A stochastic model of neurogenesis controlled by a single factor

A stochastic model of adult neurogenesis coupling cell cycle progression and differentiation

Long-term tissue homeostasis requires a precise balance between stem cell self-renewal and the generation of differentiated progeny. Recently, it has been shown that in the adult murine brain, neural stem cells (NSCs) divide mostly symmetrically. This finding suggests that the required balance for tissue homeostasis is accomplished at the population level. However, it remains unclear how this balance is enabled. Furthermore, there is experimental evidence that proneural differentiation factors not only promote differentiation, but also cell cycle progression, suggesting a link between the two processes in NSCs. To study the effect of such a link on NSC dynamics, we developed a stochastic model in which stem cells have an intrinsic probability to progress through cell cycle and to differentiate. Our results show that increasing heterogeneity in differentiation probabilities leads to a decreased probability of long-term tissue homeostasis, and that this effect can be compensated when cell cycle progression and differentiation are positively coupled. Using single-cell RNA-Seq profiling of adult NSCs, we found a positive correlation in the expression levels of cell cycle and differentiation markers. Our findings suggest that a coupling between cell cycle progression and differentiation on the cellular level is part of the process that maintains tissue homeostasis in the adult brain.

Neutral dynamics and cell renewal of colonic crypts in homeostatic regime

The self renewal process in colonic crypts is the object of several studies. We present here a new compartment model with the following characteristics: (a) we distinguish different classes of cells: stem cells, six generations of transit amplifying cells and the differentiated cells; (b) in order to take into account the monoclonal character of crypts in homeostatic regimes we include symmetric divisions of the stem cells. We first consider the dynamic differential equations that describe the evolution of the mean values of the populations, but the small observed value of the total number of cells involved plus the huge dispersion of experimental data found in the literature leads us to study the stochastic discrete process. This analysis allows us to study fluctuations, the neutral drift that leads to monoclonality, and the effects of the fixation of mutant clones.

Cyclin A2 promotes DNA repair in the brain during both development and aging

Various stem cell niches of the brain have differential requirements for Cyclin A2. Cyclin A2 loss results in marked cerebellar dysmorphia, whereas forebrain growth is retarded during early embryonic development yet achieves normal size at birth. To understand the differential requirements of distinct brain regions for Cyclin A2, we utilized neuroanatomical, transgenic mouse, and mathematical modeling techniques to generate testable hypotheses that provide insight into how Cyclin A2 loss results in compensatory forebrain growth during late embryonic development. Using unbiased measurements of the forebrain stem cell niche, we parameterized a mathematical model whereby logistic growth instructs progenitor cells as to the cell-types of their progeny. Our data was consistent with prior findings that progenitors proliferate along an auto-inhibitory growth curve. The growth retardation inCCNA2-null brains corresponded to cell cycle lengthening, imposing a developmental delay. We hypothesized that Cyclin A2 regulates DNA repair and that CCNA2-null progenitors thus experienced lengthened cell cycle. We demonstrate that CCNA2-null progenitors suffer abnormal DNA repair, and implicate Cyclin A2 in double-strand break repair. Cyclin A2's DNA repair functions are conserved among cell lines, neural progenitors, and hippocampal neurons. We further demonstrate that neuronal CCNA2 ablation results in learning and memory deficits in aged mice.

Retinogenesis: stochasticity and the competency model

The vertebrate retina is made up of seven principal cell types. These seven retinal cell types arise from multipotent retinal progenitor cells (RPCs). The competency model was proposed suggesting that RPCs undergo a series of irreversible transitions between competency states, in each of which the RPCs are competent to generate a different subset of cell types, but not retinal cells generated at previous moments. In this work, we generalize the stochastic model of neurogenesis of Barton et al. (2014), assuming that the same factor that regulates the differentiation, regulates the competency. The model reproduces the timing of production of different retinal cell types in rats such as it was experimentally measured. The results show that the evolution of the competency during retinogenesis could be explained by a single factor. Its evolution during the cell cycle and the stochastic inheritance in cell divisions determine the sequence and the overlap of production of different retinal cell types during development.