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Related Experiment Video

Updated: Dec 10, 2025

Measurement of Protein Turnover Rates in Senescent and Non-Dividing Cultured Cells with Metabolic Labeling and Mass Spectrometry

Measurement of Protein Turnover Rates in Senescent and Non-Dividing Cultured Cells with Metabolic Labeling and Mass Spectrometry

Published on: April 6, 2022

Study Aging by Fibroblasts Metabolome.

Ana Rocha¹, Sandra Magalhães¹, Alexandra Nunes¹

¹iBiMED - Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal.

Current Molecular Medicine

|September 2, 2020

Summary

This study explores using fibroblast cell lines to understand aging. Metabolomics analysis of these cells offers insights into biological aging and predicting age-related diseases.

Keywords:

Aging FTIR MS NMR fibroblasts metabolome

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Obtaining Specimens with Slowed, Accelerated and Reversed Aging in the Honey Bee Model

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Published on: August 29, 2013

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Last Updated: Dec 10, 2025

Measurement of Protein Turnover Rates in Senescent and Non-Dividing Cultured Cells with Metabolic Labeling and Mass Spectrometry

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Published on: April 6, 2022

A Quantitative Measurement of Reactive Oxygen Species and Senescence-associated Secretory Phenotype in Normal Human Fibroblasts During Oncogene-induced Senescence

A Quantitative Measurement of Reactive Oxygen Species and Senescence-associated Secretory Phenotype in Normal Human Fibroblasts During Oncogene-induced Senescence

Published on: August 12, 2018

Obtaining Specimens with Slowed, Accelerated and Reversed Aging in the Honey Bee Model

Obtaining Specimens with Slowed, Accelerated and Reversed Aging in the Honey Bee Model

Published on: August 29, 2013

Area of Science:

Gerontology
Cell Biology
Metabolomics

Background:

Aging is a complex, multifactorial process influenced by various factors, impacting health and disease onset.
Understanding biological aging versus chronological age is crucial for predicting metabolic decline and age-related diseases.

Purpose of the Study:

To review the significance of using fibroblast cell lines as models for studying the metabolome of aging.
To highlight the role of cell metabolomics in personalized and integrative medicine.

Main Methods:

Utilizing cell lines, specifically fibroblasts, to model organism-wide aging processes.
Employing modern "omic" techniques such as Fourier-transform infrared spectroscopy (FTIR), Nuclear Magnetic Resonance (NMR), and Mass Spectrometry (MS) for metabolomics analysis.

Main Results:

Fibroblast cell metabolomics provides valuable data for understanding aging at a cellular level.
Cellular aging reflects organismal aging, making cell line studies relevant.

Conclusions:

Fibroblasts are relevant cell models for investigating the metabolome of aging.
Cell metabolomics, using advanced "omic" techniques, contributes to predicting aging trajectories and related diseases.