Buchmann Institut für Molekulare Lebenswissenschaften (BMLS)
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Calcium (Ca2+) elevation is an essential secondary messenger in many cellular processes, including disease progression and adaptation to external stimuli, e.g., gravitational load. Therefore, mapping and quantifying Ca2+ signaling with a high spatiotemporal resolution is a key challenge. However, particularly on microgravity platforms, experiment time is limited, allowing only a small number of replicates. Furthermore, experiment hardware is exposed to changes in gravity levels, causing experimental artifacts unless appropriately controlled. We introduce a new experimental setup based on the fluorescent Ca2+ reporter CaMPARI2, onboard LED arrays, and subsequent microscopic analysis on the ground. This setup allows for higher throughput and accuracy due to its retrograde nature. The excellent performance of CaMPARI2 was demonstrated with human chondrocytes during the 75th ESA parabolic flight campaign. CaMPARI2 revealed a strong Ca2+ response triggered by histamine but was not affected by the alternating gravitational load of a parabolic flight.
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.
The Mediterranean fruit fly (medfly), Ceratitis capitata, is an important model organism in biology and agricultural research with high economic relevance. However, information about its embryonic development is still sparse. We share nine long-term live imaging datasets acquired with light sheet fluorescence microscopy (484.5 h total recording time, 373 995 images, 256 Gb) with the scientific community. Six datasets show the embryonic development in toto for about 60 hours at 30 minutes intervals along four directions in three spatial dimensions, covering approximately 97% of the entire embryonic development period. Three datasets focus on germ cell formation and head involution. All imaged embryos hatched morphologically intact. Based on these data, we suggest a two-level staging system that functions as a morphogenetic framework for upcoming studies on medfly. Our data supports research on wild-type or aberrant morphogenesis, quantitative analyses, comparative approaches to insect development as well as studies related to pest control. Further, they can be used to test advanced image processing approaches or to train machine learning algorithms and/or neuronal networks.
We present a deterministic workflow for genotyping single and double transgenic individuals directly upon nascence that prevents overproduction and reduces wasted animals by two-thirds. In our vector concepts, transgenes are accompanied by two of four clearly distinguishable transformation markers that are embedded in interweaved, but incompatible Lox site pairs. Following Cre-mediated recombination, the genotypes of single and double transgenic individuals were successfully identified by specific marker combinations in 461 scorings.
The original version of this Article contained errors where Table S5 and Table S6 were incorrectly cited. As the result, in the Methods section, under the subheading ‘Germline transformation, crossing setups and insertion junction sequencing’, “Progeny were scored for transformation marker presence during either the larval, pupal and adult stage by using a fluorescence stereo microscope (SteREO Discovery.V8, Zeiss) with appropriate filter sets (Table S4).” now reads: “Progeny were scored for transformation marker presence during either the larval, pupal and adult stage by using a fluorescence stereo microscope (SteREO Discovery.V8, Zeiss) with appropriate filter sets (Table S5).” And, under the subheading ‘Light sheet-based fluorescence microscopy’, “Metadata for the three datasets are provided in Table S5.” now reads: “Metadata for the three datasets are provided in Table S6.” In Data availability section, “Microscopy data can be accessed as described in Table S5.” now reads: “Microscopy data can be accessed as described in Table S6.” Additionally, in the Supplementary Information 8 file, the “Data Access” row was omitted in Table S6. The “Data Access” row now reads: Dataset (DS) DS0001 DS0002 DS0003 Dataset Access DOI: 10.5281/zenodo.4892363 DOI: 10.5281/zenodo.4892373 DOI: 10.5281/zenodo.4892381 The original Supplementary Information 8 file is provided below. Finally, the Supplementary Information 1 and 5 files published with this Article contained tracked changes, these have now been removed. The original Article and accompanying Supplementary Information files have been corrected.