Preservation of biomarkers immunoreactivity on cytospins protected with polyethylene glycol.

INTRODUCTION: The aim of this retrospective study was to evaluate the preservation of biomarkers immunoreactivity on cytospins protected with polyethylene glycol (PEG). METHODS: In two independent cytopathology laboratories, immunocytochemical reactions were retrospectively evaluated on methanol-fixed and PEG-protected cytospins stored at room temperature (RT) for different time periods and compared with immunocytochemical reactions on corresponding baseline methanol-fixed cytospins. Semi-quantitatively assessed immunoreactivity, using scores from 0 to 3, was considered reduced if two sequential scores were lowered by at least one point. RESULTS: Immunocytochemical reactions for 40 biomarkers with membrane (10), cytoplasmic (22) and nuclear (8) localisation were performed on 921 slides prepared from 183 cytological samples. For the majority of biomarkers (29/37, 78%), immunoreactivity on PEG-protected cytospins stored at RT remained unchanged in the first 12 months. Immunoreactivity for GFAP, p40 and hepatocyte antigen was monitored and remained unchanged for 1, 8 and 7 months, respectively. Partial or complete loss of immunoreactivity on PEG-protected cytospins stored for less than 12 months was found on a single sample out of the total evaluated for CD3 (1/7), CD30 (1/4), CD45 (1/10), CK5/6 (1/7), MelanA (1/7) and vimentin (1/7), while more frequent changes of immunoreactivity were found for Ki67 (4/7) and p63 (2/7). CONCLUSION: Immunoreactivity on cytospins protected with PEG and stored at RT is well-preserved for at least 12 months for the majority of biomarkers.

Bayesian analysis of data from segmented super-resolution images for quantifying protein clustering.

Super-resolution imaging techniques have largely improved our capabilities to visualize nanometric structures in biological systems. Their application further permits the quantitation relevant parameters to determine the molecular organization and stoichiometry in cells. However, the inherently stochastic nature of fluorescence emission and labeling strategies imposes the use of dedicated methods to accurately estimate these parameters. Here, we describe a Bayesian approach to precisely quantitate the relative abundance of molecular aggregates of different stoichiometry from segmented images. The distribution of proxies for the number of molecules in a cluster, such as the number of localizations or the fluorescence intensity, is fitted via a nested sampling algorithm to compare mixture models of increasing complexity and thus determine the optimum number of mixture components and their weights. We test the performance of the algorithm on in silico data as a function of the number of data points, threshold, and distribution shape. We compare these results to those obtained with other statistical methods, showing the improved performance of our approach. Our method provides a robust tool for model selection in fitting data extracted from fluorescence imaging, thus improving the precision of parameter determination. Importantly, the largest benefit of this method occurs for small-statistics or incomplete datasets, enabling an accurate analysis at the single image level. We further present the results of its application to experimental data obtained from the super-resolution imaging of dynein in HeLa cells, confirming the presence of a mixed population of cytoplasmic single motors and higher-order structures.