One contribution of 16 to a theme issue 'Silent cancer agents: multi-disciplinary modelling of human DNA oncoviruses'. Subject Areas:health and disease and epidemiology, immunology
Understanding dose-dependent survival of irradiated cells is a pivotal goal in radiotherapy and radiobiology. To this end, the clonogenic assay is the standard in vitro method, classifying colonies into either clonogenic or non-clonogenic based on a size threshold at a fixed time. Here we developed a methodological framework for the automated analysis of time course live-cell image data to examine in detail the growth dynamics of large numbers of colonies that occur during such an experiment. We developed a segmentation procedure that exploits the characteristic composition of phase-contrast images to identify individual colonies. Colony tracking allowed us to characterize colony growth dynamics as a function of dose by extracting essential information: (a) colony size distributions across time; (b) fractions of differential growth behavior; and (c) distributions of colony growth rates across all tested doses. We analyzed three data sets from two cell lines (H3122 and RENCA) and made consistent observations in line with already published results: (i) colony growth rates are normally distributed with a large variance; (ii) with increasing dose, the fraction of exponentially growing colonies decreases, whereas the fraction of delayed abortive colonies increases; as a novel finding, we observed that (iii) mean exponential growth rates decrease linearly with increasing dose across the tested range (0–10 Gy). The presented method is a powerful tool to examine live colony growth on a large scale and will help to deepen our understanding of the dynamic, stochastic processes underlying the radiation response in vitro.
Background: Helicobacter pylori (H. pylori) is a bacterial carcinogen and the leading risk factor for noncardia gastric cancer (NCGC). Detecting antibodies against specific H. pylori proteins in peripheral blood can be applied to characterize infection and determine disease associations. Most studies analyzing the association between H. pylori infection and gastric cancer have focused on previously identified antigens, predominantly the virulence factor cytotoxin-associated gene A (CagA). Selecting antigens in an unbiased approach may, however, allow the identification of novel biomarkers. Methods: Using a combination of multiple spotting technique and cell-free, on-chip protein expression, we displayed the H. pylori genome (strain 26695) on high-density microarrays. Immunogenic proteins were identified by serum pool incubations and henceforth analyzed in individual samples. To test its applicability, we used sera from a multicase–control (MCC)-Spain study. Serologic responses between NCGC cases and controls were assessed by conditional logistic regression estimating ORs and 95% confidence intervals. Results: We successfully expressed 93% of the 1,440 H. pylori open reading frames in situ. Of these, 231 (17%) were found to be immunogenic. By comparing 58 NCGC cases with 58 matched controls, we confirmed a higher seroprevalence of CagA among cases (66%) than controls (31%). We further identified a potential novel marker, the Helicobacter outer membrane protein A (HopA). Conclusions: In this study, we provide evidence that our H. pylori whole-proteome microarray offers a platform for unbiased de novo identification of serologic biomarkers. Impact: Given its versatile workflow, antibody responses against other H. pylori strains and possible associations with diverse H. pylori–related outcomes can be systematically analyzed.
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