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The transcriptome is the collection of all RNAs transcribed from a particular tissue or cell at a particular stage of development or functional state. Transcriptomics refers to a discipline that studies the transcription of genes and the laws of transcriptional regulation in cells at a holistic level. Transcriptome research is able to study gene function as well as gene structure at a holistic level, revealing the molecular mechanisms involved in specific biological processes as well as in the development of diseases. Lipidome is the collection of all the lipids in the cells under specific developmental stages or physiological conditions. Lipidomics, as an important branch of metabolomics, is the comprehensive and systematic analysis of lipids and the molecules interacting with them in organisms, tissues, or cells, and the identification of lipid structure and function, which reveals the relationship between lipid metabolism and the physiological and pathological processes of cells, organs, and even the organism. By combining transcriptomics and lipidomics data, we can more systematically and comprehensively analyze the functions and regulatory mechanisms of biological lipid molecules, and select key lipid metabolic pathways and related genes, as well as lipid metabolites for subsequent in-depth research.
Creative Proteomics has many years of experience in multi-omics integration services. Based on our advanced high-throughput transcriptome sequencing platform and lipidomics analysis service combined with tandem mass spectrometry and analytical software, we aim to provide one-stop combined transcriptomics and lipidomics analysis service for our clients.
Ntegrated lipidomics, transcriptomics and network pharmacology analysis to reveal the mechanisms of Danggui Buxue Decoction in the treatment of diabetic nephropathy in type 2 diabetes mellitus（Sun L, et al,. 2022)
Purpose and advantages of combined transcriptomics and lipidomics analysis
Combined transcriptomics and lipidomics analysis can provide valuable insights into the complex relationship between gene expression and lipid metabolism，which can help researchers obtain a more comprehensive understanding of cellular processes and regulatory mechanisms.
- Enhanced understanding of lipid metabolism. Lipids play critical roles in various biological processes, including energy storage, membrane structure, and signaling. By combining transcriptomics and lipidomics, researchers can identify key genes involved in lipid metabolism and gain insights into how they are regulated.
- Identification of novel biomarkers. Facilitate the discovery of potential biomarkers for disease diagnosis, prognosis, and therapeutic targets. By examining gene expression patterns and lipid profiles simultaneously, researchers can identify specific lipid species that are associated with altered gene expression and disease states.
- Uncovering regulatory networks. Combined transcriptomics and lipidomics analysis enables the identification of regulatory networks that govern lipid metabolism.
- Validation of functional genomics studies. By examining both gene expression and lipid levels, researchers can assess the functional consequences of genetic perturbations or interventions.
Applications of combined transcriptomics and lipidomics analyses
- Agroforestry. Mechanisms of growth and development, stress resistance, breeding, species conservation research, etc.
- Animal husbandry. Growth and development mechanisms, pathogenic mechanisms research, meat and dairy product quality research, etc.
- Marine aquaculture. Growth and development mechanisms, fishery resources, the fishery environment and aquatic product safety, etc.
- Microbiology. Pathogenesis, drug resistance mechanisms, pathogen-host interaction, etc.
- Biomedicine. Biomarkers, disease mechanisms, disease typing, drug development, personalized treatment, etc.
- Environmental science. Optimization of fermentation processes, biofuel production, environmental hazard risk assessment studies, etc.
- Food science. Optimization of food storage and processing conditions, identification of food components and quality, functional food development, food safety monitoring and testing, etc.
Our service workflow
What do we offer?
Creative Proteomics will provide you with a detailed technical report on the following.
- Experimental steps.
- Relevant experimental parameters.
- Mass spectrometry images.
- Raw data.
- Results of transcriptomics and lipidomics analyses
Creative Proteomics combines advanced technology platforms and years of service experience to provide customizable analytical solutions, rapid and accurate analytical services. We provide you with one-stop transcriptomics and lipidomics integrated analysis services from experimental design, sample testing and data analysis, which can meet a variety of testing needs. If you are interested in us, please feel free to contact us.
- Sun L,Yang Z, Zhao W, et al. Integrated lipidomics, transcriptomics and network pharmacology analysis to reveal the mechanisms of Danggui Buxue Decoction in the treatment of diabetic nephropathy in type 2 diabetes mellitus. J Ethnopharmacol. 2022;283:114699.
Integration of Lipidomics and Transcriptomics Reveals Reprogramming of the Lipid Metabolism and Composition in Clear Cell Renal Cell Carcinoma
Renal cell carcinoma (RCC) accounts for approximately 2-3% of all malignant diseases in adults. Clear cell renal cell carcinoma (ccRCC) is a metabolic disease, and many of the genes altered in this tumor play a significant role in controlling cellular metabolic activity.
Given the importance of metabolic reprogramming in cancer cells and the involvement of lipids in many cellular processes such as membrane remodeling and cell signaling, In this study, the authors characterize the lipidomics of human ccRCC and combine it with transcriptomic data to link changes in cancer lipid metabolism with changes in gene expression.
The authors performed untargeted lipidomic analysis of 40 renal-derived tissues (including 20 ccRCC and 20 paired normal tissues) using LC-MS and GC-MS platforms. A total of 158 lipids were identified, of which 93 were differentially expressed in tumor tissues compared to normal samples (57 higher and 36 lower). Principal component analysis (PCA) was applied to differentiate normal and pathological samples as a function of the overall tissue lipid group, demonstrating that the two groups were significantly different. Based on PCA, hierarchical cluster analysis and heat map visualization showed a clear distinction between ccRCC and non-tumor tissues (Figure 1).
Long chain fatty acids (LCFAs), saturated fatty acids (SFAs), monounsaturated fatty acid (MUFAs), and polyunsaturated fatty acids (PUFAs) were significantly accumulated in cancer (ccRCC) compared to normal tissue(Figure 2).
Comprehensive lipidomic and transcriptomic analyses revealed that fatty acid desaturation and elongation pathways were enriched in tumor tissues, with increased expression of stearoyl-CoA desaturase-1 (SCD1) and fatty acid elongase 2 and 5 (ELOVL2 and ELOVL5)in ccRCC (Figure 3).
Primary renal cancer cells treated by the authors with the small molecule SCD1 inhibitor A939572 proliferated at a slower rate than untreated cancer cells. In addition, after cisplatin treatment, the mortality rate of tumor cells treated with A939572 was significantly higher than that of untreated cancer cells. It was shown that SCD1 inhibition significantly decreased cancer cell proliferation and increased cisplatin sensitivity (Figure 4).
By combining transcriptomic and lipidomic analyses, the authors show that ccRCC is characterized by reprogramming of lipid metabolism associated with a switch in adipogenic gene signatures. Accumulation of ultra-long-chain FA and PUFA is maintained by overexpression of SCD1 and ELOVL. Inhibition of SCD1 activity decreases cell viability and increases cisplatin sensitivity, suggesting that this pathway modulates chemoresistance in ccRCC.
- Lucarelli G,Ferro M,Loizzo D, et al. Integration of Lipidomics and Transcriptomics Reveals Reprogramming of the Lipid Metabolism and Composition in Clear Cell Renal Cell Carcinoma. Metabolites. 2020;10 (12):509.