Statistical analysis of a high-content screening assay of microtubule polymerization status

The present work describes the analysis of the first high-content, double-immunofluorescence assay of microtubule polymerization status. Two novel features of the work are the extraction of a new class of cell metrics that target fiber-based cell phenotypes (using the Fiberscore algorithm) in a high-content assay, and the development of a non-uniformity correction algorithm that allows the unbiased analysis of dim cells.;The correlation texture metric was identified as showing greatly increased sensitivity, when used on the Tyr-tubulin-specific channel. The identification of this cell-level metric provides a preliminary demonstration that high-content assays have the potential to provide superior performance over conventional whole-well HTS assays. An image-processing related issue termed the 'area-intensity confound', was also identified as a possible major source of variability that limited the performance of the alternative cell-level metrics that were developed. A resolution to this issue is proposed.;Many open questions and avenues of further investigation remain, and the current study represents only a preliminary step in the ongoing analysis of the HCS microtubule polymerization status assay, and the development of pertinent statistical inference methods.;Findings relevant to HCS data analysis in general include: (1) Spatial plate biases are significant in HCS data and manifest differently for different cell-level metrics; (2) Individual plates are separate statistical entities; thus cellular data in HCS cannot in general be pooled before proper normalization procedures are applied; (3) Inter-plate variance is significant in HCS data such that inter-plate replicates are a necessity. However HCS data also appears to be amenable for empirical Bayes methods for improving sensitivity to 'hit' compounds; (4) Cell populations are observed to respond heterogeneously to treatment compounds. Initial tests of an alternative cell population summary statistic (the AUC) thought to be suited for the detection of cell subpopulations, did not indicate significant improvements in sensitivity over conventional measures (such as the population median) however.