Sorbonne Université, Université Paris-Diderot SPC, CNRS, Laboratoire Jacques-Louis Lions, LJLL, Paris, France.
Sorbonne Université, CNRS UMR7622, Inserm U1156, Institut de Biologie Paris-Seine (IBPS), Laboratoire de Biologie du développement (LBD), Paris, France.
BMC Bioinformatics. 2019 Sep 14;20(1):470. doi: 10.1186/s12859-019-3018-8.
Neurogenesis in the murine cerebral cortex involves the coordinated divisions of two main types of progenitor cells, whose numbers, division modes and cell cycle durations set up the final neuronal output. To understand the respective roles of these factors in the neurogenesis process, we combine experimental in vivo studies with mathematical modeling and numerical simulations of the dynamics of neural progenitor cells. A special focus is put on the population of intermediate progenitors (IPs), a transit amplifying progenitor type critically involved in the size of the final neuron pool.
A multiscale formalism describing IP dynamics allows one to track the progression of cells along the subsequent phases of the cell cycle, as well as the temporal evolution of the different cell numbers. Our model takes into account the dividing apical progenitors (AP) engaged into neurogenesis, both neurogenic and proliferative IPs, and the newborn neurons. The transfer rates from one population to another are subject to the mode of division (proliferative, or neurogenic) and may be time-varying. The model outputs are successfully fitted to experimental cell numbers from mouse embryos at different stages of cortical development, taking into account IPs and neurons, in order to adjust the numerical parameters. We provide additional information on cell kinetics, such as the mitotic and S phase indexes, and neurogenic fraction.
Applying the model to a mouse mutant for Ftm/Rpgrip1l, a gene involved in human ciliopathies with severe brain abnormalities, reveals a shortening of the neurogenic period associated with an increased influx of newborn IPs from apical progenitors at mid-neurogenesis. Our model can be used to study other mouse mutants with cortical neurogenesis defects and can be adapted to study the importance of progenitor dynamics in cortical evolution and human diseases.
小鼠大脑皮层中的神经发生涉及两种主要祖细胞类型的协调分裂，其数量、分裂模式和细胞周期持续时间决定了最终的神经元输出。为了了解这些因素在神经发生过程中的各自作用，我们将体内实验研究与神经祖细胞动力学的数学建模和数值模拟相结合。特别关注中间祖细胞（IPs）群体，这是一种过渡扩增祖细胞类型，对于最终神经元池的大小至关重要。
描述 IP 动力学的多尺度形式主义允许跟踪细胞沿着细胞周期的后续阶段的进展，以及不同细胞数量的时间演化。我们的模型考虑了参与神经发生的分裂顶端祖细胞（AP）、神经发生和增殖的 IPs 以及新生神经元。从一个群体到另一个群体的转移率取决于分裂模式（增殖或神经发生），并且可能随时间变化。模型输出成功拟合了来自处于不同皮层发育阶段的小鼠胚胎的实验细胞数量，同时考虑了 IPs 和神经元，以调整数值参数。我们提供了有关细胞动力学的其他信息，例如有丝分裂和 S 期指数以及神经发生分数。
将该模型应用于 Ftm/Rpgrip1l 基因的小鼠突变体，该基因涉及具有严重脑异常的人类纤毛病，揭示了神经发生期的缩短与从中期神经发生的顶端祖细胞中新生的 IPs 的流入增加有关。我们的模型可用于研究其他具有皮层神经发生缺陷的小鼠突变体，并可用于研究祖细胞动力学在皮层进化和人类疾病中的重要性。
