X-ray Fluorescence Microscopy Reveals the Role of Selenium in Spermatogenesis

Selenium (Se) is a trace element with important roles in human health. Several selenoproteins have essential functions in development. However, the cellular and tissue distribution of Se remains largely unknown because of the lack of analytical techniques that image this element with sufficient sensitivity and resolution. Herein, we report that X-ray fluorescence microscopy (XFM) can be used to visualize and quantify the tissue, cellular and subcellular topography of Se. We applied this technique to characterize the role of Se in spermatogenesis and identified a dramatic Se enrichment specifically in late spermatids, a pattern that was not seen in any other elemental maps. This enrichment was due to elevated levels of the mitochondrial form of glutathione peroxidase 4 and was fully dependent on the supplies of Se by Selenoprotein P. High-resolution scans revealed that Se concentrated near the lumen side of elongating spermatids, where structural components of sperm are formed. During spermatogenesis, maximal Se associated with decreased phosphorus, whereas Zn did not change. In sperm, Se was primarily in the midpiece and co-localized with Cu and Fe. XFM allowed quantification of Se in the midpiece (0.8 fg) and head (0.14 fg) of individual sperm cells, revealing the ability of sperm cells to handle the amounts of this element well above its toxic levels. Overall, the use of XFM allowed visualization of tissue and cellular Se and provided important insights in the role of this and other trace elements in spermatogenesis

Genome alignment with graph data structures: a comparison

Background Recent advances in rapid, low-cost sequencing have opened up the opportunity to study complete genome sequences. The computational approach of multiple genome alignment allows investigation of evolutionarily related genomes in an integrated fashion, providing a basis for downstream analyses such as rearrangement studies and phylogenetic inference. Graphs have proven to be a powerful tool for coping with the complexity of genome-scale sequence alignments. The potential of graphs to intuitively represent all aspects of genome alignments led to the development of graph-based approaches for genome alignment. These approaches construct a graph from a set of local alignments, and derive a genome alignment through identification and removal of graph substructures that indicate errors in the alignment. Results We compare the structures of commonly used graphs in terms of their abilities to represent alignment information. We describe how the graphs can be transformed into each other, and identify and classify graph substructures common to one or more graphs. Based on previous approaches, we compile a list of modifications that remove these substructures. Conclusion We show that crucial pieces of alignment information, associated with inversions and duplications, are not visible in the structure of all graphs. If we neglect vertex or edge labels, the graphs differ in their information content. Still, many ideas are shared among all graph-based approaches. Based on these findings, we outline a conceptual framework for graph-based genome alignment that can assist in the development of future genome alignment tools