and assess the agreement of outputs from all tested software. A final test using a novel ASAPseq dataset provides evidence that CountASAP can generate biologically meaningful results that correlate well with paired chromatin accessibility data. CONCLUSIONS: CountASAP shows good agreement with existing, well-tested data processing tools in the analysis of similarly-structured benchmarking data. CountASAP runs efficiently on a standard laptop, has user-friendly documentation, a one-step installation, and represents the first and only tool designed specifically for the processing of ASAPseq data.
Autism Spectrum Disorder (ASD) involves a multi-system interaction mechanism among genetics, immunity, and gut microbiota, yet its regulatory network remains undefined. This study conducted a meta-analysis on Genome-Wide Association Study data from four independent ASD cohorts to identify potential genetic loci. By integrating Polygenic Priority Score, brain region, and brain cell eQTL enrichment analyses, and combining summary-data-based Mendelian Randomisation (SMR) analyses of brain cis-eQTL and mQTL, bidirectional Mendelian Randomisation analyses of 473 gut microbiota, and SMR analysis of blood eQTL, SNPs such as rs2735307 and rs989134 with significant multi-dimensional associations were identified. These loci exert cross-tissue regulatory effects by participating in gut microbiota regulation, involving immune pathways such as T cell receptor signal activation and neutrophil extracellular trap formation, as well as cis-regulating neurodevelopmental genes (HMGN1 and H3C9P), or synergistically influencing epigenetic methylation modifications to regulate the expression of BRWD1 and ABT1. The cross-scale evidence chain constructed in this study provides a theoretical foundation for precision medicine research in ASD, holding promise to advance the development of innovative therapeutic strategies.
Cervical cancer remains a major threat to women's health worldwide, and reliable serum biomarkers for early detection and therapeutic stratification remain limited. Human wings-apart-like (hWAPL) protein has been implicated in cervical carcinogenesis, but its diagnostic and clinical value has not been fully elucidated. To address this gap, this study integrated public multi-omics datasets, including The Cancer Genome Atlas, GEPIA2, the Human Protein Atlas, and single-cell transcriptomic data, to characterize hWAPL expression, clinicopathological associations, immune infiltration, co-expression networks, post-translational modifications, and drug sensitivity predictions. These findings were evaluated in an independent single-center serum cohort comprising 89 patients with histologically confirmed cervical squamous cell carcinoma and 89 healthy female controls. Serum hWAPL and squamous cell carcinoma antigen (SCC) levels were measured, and diagnostic performance was assessed by receiver operating characteristic curve analysis. In silico, hWAPL was broadly upregulated across multiple malignancies, particularly cervical cancer, enriched in malignant epithelial cells and monocytes/macrophages, and associated with shorter progression-free interval, predicted reduced sensitivity to cisplatin, paclitaxel, and 5-fluorouracil, and predicted sensitivity to MCL-1 and Wee1 inhibitors. In the serum cohort, hWAPL levels were significantly higher in patients than controls and discriminated cervical cancer with an area under the curve of 0.961, exceeding SCC alone. Combining hWAPL with SCC further improved diagnostic performance (area under the curve, 0.974; sensitivity, 93.3%; specificity, 95.5%). These findings suggest that serum hWAPL is a potential novel diagnostic biomarker for cervical squamous cell carcinoma whose performance is enhanced by SCC, whereas the observed associations with chemoresistance and immune microenvironment remodeling are hypothesis-generating and require experimental confirmation.
Rice growth is highly dependent on water availability, and drought stress significantly impacts its entire life cycle. However, previous studies lack systematic investigations into drought-responsive candidate genes across the full life cycle of rice. This study integrates transcriptomic and phenotypic data from two rice lines, IR64 (drought-sensitive) and DK151 (drought-tolerant), under varied environmental conditions at distinct growth stages. Using k-means clustering, 13 369 genes were categorized into 17 distinct expression patterns, revealing drought-responsive genes specifically upregulated or downregulated under drought stress. Weighted co-expression network analysis (WGCNA) further identified four gene modules strongly correlated with drought-related phenotypes, co-localizing 2859 drought-responsive genes through both approaches. Proteomics and metabolomics were supplemented at the booting stage, where phenotypic and transcriptomic differences under drought were most pronounced. Integrated omics results demonstrate gibberellin (GA) and abscisic acid (ABA) pathways play a key role during drought tolerance in rice, and 79 high-confidence drought-resistant candidate genes were prioritized from the 2859 drought-responsive genes. Among these, Gibberellin 20-oxidase 5 (OsGA20ox5) was identified as a key negative regulator of drought tolerance. Furthermore, the transcription factor zinc finger protein 252 (OsZFP252) directly binds to the OsGA20ox5 promoter, repressing its expression and enhancing ABA biosynthesis, thereby improving drought tolerance by increasing stomatal closure and expanding vascular bundle water transport capacity. Notably, the drought-tolerant haplotype 2-4 (Hap2-4) of OsGA20ox5 provides valuable insights for drought-resistant breeding.
It is remarkable that microarray technologies have nearly reached a pinnacle. Establishment of further analysis and management of enormous data derived from microarray technology is currently the highest priority. The heterogeneous functions of cholangiocytes regulate the pathophysiology of the biliary epithelium in relation to secretory, proliferative and apoptotic activities. Distinct expression profiles of two murine cholangiocyte lines, termed small and large have been revealed by microarray analysis. The features of the two cholangiocyte cell lines, categorized partly according to gene ontology, indicate the specific physiological role of each cell line. The large cholangiocytes are characterized as "transport" and "immune/ inflammatory responses". In contrast, small cholangiocytes are associated with properties of limited physiological functional ability and proliferating/migrating potential with specific molecules like Eph receptors, comparable to mesenchymal cells. 'Omic study will be of great help in understanding the heterogeniety of cholangiocytes.
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.
Radiation-induced rectal injury (RRI) significantly affects the quality of life in patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy (NCRT). Non-targeted liquid chromatography-mass spectrometry metabolomics analysis and transcriptomic analysis were conducted to explore RRI characteristics. Hematoxylin-eosin and Masson staining confirmed radiation-induced injury in rectal tissue within the radiotherapy target region. Orthogonal partial least squares discriminant analysis identified 823 differentially expressed metabolites (DEMs). Transcriptomic analysis revealed 400 differentially expressed genes (DEGs). Enrichment analysis revealed that DEMs and DEGs were primarily involved in metabolic, immune, and signal transduction pathways. Integrated analysis demonstrated significant enrichment of DEMs and DEGs in the arachidonic acid metabolism pathway. Pearson's correlation and canonical correlation analyses were used to assess the association between DEMs and DEGs within this pathway. In conclusion, this study identified key biological regulatory pathways involved in RRI through a multi-omics approach, offering potential targets for its diagnosis and treatment.
Cytokines and chemoattractive cytokines (chemokines) are present in a wide variety of body fluids such as plasma, cerebrospinal fluid, bronchoaveolar fluid, amniotic fluid, synovial fluid, middle ear effusion fluid, and urine. Cytokines can be detected using classical solid-phase sandwich immunoassays such as enzyme-linked immunosorbent assay (ELISA) or with a bead based multiplex immunoassay (MIA). The physical chemical properties of the different body fluids (such as pH and total protein content) differ, which may have an impact on the outcome of the cytokine assay. Both ELISA as well as MIA cytokine detection systems are constructed by sandwiching the protein of interest between a capture and reporter antibody. When the biological sample contains heterophilic antibodies (such as in patients with auto-immune diseases), these non-specific antibodies can cause false positive results. During pathological conditions, cytokines may be found over a wide concentration range; likewise have to cover this dynamic range in a similar fashion. The correct (statistical) analysis of standard curves and (multiplexed) data are critical for proper interpretation. Classical ELISA based cytokine assays are robust, easy to use and very well suited for measurement of single cytokines. Due to an increased interest in the integral approach to understand biological processes (the omics era), multiplex immunoassays for detection of cytokines and the interpretation of these assays are gaining popularity.
Biomolecular condensates formed through liquid-liquid phase separation regulate cellular processes, and their dysregulation causes disease. Current methods for identifying endogenous phase-separating proteins have low throughput and cannot capture dynamic responses to stimuli. Here we present a protocol combining osmotic compression or transforming growth factor-β (TGF-β) treatment to induce condensation with sucrose density gradient centrifugation and quantitative mass spectrometry to enable systematic, high-throughput identification of endogenous condensates and phase-separating proteins. The method exploits the density changes that occur when phase-separating proteins undergo oligomerization during condensate formation. In H1975 cells, we identified over 1,500 phase-separating proteins under osmotic compression or TGF-β treatment; 538 of these candidates were not present in PhaSepDB, a database that compiles in vivo, in vitro and omics-derived proteins. The approach detects constitutive condensates and proteins that dynamically phase-separate in response to osmotic stress or TGF-β signaling. This protocol provides proteome-wide analysis of fractions of proteins having different densities and enables temporal resolution of phase-separation events. The procedure takes ~9 d and requires expertise in cell culture, biochemistry and mass spectrometry. This method enables systematic study of biomolecular condensates and disease-associated phase-separation mechanisms.
Fish play host to viral, bacterial, and parasitic diseases in addition to non-infectious conditions such as cancer. The National Marine Monitoring Programme (NMMP) provides information to the U.K. Government on the health status of marine fish stocks. An aspect of this work relates to the presence of tumors and other pathologies in the liver of the offshore sentinel flatfish species, dab (Limanda limanda). Using internationally agreed quality assurance criteria, tumors and pre-tumors are diagnosed using histopathology. The current study has expanded upon this work by integrating these traditional diagnostic approaches with ones utilizing modern technologies for analysis of proteomic and metabolomic profiles of selected lesions. We have applied SELDI and FT-ICR technologies (for proteomic and metabolomic analyses, respectively) to tumor and non-tumor samples resected from the liver of dab. This combined approach has demonstrated how these technologies are able to identify protein and metabolite profiles that are specific to liver tumors. Using histopathology to classify "analysis groups" is key to the success of such an approach since it allows for elimination of spurious samples (e.g., those containing parasite infections) that may confuse interpretation of "omic" data. As such, the pathology laboratory plays a central role in collating information relating to particular specimens and in establishing sampling groups relative to specific diagnostic questions. In this study, we present pilot data, which illustrates that proteomics and metabolomics can be used to discriminate fish liver tumors and suggest future directions for work of this type.