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The human amygdala is involved in processing of memory, decision-making, and emotional responses. Previous studies suggested that the amygdala may represent a neurogenic niche in mammals. By combining two distinct methodological approaches, lipofuscin quantification and 14C-based retrospective birth dating of neurons, along with mathematical modelling, we here explored whether postnatal neurogenesis exists in the human amygdala. We investigated post-mortem samples of twelve neurologically healthy subjects. The average rate of lipofuscin-negative neurons was 3.4%, representing a substantial proportion of cells substantially younger than the individual. Mass spectrometry analysis of genomic 14C-concentrations in amygdala neurons compared with atmospheric 14C-levels provided evidence for postnatal neuronal exchange. Mathematical modelling identified a best-fitting scenario comprising of a quiescent and a renewing neuronal population with an overall renewal rate of >2.7% per year. In conclusion, we provide evidence for postnatal neurogenesis in the human amygdala with cell turnover rates comparable to the hippocampus.

Weakly coupled oscillators can exhibit seemingly incongruous synchronization patterns comprised of coherent and incoherent spatial domains known as chimera states. However, the weak coupling approximation is invalid when the characteristic phase response curve of an oscillator does not scale linearly with the coupling strength and instead changes its shape. In chemical experiments with photo-coupled relaxation oscillators, we find that beyond weak coupling chimera patterns consist of different coexisting cluster states. Numerical modeling reveals that the observed cluster states result from a phase-dependent excitability that is also commonly observed in neural tissue and cardiac pacemaker cells.

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Correct estimates of cell proliferation rates are crucial for quantitative models of the development, maintenance and regeneration of tissues. Continuous labeling assays are used to infer proliferation rates in vivo.So far, the experimental and theoretical study of continuous labeling assays focused on the dynamics of the mean labeling-fraction but neglected stochastic effects. To study the dynamics of the labeling-fraction in detail and fully exploit the information hidden in fluctuations, we developed a probabilistic model of continuous labeling assays which incorporates biological variability at different levels, between cells within a tissue sample but also between multiple tissue samples. Using stochastic simulations, we find systematic shifts of the mean-labeling fraction due to variability in cell cycle lengths. Using simulated data as ground truth, we show that current inference methods can give biased proliferation rate estimates with an error of up to 40 %. We derive the analytical solution for the Likelihood of our probabilistic model. We use this solution to infer unbiased proliferation rate estimates in a parameter recovery study. Furthermore, we show that the biological variability on different levels can be disentangled from the fluctuations in the labeling data. We implemented our model and the unbiased parameter estimation method as an open source Python tool and provide an easy to use web service for cell cycle length estimation from continuous labeling assays (https://imc.zih.tu-dresden.de/cellcycle).

SummaryPhysiological liver cell replacement is central to maintaining the organ’s high metabolic activity, although its characteristics are difficult to study in humans. Using retrospective 14C birth dating of cells, we report that human hepatocytes show continuous and lifelong turnover, maintaining the liver a young organ (average age < 3 years). Hepatocyte renewal is highly dependent on the ploidy level. Diploid hepatocytes show an seven-fold higher annual exchange rate than polyploid hepatocytes. These observations support the view that physiological liver cell renewal in humans is mainly dependent on diploid hepatocytes, whereas polyploid cells are compromised in their ability to divide. Moreover, cellular transitions between these two subpopulations are limited, with minimal contribution to the respective other ploidy class under homeostatic conditions. With these findings, we present a new integrated model of homeostatic liver cell generation in humans that provides fundamental insights into liver cell turnover dynamics.