Pommerencke Thora, Steinberg Thorsten, Dickhaus Hartmut, Tomakidi Pascal, Grabe Niels
Hamamatsu Tissue Imaging and Analysis TIGA Center, BIOQUANT, University Heidelberg, Heidelberg, Germany.
BMC Bioinformatics. 2008 Nov 6;9:473. doi: 10.1186/1471-2105-9-473.
The epidermal physiology results from a complex regulated homeostasis of keratinocyte proliferation, differentiation and death and is tightly regulated by a specific protein expression during cellular maturation. Cellular in silico models are considered a promising and inevitable tool for the understanding of this complex system. Hence, we need to incorporate the information of the differentiation dependent protein expression in cell based systems biological models of tissue homeostasis. Such methods require measuring tissue differentiation quantitatively while correlating it with biomarker expression intensities.
Differentiation of a keratinocyte is characterized by its continuously changing morphology concomitant with its movement from the basal layer to the surface, leading to a decreased average nuclei density throughout the tissue. Based thereon, we designed and evaluated three different mathematical measures (nuclei based, distance based, and joint approach) for quantifying differentiation in epidermal keratinocytes. We integrated them with an immunofluorescent staining and image analysis method for tissue sections, automatically quantifying epidermal differentiation and measuring the corresponding expression of biomarkers. When studying five well-known differentiation related biomarkers in an epidermal neck sample only the resulting biomarker profiles incorporating the relative distance information of cells to the tissue borders (distance based and joint approach) provided a high-resolution view on the whole process of keratinocyte differentiation. By contrast, the inverse nuclei density approach led to an increased resolution at early but heavily decreased resolution at late differentiation. This effect results from the heavy non-linear decay of DAPI intensity per area, probably caused by cytoplasmic growth and chromatin decondensation. In the joint approach this effect could be compensated again by incorporating distance information.
We suppose that key mechanisms regulating tissue homeostasis probably depend more on distance information rather than on nuclei reorganization. Concluding, the distance approach appears well suited for comprehensively observing keratinocyte differentiation.
表皮生理学源于角质形成细胞增殖、分化和死亡的复杂调节稳态，并且在细胞成熟过程中受到特定蛋白质表达的严格调控。细胞计算机模拟模型被认为是理解这一复杂系统的一种有前途且不可避免的工具。因此，我们需要在基于细胞的组织稳态系统生物学模型中纳入依赖于分化的蛋白质表达信息。此类方法需要在将组织分化与生物标志物表达强度相关联的同时对其进行定量测量。
角质形成细胞的分化特征在于其形态不断变化，同时伴随着从基底层向表面的移动，导致整个组织中平均细胞核密度降低。基于此，我们设计并评估了三种不同的数学方法（基于细胞核、基于距离和联合方法）来量化表皮角质形成细胞的分化。我们将它们与用于组织切片的免疫荧光染色和图像分析方法相结合，自动量化表皮分化并测量生物标志物的相应表达。在研究表皮颈部样本中的五种著名的与分化相关的生物标志物时，只有结合了细胞到组织边界的相对距离信息的生物标志物图谱（基于距离和联合方法）提供了角质形成细胞分化全过程的高分辨率视图。相比之下，细胞核密度反比法在分化早期导致分辨率提高，但在分化后期分辨率大幅下降。这种效应源于每单位面积DAPI强度的严重非线性衰减，可能是由细胞质生长和染色质解聚引起的。在联合方法中，可以通过纳入距离信息再次补偿这种效应。
我们推测调节组织稳态的关键机制可能更多地依赖于距离信息而非细胞核重组。总之，距离方法似乎非常适合全面观察角质形成细胞的分化。
