University of Groningen - Bernoulli Institute, 9747AG Groningen, The Netherlands.
University of Nottingham - School of Mathematical Sciences, Nottingham NG72RD, United Kingdom.
Bioinformatics. 2023 Oct 3;39(10). doi: 10.1093/bioinformatics/btad605.
Investigating cell differentiation under a genetic disorder offers the potential for improving current gene therapy strategies. Clonal tracking provides a basis for mathematical modelling of population stem cell dynamics that sustain the blood cell formation, a process known as haematopoiesis. However, many clonal tracking protocols rely on a subset of cell types for the characterization of the stem cell output, and the data generated are subject to measurement errors and noise.
We propose a stochastic framework to infer dynamic models of cell differentiation from clonal tracking data. A state-space formulation combines a stochastic quasi-reaction network, describing cell differentiation, with a Gaussian measurement model accounting for data errors and noise. We developed an inference algorithm based on an extended Kalman filter, a nonlinear optimization, and a Rauch-Tung-Striebel smoother. Simulations show that our proposed method outperforms the state-of-the-art and scales to complex structures of cell differentiations in terms of nodes size and network depth. The application of our method to five in vivo gene therapy studies reveals different dynamics of cell differentiation. Our tool can provide statistical support to biologists and clinicians to better understand cell differentiation and haematopoietic reconstitution after a gene therapy treatment. The equations of the state-space model can be modified to infer other dynamics besides cell differentiation.
The stochastic framework is implemented in the R package Karen which is available for download at https://cran.r-project.org/package=Karen. The code that supports the findings of this study is openly available at https://github.com/delcore-luca/CellDifferentiationNetworks.
研究遗传疾病下的细胞分化有潜力改进当前的基因治疗策略。克隆追踪为数学建模提供了基础，可用于研究维持血细胞形成的群体干细胞动力学，这一过程被称为造血。然而，许多克隆追踪方案依赖于细胞类型的子集来描述干细胞输出，并且所生成的数据受到测量误差和噪声的影响。
我们提出了一种随机框架，可从克隆追踪数据中推断细胞分化的动态模型。状态空间公式将描述细胞分化的随机拟反应网络与高斯测量模型相结合，该模型可解释数据误差和噪声。我们开发了一种基于扩展卡尔曼滤波器、非线性优化和 Rauch-Tung-Striebel 平滑器的推断算法。模拟表明，我们的方法优于最先进的方法，并且在节点大小和网络深度方面扩展到了更复杂的细胞分化结构。我们的方法在五项体内基因治疗研究中的应用揭示了细胞分化的不同动力学。我们的工具可以为生物学家和临床医生提供统计支持，以更好地了解基因治疗后的细胞分化和造血重建。状态空间模型的方程可以修改为推断除细胞分化以外的其他动态。
随机框架在 R 包 Karen 中实现，可在 https://cran.r-project.org/package=Karen 下载。支持本研究结果的代码可在 https://github.com/delcore-luca/CellDifferentiationNetworks 上公开获取。
