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

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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.