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Abstract：
Solid tumors consist of tumor cells associated with stromal and immune cells, secreted factors and extracellular matrix (ECM), which together constitute the tumor microenvironment. Among stromal cells, activated fibroblasts, known as cancer-associated fibroblasts (CAFs) are of particular interest. CAFs secrete a plethora of ECM components including collagen and modulate the architecture of the ECM, thereby influencing cancer cell migration. The characterization of the collagen fibre network and its space and time-dependent microstructural modifications is key to investigating the interactions between cells and the ECM. Developing image analysis tools for that purpose is still a challenge because the structural complexity of the collagen network calls for specific statistical descriptors. Moreover, the low signal-to-noise ratio of imaging techniques available for time-resolved studies rules out standard methods based on image segmentation.
In this work, we develop a novel approach based on the stochastic modelling of the gel structure and on grey-tone image analysis. The method is then used to study the remodelling of a collagen matrix by migrating breast cancer-derived CAFs in a three-dimensional spheroid model of cellular invasion imaged by time-lapse confocal microscopy.
The structure of the collagen at the scale of a few microns consists in regions with high fibre density separated by depleted regions, which can be thought of as aggregates and pores. The approach developped captures this two-scale structure with a clipped Gaussian field model to describe the aggregates-and-pores large-scale structure, and a homogeneous Boolean model to describe the small-scale fibre network within the aggregates. The model parameters are identified by fitting the grey-tone histograms and correlation functions of the images. The method applies to unprocessed grey-tone images, and it can therefore be used with low magnification, noisy time-lapse reflectance images. When applied to the CAF spheroid time-resolved images, the method reveals different matrix densification mechanisms for the matrix in direct contact or far from the cells.
We developed a novel and multidisciplinary image analysis approach to investigate the remodelling of fibrillar collagen in a 3D spheroid model of cellular invasion. The specificity of the method is that it applies to the unprocessed grey-tone images, and it can therefore be used with noisy time-lapse reflectance images of non-fluorescent collagen. When applied to the CAF spheroid time-resolved images, the method reveals different matrix densification mechanisms for the matrix in direct contact or far from the cells.
In this work, we develop a novel approach based on the stochastic modelling of the gel structure and on grey-tone image analysis. The method is then used to study the remodelling of a collagen matrix by migrating breast cancer-derived CAFs in a three-dimensional spheroid model of cellular invasion imaged by time-lapse confocal microscopy.
The structure of the collagen at the scale of a few microns consists in regions with high fibre density separated by depleted regions, which can be thought of as aggregates and pores. The approach developped captures this two-scale structure with a clipped Gaussian field model to describe the aggregates-and-pores large-scale structure, and a homogeneous Boolean model to describe the small-scale fibre network within the aggregates. The model parameters are identified by fitting the grey-tone histograms and correlation functions of the images. The method applies to unprocessed grey-tone images, and it can therefore be used with low magnification, noisy time-lapse reflectance images. When applied to the CAF spheroid time-resolved images, the method reveals different matrix densification mechanisms for the matrix in direct contact or far from the cells.
We developed a novel and multidisciplinary image analysis approach to investigate the remodelling of fibrillar collagen in a 3D spheroid model of cellular invasion. The specificity of the method is that it applies to the unprocessed grey-tone images, and it can therefore be used with noisy time-lapse reflectance images of non-fluorescent collagen. When applied to the CAF spheroid time-resolved images, the method reveals different matrix densification mechanisms for the matrix in direct contact or far from the cells.
摘要：
未经证实：实体瘤由与基质和免疫细胞相关的肿瘤细胞组成，分泌因子和细胞外基质（ECM），它们共同构成了肿瘤微环境。在基质细胞中，活化成纤维细胞，被称为癌症相关成纤维细胞(CAF)是特别感兴趣的。CAFs分泌过多的ECM成分，包括胶原蛋白，并调节ECM的结构，从而影响癌细胞迁移。胶原纤维网络及其空间和时间依赖性微观结构修饰的表征是研究细胞与ECM之间相互作用的关键。为此目的开发图像分析工具仍然是一个挑战，因为胶原蛋白网络的结构复杂性需要特定的统计描述符。此外，可用于时间分辨研究的低信噪比成像技术排除了基于图像分割的标准方法。
UNASSIGNED：在这项工作中，我们开发了一种基于凝胶结构的随机建模和灰度图像分析的新方法。然后，该方法用于通过在延时共聚焦显微镜成像的细胞侵袭的三维球体模型中迁移乳腺癌衍生的CAF来研究胶原蛋白基质的重塑。
UNASSIGNED：胶原蛋白的结构在几微米的尺度上由被耗尽区域隔开的高纤维密度区域组成，可以被认为是聚集体和毛孔。所开发的方法利用限幅高斯场模型捕获了这种双尺度结构，以描述聚集体和孔隙的大尺度结构，和一个齐次布尔模型来描述骨料中的小规模光纤网络。通过拟合图像的灰度直方图和相关函数来识别模型参数。该方法适用于未处理的灰度图像，因此它可以以低放大率使用，嘈杂的延时反射图像。当应用于CAF球体时间分辨图像时，该方法揭示了基质直接接触或远离细胞的不同基质致密化机制。
UNASSIGNED：我们开发了一种新颖的多学科图像分析方法，以研究纤维胶原在细胞侵袭的3D球体模型中的重塑。该方法的特殊性在于它适用于未处理的灰度图像，因此，它可以与非荧光胶原蛋白的嘈杂延时反射图像一起使用。当应用于CAF球体时间分辨图像时，该方法揭示了基质直接接触或远离细胞的不同基质致密化机制。
UNASSIGNED：在这项工作中，
UNASSIGNED：胶原蛋白的结构在几微米的尺度上由被耗尽区域隔开的高纤维密度区域组成，
UNASSIGNED：我们开发了一种新颖的多学科图像分析方法，以研究纤维胶原在细胞侵袭的3D球体模型中的重塑。
