Detecting hybridization in African schistosome species: does egg morphology complement molecular species identification?

Hybrid parasites may have an increased transmission potential and higher virulence compared to their parental species. Consequently, hybrid detection is critical for disease control. Previous crossing experiments showed that hybrid schistosome eggs have distinct morphotypes. We therefore compared the performance of egg morphology with molecular markers with regard to detecting hybridization in schistosomes. We studied the morphology of 303 terminal-spined eggs, originating from 19 individuals inhabiting a hybrid zone with natural crosses between the human parasite Schistosoma haematobium and the livestock parasite Schistosoma bovis in Senegal. The egg sizes showed a high variability and ranged between 92·4 and 176·4 µm in length and between 35·7 and 93·0 µm in width. No distinct morphotypes were found and all eggs resembled, to varying extent, the typical S. haematobium egg type. However, molecular analyses on the same eggs clearly showed the presence of two distinct partial mitochondrial cox1 profiles, namely S. bovis and S. haematobium, and only a single nuclear ITS rDNA profile (S. haematobium). Therefore, in these particular crosses, egg morphology appears not a good indicator of hybrid ancestry. We conclude by discussing strengths and limitations of molecular methods to detect hybrids in the context of high-throughput screening of field samples.

Multi-scale dynamic imaging reveals that cooperative motility behaviors promote efficient predation in bacteria.

Many species, such as fish schools or bird flocks, rely on collective motion to forage, prey, or escape predators. Likewise, Myxococcus xanthus forages and moves collectively to prey and feed on other bacterial species. These activities require two distinct motility machines enabling adventurous (A) and social (S) gliding, however when and how these mechanisms are used has remained elusive. Here, we address this long-standing question by applying multiscale semantic cell tracking during predation. We show that: (1) foragers and swarms can comprise A- and S-motile cells, with single cells exchanging frequently between these groups; (2) A-motility is critical to ensure the directional movement of both foragers and swarms; (3) the combined action of A- and S-motile cells within swarms leads to increased predation efficiencies. These results challenge the notion that A- and S-motilities are exclusive to foragers and swarms, and show that these machines act synergistically to enhance predation efficiency.

RNA imaging in bacteria.

The spatiotemporal regulation of gene expression plays an essential role in many biological processes. Recently, several imaging-based RNA labeling and detection methods, both in fixed and live cells, were developed and now enable the study of transcript abundance, localization and dynamics. Here, we review the main single-cell techniques for RNA visualization with fluorescence microscopy and describe their applications in bacteria.

Misic, a general deep learning-based method for the high-throughput cell segmentation of complex bacterial communities.

Studies of bacterial communities, biofilms and microbiomes, are multiplying due to their impact on health and ecology. Live imaging of microbial communities requires new tools for the robust identification of bacterial cells in dense and often inter-species populations, sometimes over very large scales. Here, we developed MiSiC, a general deep-learning-based 2D segmentation method that automatically segments single bacteria in complex images of interacting bacterial communities with very little parameter adjustment, independent of the microscopy settings and imaging modality. Using a bacterial predator-prey interaction model, we demonstrate that MiSiC enables the analysis of interspecies interactions, resolving processes at subcellular scales and discriminating between species in millimeter size datasets. The simple implementation of MiSiC and the relatively low need in computing power make its use broadly accessible to fields interested in bacterial interactions and cell biology.