Standardized mounting method of (zebrafish) embryos using a 3D-printed stamp for high-content, semi-automated confocal imaging.

BACKGROUND: Developmental biology relies to a large extent on the observation and comparison of phenotypic traits through time using high resolution microscopes. In this context, transparent model organisms such as the zebrafish Danio rerio in which developing tissues and organs can be easily observed and imaged using fluorescent proteins have become very popular. One limiting factor however is the acquisition of a sufficient amount of data, in standardized and reproducible conditions, to allow robust quantitative analysis. One way to improve this is by developing mounting methods to increase the number of embryos that can be imaged simultaneously in near-to-identical orientation. RESULTS: Here we present an improved mounting method allowing semi-automated and high-content imaging of zebrafish embryos. It is based on a 3D-printed stamp which is used to create a 2D coordinate system of multiple µ-wells in an agarose cast. Each µ-well models a negative of the average zebrafish embryo morphology between 22 and 96 h-post-fertilization. Due to this standardized and reproducible arrangement, it is possible to define a custom well plate in the respective imaging software that allows for a semi-automated imaging process. Furthermore, the improvement in Z-orientation significantly reduces post-processing and improves comparability of volumetric data while reducing light exposure and thus photo-bleaching and photo-toxicity, and improving signal-to-noise ratio (SNR). CONCLUSIONS: We present here a new method that allows to standardize and improve mounting and imaging of embryos. The 3D-printed stamp creates a 2D coordinate system of µ-wells in an agarose cast thus standardizing specimen mounting and allowing high-content imaging of up to 44 live or mounted zebrafish embryos simultaneously in a semi-automated, well-plate like manner on inverted confocal microscopes. In summary, image data quality and acquisition efficiency (amount of data per time) are significantly improved. The latter might also be crucial when using the services of a microscopy facility.

A Photorhabdus natural product inhibits insect juvenile hormone epoxide hydrolase.

Simple urea compounds ("phurealipids") have been identified from the entomopathogenic bacterium Photorhabdus luminescens, and their biosynthesis was elucidated. Very similar analogues of these compounds have been previously developed as inhibitors of juvenile hormone epoxide hydrolase (JHEH), a key enzyme in insect development and growth. Phurealipids also inhibit JHEH, and therefore phurealipids might contribute to bacterial virulence.

Seascape analysis reveals regional gene flow patterns among populations of a marine planktonic diatom.

We investigated the gene flow of the common marine diatom, Skeletonema marinoi, in Scandinavian waters and tested the null hypothesis of panmixia. Sediment samples were collected from the Danish Straits, Kattegat and Skagerrak. Individual strains were established from germinated resting stages. A total of 350 individuals were genotyped by eight microsatellite markers. Conventional F-statistics showed significant differentiation between the samples. We therefore investigated whether the genetic structure could be explained using genetic models based on isolation by distance (IBD) or by oceanographic connectivity. Patterns of oceanographic circulation are seasonally dependent and therefore we estimated how well local oceanographic connectivity explains gene flow month by month. We found no significant relationship between genetic differentiation and geographical distance. Instead, the genetic structure of this dominant marine primary producer is best explained by local oceanographic connectivity promoting gene flow in a primarily south to north direction throughout the year. Oceanographic data were consistent with the significant FST values between several pairs of samples. Because even a small amount of genetic exchange prevents the accumulation of genetic differences in F-statistics, we hypothesize that local retention at each sample site, possibly as resting stages, is an important component in explaining the observed genetic structure.

On the impact of dispersal asymmetry on metapopulation persistence.

Metapopulation theory for a long time has assumed dispersal to be symmetric, i.e. patches are connected through migrants dispersing bi-directionally without a preferred direction. However, for natural populations symmetry is often broken, e.g. for species in the marine environment dispersing through the transport of pelagic larvae with ocean currents. The few recent studies of asymmetric dispersal concluded that asymmetry has a distinct negative impact on the persistence of metapopulations. Detailed analysis, however, revealed that these previous studies might have been unable to properly disentangle the effect of symmetry from other potentially confounding properties of dispersal patterns. We resolve this issue by systematically investigating the symmetry of dispersal patterns and its impact on metapopulation persistence. Our main analysis based on a metapopulation model equivalent to previous studies but now applied on regular dispersal patterns aims to isolate the effect of dispersal symmetry on metapopulation persistence. Our results suggest that asymmetry in itself does not imply negative effects on metapopulation persistence. For this reason we recommend to investigate it in connection with other properties of dispersal instead of in isolation.

Markov properties in presence of measurement noise.

Recently, several powerful tools for the reconstruction of stochastic differential equations from measured data sets have been proposed [e.g., Siegert, Phys. Lett. A 243, 275 (1998); Hurn, J. Time Series Anal. 24, 45 (2003)]. Efficient application of the methods, however, generally requires Markov properties to be fulfilled. This constraint typically seems to be violated on small scales, which frequently is attributed to physical effects. On the other hand, measurement noise such as uncorrelated measurement and discretization errors has large impacts on the statistics of measurements on small scales. We demonstrate that the presence of measurement noise, likewise, spoils Markov properties of an underlying Markov process. This fact is promising for the further development of techniques for the reconstruction of stochastic processes from measured data, since limitations at small scales might stem from artificial noise sources rather than from intrinsic properties of the dynamics of the underlying process. Measurement noise, however, can be controlled much better than the intrinsic dynamics of the underlying process.

Directional genetic differentiation and relative migration.

Understanding the population structure and patterns of gene flow within species is of fundamental importance to the study of evolution. In the fields of population and evolutionary genetics, measures of genetic differentiation are commonly used to gather this information. One potential caveat is that these measures assume gene flow to be symmetric. However, asymmetric gene flow is common in nature, especially in systems driven by physical processes such as wind or water currents. As information about levels of asymmetric gene flow among populations is essential for the correct interpretation of the distribution of contemporary genetic diversity within species, this should not be overlooked. To obtain information on asymmetric migration patterns from genetic data, complex models based on maximum-likelihood or Bayesian approaches generally need to be employed, often at great computational cost. Here, a new simpler and more efficient approach for understanding gene flow patterns is presented. This approach allows the estimation of directional components of genetic divergence between pairs of populations at low computational effort, using any of the classical or modern measures of genetic differentiation. These directional measures of genetic differentiation can further be used to calculate directional relative migration and to detect asymmetries in gene flow patterns. This can be done in a user-friendly web application called divMigrate-online introduced in this study. Using simulated data sets with known gene flow regimes, we demonstrate that the method is capable of resolving complex migration patterns under a range of study designs.

Estimation of drift and diffusion functions from time series data: a maximum likelihood framework.

Complex systems are characterized by a huge number of degrees of freedom often interacting in a nonlinear manner. In many cases macroscopic states, however, can be characterized by a small number of order parameters that obey stochastic dynamics in time. Recently, techniques for the estimation of the corresponding stochastic differential equations from measured data have been introduced. This paper develops a framework for the estimation of the functions and their respective (Bayesian posterior) confidence regions based on likelihood estimators. In succession, approximations are introduced that significantly improve the efficiency of the estimation procedure. While being consistent with standard approaches to the problem, this paper solves important problems concerning the applicability and the accuracy of estimated parameters.

Reconstruction of complex dynamical systems affected by strong measurement noise.

This Letter reports on a new approach to properly analyze time series of dynamical systems which are spoilt by the simultaneous presence of dynamical noise and measurement noise. It is shown that even strong external measurement noise as well as dynamical noise which is an intrinsic part of the dynamical process can be quantified correctly, solely on the basis of measured time series and proper data analysis. Finally, real world data sets are presented pointing out the relevance of the new approach.