Advances in high-throughput technologies have provided new opportunities for histological studies to understand the mechanisms of aging and the pathological processes of various complex age-related diseases. In the complex aging process, multiple biological pathways such as genome, transcriptome, proteome, and metabolome usually work together to influence and perform biological functions, therefore, by a single histological technique can only provide limited insights into the biological mechanisms of aging and diseases, we can systematically reveal the molecular mechanisms of aging from a multi-molecular level by integrating multi-omics techniques.
We hope that our unique integrated multi-omics aging clock technology will provide new perspectives on aging biomarker discovery, aging mechanisms and therapeutic development for aging-related diseases.
The large volume and type of transcriptomic data often require normalized cohorts for normalized data processing to characterize age-related predictors and improve the level of accuracy through machine learning techniques.
Proteomic changes reflect different biological pathways and may reveal the association of various genomic and proteomic and age-related phenotypic features, and by studying aging may provide potential new therapeutic targets for age-related diseases.
Metabolomics generates a large amount of information that can reveal metabolic pathways associated with aging, quantify age-related changes in metabolic profiles, and can be a powerful tool for assessing aging with highly sensitive and specific analysis techniques.
An innovative transcriptomics-based biological age measurement approach to better understand the major changes that occur in human age at the RNA level. We can perform machine learning, differential expression and enrichment analysis on a large number of RNA samples from different tissues of different species to generate specific age predictors to explore the aging clock.
An innovative proteomics-based biological age measurement method to study proteomic changes during aging. We can systematically evaluate different proteomes and identify proteins that change significantly with age and can be used as aging biomarker candidates to compose a proteomic aging clock.
An innovative metabolomics-based biological age measurement approach reveals metabolite biomarkers associated with aging and age-related diseases through metabolomics. We can develop age-related metabolite databases and capture and identify differential metabolites, enabling them to be used as predictors to build age models. Metabolomics can be used as a powerful tool to complement other biological age markers.
We provide large-scale, high-quality and high-throughput data analysis of various omics approaches, integrating correlations between different omics levels to gain insight into the aging process and its mechanisms. The integrated multi-omics age clock technology that enables multi-level identification of interrelated processes in the aging process provides a breakthrough in the study of anti-aging interventions and helps determine the optimal timing of aging biomarker measurements and anti-aging therapies.
Wu L, et al. Integrated Multi-Omics for Novel Aging Biomarkers and Antiaging Targets. Biomolecules, 2022; 12(1):39.
To work with us or to learn more about our DNA methylation clock technology, please contact us.