An important tool in cell biology is the combination of immunogold labelling and transmission electron microscopy (TEM) by which target molecules (e.g. antigens) are bound specifically to affinity markers (primary antibodies) and then detected and localised with visualisation probes (e.g. colloidal gold particles bound to protein A). Gold particles are electron-dense, punctate and available in different sizes whilst TEM provides high-resolution images of particles and cell compartments. By virtue of these properties, the combination can be used also to quantify one or more defined targets in cell compartments. During the past decade, new ways of quantifying gold labelling within cells have been devised. Their efficiency and validity rely on sound principles of specimen sampling, event counting and inferential statistics. These include random selection of items at each sampling stage (e.g. specimen blocks, thin sections, microscopical fields), stereological analysis of cell ultrastructure, unbiased particle counting and statistical evaluation of a suitable null hypothesis (no difference in the intensity or pattern of labelling between compartments or groups of cells). The following approaches are possible: (i) A target molecule can be tested for preferential labelling by mapping the localisation of gold particles across a set of compartments. (ii) Data from wild-type and knockdown/knockout control cells can be used to correct raw gold particle counts, estimate specific labelling densities and then test for preferential labeling. (iii) The same antigen can be mapped in two or more groups of cells to test whether there are experimental shifts in compartment labelling patterns. (iv) A variant of this approach uses more than one size of gold particle to test whether or not different antigens colocalise in one or more compartments. (v) In studies involving antigen translocation, absolute numbers of gold particles can be mapped over compartments at specific positions within polarised, oriented or dividing cells. Here, the current state of the art is reviewed and approaches are illustrated with virtual datasets.