Multi-scale modeling of GMP differentiation based on single-cell genealogies
Multi-scale modeling of GMP differentiation based on single-cell genealogies
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DOI：
10.1111/j.1742-4658.2012.08664.x
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发表时间：
2012-09-01
期刊：
影响因子：
5.4
通讯作者：
Theis, Fabian J.
中科院分区：
文献类型：
--
作者：
Marr, Carsten;Strasser, Michael;Theis, Fabian J.
造血通常被描述为分支决定的层次结构，从单个细胞（造血干细胞）逐步分化产生所有成熟的血细胞类型。这一过程的各个方面已经用不同的实验和理论技术在不同的尺度上进行了模拟。在这里，我们将更常见的基于群体的方法与单细胞分辨分子分化模型结合起来，研究推断分化过程机制知识的可能性。我们专注于造血的一个子模块：粒细胞-单核细胞祖细胞（gmp）分化为粒细胞或单核细胞。在分支过程模型中，我们从实验量化的集落分析异质性中推断出gmp的分化概率，在允许的条件下，粒细胞和单核细胞都可以出现。我们将预测结果与单细胞延时显微镜测定的谱系分化概率进行比较。与分支过程模型相反，我们发现由分化标记开始决定的分化概率随着谱系内细胞的产生而增加。为了从分子角度研究这一特征，我们建立了一个随机开关模型，其中内在谱系决定是由两个拮抗转录因子执行的。我们确定了允许时间相关和时间无关的微分概率的参数制度。最后，我们通过近似贝叶斯计算推断出模型与实验观察到的微分概率相匹配的参数。这些参数提示了粒细胞和单核细胞分化动力学的不同时间尺度。因此，我们提供了小鼠gmp中细胞分化的多尺度图像，并说明了对细胞决策的单细胞时间分辨观察的必要性。
Hematopoiesis is often pictured as a hierarchy of branching decisions, giving rise to all mature blood cell types from stepwise differentiation of a single cell, the hematopoietic stem cell. Various aspects of this process have been modeled using various experimental and theoretical techniques on different scales. Here we integrate the more common population-based approach with a single-cell resolved molecular differentiation model to study the possibility of inferring mechanistic knowledge of the differentiation process. We focus on a sub-module of hematopoiesis: differentiation of granulocyte-monocyte progenitors GMPs) to granulocytes or monocytes. Within a branching process model, we infer the differentiation probability of GMPs from the experimentally quantified heterogeneity of colony assays under permissive conditions where both granulocytes and monocytes can emerge. We compare the predictions with the differentiation probability in genealogies determined from single-cell time-lapse microscopy. In contrast to the branching process model, we found that the differentiation probability as determined by differentiation marker onset increases with the generation of the cell within the genealogy. To study this feature from a molecular perspective, we established a stochastic toggle switch model, in which the intrinsic lineage decision is executed using two antagonistic transcription factors. We identified parameter regimes that allow for both time-dependent and time-independent differentiation probabilities. Finally, we infer parameters for which the model matches experimentally observed differentiation probabilities via approximate Bayesian computing. These parameters suggest different timescales in the dynamics of granulocyte and monocyte differentiation. Thus we provide a multi-scale picture of cell differentiation in murine GMPs, and illustrate the need for single-cell time-resolved observations of cellular decisions.