## 2 Materials and Methods  
### Methods for SMLM Image and Nanoclusters Analysis  
In spatial statistics, Ripley's K function is an analysis method used to characterize spatial point patterns over a given area of interest (i.e., clustered, dispersed or randomly distributed). [PERSON]'s K function is defined as [13; 14],  
\[\mathrm{K_{ij}(r)}\ =\ \lambda_{j}^{-1}\mathrm{E[M(r)]}\]  
where the density of points in the domain is denoted as \(\lambda\), \(\mathrm{M(r)}\) is the number of j points within r distance of a randomly chosen i point. E is the expectation number of events within radius r. For random distribution, a state of randomness where the events are located in a more or less uniform manner is given as, \(\mathrm{E[M(r)]}\ =\ \lambda\pi r^{2}\). To normalize the Ripley's K function [14], [PERSON]'s L function is introduced here as \(\mathrm{L(r)}\ =\ \sqrt{\mathrm{K(r)}/\pi}\). Furthermore, Ripley's H function is yielded, \(\mathrm{H(r)}\ =\ \mathrm{L(r)}\ -\ \mathrm{r}\). In the case of spatial point pattern of random distribution, without any point clustering, \(\mathrm{H(r)}\ =\ 0\). In clustered or dispersed cases, \(\mathrm{H(r)}\ >\ 0\) or \(\mathrm{H(r)}\ <\ 0\), respectively. The average radius of clusters is given by r value corresponding to the maximum of \(\mathrm{H(r)}\), termed as the 'peak analysis method'. In our simulation, we first generated a null image with a certain size, for example, a size of \(4\times 4\ \mathrm{\mu m^{2}}\). On the image, random distribution of spatial point patterns at lower density of \(\mathrm{D_{out}}\) was generated and the positions of the points were defined by the 'rand' function in Matlab (MathWorks Inc. Natick, MA, USA). To generate the clusters, we randomly distributed, for example, 10 points on the image, which were set as the centers of the clusters. Given an average radius of the clusters \(\mathrm{R_{cluster}^{set}}\), 10 circles could be drawn. Then to coincide with the naive state of the protein clusters on the cell plasma membrane, the points (proteins) at higher density of \(\mathrm{D_{in}}\) were distributed within these clusters randomly. It is worth noting that in our simulation, the clusters of each modeled SMLM image would have neither uniform locations, nor uniform distribution of proteins inside/outside clusters. Moreover, the random distribution of proteins serve as a 'buffer zone' to obviate the edge effects [17]. Finally, by applying the Gaussian convolution of these points (see in Supplementary Information, Supplementary Text), a simulated SMLM was obtained. After constructing 2D spatial point patterns of protein distribution, Ripley's H function was calculated as described in [18], (see in Supplementary Information, Supplementary Text). \(\mathrm{H(r)}\) was generated at each point to measure the degree of clustering. If \(\mathrm{H(r)}\ >\ 0\), the clustering was identified, the mean radius of clusters was given by the r value corresponding to peak value of \(\mathrm{H(r)}\), then the cluster size, number of clusters, and other parameters could be readily extracted and calculated. For comparison, we simply applied the well-known image processing tool ImageJ (National Institutesof Health, USA) to process the modeled SMLM image [19]. The cluster binary map was generated with a threshold-based approach using the intensity plot plugin of ImageJ [20]. Material for EGFR (Epidermal Growth Factor Receptor) Measurements at Fibroblast-Like Cell Lines Derived from Monkey Kidney Tissue--COS7 Cell Surface  
COS7 cells were cultured on ethanol and phosphate-buffered saline (PBS), (Sigma-Aldrich, St Louis, MO, 63103, USA) cleaned round glass coverslips in Dulbecco's modified eagle medium (DMEM) (Gibco, Invitrogen, Carlsbad, CA, USA) supplemented with 10% fetal bovine serum (FBS) at 37 \({}^{\circ}\)C in 5% CO\({}_{2}\) for 3-5 days. The day of the experiment, the cells were washed twice with PBS and fixed using 4% paraformaldehyde (PFA) at room temperature for 30 min. Before imaging, fixed cells were washed again with PBS.