Omics Levels Research Articles

GREMI: An Explainable Multi-Omics Integration Framework for Enhanced Disease Prediction and Module Identification.

Multi-omics integration has demonstrated promising performance in complex disease prediction. However, existing research typically focuses on maximizing prediction accuracy, while often neglecting the essential task of discovering meaningful biomarkers. This issue is particularly important in biomedicine, as molecules often interact rather than function individually to influence disease outcomes. To this end, we propose a two-phase framework named GREMI to assist multi-omics classification and explanation. In the prediction phase, we propose to improve prediction performance by employing a graph attention architecture on sample-wise co-functional networks to incorporate biomolecular interaction information for enhanced feature representation, followed by the integration of a joint-late mixed strategy and the true-class-probability block to adaptively evaluate classification confidence at both feature and omics levels. In the interpretation phase, we propose a multi-view approach to explain disease outcomes from the interaction module perspective, providing a more intuitive understanding and biomedical rationale. We incorporate Monte Carlo tree search (MCTS) to explore local-view subgraphs and pinpoint modules that highly contribute to disease characterization from the global-view. Extensive experiments demonstrate that the proposed framework outperforms state-of-the-art methods in seven different classification tasks, and our model effectively addresses data mutual interference when the number of omics types increases. We further illustrate the functional- and disease-relevance of the identified modules, as well as validate the classification performance of discovered modules using an independent cohort.

Priority-Elastic net for binary disease outcome prediction based on multi-omics data

BackgroundHigh-dimensional omics data integration has emerged as a prominent avenue within the healthcare industry, presenting substantial potential to improve predictive models. However, the data integration process faces several challenges, including data heterogeneity, priority sequence in which data blocks are prioritized for rendering predictive information contained in multiple blocks, assessing the flow of information from one omics level to the other and multicollinearity.MethodsWe propose the Priority-Elastic net algorithm, a hierarchical regression method extending Priority-Lasso for the binary logistic regression model by incorporating a priority order for blocks of variables while fitting Elastic-net models sequentially for each block. The fitted values from each step are then used as an offset in the subsequent step. Additionally, we considered the adaptive elastic-net penalty within our priority framework to compare the results.ResultsThe Priority-Elastic net and Priority-Adaptive Elastic net algorithms were evaluated on a brain tumor dataset available from The Cancer Genome Atlas (TCGA), accounting for transcriptomics, proteomics, and clinical information measured over two glioma types: Lower-grade glioma (LGG) and glioblastoma (GBM).ConclusionOur findings suggest that the Priority-Elastic net is a highly advantageous choice for a wide range of applications. It offers moderate computational complexity, flexibility in integrating prior knowledge while introducing a hierarchical modeling perspective, and, importantly, improved stability and accuracy in predictions, making it superior to the other methods discussed. This evolution marks a significant step forward in predictive modeling, offering a sophisticated tool for navigating the complexities of multi-omics datasets in pursuit of precision medicine’s ultimate goal: personalized treatment optimization based on a comprehensive array of patient-specific data. This framework can be generalized to time-to-event, Cox proportional hazards regression and multicategorical outcomes. A practical implementation of this method is available upon request in R script, complete with an example to facilitate its application.

Multi-Omics Analysis Identified Drug Repurposing Targets for Chronic Obstructive Pulmonary Disease.

Despite recent advances in chronic obstructive pulmonary disease (COPD) research, few studies have identified the potential therapeutic targets systematically by integrating multiple-omics datasets. This project aimed to develop a systems biology pipeline to identify biologically relevant genes and potential therapeutic targets that could be exploited to discover novel COPD treatments via drug repurposing or de novo drug discovery. A computational method was implemented by integrating multi-omics COPD data from unpaired human samples of more than half a million subjects. The outcomes from genome, transcriptome, proteome, and metabolome COPD studies were included, followed by an in silico interactome and drug-target information analysis. The potential candidate genes were ranked by a distance-based network computational model. Ninety-two genes were identified as COPD signature genes based on their overall proximity to signature genes on all omics levels. They are genes encoding proteins involved in extracellular matrix structural constituent, collagen binding, protease binding, actin-binding proteins, and other functions. Among them, 70 signature genes were determined to be druggable targets. The in silico validation identified that the knockout or over-expression of SPP1, APOA1, CTSD, TIMP1, RXFP1, and SMAD3 genes may drive the cell transcriptomics to a status similar to or contrasting with COPD. While some genes identified in our pipeline have been previously associated with COPD pathology, others represent possible new targets for COPD therapy development. In conclusion, we have identified promising therapeutic targets for COPD. This hypothesis-generating pipeline was supported by unbiased information from available omics datasets and took into consideration disease relevance and development feasibility.

Optimizing phage-based mutant recovery and minimizing heat effect in the construction of transposon libraries in Staphylococcus aureus

Staphylococcus aureus (S. aureus), particularly Methicillin-resistant S. aureus (MRSA), poses a significant global public health threat, necessitating advanced methodologies to enhance our understanding of this organism at the omics levels. This study introduces a refined protocol for constructing and curing high-density transposon mutant (tn-mutant) libraries in S. aureus, addressing the challenges associated with low transductant yields, and the complex genetic manipulation mechanism in Gram-positive bacteria. Our methodology employs a Himar1 transposon based on a two-plasmid system, leveraging Himar1’s high insertional efficiency in AT-rich organisms. Enhanced transduction efficiency was achieved through chloramphenicol pre-treatment and the use of modified enriched media. Complementing this, an optimized plasmid curing procedure ensured a representative and stable tn-mutant library. The protocol was successfully applied to multiple S. aureus strains, demonstrating an increase in mutant recovery and reduced post-curing impact. The method offers a robust approach for Transposon Insertion Sequencing (TIS) applications in S. aureus, enabling deeper insights into survival, resistance, and pathogenicity mechanisms. This protocol holds a significant potential for accelerating the construction of tn-mutant libraries in various S. aureus strains.

CVD Atlas: a multi-omics database of cardiovascular disease.

Cardiovascular disease (CVD) is the leading cause of illness and death worldwide. Numerous studies have been conducted into the underlying mechanisms and molecular characteristics of CVD using various omics approaches. However, there is still a need for comprehensive resources on CVD. To fill this gap, we present the CVD Atlas, accessed at https://ngdc.cncb.ac.cn/cvd. This database compiles knowledge and information from manual curation, large-scale data analysis, and existing databases, utilizing multi-omics data to understand CVDs comprehensively. The current version of CVD Atlas contains 215,333 associations gathered from 308 publications, 652 datasetsand 7 databases. It covers 190 diseases and 44 traits across multiple omics levels. Additionally, it provides an interactive knowledge graph that integrates disease-gene associations and two types of analysis tools, offering an engaging way to query and display relationships. CVD Atlas also features a user-friendly web interface that allows users to easily browse, search, and download all association information, research metadata, and annotation details. In conclusion, CVD Atlas is a valuable resource that enhances the accessibility and utility of knowledge and information related to CVD, benefiting human health and CVD research communities.

Multi-Omics Analysis Unravels the Impact of Stool Sample Logistics on Metabolites and Microbial Composition.

Human health and the human microbiome are inevitably intertwined, increasing their relevance in clinical research. However, the collection, transportation and storage of faecal samples may introduce bias due to methodological differences, especially since postal shipping is a common practise in large-scale clinical cohort studies. Using four different Omics layer, we determined the structural (16S rRNA sequencing, cytometric microbiota profiling) and functional integrity (SCFAs, global metabolome) of the microbiota in relation to different easy-to-handle conditions. These conditions were storage at -20 °C, -20 °C as glycerol stock, 4 °C and room temperature with and without oxygen exposure for a maximum of one week. Storage time affected the microbiota on all Omics levels. However, the magnitude was donor-dependent, highlighting the need for purpose-optimized sample collection in clinical multi-donor studies. The effects of oxygen exposure were negligible for all analyses. At ambient temperature, SCFA and compositional profiles were stable for 24 h and 48 h, respectively, while at 4 °C, SCFA profiles were maintained for 48 h. The global metabolome was highly susceptible, already changing at 24 h in non-frozen conditions. Thus, faecal microbiota was best preserved on all levels when transported as a native sample frozen within 24 h, leading to the least biased outcomes in the analysis. We conclude that the immediate freezing of native stool samples for transportation to the lab is best suited for planned multi-Omics analyses that include metabolomics to extend standard sequencing approaches.

Direct mapping of tyrosine sulfation states in native peptides by nanopore.

Sulfation is considered the most prevalent post-translational modification (PTM) on tyrosine; however, its importance is frequently undervalued due to difficulties in direct and unambiguous determination from phosphorylation. Here we present a sequence-independent strategy to directly map and quantify the tyrosine sulfation states in universal native peptides using an engineered protein nanopore. Molecular dynamics simulations and nanopore mutations reveal specific interactions between tyrosine sulfation and the engineered nanopore, dominating identification across diverse peptide sequences. We show a nanopore framework to discover tyrosine sulfation in unknown peptide fragments digested from a native protein and determine the sequence of the sulfated fragment based on current blockade enhancement induced by sulfation. Moreover, our method allows direct observation of peptide sulfation in ultra-low abundance, down to 1%, and distinguishes it from isobaric phosphorylation. This sequence-independent strategy suggests the potential of nanopore to explore specific PTMs in real-life samples and at the omics level.

Stable biomarker discovery in multi-omics data via canonical correlation analysis.

Multi-omics analysis offers a promising avenue to a better understanding of complex biological phenomena. In particular, untangling the pathophysiology of multifactorial health conditions such as the inflammatory bowel disease (IBD) could benefit from simultaneous consideration of several omics levels. However, taking full advantage of multi-omics data requires the adoption of suitable new tools. Multi-view learning, a machine learning technique that natively joins together heterogeneous data, is a natural source for such methods. Here we present a new approach to variable selection in unsupervised multi-view learning by applying stability selection to canonical correlation analysis (CCA). We apply our method, StabilityCCA, to simulated and real multi-omics data, and demonstrate its ability to find relevant variables and improve the stability of variable selection. In a case study on an IBD microbiome data set, we link together metagenomics and metabolomics, revealing a connection between their joint structure and the disease, and identifying potential biomarkers. Our results showcase the usefulness of multi-view learning in multi-omics analysis and demonstrate StabilityCCA as a powerful tool for biomarker discovery.

MosGraphFlow: a novel integrative graph AI model mining disease targets from multi-omic data.

Multi-omic data can better characterize complex cellular signaling pathways from multiple views compared to individual omic data. However, integrative multi-omic data analysis to rank key disease biomarkers and infer core signaling pathways remains an open problem. In this study, our novel contributions are that we developed a novel graph AI model, mosGraphFlow, for analyzing multi-omic signaling graphs (mosGraphs), 2) analyzed multi-omic mosGraph datasets of AD, and 3) identified, visualized and evaluated a set of AD associated signaling biomarkers and network. The comparison results show that the proposed model not only achieves the best classification accuracy but also identifies important AD disease biomarkers and signaling interactions. Moreover, the signaling sources are highlighted at specific omic levels to facilitate the understanding of the pathogenesis of AD. The proposed model can also be applied and expanded for other studies using multi-omic data. Model code is accessible via GitHub: https://github.com/FuhaiLiAiLab/mosGraphFlow.

Integrative in silico approaches to analyse microRNA-mediated responses in human diseases.

Advancements in sequencing technologies have facilitated omics level information generation for various diseases in human. High-throughput technologies have become a powerful tool to understand differential expression studies and transcriptional network analysis. An understanding of complex transcriptional networks in human diseases requires integration of datasets representing different RNA species including microRNA (miRNA) and messenger RNA (mRNA). This review emphasises on conceptual explanation of generalized workflow and methodologies to the miRNA mediated responses in human diseases by using different in silico analysis. Although,there have been many prior explorations in miRNA-mediated responses in human diseases, the advantages, limitations and overcoming the limitation through different statistical techniques have not yet been discussed. This review focuses on miRNAs as important gene regulators in human diseases, methodologies for miRNA-target gene prediction and data driven methods for enrichment and network analysis for miRnome-targetome interactions. Additionally, it proposes an integrative workflow to analyse structural components of networks obtained from high-throughput data. This review explains how to apply the existing methods to analyse miRNA-mediated responses in human diseases. It addresses unique characteristics of different analysis, its limitations and its statistical solutions influencing the choice of methods for the analysis through a workflow. Moreover, it provides an overview of promising common integrative approaches to comprehend miRNA-mediated gene regulatory events in biological processes in humans. The proposed methodologies and workflow shall help in the analysis of multi-source data to identify molecular signatures of various human diseases.

Identification of candidate genes and chemicals associated with osteonecrosis of femoral head by multiomics studies and chemical-gene interaction analysis.

In-depth understanding of osteonecrosis of femoral head (ONFH) has revealed that degeneration of the hip cartilage plays a crucial role in ONFH progression. However, the underlying molecular mechanisms and susceptibility to environmental factors in hip cartilage that contribute to ONFH progression remain elusive. We conducted a multiomics study and chemical-gene interaction analysis of hip cartilage in ONFH. The differentially expressed genes (DEGs) involved in ONFH progression were identified in paired hip cartilage samples from 36 patients by combining genome-wide DNA methylation profiling, gene expression profiling, and quantitative proteomics. Gene functional enrichment and pathway analyses were performed via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Functional links between proteins were discovered through protein-protein interaction (PPI) networks. The ONFH-associated chemicals were identified by integrating the DEGs with the chemical-gene interaction sets in the Comparative Toxicogenomics Database (CTD). Finally, the DEGs, including MMP13 and CHI3L1, were validated via quantitative real-time PCR (qRT-PCR) and immunohistochemistry (IHC). Twenty-two DEGs were identified across all three omics levels in ONFH cartilage, 16 of which were upregulated and six of which were downregulated. The collagen-containing extracellular matrix (ECM), ECM structural constituents, response to amino acids, the relaxin signaling pathway, and protein digestion and absorption were found to be primarily involved in cartilage degeneration in ONFH. Moreover, ten major ONFH-associated chemicals were identified, including, benzo(a)pyrene, valproic acid, and bisphenol A. Overall, our study identified several candidate genes, pathways, and chemicals associated with cartilage degeneration in ONFH, providing novel clues into the etiology and biological processes of ONFH progression.

Multimodal functional deep learning for multiomics data.

With rapidly evolving high-throughput technologies and consistently decreasing costs, collecting multimodal omics data in large-scale studies has become feasible. Although studying multiomics provides a new comprehensive approach in understanding the complex biological mechanisms of human diseases, the high dimensionality of omics data and the complexity of the interactions among various omics levels in contributing to disease phenotypes present tremendous analytical challenges. There is a great need of novel analytical methods to address these challenges and to facilitate multiomics analyses. In this paper, we propose a multimodal functional deep learning (MFDL) method for the analysis of high-dimensional multiomics data. The MFDL method models the complex relationships between multiomics variants and disease phenotypes through the hierarchical structure of deep neural networks and handles high-dimensional omics data using the functional data analysis technique. Furthermore, MFDL leverages the structure of the multimodal model to capture interactions between different types of omics data. Through simulation studies and real-data applications, we demonstrate the advantages of MFDL in terms of prediction accuracy and its robustness to the high dimensionality and noise within the data.

Memory effects of prior subculture may impact the quality of multiomic perturbation profiles

Mass spectrometry-based omics technologies are increasingly used in perturbation studies to map drug effects to biological pathways by identifying significant molecular events. Significance is influenced by fold change and variation of each molecular parameter, but also by multiple testing corrections. While the fold change is largely determined by the biological system, the variation is determined by experimental workflows. Here, it is shown that memory effects of prior subculture can influence the variation of perturbation profiles using the two colon carcinoma cell lines SW480 and HCT116. These memory effects are largely driven by differences in growth states that persist into the perturbation experiment. In SW480 cells, memory effects combined with moderate treatment effects amplify the variation in multiple omics levels, including eicosadomics, proteomics, and phosphoproteomics. With stronger treatment effects, the memory effect was less pronounced, as demonstrated in HCT116 cells. Subculture homogeneity was controlled by real-time monitoring of cell growth. Controlled homogeneous subculture resulted in a perturbation network of 321 causal conjectures based on combined proteomic and phosphoproteomic data, compared to only 58 causal conjectures without controlling subculture homogeneity in SW480 cells. Some cellular responses and regulatory events were identified that extend the mode of action of arsenic trioxide (ATO) only when accounting for these memory effects. Controlled prior subculture led to the finding of a synergistic combination treatment of ATO with the thioredoxin reductase 1 inhibitor auranofin, which may prove useful in the management of NRF2-mediated resistance mechanisms.

Twin Scholarships of Glycomedicine and Precision Medicine in Times of Single-Cell Multiomics.

Systems biology and multiomics research expand the prospects of planetary health innovations. In this context, this mini-review unpacks the twin scholarships of glycomedicine and precision medicine in the current era of single-cell multiomics. A significant growth in glycan research has been observed over the past decade, unveiling and establishing co- and post-translational modifications as dynamic indicators of both pathological and physiological conditions. Systems biology technologies have enabled large-scale and high-throughput glycoprofiling and access to data-intensive biological repositories for global research. These advancements have established glycans as a pivotal third code of life, alongside nucleic acids and amino acids. However, challenges persist, particularly in the simultaneous analysis of the glycome and transcriptome in single cells owing to technical limitations. In addition, holistic views of the complex molecular interactions between glycomics and other omics types remain elusive. We underscore and call for a paradigm shift toward the exploration of integrative glycan platforms and analysis methods for single-cell multiomics research and precision medicine biomarker discovery. The integration of multiple datasets from various single-cell omics levels represents a crucial application of systems biology in understanding complex cellular processes and is essential for advancing the twin scholarships of glycomedicine and precision medicine.

Integrative Analysis of Hepatopancreas Transcriptome and Proteome in Female Eriocheir sinensis under Thermal Stress.

The Chinese mitten crab (Eriocheir sinensis), an economically important crustacean that is endemic to China, has recently experienced high-temperature stress. The high thermal tolerance of E. sinensis points to its promise in being highly productive in an aquacultural context. However, the mechanisms underlying its high thermal tolerance remain unknown. In this study, female E. sinensis that were heat exposed for 24 h at 38.5 °C and 33 °C were identified as high-temperature-stressed (HS) and normal-temperature-stressed (NS) groups, respectively. The hepatopancreas of E. sinensis from the HS and NS groups were used for transcriptome and proteomic analyses. A total of 2350 upregulated and 1081 downregulated differentially expressed genes (DEGs) were identified between the HS and NS groups. In addition, 126 differentially expressed proteins (DEPs) were upregulated and 35 were downregulated in the two groups. An integrated analysis showed that 2641 identified genes were correlated with their corresponding proteins, including 25 genes that were significantly differentially expressed between the two omics levels. Ten Gene Ontology terms were enriched in the DEGs and DEPs. A functional analysis revealed three common pathways that were significantly enriched in both DEGs and DEPs: fluid shear stress and atherosclerosis, leukocyte transendothelial migration, and thyroid hormone synthesis. Further analysis of the common pathways showed that MGST1, Act5C, HSP90AB1, and mys were overlapping genes at the transcriptome and proteome levels. These results demonstrate the differences between the HS and NS groups at the two omics levels and will be helpful in clarifying the mechanisms underlying the thermal tolerance of E. sinensis.

LGG-14. INTEGRATED MULTI-OMICS EXPLORATION OF MAPK PATHWAY INHIBITION IN PEDIATRIC PILOCYTIC ASTROCYTOMA

Abstract Pilocytic astrocytomas (PA), the most common pediatric brain tumors, exhibit MAPK pathway alterations leading to its constitutive activation. They are also associated with low proliferation index due to the oncogene-induced senescence (OIS), sustained by senescence-associated secretory phenotype (SASP) factors. Little is known about the downstream consequences of MAPK activation leading to OIS-SASP, and of the molecular implications of MAPK pathway inhibition in senescent PA cells. Senescent DKFZ-BT66 cells derived from a primary KIAA::BRAF-fusion positive PA were used to generate RNA-sequencing and phospho-/proteomic datasets, before and after treatment with the MEK inhibitor (MEKi) trametinib for different time-spans. A multi-omics factor analysis tool (MEFISTO) and single sample gene set enrichment analysis (ssGSEA) were used to identify key OIS effectors and differentially regulated pathways upon MAPK inhibition. Trametinib treatment inhibited OIS and SASP signatures across all omics levels in the senescent DKFZ-BT66 cells, associated with reduced sensitivity towards senolytics drugs, indicating inhibition of senescence features upon MEKi. MEFISTO analysis identified key effectors involved in MAPK-induced OIS in the senescent PA cells. We built a pathway network using a prior knowledge network approach allowing to map the direct downstream effectors of MAPK leading to OIS-SASP. We identified several parallel pathways (JAK/STAT, p38, GSK3B) potentially involved in OIS and under the control of the MAPK pathway. In addition, ssGSEA showed that pathways related to upstream MAPK activators (SOS1, CK2, several RTKs) were predicted to be upregulated upon treatment, highlighting putative targets to be further investigated. Our data suggest that MAPKi reverses OIS signaling in senescent PA cells, while inducing the activation of MAPK upstream regulators, identifying putative co-targets for the treatment of PA. Further validation of the targetability of these pathways is pending. Furthermore, the identification of the MAPK-related OIS-SASP genes provides insight into the regulation of OIS-SASP by the MAPK pathway.

Identifying dysregulated regions in amyotrophic lateral sclerosis through chromatin accessibility outliers

The high heritability of ALS contrasts with its low molecular diagnosis rate post-genetic testing, pointing to potential undiscovered genetic factors. To aid the exploration of these factors, we introduced EpiOut, an algorithm to identify chromatin accessibility outliers that are regions exhibiting divergent accessibility from the population baseline in a single or few samples. Annotation of accessible regions with histone ChIP-seq and Hi-C indicates that outliers are concentrated in functional loci, especially among promoters interacting with active enhancers. Across different omics levels, outliers are robustly replicated, and chromatin accessibility outliers are reliable predictors of gene expression outliers and aberrant protein levels. When promoter accessibility does not align with gene expression, our results indicate that molecular aberrations are more likely to be linked to post-transcriptional regulation rather than transcriptional regulation. Our findings demonstrate that the outlier detection paradigm can uncover dysregulated regions in rare diseases. EpiOut is available at github.com/uci-cbcl/EpiOut.

Spatiotemporal expression and control of haemoglobin in space

It is now widely recognised that the environment in space activates a diverse set of genes involved in regulating fundamental cellular pathways. This includes the activation of genes associated with blood homeostasis and erythropoiesis, with a particular emphasis on those involved in globin chain production. Haemoglobin biology provides an intriguing model for studying space omics, as it has been extensively explored at multiple -omic levels, spanning DNA, RNA, and protein analyses, in both experimental and clinical contexts. In this study, we examined the developmental expression of haemoglobin over time and space using a unique suite of multi-omic datasets available on NASA GeneLab, from the NASA Twins Study, the JAXA CFE study, and the Inspiration4 mission. Our findings reveal significant variations in globin gene expression corresponding to the distinct spatiotemporal characteristics of the collected samples. This study sheds light on the dynamic nature of globin gene regulation in response to the space environment and provides valuable insights into the broader implications of space omics research.

Extracellular vesicles from dental pulp mesenchymal stem cells modulate macrophage phenotype during acute and chronic cardiac inflammation in athymic nude rats with myocardial infarction

Background/aimsExtracellular vesicles (EVs) derived from dental pulp mesenchymal stem cells (DP-MSCs) are a promising therapeutic option for the treatment of myocardial ischemia. The aim of this study is to determine whether MSC-EVs could promote a pro-resolving environment in the heart by modulating macrophage populations.MethodsEVs derived from three independent biopsies of DP-MSCs (MSC-EVs) were isolated by tangential flow-filtration and size exclusion chromatography and were characterized by omics analyses. Biological processes associated with these molecules were analyzed using String and GeneCodis platforms. The immunomodulatory capacity of MSC-EVs to polarize macrophages towards a pro-resolving or M2-like phenotype was assessed by evaluating surface markers, cytokine production, and efferocytosis. The therapeutic potential of MSC-EVs was evaluated in an acute myocardial infarction (AMI) model in nude rats. Infarct size and the distribution of macrophage populations in the infarct area were evaluated 7 and 21 days after intramyocardial injection of MSC-EVs.ResultsLipidomic, proteomic, and miRNA-seq analysis of MSC-EVs revealed their association with biological processes involved in tissue regeneration and regulation of the immune system, among others. MSC-EVs promoted the differentiation of pro-inflammatory macrophages towards a pro-resolving phenotype, as evidenced by increased expression of M2 markers and decreased secretion of pro-inflammatory cytokines. Administration of MSC-EVs in rats with AMI limited the extent of the infarcted area at 7 and 21 days post-infarction. MSC-EV treatment also reduced the number of pro-inflammatory macrophages within the infarct area, promoting the resolution of inflammation.ConclusionEVs derived from DP-MSCs exhibited similar characteristics at the omics level irrespective of the biopsy from which they were derived. All MSC-EVs exerted effective pro-resolving responses in a rat model of AMI, indicating their potential as therapeutic agents for the treatment of inflammation associated with AMI.

Multiomics approach towards characterization of tumor cell plasticity and its significance in precision and personalized medicine.

Cellular plasticity refers to the ability of cells to change their identity or behavior, which can be advantageous in some cases (e.g., tissue regeneration) but detrimental in others (e.g., cancer metastasis). With a better understanding of cellular plasticity, the complexity of cancer cells, their heterogeneity, and their role in metastasis is being unraveled. The plasticity of the cells could also prove as a nemesis to their characterization. In this review, we have attempted to highlight the possibilities and benefits of using multiomics approach in characterizing the plastic nature of cancer cells. There is a need to integrate fragmented evidence at different levels of cellular organization (DNA, RNA, protein, metabolite, epigenetics, etc.) to facilitate the characterization of different forms of plasticity and cell types. We have discussed the role of cellular plasticity in generating intra-tumor heterogeneity. Different omics level evidence is being provided to highlight the variety of molecular determinants discovered using different techniques. Attempts have been made to integrate some of this information to provide a quantitative assessment and scoring of the plastic nature of the cells. However, there is a huge gap in our understanding of mechanisms that lead to the observed heterogeneity. Understanding of these mechanism(s) is necessary for finding targets for early detection and effective therapeutic interventions in metastasis. Targeting cellular plasticity is akin to neutralizing a moving target. Along with the advancements in precision and personalized medicine, these efforts may translate into better clinical outcomes for cancer patients, especially in metastatic stages.