Techniques for analysing pattern formation in populations of stem cells and their progeny.
Techniques for analysing pattern formation in populations of stem cells and their progeny.
复制标题
DOI：
10.1186/1471-2105-12-396
复制
发表时间：
2011-10-12
影响因子：
3
通讯作者：
Byrne HM
中科院分区：
文献类型：
--
作者：
Fozard JA;Kirkham GR;Buttery LD;King JR;Jensen OE;Byrne HM
为了研究细胞分化模式如何与潜在的细胞内和细胞间信号通路相关，我们使用基于随机个体的模型来模拟干细胞及其后代作为单层培养时的模式形成。我们假设单个细胞的命运受到其通过扩散或近分泌信号从邻近细胞接收的信号的调节。我们使用两种适合平面多细胞系统的不同空间统计测量来分析模拟模式：配对相关函数（PCF）和样方直方图（QH）。通过扩散信号机制，图案大小（由 PCF 显示）由形态发生素衰减率和决定形态发生素偏向分化程度的灵敏度参数决定；高灵敏度和缓慢的衰减会产生大规模的图案。相比之下，对于近分泌信号传导，高灵敏度可在较短的长度尺度上产生明确的模式。 QH 比 PCF 更容易计算，并且使我们能够区分低灵敏度下的随机微分和较高灵敏度下生成的图案状态。 PCF 和 QH 共同提供了一种表征平面多细胞聚集体中新兴分化模式的有效方法。
To investigate how patterns of cell differentiation are related to underlying intra- and inter-cellular signalling pathways, we use a stochastic individual-based model to simulate pattern formation when stem cells and their progeny are cultured as a monolayer. We assume that the fate of an individual cell is regulated by the signals it receives from neighbouring cells via either diffusive or juxtacrine signalling. We analyse simulated patterns using two different spatial statistical measures that are suited to planar multicellular systems: pair correlation functions (PCFs) and quadrat histograms (QHs). With a diffusive signalling mechanism, pattern size (revealed by PCFs) is determined by both morphogen decay rate and a sensitivity parameter that determines the degree to which morphogen biases differentiation; high sensitivity and slow decay give rise to large-scale patterns. In contrast, with juxtacrine signalling, high sensitivity produces well-defined patterns over shorter lengthscales. QHs are simpler to compute than PCFs and allow us to distinguish between random differentiation at low sensitivities and patterned states generated at higher sensitivities. PCFs and QHs together provide an effective means of characterising emergent patterns of differentiation in planar multicellular aggregates.
登录
查看更多内容
影响因子：
4.3
作者：
Agrawal S;Archer C;Schaffer DV
通讯作者：
Schaffer DV
影响因子：
2.7
作者：
COTTRILL, CP;ARCHER, CW;WOLPERT, L
通讯作者：
WOLPERT, L
影响因子：
46.9
作者：
Blum, Barak;Bar-Nur, Ori;Benvenisty, Nissim
通讯作者：
Benvenisty, Nissim
影响因子：
5.8
作者：
Baddeley, A;Turner, R
通讯作者：
Turner, R
影响因子：
64.8
作者：
Chang, Hannah H.;Hemberg, Martin;Huang, Sui
通讯作者：
Huang, Sui