Mitochondrial Dysfunction-Induced Senescence Model Customization Services

As organisms age, dysfunctional mitochondria accumulate in tissues composed mainly of non-dividing cells such as muscle cells and neurons. When mitochondria become dysfunctional, cells enter a state of senescence in which their ability to divide permanently ceases during a complex stress response. Researchers are using the robust model of mitochondrial dysfunction-induced senescence to explore molecular mechanisms that will improve the understanding of age-related diseases.

Fig. 1 Comparison between mitochondrial dysfunction associated-senescence (MiDAS) and stress-induced senescence. (Gallage S and Gil J, 2016)

At CD BioSciences, we specialize in providing customization services for mitochondrial dysfunction-induced senescence models. With years of experience in the industry, our team of expert biologists and advanced technologies enable us to offer comprehensive analysis services for mitochondrial dysfunction and senescent phenotypes.

How Do We Construct Mitochondrial Dysfunction-Induced Senescence Models?

Cell line selection

We guide our clients to choose suitable cell lines for constructing mitochondrial dysfunction-induced senescence models. Depending on the specific research interests, consider cell lines with relevant characteristics, such as human fibroblasts or epithelial cells.

Induction of mitochondrial dysfunction

We assist clients in choosing suitable mitochondrial toxins, including rotenone, antimycin A, and oligomycin for producing mitochondrial dysfunction. Our cellular model construction process uses these toxins while accounting for their concentrations, exposure times, and possible downstream cellular physiological effects.

Assessment of mitochondrial dysfunction

We help clients evaluate mitochondrial function and dysfunction within our cellular models. We offer assessments for mitochondrial membrane potential, ATP production measurements, reactive oxygen species (ROS) levels, mitochondrial DNA damage, and mitochondrial morphology alterations.

Characterization of senescent phenotypes

We perform a comprehensive characterization of the senescent phenotype to validate the cellular models, such as assessing senescence-associated beta-galactosidase (SA-β-gal) activity, cell cycle arrest, senescence-associated secretory phenotype (SASP), and alterations in gene expression profiles associated with senescence.

Optimization of aging models

If requested, we also help clients carry out our iterative optimization steps to refine the cellular models, ensuring their stability, reproducibility, and relevance to your specific research needs.

Extensive Applications of Mitochondrial Dysfunction-Induced Senescence Models

• Mechanistic studies. Our complete services assist clients in elucidating the signaling pathways, regulatory networks, and molecular interactions involved in MiDAS, which can reveal the fundamental biology of mitochondria and cellular aging.
• Disease modeling. Mitochondrial dysfunction is implicated in a wide range of diseases, including neurodegenerative disorders, metabolic syndromes, cardiovascular diseases, and cancer. We help clients customize mitochondrial dysfunction-induced senescence models as platforms to investigate the role of mitochondrial dysfunction in disease pathogenesis and progression
• Drug discovery and development. Our clients can discover new drugs for age-related diseases that are linked to mitochondrial dysfunction through models of mitochondrial dysfunction-induced senescence.

Our Service Features

• Customization and flexibility
• Timely project execution
• Confidentiality and data security

CD BioSciences offers comprehensive customization services for mitochondrial dysfunction-induced senescence models, supported by our expertise, advanced technologies, and commitment to delivering high-quality results. If you are interested in our services, please feel free to contact us or make an online inquiry.

Reference

1. Gallage S and Gil J. Mitochondrial dysfunction meets senescence. Trends in Biochemical Sciences, 2016, 41 (3): 207-209.