Relative labelling index: a novel stereological approach to test for non‐random immunogold labelling of organelles and membranes on transmission electron microscopy thin sections

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Abstract
Summary: Simple and efficient protocols for quantifying immunogold labelling of antigens localized in different cellular compartments (organelles or membranes) and statistically evaluating resulting labelling distributions are presented. Two key questions are addressed: (a) is compartmental labelling within an experimental group (e.g. control or treated) consistent with a random distribution? and (b) do labelling patterns vary between groups (e.g. control vs. treated)? Protocols rely on random sampling of cells and compartments. Numbers of gold particles lying on specified organelle compartments provide an observed frequency distribution. By superimposing test‐point lattices on cell profiles, design‐based stereology is used to determine numbers of points lying on those same compartments. Random points hit compartments with probabilities determined by their relative sizes and so provide a convenient internal standard, namely, the expected distribution if labelling is purely random. By applying test‐line lattices, and counting sites at which these intersect membrane traces, analogous procedures provide observed and expected labelling distributions for different classes of membranes. Dividing observed golds by expected golds provides a relative labelling index (RLI) for each compartment and, for random labelling, the predicted RLI = 1. In contrast to labelling densities of organelles (golds µm−2) or membranes (golds µm−1), RLI values are estimated without needing to know lattice constants (area per point or length per intersection) or specimen magnification. Gold distributions within a group are compared by chi‐squared analysis to test if the observed distribution differs significantly from random and, if it is non‐random, to identify compartments which are preferentially labelled (RLI > 1). Contingency table analysis allows labelling distributions in different groups of cells to be compared. Protocols are described and illustrated using worked specimen examples and real data.