Análisis de Clustering en el estudio del envejecimiento cronológico en células de levadura Saccharomyces cerevisiae

Abstract

Aging is a fundamental area of ​​study for humans due to its impact at both the physical and cellular level, for example, neuronal deterioration is directly related to aging and can lead to an increased risk of chronic diseases such as Alzheimer's. However, it is a booming area of ​​research and there are not many articles that analyze aging markers in cells with flow cytometry, and even fewer that incorporate machine learning methods. In this internship, chronological aging was studied at the cellular level in the yeast Saccharomyces cerevisiae, using HSP12 as a quiescence marker through flow cytometry. 100,000 daily data were collected over a 10-day period and analyzed using several machine learning methods, such as Clustering algorithms and Principal Component Analysis (PCA). These analyses allowed to identify subgroups associated with aging, providing a more detailed understanding of the four potential states of cellular aging: proliferation, senescence, quiescence, and death.