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The transcriptome represents the intermediate state of gene expression and can react to transcriptional and post-transcriptional regulation. Proteins are direct function performers in organisms, and changes in protein content play a crucial role in organism study. Metabolites are closely related to physiological phenotypes, and changes in metabolites in organisms can explore the metabolic mechanism of the whole organism by detecting changes in metabolites after exogenous stimuli or genetic modifications; moreover, comprehensive analysis of products in the life process of organisms can help to reveal the link between genotypes and phenotypes. Therefore, in order to comprehensively investigate the disease and stress mechanisms of organisms, and precisely study the expression patterns and regulatory mechanisms of important genes, only by integrating transcriptomics, proteomics, and metabolomics data and conducting multifaceted studies on biological samples can we uncover the molecular regulatory mechanisms of the life process, and then explain the biological problems from a holistic point of view.
Creative Proteomics has many years of experience in integrated multi-omics analysis. By combining data from multiple genomics platforms, we can help researchers gain a more comprehensive understanding of cellular processes. We can identify new biomarkers, elucidate complex networks, and improve their understanding of disease mechanisms, leading to a deeper understanding of biological phenomena.
Longitudinal Transcriptomic, Proteomic, and Metabolomic Analysis of Citrus limon Response to Graft Inoculation by Candidatus Liberibacter asiaticus. (Ramsey JS, et al. 2020)
What are the main analyses we provide?
- Data quality control.
- Differential expression analysis of mRNA, protein data, and metabolomic data.
- mRNA and protein correlation analysis.
- Target gene GO annotation, KEGG enrichment analysis.
- GO annotation and KEGG enrichment analysis of associated proteins.
- mRNA and protein correlation result interactions analysis.
- Protein interactions network diagram.
- Differential expression metabolite analysis and Pearson correlation coefficient analysis.
- Combined mRNA, protein, and metabolome analysis.
- Pathway integration analysis, KEGG annotation-based screening of differentially expressed metabolites.
- Differential metabolite classification.
- mRNA, protein, and metabolite pathway statistics and cluster analysis.
- Regulatory networks.
Applications of integrated transcriptomic, proteomic, and metabolomic analysis
- Plant research. Research on plant biotic and abiotic stress mechanisms, plant growth and development, color and nutritional metabolism of fruits, vegetables and flowers, functional active ingredient pathways in medicinal plants, and crop breeding and conservation.
- Animal research. Animal disease research, animal genetic breeding, animal nutritional quality research, meat and milk quality research, etc.
- Biomedical research. Research on molecular mechanisms of complex diseases, biomarkers, disease typing, personalized treatment, embryonic development, etc.
- Pharmaceutical research. Drug action mechanism, drug action target research, drug efficiency evaluation, drug development, etc.
Advantages of integrated transcriptomic, proteomic, and metabolomic analysis
- Enhanced data interpretation. Integration of transcriptomic, proteomic, and metabolomic data provides a more complete picture of cellular processes. By considering the dynamic interactions between genes, proteins, and metabolites, researchers can decipher complex biological pathways and gain insights into functional relationships between molecules.
- Identification of novel biomarkers. Integration of transcriptomic, proteomic, and metabolomic data from both healthy and diseased individuals, researchers can identify specific molecular signatures associated with various physiological and pathological conditions. These biomarkers can be utilized for early disease detection, prognosis, and monitoring treatment responses.
- Elucidation of complex biological networks. The integration of multiple omics datasets enables the construction of comprehensive biological networks, unraveling the intricate interplay between genes, proteins, and metabolites. By employing network-based approaches, researchers can identify key regulatory elements, signaling pathways, and functional modules involved in specific cellular processes.
- Improved understanding of disease mechanisms. By examining changes in gene expression, protein abundance, and metabolite levels simultaneously, researchers can identify dysregulated pathways and key molecular players driving disease progression. This knowledge can aid in the development of targeted therapies and the discovery of novel drug targets, ultimately leading to improved treatment strategies.
- Facilitating personalized medicine. Transcriptomic, proteomic, and metabolomic data integration holds significant promise in advancing personalized medicine. This can be utilized to tailor medical interventions to individual patients, optimizing therapeutic outcomes and minimizing adverse effects.
Our service workflow
Creative Proteomics provides integrated multi-omics analysis services that play an important role in deepening biological research and enhancing discoveries that help researchers in biomedicine.If you are interested in us, please feel free to contact us.
- Ramsey JS, Chin EL, Chavez JD, et al. Longitudinal Transcriptomic, Proteomic, and Metabolomic Analysis of Citrus limon Response to Graft Inoculation by Candidatus Liberibacter asiaticus. J Proteome Res. 2020 Jun 5;19(6):2247-2263.
The use of metabolomics integrated with transcriptomic and proteomic studies for identifying key steps involved in the control of nitrogen metabolism in crops such as maize.
Journal: J Exp Bot
Linking plant phenotypes to gene and protein expression and metabolite synthesis and accumulation is one of the major challenges for improving global agricultural production. This challenge is particularly relevant to crop nitrogen utilization efficiency (NUE). In this article, the authors investigated differences in the accumulation of gene transcripts, proteins, and metabolites in maize leaves under long-term nitrogen (N)-deficient growing conditions at two important stages of plant development. The effects of nitrogen deficiency were examined at the transcriptomic, proteomic, and metabolomic levels. By integrating transcriptomic, proteomic, and metabolomic data analyses, the authors contribute to improving researchers' understanding of plant nitrogen economics for better application in breeding and agronomy.
The leaf proteome was not significantly altered functionally under chronic nitrogen deficiency. Comparison of the proteomes of N- and N+ leaves at the nutrient V stage revealed significant differences (P < 0.05) in the volumes of 45 protein spots. Only 40 proteins were identified. Quantification of the optical density of these differences is shown in JXB Online Supplemental Table S3. At the M maturation stage, the volume of 40 protein spots was significantly altered in N-deficient leaves. Examples of modifications occurring in leaf protein profiles show differences between V-stage N+ and N- plants ( Figure 1).
Functional classes of metabolites, proteins and gene transcripts isolated from leaves of maize plants grown under low and high nitrogen fertilization conditions showed differences in accumulation levels. Pie charts show the number of metabolites, proteins, and transcripts identified in the three "omics" experiments, which increased at the nutrient (V) and maturity (M) stages of leaf development under either high (N+) or low (N-) nitrogen supply ( Figure 2).
The authors found that many key plant biological functions were either up- or down-regulated when N was limited, including major alterations in photosynthesis, carbon (C) metabolism, and to a lesser extent downstream metabolic pathways. The authors also found that the effects of N deficiency stress were similar to plant responses to many other biotic and abiotic stresses in terms of transcript, protein, and metabolite accumulation. Genetic and metabolic changes differed between the nitrogen assimilation and irrigation phases, suggesting that plant development is an important component in determining the key elements of the control of nitrogen use efficiency in plants. The integration of the three "omics" studies is not straightforward, as the different levels of regulation appear to occur in a stepwise manner from gene expression to metabolite accumulation.
- Amiour N,Imbaud S,Clément G, et al. The use of metabolomics integrated with transcriptomic and proteomic studies for identifying key steps involved in the control of nitrogen metabolism in crops such as maize. J Exp Bot. 2012;63 (14):5017-33.