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Identification of senescent cells in multipotent mesenchymal stromal cell cultures: Current methods and future directions

期刊

CYTOTHERAPY
卷 21, 期 8, 页码 803-819

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.jcyt.2019.05.001

关键词

label-free; multipotent mesenchymal stromal cells; non-destructive; replicative aging; senescence

资金

  1. Leeds Institute of Rheumatic and Musculoskeletal Medicine, Leeds University, UK
  2. Singapore Institute of Manufacturing Technology, A*STAR, Singapore

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Regardless of their tissue of origin, multipotent mesenchymal stromal cells (MSCs) are commonly expanded in vitro for several population doublings to achieve a sufficient number of cells for therapy. Prolonged MSC expansion has been shown to result in phenotypical, morphological and gene expression changes in MSCs, which ultimately lead to the state of senescence. The presence of senescent cells in therapeutic MSC batches is undesirable because it reduces their viability, differentiation potential and trophic capabilities. Additionally, senescent cells acquire senescence-activated secretory phenotype, which may not only induce apoptosis in the neighboring host cells following MSC transplantation, but also trigger local inflammatory reactions. This review outlines the current and promising new methodologies for the identification of senescent cells in MSC cultures, with a particular emphasis on non-destructive and label-free methodologies. Technologies allowing identification of individual senescent cells, based on new surface markers, offer potential advantage for targeted senescent cell removal using new-generation senolytic agents, and subsequent production of therapeutic MSC batches fully devoid of senescent cells. Methods or a combination of methods that are non-destructive and label-free, for example, involving cell size and spectroscopic measurements, could be the best way forward because they do not modify the cells of interest, thus maximizing the final output of therapeutic-grade MSC cultures. The further incorporation of machine learning methods has also recently shown promise in facilitating, automating and enhancing the analysis of these measured data.

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