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Parigini, C.

Publications and source records attributed to Parigini, C..

2 recordsLinked to original sources

Universal principles of lineage architecture and stem cell identity in renewing tissues

Adult tissues in multicellular organisms typically contain a variety of stem, progenitor and differentiated cell types arranged in a lineage hierarchy that regulates healthy tissue turnover and repair. Lineage hierarchies in disparate tissues often exhibit common features, yet the general principles regulating their architecture are not known. Here, we provide a formal framework for understanding the relationship between cell molecular states (patterns of gene, protein expression etc. in the cell) and cell types that uses notions from network science to decompose the structure of cell state trajectories into functional units. Using this framework we show that many widely experimentally observed features of cell lineage architectures - including the fact that a single adult stem cell type always resides at the apex of a lineage hierarchy - arise as a natural consequence of homeostasis, and indeed are the only possible way that lineage architectures can be constructed to support homeostasis in renewing tissues. Furthermore, under suitable feedback regulation, for example from the stem cell niche, we show that the property of stemness is entirely determined by the cell environment. Thus, we argue that stem cell identities are contextual and not determined by hard-wired, cell-intrinsic, characteristics.

systems biology

Universality of clonal dynamics poses fundamental limits to identify stem cell self-renewal strategies

How adult stem cells maintain self-renewing tissues is in vivo commonly assessed by analysing clonal data from cell lineage tracing assays. To identify strategies of stem cell self-renewal requires that different models of stem cell fate choice predict sufficiently different clonal statistics. Here we show that models of cell fate choice can, in homeostatic tissues, be categorized by exactly two universality classes, whereby models of the same class predict, under asymptotic conditions, the same clonal statistics. Those classes relate to generalizations of the canonical asymmetric vs. symmetric stem cell self-renewal strategies and are differentiated by a conservation law. This poses both challenges and opportunities to identify stem cell self-renewal strategies: while under asymptotic conditions, self-renewal models of the same universality class cannot be distinguished by clonal data only, models of different classes can be distinguished by simple means.

systems biology