Patterning of cell-to-cell variability through cross talk between cell adhesion and endocytosis

一般来说，实验读数的可变性被认为是一种负担，应尽可能减少。然而，可以利用来自细胞群体的单个细胞的读数的可变性来揭示因果网络和新的分子机制。本研究首次通过分析细胞间差异性来研究整合素受体丰度、排列和信号传导以及细胞外基质组成作为细胞-基质粘附的要素。这种分析可能会确定这种基本细胞能力的新调控机制，影响胚胎发育，组织维护以及疾病，如癌症和自身免疫。我将研究这种变异性的假定决定因素，特别是细胞微环境的参数，例如局部细胞密度或细胞在群体中的相对定位，这将增加我们对细胞-基质粘附的基本水平的理解。此外，本研究的一个主要目标是创建第一次的细胞-基质粘附的决定因素和它的串扰与内吞作用的基础上确定的模式的细胞-细胞的变异性的综合视图。这将揭示使细胞的粘附能力和内吞活性适应其微环境的关键机制，这对于理解单个异常细胞如何促进癌症和自身免疫的发展或进展至关重要。目前，适用于建模的定量单细胞数据有限。在这项研究中，我将应用高通量荧光显微镜和高内容的图像分析，以测量在大量的单细胞的整合素介导的粘附和内吞作用的串扰数百个定量特征。结合参数化每个细胞的微环境，这将为数据驱动的建模提供广泛的数据集，以推断因果关系并识别反馈机制。将在RNAi或抑制剂处理后进行基于单细胞图像的分析和经典群体平均分析，以验证确定的相关性。这些方法已被证明有助于更深入地了解细胞过程和开发治疗疾病的新策略。沿着新的生物学数据的生成，本项目将同时推进单细胞数据的实验采集和计算分析方法。一种新的方法，实验复用将被开发和应用于测量的数量，活性和定位的表面受体，信号分子，外壳蛋白和标记物的内吞隔室同时在同一个样品中。这种方法将大大改善未来对任何细胞过程的研究。

In general, variability in experimental readouts is regarded as a burden to be diminished where possi-ble. However, variability in readouts from individual cells of a cell population can be harnessed to reveal causal networks and novel molecular mechanisms. This study is the first to investigate integrin receptor abundance, arrangement and signalling as well as extracellular matrix composition as ele-ments of cell-matrix adhesion by analysis of cell-to-cell variability. Such analysis may identify new regulatory mechanisms for this essential cellular capability, influencing embryogenesis, tissue mainte-nance as well as diseases, such as cancer and autoimmunity. I will examine putative determinants of this variability, particularly parameters of the cell`s microenvironment, such as the local cell density or the relative localisation of a cell within a population, which will increase our understanding of cell-matrix adhesion at the fundamental level. Further, a major goal of this study is to create for the first time an integrated view of determinants of cell-matrix adhesion and its cross talk with endocytosis based on the identified patterns of cell-to-cell variability. This will reveal crucial mechanisms to adapt the adhesive capacity and endocytic activity of a cell to its microenvironment, which is essential to understand how single anomalous cells contribute to the development or progression of cancer and autoimmunity. Currently, there is limited availability of quantitative single-cell data suitable for modelling. In this study I will apply high-throughput fluorescence microscopy and high-content image analysis to measure in large numbers of single cells hundreds of quantitative features of integrin-mediated adhesion and its crosstalk with endocytosis. In combination with parameterising each cell`s microenvironment this will provide an extensive dataset for data-driven modelling to infer causalities and identify feedback mechanisms. Single-cell image-based analyses and classical population averaging analyses will be performed following RNAi or inhibitor treatments to validate determined correlations. Such approaches have proven useful for providing deeper insights into cellular processes and developing new strategies for the treatment of diseases. Along with generating novel biological data, this project will simultaneously advance methods for experimental acquisition and computational analysis of single-cell data. A novel method for experimental multiplexing will be developed and applied to measure the amount, the activity and the localisation of surface receptors, signalling molecules, coat proteins and markers of endocytic compartments simultaneously in the same sample. This method will greatly improve future studies into any cellular process.