Measurable Metrics of Mesenchymal Stem Cell Aging

Area of Science:

• Cellular and Molecular Biology
• Stem Cell Biology
• Gerontology

Background:

• Cellular senescence is a key hallmark of aging, impacting tissue function.
• Human mesenchymal stem cells (hMSCs) are crucial for regenerative medicine and their aging affects therapeutic potential.
• Understanding senescence markers and developing predictive models is vital for hMSC applications.

Purpose of the Study:

• To analyze cellular senescence markers in replicative, stress-induced, and chronological aging models of hMSCs.
• To assess the feasibility of predictive models for estimating chronological age and in vitro cultivation duration using transcriptomic and morphological data.

Main Methods:

• Real-time PCR for gene expression dynamics and telomere length.
• High-throughput transcriptome sequencing of hMSCs from donors of varying ages.
• Machine learning algorithms for cell morphology analysis and a segmentation neural network for nuclear morphology.

Main Results:

• CDKN1A, LMNB1, and HMGB2 gene dynamics were consistent across senescence models.
• Donor-dependent heterogeneity in hMSC transcriptomes complicates precise predictive modeling.
• Telomere length analysis is applicable for assessing replicative senescence dynamics.
• A neural network model successfully detected senescence-associated nuclear morphology alterations.

Conclusions:

• Specific gene expression patterns and telomere length changes can indicate senescence in hMSCs.
• Cellular morphology, particularly nuclear alterations, provides valuable insights into aging and cultivation duration.
• Predictive models integrating transcriptomic and morphological data show promise for age and cultivation time assessment, despite donor heterogeneity challenges.