*2.10. Quantification of TCTP Distribution*

To study the distribution of the protein within cells, we acquired images at high magnification 63 × zoom 5 (bar = 5 μm) of tumour cell lines stained with the indicated primary antibody and Hoechst (blue) at two di fferent spectral range. Excitation/emission wavelengths used were 346/460 nm (first channel) for chromosome detection and 555/580 nm for protein detection (second channel). At least 12 cells were analysed for each sample. Thus, we performed a semi-automatic segmentation of chromosomes and cells by applying ImageJ plugin (Trainable Weka segmentation plugin) to both channels and then excluding all the cells at the image border. From these two binary masks (one for the whole cell and another for the chromosomes), we calculated pixel by pixel the distance from chromosomes of each point of the cells, using Matlab (Mathworks, USA). Collecting all the values of the second channel intensity for each distance from the chromosome, we obtained a line profile, which shows the distribution of the protein as a function of the distance from the chromosome. Signal intensity in the second channel is directly related to the quantity of protein. These results are based on a comparison between the fluorescence intensity for treated and untreated cells. The experimental setup has been kept constant for all acquisitions for cells of the same cellular line. Fluorescently labelled samples have been imaged using a confocal LEICA TCS SP5 microscope (Leica, Heidelberg, Germany) equipped with an argon/krypton laser.

### *2.11. Quantification of Ki-67 Positive Cells*

We quantified Ki-67 positive cells from the analysis of fluorescence images deriving from a sample stained with Hoechst (blue) for Nuclei (first channel) and Ki-67 (red) (second channel). Excitation/emission wavelengths used were 346/460 nm for first channel and 555/580 nm for second channel. The signals from these fluorophores, acquired in two di fferent spectral channels, allowed to contextually count the whole population of cells and distinguish the KI-67 positive cells. The count and segmentation of the whole cell population from the signal in the first channel have been performed using a custom made program of image analysis (Matlab, Mathworks, USA) based on a Gaussian

filter for the background remotion and a watershed algorithm for cell segmentation. The algorithm produced a binary mask of the cells in the original image. By applying this mask to the image of the second channel, we calculated a mean value of signal intensity for each cell, and then distinguished two di fferent groups within the whole population, through the set of a threshold value in intensity. In each group, we analysed: i) 2380 cells for MCF10A-pBabe; ii) 3612 cells for MCF10A-AATCTP; iii) 2483 cells for MCF10A-WTTCTP (right panel).
