Find research datasets worth reusing

opencc-by-4.0Sep 2024View details →

Fluorescent oil droplet in developing zebrafish embryo

Multichannel image data of fluorescently-labelled oild droplet injected into developing Zebrafish embryo. The frame rate is 3min and the interfacial tension of the injected droplet equals 3.3 mN/m²

opencc-by-4.0May 2021View details →

Toxicogenomic profiles of neuronal targeting insecticides in zebrafish embryos as non-target aquatic vertebrate model

We have conducted semi-static exposure studies with six neuronal targeting insecticides on fertilized zebrafish (Danio rerio) eggs, similar to the OECD 236 guideline for the 96h zebrafish embryo toxicity test. The aim of these transcriptomic profiling experiments was to screen for ecotoxicogenomic fingerprints in zebrafish (Danio rerio) embryos as aquatic vertebrate non-target model exposed to sub lethal concentrations of pesticides. Data published in <a href="https://doi.org/10.1016/j.chemosphere.2021.132746">Reinwald et al. 2021</a> (PMID:34748799). For experimental details please refer to the publicly accessible experiment description and treatment protocols deposited in the <a href="https://www.ebi.ac.uk/arrayexpress/">ArrayExpress database </a>at EMBL-EBI (www.ebi.ac.uk/arrayexpress) under the following accession numbers: <a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-9852/">E-MTAB-9852</a> (Abamectin), <a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-9855/">E-MTAB-9855 </a>(Carbaryl),<a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-9853/"> E-MTAB-9853</a> (Chlorpyrifos), <a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-9854/">E-MTAB-9854</a> (Fipronil), <a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-9859/">E-MTAB-9859</a> (Imidacloprid), <a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-9860/">E-MTAB-9860</a> (Methoxychlor). The uploaded data archives (<a href="https://www.7-zip.org/">7-zip</a> compressed) consists of three major data types: 1. MultiQC reports from raw RNA-Seq read processing and QC (50bp SR) ( <a href="https://zenodo.org/api/files/5c06b1b3-0d96-4ab8-8925-a7419f0a379d/Neuotox_multiQCreports.7z">Neuotox_multiQCreports.7z </a>) 2. Result tables of differential gene expression analysis (DGEA) with DESEq2 ( <a href="https://zenodo.org/api/files/5c06b1b3-0d96-4ab8-8925-a7419f0a379d/Neurotox_DESeq2_ResultTables.7z">Neurotox_DESeq2_ResultTables.7z </a>) 3. Result tables of gene set enrichment analysis (GSEA) with clusterProfiler ( <a href="https://zenodo.org/api/files/5c06b1b3-0d96-4ab8-8925-a7419f0a379d/Neurotox_clusterProfiler_ResultTables.7z">Neurotox_clusterProfiler_ResultTables.7z </a>) 4. Result tables of overrepresentation analysis (ORA) via clusterProfiler::compareCluster() ( <a href="https://zenodo.org/api/files/c3fb14b4-6a90-4b12-a2be-ed5661d19683/Neurotox_clusterProfiler_ORA_on_core_DEGs.7z?versionId=2156d2be-b815-4157-b0f9-3eeed117b812">Neurotox_clusterProfiler_ORA_on_core_DEGs.7z </a>) Each data archive contains a README file describing the methods applied to generate the respective result tables / reports. For each tested substance and exposure condition a DGEA and GSEA result table is uploaded. The corresponding bash and R codes for each analysis step are available on github under: <a href="https://github.com/hreinwal/zfeNeurotox">https://github.com/hreinwal/zfeNeurotox</a> Gene count normalization and DGEA was conducted with DESeq2 (<a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0550-8">Love et al., 2014</a>, DOI 10.1186/s13059-014-0550-8). Three biological replicates per condition, exposure treatments were compared with respect to the control group in a pairwise fashion, applying Wald’s t-test. P values were corrected for multiple testing with independent hypothesis weighting (IHW) (<a href="https://www.nature.com/articles/nmeth.3885">Ignatiadis et al., 2016</a>, DOI 10.1038/nmeth.3885) after Benjamini-Hochberg (BH). To improve the signal to statistical noise ratio, the obtained log2-fold change (lfc) values were shrunk with the apeglm method described by Zhu and colleagues (<a href="https://academic.oup.com/bioinformatics/article/35/12/2084/5159452?login=true">2019</a>, DOI 10.1093/bioinformatics/bty895) before DGEA result tables were subjected to GSEA via clusterProfiler (<a href="https://www.liebertpub.com/doi/10.1089/omi.2011.0118">Yu et al., 2012</a>, DOI 10.1089/omi.2011.0118) and reactomePA (<a href="https://pubs.rsc.org/en/content/articlehtml/2015/mb/c5mb00663e">Yu and He, 2016</a>, DOI 10.1039/C5MB00663E). The linked ArrayExpress accession numbers above, provide access to the raw and DESeq2 normalized gene count matrices upon which these analysis were performed. Genes were annotated through the biomaRt package (<a href="https://www.nature.com/articles/nprot.2009.97.pdf?origin=ppub">Durinck et al., 2009</a>, DOI 10.1038/nprot.2009.97) in R (<a href="https://www.r-project.org/">R Core Team 2021</a>).

Figure 5 in The ontogeny and homology of the Weberian apparatus in the zebrafish Danio rerio (Ostariophysi: Cypriniformes)

Figure 5. Illustration of the differentiating saddle-shaped complex cartilage from a dorsolateral aspect (LU F.082313, 10.5 mm SL). Associated neural arches are not shown. Anterior to the left.

Figure 3 in The ontogeny and homology of the Weberian apparatus in the zebrafish Danio rerio (Ostariophysi: Cypriniformes)

Figure 3. Complex cartilage and neural arch three association in (A). Cleared and stained specimen (LU F.082315, 4.9 mm SL) and (B) histological transverse section (LU F.082320, 5.7 mm TL).

Figure 4 in The ontogeny and homology of the Weberian apparatus in the zebrafish Danio rerio (Ostariophysi: Cypriniformes)

Figure 4. Photomicrograph of transverse section through (A) occipital region showing the connective tissue perineural tube surrounding the neural tube (LU F.082320, 5.7 mm TL). (B) Photograph of vertebra one showing the developing fibrocartilage pad (LU F.082320, 5.7 mm TL).

Figure 1 in The ontogeny and homology of the Weberian apparatus in the zebrafish Danio rerio (Ostariophysi: Cypriniformes)

Figure 1. Weberian apparatus of Opsariichthys bidens. Modified from Fink & Fink (1981). Bone is stippled and cartilage is shaded black. Anterior to the left.

opencc-by-4.0Nov 2022View details →

Data for "An optofluidic platform for interrogating chemosensory behavior and brainwide neural representation in larval zebrafish"

This dataset contains the free arena data and imaging data for the following research paper. Sy, S.K.H., Chan, D.C.W., Chan, R.C.H. et al. An optofluidic platform for interrogating chemosensory behavior and brainwide neural representation in larval zebrafish. Nat Commun 14, 227 (2023). https://doi.org/10.1038/s41467-023-35836-2

opencc-zeroApr 2023View details →

Size-selective harvesting impacts learning and decision-making in zebrafish

Size-selective harvesting common to fisheries is known to evolutionarily alter life-history and behavioural traits in exploited fish populations. Changes in these traits may in turn modify learning and decision-making abilities through energetic trade-offs with brain investment that can vary across development or via correlations with personality traits. We examined the hypothesis of size-selection-induced alteration of learning performance in three selection lines of zebrafish (Danio rerio) generated through intensive harvesting for large, small and random body-size for five generations followed by no further selection for ten generations that allowed examining evolutionarily fixed outcomes. We tested associative learning ability throughout ontogeny in fish groups using a colour-discrimination paradigm with a food reward, and the propensity to make group decisions in an associative task. All selection lines showed significant associative abilities that improved across ontogeny. The large-harvested line fish showed a significantly slower associative learning speed as subadults and adults than the controls. We found no evidence of memory decay as a function of size-selection. Decision-making speed did not vary across lines, but the large-harvested line made faster decisions during the probe trial. Collectively, our results show that size-selective harvesting evolutionarily alters associative and decision-making abilities in zebrafish, which could affect resource acquisition and survival in exploited fish populations.

opencc-by-4.0May 2023View details →

Morphological changes of zebrafish macrophages during wound healing

Tg(mpeg1:gal4/UAS:Kaede) larvae with mosaic expression of Kaede protein in macrophages were amputated at 3 dpf and imaged using high resolution Spinning Disk microscopy between 8 to 13 h,every 2.5 min  (C) Quantification of perimeter (upper graph) and circularity value (lower graph) for individual macrophages present at the wound, during a time lapse sequence from 8 hpA to 13 hpA every 2.5 min. For quantification, we used  automated image analysis, the image segmentation was carried out from 8 to 13 hpA. At first, the images were filtered by using space-time filtering that keeps the temporal coherence of moving macrophages (Sarti et al., 1999). Then, the filtered images were segmented by a combination of the local Otsu method (Otsu, 1979; Saddami et al., 2019) and (Park et al., unpublished) and the subjective surface segmentation (SUBSURF) method  (Sarti et al., 2000) where the binarized images from the local Otsu method were considered as an initial condition of the SUBSURF equation (Park et al., 2023). Trajectories of macrophages in segmented images were reconstructed based on automatic cell tracking (Park et al., 2023), and shape descriptors (perimeter and circularity) were measured.  Refernces: Sipka T, Park SA, Ozbilgic R, Balas L, Durand T, Mikula K, Lutfalla G, Nguyen-Chi M. (2022). Macrophages undergo a behavioural switch during wound healing in zebrafish. Free Radical Biology and Medicine 192:200–212. doi:10.1016/j.freeradbiomed.2022.09.021 and Park SA, Sipka T, Kriva Z, Lutfalla G, Nguyen-Chi M, Mikula K. (2023). Segmentation-based tracking of macrophages in 2D+time microscopy movies inside a living animal. Computers in Biology and Medicine    153, 106499. doi: 10.1016/j.compbiomed.2022.106499

opencc-zeroMay 2023View details →

Asymmetric mechanotransduction by hair cells of the zebrafish lateral line (Part 1/2)

In the lateral line system, water motion is detected by neuromast organs, fundamental units that are arrayed on a fish's surface. Each neuromast contains hair cells, specialized mechanoreceptors that convert mechanical stimuli, in the form of water movement, into electrical signals. The orientation of hair cells' mechanosensitive structures ensures that the opening of mechanically-gated channels is maximal when deflected in a single direction. In each neuromast organ, hair cells have two opposing orientations, enabling bi-directional detection of water movement. Interestingly, Tmc2b and Tmc2a proteins, which constitute the mechanotransduction channels in neuromasts, distribute asymmetrically so that Tmc2a is expressed in hair cells of only one orientation. Here, using both in vivo recording of extracellular potentials and calcium imaging of neuromasts, we demonstrate that hair cells of one orientation have larger mechanosensitive responses. The associated afferent neuron processes that innervate neuromast hair cells faithfully preserve this functional difference. Moreover, Emx2, a transcription factor required for the formation of hair cells with opposing orientations, is necessary to establish this functional asymmetry within neuromasts. Remarkably, loss of Tmc2a does not impact hair cell orientation but abolishes the functional asymmetry as measured by recording extracellular potentials and calcium imaging. Overall, our work indicates that oppositely oriented hair cells within a neuromast employ different proteins to alter mechanotransduction to sense the direction of water motion.

opencc-zeroMay 2023View details →

Asymmetric mechanotransduction by hair cells of the zebrafish lateral line (Part 2/2)

opencc-by-4.0Jun 2023View details →

Immobilized fluorescently stained zebrafish through the eXtended Field of view Light Field Microscope 2D-3D dataset

Immobilized fluorescently stained zebrafish through the eXtended Field of view Light Field Microscope 2D-3D dataset This dataset comprises three immobilized fluorescently stained zebrafish imaged through the eXtended Field of view Light Field Microscope (XLFM, also known as Fourier Light Field Microscope). The images were preprocessed with the <a href="https://github.com/pvjosue/SLNet_XLFMNet">SLNet</a>, which extracts the sparse signals from the images (a.k.a. the neural activity). If you intend to use this with Pytorch, you can find a data loader and working source code to load and train networks <a href="https://github.com/pvjosue/CWFA">here</a>. This dataset is part of the publication: Fast light-field 3D microscopy with out-of-distribution detection and adaptation through Conditional Normalizing Flows.   The fish present are: <ul> <li>1x NLS GCaMP6s</li> <li>1x Pan-neuronal nuclear localized GCaMP6s Tg(HuC:H2B:GCaMP6s)</li> <li>1x Soma localized GCaMP7f Tg(HuC:somaGCaMP7f)</li> </ul>   The dataset is structured as follows:: XLFM_dataset <ul> <li>Dataset/ <ul> <li>GCaMP6s_NLS_1/ <ul> <li>SLNet_preprocessed/ <ul> <li>XLFM_image/ <ul> <li>XLFM_image_stack.tif: tif stack of 600 preprocessed XLFM images.</li> </ul> </li> <li>XLFM_stack/ <ul> <li>XLFM_stack_nnn.tif: 3D stack corresponding to frame nnn.</li> </ul> </li> <li>Neural_activity_coordinates.csv: 3D coordinates of neurons found through the <a href="https://www.biorxiv.org/content/10.1101/061507v2">suite2p framework</a>.</li> </ul> </li> <li>Raw/ <ul> <li>XLFM_image/ <ul> <li>XLFM_image_stack.tif: tif stack of 600 raw XLFM images.</li> </ul> </li> </ul> </li> </ul> </li> <li>(other samples)</li> </ul> </li> <li>lenslet_centers_python.txt: 2D coordinates of the lenset in the XLFM images.</li> <li>PSF_241depths_16bit.tif: 3D PSF of the microscope can be used for 3D deconvolution. Spanning  734 × 734 × 550𝜇𝑚3 used to deconvolve this volumes. </li> </ul>   In this dataset, we provide a subset of the images and volumes. Due to space constraints, we provide the 3D volumes only for: <ul> <li>SLNet_preprocessed/XLFM_stack/ <ul> <li>10 interleaved frames between frames 0-499 (can be used for training a network).</li> <li>20 consecutive frames, 500-520 (can be used for testing).</li> </ul> </li> <li>raw/ <ul> <li>No volumes are provided for raw data, but they can be reconstructed through 3D deconvolution.</li> </ul> </li> </ul>   Enjoy, and feel free to contact us for any information request, like the full PSF, 3 more samples or longer image sequences.

opencc-zeroJun 2023View details →

Dataset for: Female reproductive fluids attracts more and better sperm in zebrafish

Mounting evidence shows that the female reproductive fluid (FRF) can differently affect sperm performance of different males by biasing paternity share among competing males. Here, we tested for the first time the potential of 'within-ejaculate cryptic female choice' mediated by the FRF in the zebrafish (Danio rerio). Using a recently developed sperm selection chamber, we separated and collected FRF-selected from non-selected sperm to compare the two subpopulations of sperm in terms of sperm number, viability, DNA integrity and fertilizing ability. We showed that the sperm attracted by FRF are more numerous, more viable and with higher DNA integrity. In addition, FRF-selected sperm fertilized more eggs, but if this is due to fertilization ability per se or numerical advantage is unknown. Our results suggest that the FRF can select sperm with a better phenotype, highlighting the crucial and impactful role that the FRF has in the process of fertilization and post-mating sexual selection dynamics and the potential implications for sperm selection in assisted reproductive techniques.

opencc-zeroOct 2023View details →

Single-cell RNA sequencing of sclerotome-derived fibroblasts in zebrafish

Despite their importance in tissue maintenance and repair, fibroblast diversity and plasticity remain poorly understood. Using single-cell RNA sequencing, we uncover distinct sclerotome-derived fibroblast populations in zebrafish, including progenitor-like perivascular/interstitial fibroblasts, and specialized fibroblasts such as tenocytes. To determine fibroblast plasticity in vivo, we develop a laser-induced tendon ablation and regeneration model. Lineage tracing reveals that laser-ablated tenocytes are quickly regenerated by preexisting fibroblasts. By combining single-cell clonal analysis and live imaging, we demonstrate that perivascular/interstitial fibroblasts actively migrate to the injury site, where they proliferate and give rise to new tenocytes. By contrast, perivascular fibroblast-derived pericytes or specialized fibroblasts, including tenocytes, exhibit no regenerative plasticity. Interestingly, active Hedgehog (Hh) signaling is required for the proliferation of activated fibroblasts to ensure efficient tenocyte regeneration. Together, our work highlights the functional diversity of fibroblasts and establishes perivascular/interstitial fibroblasts as tenocyte progenitors that promote tendon regeneration in a Hh signaling-dependent manner.