Faculté des sciences

Biological noise and positional effects influence cell stemness

Blum, Walter ; Henzi, Thomas ; Schwaller, Beat ; Pecze, László

In: Journal of Biological Chemistry, 2018, vol. 293, no. 14, p. 5247–5258

Biological (or cellular) noise is the random quantitative variability of proteins and other molecules in individual, genetically identical cells. As the result of biological noise in the levels of some transcription factors that determine a cell's differentiation status, differentiated cells may dedifferentiate to a stem cell state given a sufficiently long time period. Here, to provide... Di più

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    Summary
    Biological (or cellular) noise is the random quantitative variability of proteins and other molecules in individual, genetically identical cells. As the result of biological noise in the levels of some transcription factors that determine a cell's differentiation status, differentiated cells may dedifferentiate to a stem cell state given a sufficiently long time period. Here, to provide direct evidence supporting this hypothesis, we used a live-cell monitoring system based on enhanced green fluorescent protein (eGFP) expression to continuously assess the “stemness” of individual human and murine malignant mesothelioma cells over a period of up to 3 months. Re-expression of the transcription factors, the top hierarchical stemness markers Sox2 (SRY-box 2) and Oct4 (octamer-binding transcription factor), monitored as cell eGFP expression was observed in a subpopulation of differentiated eGFP(−) malignant mesothelioma cells. However, we found that this transition was extremely rare. Of note, when it did occur, neighboring cells that were not direct descendants of a newly emerged eGFP(+) stem cell were more likely than non-neighboring cells to also become an eGFP(+) stem cell. This observation suggested a positional effect and led to a clustered “mosaic” reappearance of eGFP(+) stem cells. Moreover, stem cells reappeared even in cell cultures derived from one single differentiated eGFP(−) cell. On the basis of our experimental in vitro and in vivo findings, we developed a tumor growth model to predict the clustered localization of cancer stem cells within a tumor mass.