A deterministic genotyping workflow reduces waste of transgenic individuals by two-thirds

Next generation marker-based vector concepts for rapid and unambiguous identification of single and double homozygous transgenic organisms

For diploid model organisms, the actual transgenesis processes require subsequent periods of transgene management, which are challenging in emerging model organisms due to the lack of suitable methodology. We used the red flour beetle Tribolium castaneum, a stored-grain pest, to perform a comprehensive functional evaluation of our AClashOfStrings (ACOS) and the combined AGameOfClones/AClashOfStrings (AGOC/ACOS) vector concepts, which use four clearly distinguishable markers to provide full visual control over up to two independent transgenes. We achieved comprehensive statistical validation of our approach by systematically creating seventeen novel single and double homozygous sublines intended for fluorescence live imaging, including several sublines in which the microtubule cytoskeleton is labeled. During the mating procedures, we genotyped more than 20,000 individuals in less than 80 working hours, which corresponds to about 10 to 15 s per individual. We also confirm the functionality of our combined concept in two double transgene special cases, i.e. integration of both transgenes in close proximity on the same chromosome and integration of one transgene on the X allosome. Finally, we discuss our vector concepts regarding performance, genotyping accuracy, throughput, resource saving potential, fluorescent protein choice, modularity, adaptation to other diploid model organisms and expansion capability.

QuickPIV: Efficient 3D particle image velocimetry software applied to quantifying cellular migration during embryogenesis

BACKGROUND

The technical development of imaging techniques in life sciences has enabled the three-dimensional recording of living samples at increasing temporal resolutions. Dynamic 3D data sets of developing organisms allow for time-resolved quantitative analyses of morphogenetic changes in three dimensions, but require efficient and automatable analysis pipelines to tackle the resulting Terabytes of image data. Particle image velocimetry (PIV) is a robust and segmentation-free technique that is suitable for quantifying collective cellular migration on data sets with different labeling schemes. This paper presents the implementation of an efficient 3D PIV package using the Julia programming language-quickPIV. Our software is focused on optimizing CPU performance and ensuring the robustness of the PIV analyses on biological data.

RESULTS

QuickPIV is three times faster than the Python implementation hosted in openPIV, both in 2D and 3D. Our software is also faster than the fastest 2D PIV package in openPIV, written in C++. The accuracy evaluation of our software on synthetic data agrees with the expected accuracies described in the literature. Additionally, by applying quickPIV to three data sets of the embryogenesis of Tribolium castaneum, we obtained vector fields that recapitulate the migration movements of gastrulation, both in nuclear and actin-labeled embryos. We show normalized squared error cross-correlation to be especially accurate in detecting translations in non-segmentable biological image data.

CONCLUSIONS

The presented software addresses the need for a fast and open-source 3D PIV package in biological research. Currently, quickPIV offers efficient 2D and 3D PIV analyses featuring zero-normalized and normalized squared error cross-correlations, sub-pixel/voxel approximation, and multi-pass. Post-processing options include filtering and averaging of the resulting vector fields, extraction of velocity, divergence and collectiveness maps, simulation of pseudo-trajectories, and unit conversion. In addition, our software includes functions to visualize the 3D vector fields in Paraview.