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Fertility of a male is sometimes difficult to assess and results obtained using regular sperm analysis methods are often inconclusive. Flow cytometry was proven to generate crucial information, that allows specialists to conclude upon the reproductive capacity of an individual. Together with computer assisted sperm analysis CASA systems, flow cytometers allow an in-depth analysis of fresh, chilled or frozen/thawed semen, which is currently essential for research purposes, but also for diagnosis of various male-related fertility disorders, both in veterinary and human medicine. Several parameters can be assessed using this method, such as sperm viability, acrosome integrity, mitochondrial activity, concentration and total sperm number, DNA fragmentation, capacitation, oxidative stress, etc. This paper provides a rapid reference to specialists involved in semen analysis by flow cytometry, regarding semen sample preparation, instrument setup, and evaluation of the most important sperm parameters (viability, acrosome reaction and mitochondrial activity).

Background The importance of peptide microarrays as a tool for serological diagnostics has strongly increased over the last decade. However, interpretation of the binding signals is still hampered by our limited understanding of the technology. This is in particular true for arrays probed with antibody mixtures of unknown complexity, such as sera. To gain insight into how signals depend on peptide amino acid sequences, we probed random-sequence peptide microarrays with sera of healthy and infected mice. We analyzed the resulting antibody binding profiles with regression methods and formulated a minimal model to explain our findings. Results Multivariate regression analysis relating peptide sequence to measured signals led to the definition of amino acid-associated weights. Although these weights do not contain information on amino acid position, they predict up to 40-50% of the binding profiles' variation. Mathematical modeling shows that this position-independent ansatz is only adequate for highly diverse random antibody mixtures which are not dominated by a few antibodies. Experimental results suggest that sera from healthy individuals correspond to that case, in contrast to sera of infected ones. Conclusions Our results indicate that position-independent amino acid-associated weights predict linear epitope binding of antibody mixtures only if the mixture is random, highly diverse, and contains no dominant antibodies. The discovered ensemble property is an important step towards an understanding of peptide-array serum-antibody binding profiles. It has implications for both serological diagnostics and B cell epitope mapping.