Fractal dimension to characterize interactions between blood and lymphatic endothelial cells.

IF 2 4区 生物学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY Physical biology Pub Date : 2023-06-12 DOI:10.1088/1478-3975/acd898
Donghyun Paul Jeong, Daniel Montes, Hsueh-Chia Chang, Donny Hanjaya-Putra
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Abstract

Spatial patterning of different cell types is crucial for tissue engineering and is characterized by the formation of sharp boundary between segregated groups of cells of different lineages. The cell-cell boundary layers, depending on the relative adhesion forces, can result in kinks in the border, similar to fingering patterns between two viscous partially miscible fluids which can be characterized by its fractal dimension. This suggests that mathematical models used to analyze the fingering patterns can be applied to cell migration data as a metric for intercellular adhesion forces. In this study, we develop a novel computational analysis method to characterize the interactions between blood endothelial cells (BECs) and lymphatic endothelial cells (LECs), which form segregated vasculature by recognizing each other through podoplanin. We observed indiscriminate mixing with LEC-LEC and BEC-BEC pairs and a sharp boundary between LEC-BEC pair, and fingering-like patterns with pseudo-LEC-BEC pairs. We found that the box counting method yields fractal dimension between 1 for sharp boundaries and 1.3 for indiscriminate mixing, and intermediate values for fingering-like boundaries. We further verify that these results are due to differential affinity by performing random walk simulations with differential attraction to nearby cells and generate similar migration pattern, confirming that higher differential attraction between different cell types result in lower fractal dimensions. We estimate the characteristic velocity and interfacial tension for our simulated and experimental data to show that the fractal dimension negatively correlates with capillary number (Ca), further indicating that the mathematical models used to study viscous fingering pattern can be used to characterize cell-cell mixing. Taken together, these results indicate that the fractal analysis of segregation boundaries can be used as a simple metric to estimate relative cell-cell adhesion forces between different cell types.

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用分形维度描述血液和淋巴内皮细胞之间的相互作用。
不同类型细胞的空间模式化对组织工程至关重要,其特点是不同系的细胞群之间形成鲜明的边界。细胞-细胞边界层取决于相对的粘附力,会导致边界出现扭结,类似于两种粘性部分混溶流体之间的指状模式,可以用分形维度来表征。这表明,用于分析指状模式的数学模型可应用于细胞迁移数据,作为细胞间粘附力的度量指标。在本研究中,我们开发了一种新的计算分析方法来描述血液内皮细胞(BECs)和淋巴内皮细胞(LECs)之间的相互作用。我们观察到 LEC-LEC 和 BEC-BEC 成对的无差别混合、LEC-BEC 成对的锐边界以及假 LEC-BEC 成对的指状模式。我们发现盒计数法得出的分形维度在尖锐边界为 1 和无差别混合为 1.3 之间,指状边界为中间值。我们进一步验证了这些结果是由不同的亲和力造成的,我们进行了随机漫步模拟,对附近的细胞进行了不同的吸引,并产生了类似的迁移模式,证实了不同类型细胞之间较高的不同吸引力会导致较低的分形维度。我们估算了模拟数据和实验数据的特征速度和界面张力,结果表明分形维度与毛细管数(Ca)呈负相关,这进一步表明用于研究粘性指状模式的数学模型可用于表征细胞-细胞混合。综上所述,这些结果表明,隔离边界的分形分析可作为一种简单的指标来估算不同类型细胞之间的相对细胞粘附力。
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来源期刊
Physical biology
Physical biology 生物-生物物理
CiteScore
4.20
自引率
0.00%
发文量
50
审稿时长
3 months
期刊介绍: Physical Biology publishes articles in the broad interdisciplinary field bridging biology with the physical sciences and engineering. This journal focuses on research in which quantitative approaches – experimental, theoretical and modeling – lead to new insights into biological systems at all scales of space and time, and all levels of organizational complexity. Physical Biology accepts contributions from a wide range of biological sub-fields, including topics such as: molecular biophysics, including single molecule studies, protein-protein and protein-DNA interactions subcellular structures, organelle dynamics, membranes, protein assemblies, chromosome structure intracellular processes, e.g. cytoskeleton dynamics, cellular transport, cell division systems biology, e.g. signaling, gene regulation and metabolic networks cells and their microenvironment, e.g. cell mechanics and motility, chemotaxis, extracellular matrix, biofilms cell-material interactions, e.g. biointerfaces, electrical stimulation and sensing, endocytosis cell-cell interactions, cell aggregates, organoids, tissues and organs developmental dynamics, including pattern formation and morphogenesis physical and evolutionary aspects of disease, e.g. cancer progression, amyloid formation neuronal systems, including information processing by networks, memory and learning population dynamics, ecology, and evolution collective action and emergence of collective phenomena.
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