Non-Random Distribution of Interleukin Receptors on the Cell Surface

Spot the dots: Spatial organization of cell surface proteins plays a key role in the process of transmembrane signalling. Receptor clustering and changes in their cell surface distribution are often determining factors in the final outcome of ligand–receptor interactions. Herein, the authors show the applicability of Ripley's K(t) with additional algorithms a tool for analyzing the cell surface receptor patterns (see picture). Spatial organization of cell surface proteins plays a key role in the process of transmembrane signalling. Receptor clustering and changes in their cell surface distribution are often determining factors in the final outcome of ligand–receptor interactions. There are several techniques for assessing the distribution of protein molecules. Fluorescence resonance energy transfer (FRET) is an excellent tool for determining distance relationships of cell surface molecules. However, it does not provide information on the distribution of molecular clusters. Different kinds of microscopies fill this gap. The evaluation of the images provided by the listed techniques is often questionable. Herein we show the applicability of Ripley’s K(t) function as a tool for analyzing the cell surface receptor patterns (Y. Nakamura, et al., Nature 1994, 369, 330–333). We have implemented an effective image processing algorithm for fast localization of gold labels on biological samples. We investigated spatial organization of Interleukin‐2Rα and ‐15Rα (IL‐2Rα and IL‐15Rα) on a human CD4+leukaemia T‐cell line, Kit225 FT7.10 by using transmission electron microscopy (TEM). TEM analysis showed co‐clustering of the two types of α‐chains even on the few‐hundred‐nanometer scale. The analysis of our data may contribute to our understanding the action of the IL‐2/IL‐15 receptor system in T‐cell function. Spot the dots: Spatial organization of cell surface proteins plays a key role in the process of transmembrane signalling. Receptor clustering and changes in their cell surface distribution are often determining factors in the final outcome of ligand–receptor interactions. Herein, the authors show the applicability of Ripley's K(t) with additional algorithms a tool for analyzing the cell surface receptor patterns (see picture).