A network of Notch-dependent and -independent her genes controls neural stem and progenitor cells in the zebrafish thalamic proliferation zone.

Neural proliferation zones mediate brain growth and employ Delta/Notch signaling and HES/Her transcription factors to balance neural stem cell (NSC) maintenance with the generation of progenitors and neurons. We investigated Notch-dependency and function of her genes in the thalamic proliferation zone of zebrafish larvae. Nine Notch-dependent genes, her2, her4.1-4.5, her12, her15.1-15.2, and two Notch-independent genes, her6 and her9, are differentially expressed and define distinct NSC and progenitor populations. her6 prominently executes patterning information to maintain NSCs and the zona limitans intrathalamica Shh signaling activity. Surprisingly, simultaneous deletion of nine Notch-dependent her genes does not affect NSCs or progenitor formation, and her4 overexpression only caused reduction of ascl1b progenitors. Combined genetic manipulations of Notch-dependent and -independent her genes suggest that her6 in the thalamic proliferation zone prominently maintains NSCs and inhibits NSC-to-progenitor lineage transitions. The her gene network is characterized by redundant gene functions, with Notch-independent her genes better substituting for loss of Notch-dependent her genes than vice versa. Together, her gene regulatory feedback loops and cross-regulation contribute to the observed robustness of NSC maintenance.

Detecting frequency modulation in stochastic time-series data.

We propose a statistical test to identify nonstationary frequency-modulated stochastic processes from time-series data. Our method uses the instantaneous phase as a discriminatory statistics with reliable critical values derived from surrogate data. We simulated an oscillatory second-order autoregressive process to evaluate the size and power of the test. We found that the test we propose is able to correctly identify more than 99% of nonstationary data when the frequency of the simulated data is doubled after the first half of the time series. Our method is easily interpretable, computationally cheap, and does not require choosing hyperparameters that are dependent on the data.