Quick cytogenetic screening of breeding bulls using flow cytometric sperm DNA histogram analysis

The aim of the present study was to test the FXCycle PI/RNase kit for routine DNA analyses in order to detect breeding bulls and/or insemination doses carrying cytogenetic aberrations. In a series of experiments we first established basic DNA histogram parameters of cytogenetically healthy breeding bulls by measuring the intraspecific genome size variation of three animals, then we compared the histogram profiles of bulls carrying cytogenetic defects to the baseline values. With the exception of one case the test was able to identify bulls with cytogenetic defects. Therefore, we conclude that the assay could be incorporated into the laboratory routine where flow cytometry is applied for semen quality control.

Correlation of Grade of Urothelial Cell Carcinomas and DNA Histogram Features Assessed by Flow Cytometry and Automated Image Cytometry

Objective: To analyse how DNA ploidy and S-phase fraction (SPF) by flow cytometry (FCM) and an optimised fully automatic DNA image cytometer (ICM) correlate with grade in TaT1 urothelial cell carcinomas (UC) of the urinary bladder.Materials and methods: Two-hundred-and twenty-eight consensus cases were analysed. Single cell suspensions were stained (DAPI for FCM, Feulgen for ICM). There was enough material for both FCMand ICMin 202 of these cases. FCMand optimised ICM measurements were performed on the 202 UCs. To discriminate between different grades, single- and multivariate analyses was performed on DNA histogram features obtained with the MultiCycle program (using DNA index (DI) and SPF). Results: Overall measurement time of the adapted ICM method was 10.7 minutes per case (range 5.9–29.8 min.) and required little additional interactive object rejection (average 152 objects (84–298) on 3000 objects per case measured, which took 9.9 minutes on average, range 8.3–15.5 minutes). The ICM histograms looked much “cleaner” with less noise than the FCM graphs. The coefficient of variation (CV) of the diploid peak for ICM(5.4%) was significantly lower than for FCM(5.9%) (p< 0.0001). ICM features were more strongly correlated to grade than FCMfeatures. In multivariate analysis, the best discriminating set of features was DNA ploidy and SPF (both by ICM).Conclusions: The adapted fully automated DNA ICM works very well for UCs. Low CV DNA ICM histograms are obtained in a time comparable to FCM. The DNA ICM results have stronger discriminative power than DNA FCM for grade in TaT1 UCs. Colour figures can be viewed onhttp://www.esacp.org/acp/2003/25-3/bol.htm.

DNA Histogram Interpretation Based on Statistical Approaches

Image cytometric DNA measurements provide data which are most often interpreted as equivalent to the chromosomal ploidy although the chromosomal and the DNA ploidy are not identical. The common link between them is the cell cycle. Therefore, if destined for DNA ploidy interpretations, the DNA cytometry should be performed on a population‐oriented stochastic basis. Using stochastic sampling the data can be interpreted by applying the rules of stochastic processes. A set of statistical methods is given that enables a DNA histogram to be interpreted objectively and without human interaction. These statistics analyse the precision and accuracy of the entire measurement process. They give in error probabilities for accepting a measurement as reliable, for recognition of stemlines, stemline aneuploidy, and for evaluating so‐called rare events. Nearly 300 image cytometric DNA measurements from breast cancers and rat liver imprints examples have been selected to demonstrate the efficiency of the statistics in each step of interpreting DNA histograms.