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2,182 results for “Organoids”
Phindr3D: Test Data Set 2 (human MCF10A breast cancer organoids)
<p>3D confocal image stacks of human MCF10A breast cancer organoids expressing different oncogenes to test the functionality of Phindr3D. Explanatory .txt file contained in the ZIP files.</p> <p>Please see the manuscript for details and on how to access the full data set:</p> <p> </p> <p><strong>Rapid 3D phenotypic analysis of neurons and organoids using data-driven cell segmentation-free machine learning</strong></p> <p>Philipp Mergenthaler*, Santosh Hariharan*, James M. Pemberton, Corey Lourenco, Linda Z. Penn, David W. Andrews</p> <p><em>PLOS Computational Biology, DOI: <a href="https://dx.doi.org/10.1371/journal.pcbi.1008630">10.1371/journal.pcbi.1008630</a></em></p> <p> </p> <p><strong>Phindr3D is available on GitHub</strong>: <a href="https://github.com/DWALab/Phindr3D">GitHub - DWALab/Phindr3D</a></p>
Electrophysiological characterization and functionality of neurons in murine cerebral organoids.
<p>Dataset includes patch-clamp recordings aim to characterize functionality of neurons belonging to murine cerebral organoids.</p> <p>Patch-clamp recordings were performed by University of Modena and Reggio Emilia unit (PI Prof. Curia Giulia).</p> <p>Organoids were generated by University of Verona unit (PI Prof. Decimo Ilaria) and transferred to Modena for electrophysiology experiments.</p>
Long-term live imaging and multiscale analysis identify heterogeneity and core principles of epithelial organoid morphogenesis - Image data
<p>The dataset contains raw imaging data from the work:</p> <p>"Long-term live imaging and multiscale analysis identify heterogeneity and core principles of epithelial organoid morphogenesis"</p> <p>The dataset is organized as the following: the "FigureX_" or SupplementaryFigure_X" suffix in the filename refers to the figure in the paper in which the raw data is analyzed and/or visualized. The data is "raw", i.e. not processed. However, in many cases, maximum projections of the original 3D image stacks have been uploaded due to size limitations. The total size of the image stacks approaches 0.5TB. To access the full 3D image stacks please contact the corresponding author (Francesco Pampaloni, fpampalo@bio.uni-frankfurt.de).</p> <p><strong>Authors</strong></p> <p>Lotta Hof<sup>1</sup>*, Till Moreth<sup>1</sup>*, Michael Koch<sup>1</sup>, Tim Liebisch<sup>2</sup>, Marina Kurtz<sup>3</sup>, Julia Tarnick<sup>4</sup>, Susanna M. Lissek<sup>5</sup>, Monique M.A. Verstegen<sup>6</sup>, Luc J.W. van der Laan<sup>6</sup>, Meritxell Huch<sup>7</sup>, Franziska Matthäus<sup>2</sup>, Ernst H.K. Stelzer<sup>1</sup>, Francesco Pampaloni<sup>1§</sup></p> <p><sup>1</sup>Physical Biology Group, Buchmann Institute for Molecular Life Sciences (BMLS), Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany</p> <p><sup>2</sup>Faculty of Biological Sciences, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany</p> <p><sup>3</sup>Department of Physics, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany</p> <p><sup>4</sup>Deanery of Biomedical Science, University of Edinburgh, Edinburgh, United Kingdom</p> <p><sup>5</sup>Experimental Medicine and Therapy Research, University of Regensburg, Regensburg, Germany</p> <p><sup>6</sup>Department of Surgery, Erasmus MC – University Medical Center, Rotterdam, The Netherlands</p> <p><sup>7</sup>The Wellcome Trust/CRUK Gurdon Institute, University of Cambridge, Cambridge, United Kingdom. Present address: Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany</p> <p>*contributed equally</p> <p><sup>§</sup>corresponding author: fpampalo@bio.uni-frankfurt.de</p> <p><strong>Abstract</strong></p> <p><em>Background</em></p> <p>Organoids are morphologically heterogeneous three-dimensional cell culture systems and serve as an ideal model for understanding the principles of collective cell behaviour in mammalian organs during development, homeostasis, regeneration and pathogenesis. To investigate the underlying cell organisation principles of organoids, we imaged hundreds of pancreas and cholangio carcinoma organoids in parallel using light sheet and bright field microscopy for up to seven days.</p> <p><em>Results</em></p> <p>We quantified organoid behaviour at single-cell (microscale), individual-organoid (mesoscale), and entire-culture (macroscale) levels. At single-cell resolution, we monitored formation, monolayer polarisation and degeneration, and identified diverse behaviours, including lumen expansion and decline (size oscillation), migration, rotation and multi-organoid fusion. Detailed individual organoid quantifications lead to a mechanical 3D agent-based model. A derived scaling law and simulations support the hypotheses that size oscillations depend on organoid properties and cell division dynamics, which is confirmed by bright field microscopy analysis of entire cultures.</p> <p><em>Conclusion</em></p> <p>Our multiscale analysis provides a systematic picture of the diversity of cell organisation in organoids by identifying and quantifying the core regulatory principles of organoid morphogenesis.</p>
Zellige example dataset: inner ear organoid epithelium
<p><strong>Inner ear organoid at day 14 of culture. </strong></p> <p>The z-stack image encompasses half of the spherical organoid including two distinct and superimposed surfaces that correspond to the basal side of the epithelium and the apical junctional network. It was acquired with a confocal microscope (Nikon A1HD25) equipped with a Nikon Plan-Apochromat 25x lens (NA=1.05). Pixel size 0.690 µm, z step >1 µm. This dataset contains both the ground-truth height maps and the height maps generated with Zellige. The Zellige parameters used are:</p> <p><span class="math-tex">\(T_{A}=5, T_{otsu}=12, S_{min}=5, \sigma_{xy}=2, \sigma_{z}=1, T_{OSE1}=0.9, R_{1}=5, C_{1}=0.8, T_{OSE2}=0.1, R_{2}=10, C_{2}=0.8.\)</span></p> <p>Nota: to compare the ground truth height map with the Zellige height map, one first needs to substrat 1 to all values of the Zellige height map.</p> <p>See the accompanying paper: Extracting multiple surfaces from 3D microscopy images in complex biological tissues with the Zellige software tool. Trébeau <em>et al.</em> 2022: <a href="https://doi.org/10.1101/2022.04.05.485876">https://doi.org/10.1101/2022.04.05.485876</a></p>
Code and data from "Mother cells control daughter cell proliferation in intestinal organoids to minimize proliferation fluctuations"
<p>Includes the microscopy images, cell tracking data and scripts used in the publication Huelsz-Prince, Guizela, et al. "Mother cells control daughter cell proliferation in intestinal organoids to minimize proliferation fluctuations." <em>eLife </em> 11:e80682 (2022). <a href="https://doi.org/10.7554/eLife.80682"> https://doi.org/10.7554/eLife.80682</a> .</p>
Murine brain organoids developmental profiling
<p>The dataset includes the RNA sequencing data generated for the study entitled "Murine cerebral organoids develop network of functional neurons and hippocampal brain region identity" by Ciarpella et al. Starting from mouse neuronal stem cells, we generate organoids to recapitulate the hippocampal structure as new platform for modeling diseases, for drug screening and for organ transplantation in regenerative medicine. Performing RNA sequencing in organoids at different stage of proliferation and maturation, we obtained a measure of the cellular diversity within the organoids, the degree of differentiation and the affinity to hippocampus or cortex tissue (reference samples). </p>
Ilastik_organoid_segmentation
<p>This repository holds two Ilastik projects and respective training data used for the organoid segmentation of the initial organoid screen in the publication "A semi-automated intestinal organoid screening method demonstrates epigenetic control of epithelial differentiation" (<a href="https://doi.org/10.3389/fcell.2020.618552">Ostrop et al. 2020</a>).</p> <p>The image pre-processing was carried out with the ImageJ script available on GitHub: <a href="https://github.com/jennyostrop/Fiji_organoid_brightfield_processing">https://github.com/jennyostrop/Fiji_organoid_brightfield_processing</a>, archived on Zenodo as <a href="https://doi.org/10.5281/zenodo.3951125">https://doi.org/10.5281/zenodo.3951125</a></p> <p>For training, 1 well treated with DMSO vehicle control of 4 biological replicates and 5 timepoints were used (total 20 images). Training data was excluded from further analysis.</p> <p>The raw images were deposited to the Image Data Resource (<a href="https://idr.openmicroscopy.org">https://idr.openmicroscopy.org</a>) under accession number idr0092.<br> Training data corresponds to Well A01 in plate 1-plate 4.</p> <p> </p> <p><strong>Project 1: Ilastik1_PixelClass_Edges</strong></p> <p>Pixel classification, created with Ilastik 1.3.2</p> <p>Input Raw Data: Summary projection of Sobel edge detection for each z-layer (Output 2d from ImageJ script)</p> <p>Training classes:<br> Background: lines over background and debris (size 7) and inside of organoids (size 3)<br> Object: exact lines following the outer border of organoids (size 1)</p> <p> </p> <p><strong>Project 2: Ilastik2_ObjectClass_Projection</strong></p> <p>Object classification [Inputs: Raw Data, Pixel Prediction Map], created with Ilastik 1.3.2</p> <p>Input Raw Data: Minimum intensity projections of each Zstack (Output 1b from ImageJ script)<br> Input Prediction Maps: Pixel Prediction Maps generated in project Ilastik1_PixelClass_Edges</p> <p>Threshold and Size Filter: Method - Simple, Input - 0 (Background), Smooth – 1.0, 1.0, Threshold - 0.55, Size Filter – Min 10, Max 1000000<br> <br> Object classes:<br> Mislabelled: Patch of background enclosed by objects, light<br> Organoid: Typical organoid, medium dark<br> Sphere_big: Sphere, almost round, light, size of organoids or bigger<br> Sphere_small: Sphere, almost round, light to medium dark, small<br> Cluster: Cluster of several organoids/spheres and organoids segmented as single object<br> Debris: Debris in Matrigel, irregular borders and structure, mostly small, medium dark<br> AirBubble: Air bubbles in Matrigel at early timepoints, round, dark with light centre<br> Edges: Edges of well plate (empty class, included for consistency)</p>
Inferring and perturbing cell fate regulomes in human cerebral organoids
<p>Supplementary data for the manuscript: Inferring and perturbing cell fate regulomes in human cerebral organoids.</p> <p>Note: Due to the size limit of the repository, this update doesn't include all the relevant data. The following data are available via the same repository but different versions:</p> <ul> <li>Processed data in Seurat objects: <a href="https://doi.org/10.5281/zenodo.7687749" target="_blank" rel="noopener">Version 3</a> - seurat_objects.tar.gz</li> <li>scATAC-seq fragment files by Cell Ranger: <a href="https://zenodo.org/records/13254037" target="_blank" rel="noopener">Version 4</a></li> </ul>
Chronic exposure to glucocorticoids amplifies inhibitory neuron cell fate during human neurodevelopment in organoids
<p><strong>Abstract: </strong>Disruptions in the tightly regulated process of human brain development have been linked to increased risk for brain and mental illnesses. While the genetic contribution to these diseases is well established, important environmental factors have been less studied at molecular and cellular levels. In this study, we used single-cell and cell-type-specific techniques to investigate the effect of glucocorticoid (GC) exposure, a mediator of antenatal environmental risk, on gene regulation and lineage specification in unguided human neural organoids. We characterized the transcriptional response to chronic GC exposure during neural differentiation and studied the underlying gene regulatory networks by integrating single-cell transcriptomics- with chromatin accessibility data. We found lasting cell type-specific changes that included autism risk genes and several transcription factors associated with neurodevelopment. Chronic GCs influenced lineage specification primarily by priming the inhibitory neuron lineage through key transcription factors like PBX3. We provide evidence for convergence of genetic and environmental risk factors through a common mechanism of altering lineage specification.</p>
Immunogenicity of autologous and allogeneic human primary cholangiocyte organoids
<p>Primary human cells cultured in 3D organoid format have great promise as potential regenerative cellular therapies, but their immunogenicity has not yet been fully characterized. In this study, we use in vitro co-cultures and in vivo humanized mouse experimental models to examine the human immune response to autologous and allogeneic primary cholangiocyte organoids (PCOs). Our data demonstrate that PCOs upregulate the expression of HLA-I and HLA-II in inflammatory conditions. The immune response to allogeneic PCOs is driven by both HLA-I and HLA-II and is substantially ameliorated by donor-recipient HLA matching. Autologous PCOs induce a low-level immune infiltration into the graft site, while allogeneic cells display evolving stages of immune rejection in vivo. Our findings have important implications for the design and clinical translation of autologous and allogeneic organoid cellular therapies.</p>
ERK signaling of invasive cells in mammary organoid
<p>Live-cell imaging of HMT3522 T4-2 invasive mammary epithelial cell line, with fluorescent ERKTR cell signaling reporter and nuclear reporter, to extract trajectory pairs of ERKTR to nuclear cross-correlation as a measure of ERK signaling activity. Datasets X and Y capture ERK signaling state at t and t+1 respectively, while test_obs contains test trajectories and inds_test_starts contains the indices of starting times of test trajectories.</p>
An integrated transcriptomic cell atlas of human neural organoids: Full Dataset
<p>This deposition includes the full HNOCA dataset for the following pre-print: </p> <blockquote> <p>He, Z., Dony, L., Fleck, J.S. <em>et al.</em> An integrated transcriptomic cell atlas of human neural organoids. <em>Nature</em> <strong>635</strong>, 690–698 (2024). https://doi.org/10.1038/s41586-024-08172-8</p> </blockquote> <p><strong>This file contains additional data representations and metadata from intermediate processing steps of the HNOCA. For day-to-day use of the HNOCA as a resource, we recommend using the cleaned-up HNOCA object that can be found together with the disease atlas and the extended version of HNOCA in the <a href="https://doi.org/10.5281/zenodo.11203684" target="_blank" rel="noopener">original Zenodo deposition</a>.</strong></p> <p> </p> <p>Abstract:</p> <p>Neural tissues generated from human pluripotent stem cells in vitro (known as neural organoids) are becoming useful tools to study human brain development, evolution and disease. The characterization of neural organoids using single-cell genomic methods has revealed a large diversity of neural cell types with molecular signatures similar to those observed in primary human brain tissue. However, it is unclear which domains of the human nervous system are covered by existing protocols. It is also difficult to quantitatively assess variation between protocols and the specific cell states in organoids as compared to primary counterparts. Single-cell transcriptome data from primary tissue and neural organoids derived with guided or unguided approaches and under diverse conditions combined with large-scale integrative analyses make it now possible to address these challenges. Recent advances in computational methodology enable the generation of integrated atlases across many data sets. Here, we integrated 36 single-cell transcriptomics data sets spanning 26 protocols into one integrated human neural organoid cell atlas (HNOCA) totaling over 1.7 million cells. We harmonize cell type annotations by incorporating reference data sets from the developing human brain. By mapping to the developing human brain reference, we reveal which primary cell states have been generated in vitro, and which are under-represented. We further compare transcriptomic profiles of neuronal populations in organoids to their counterparts in the developing human brain. To support rapid organoid phenotyping and quantitative assessment of new protocols, we provide a programmatic interface to browse the atlas and query new data sets, and showcase the power of the atlas to annotate new query data sets and evaluate new organoid protocols. Taken together, the HNOCA will be useful to assess the fidelity of organoids, characterize perturbed and diseased states and facilitate protocol development in the future.</p>
An integrated transcriptomic cell atlas of human neural organoids: Cleaned datasets
<p>This deposition includes the datasets in h5ad format for the following publication: </p> <blockquote> <p>He, Z., Dony, L., Fleck, J.S. <em>et al.</em> An integrated transcriptomic cell atlas of human neural organoids. <em>Nature</em> <strong>635</strong>, 690–698 (2024). https://doi.org/10.1038/s41586-024-08172-8</p> </blockquote> <p><strong>The file `hnoca_cleanedmeta.h5ad` is a cleaned up version of the HNOCA dataset. While it contains all cells, some metadata and representations originating from intermediate processing steps are removed. You can find the full (and significantly larger) object in a <a href="https://doi.org/10.5281/zenodo.12536006" target="_blank" rel="noopener">second Zenodo deposition</a>.</strong></p> <p><strong>You can find the minimal HNOCA and primary reference h5ad files for query-to-reference-mapping in a <a href="https://doi.org/10.5281/zenodo.15004817">third Zenodo deposition</a>.</strong></p> <p> </p> <p>Abstract:</p> <p>Neural tissues generated from human pluripotent stem cells in vitro (known as neural organoids) are becoming useful tools to study human brain development, evolution and disease. The characterization of neural organoids using single-cell genomic methods has revealed a large diversity of neural cell types with molecular signatures similar to those observed in primary human brain tissue. However, it is unclear which domains of the human nervous system are covered by existing protocols. It is also difficult to quantitatively assess variation between protocols and the specific cell states in organoids as compared to primary counterparts. Single-cell transcriptome data from primary tissue and neural organoids derived with guided or unguided approaches and under diverse conditions combined with large-scale integrative analyses make it now possible to address these challenges. Recent advances in computational methodology enable the generation of integrated atlases across many data sets. Here, we integrated 36 single-cell transcriptomics data sets spanning 26 protocols into one integrated human neural organoid cell atlas (HNOCA) totaling over 1.7 million cells. We harmonize cell type annotations by incorporating reference data sets from the developing human brain. By mapping to the developing human brain reference, we reveal which primary cell states have been generated in vitro, and which are under-represented. We further compare transcriptomic profiles of neuronal populations in organoids to their counterparts in the developing human brain. To support rapid organoid phenotyping and quantitative assessment of new protocols, we provide a programmatic interface to browse the atlas and query new data sets, and showcase the power of the atlas to annotate new query data sets and evaluate new organoid protocols. Taken together, the HNOCA will be useful to assess the fidelity of organoids, characterize perturbed and diseased states and facilitate protocol development in the future.</p>
Bulk RNA Seq of Electrically Stimulated Retinal Organoids
<p>Considering the significant role played by both intrinsic and extrinsic electric fields in the growth and maturation of the central nervous system, the impact of short exposure to external electric fields on the development and differentiation of retinal organoids was investigated.Organoids derived from human embryonic stem cells at day 80, a critical time point in their differentiation and maturation, were used. A single 60-minute exposure to a distinct biphasic electrical field influenced neuronal development and increased the population of photoreceptors. Initial immunohistochemistry and qPCR studies confirmed the influence of electrical stimulation on the development and differentiation of various RO cell types. RNA sequencing data revealed elevated expression of rod photoreceptors, Müller cells, horizontal cells, and amacrine cells, alongside the downregulation of retinal pigment epithelium and retinal ganglion cell genes.Furthermore, our study demonstrated varying degrees of organoid development and maturation depending on the specific electrical field applied. These findings highlight the significant impact of extrinsic electrical fields on early retinal development and suggest that optimizing electrical field parameters could effectively address certain limitations in retinal organoid technology, potentially reducing the reliance on chemicals and small molecules.</p>
Data from: Identification of neural oscillations and epileptiform changes in human brain organoids.
<p>Seurat object containing processed single-cell RNA sequencing data described in:</p> <p>Samarasinghe, R.A., Miranda, O.A., Buth, J.E. <em>et al.</em> Identification of neural oscillations and epileptiform changes in human brain organoids. <em>Nat Neurosci</em> <strong>24, </strong>1488–1500 (2021). <a href="https://doi.org/10.1038/s41593-021-00906-5">https://doi.org/10.1038/s41593-021-00906-5</a></p> <p>Additional raw and processed data can be accessed at the Gene Expression Omnibus under accession number <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE165577">GSE165577</a>. </p> <p> </p>
VOnet codes and organoid single-plane image datasets
<p><span><span>Code information that composes VONet.</span></span></p> <p><span><span>VO and RO image datasets used to verify the performance of VONet.</span></span></p>
Dataset for "Surface tension induced budding drives alveologenesis in mammary gland organoids"
<p>Live-cell imaging of organoids from primary human mammary epithelial cells, including responses to laser ablation experiments and data analysis scripts.</p> <p>Please see README file for details.</p>
Annotation results of two human retina organoids
<p>Cell annotation was based on i) marker genes which were extracted from literature and expert knowledge, or (ii) via a transfer learning tool CaSTLe (Lieberman Y, Rokach L, Shay T, 2018) using the Cowan <em>et al</em>. (2020) organoid reference data set.</p> <p>Data preprocessing of both scRNA-seq data sets was done in scanpy (Wolf, F., Angerer, P. & Theis, 2018), and are included as h5ad-files.</p> <p>The annotation results are included in the .csv files</p>
Modelling Cyclic Stretch of Patient-Derived Alveolar Epithelial Cells from Organoids using a New Alveoli-on-Chip Platform
<p><span>The data originates from lung tumor samples obtained at the University Hospital Bern. Patient-derived alveolar epithelial type 2 cells (AEC2) were isolated and cultured into organoids. Grown organoids where dissociated and seeded onto a novel alveoli-on-chip system submerged in medium. After 24 h of either no mechanical stimulus (control) or cyclic breathing stretch (stretch), RNA was isolated and sequenced. The RNA-seq data underwent quality control, alignment to the reference genome, read counting, DGE and GSEA. All analyses were run in R version 4.2.1.</span></p>
High-field MRI of cerebral organoids
<p>This dataset allows the reproduction of the results presented in “An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids” [1].</p> <p>To the best or our knowledge, these are the first MRI images of cerebral organoids. This MRI dataset comprises nine growing wildtype cerebral organoids imaged over the time period of 64 days, resulting in 45 individual samples. The MRI sequences encompass T2*-w and DTI. For details we kindly refer to [1].</p> <p>Dataset structure:</p> <ol> <li>The file data_overview.csv contains one row per image with organoid number and day of differentiation</li> <li>The raw MRI and DTI data is in MRI_raw_data/</li> <li>The annotations for organoid segmentation, global cysticity classification and local cyst segmentation are in annotations/</li> </ol> <p>Use the code on GitHub (<a href="https://github.com/deiluca/cerebral_organoid_quant_mri">https://github.com/deiluca/cerebral_organoid_quant_mri</a>) to 1) cut the raw MRI images into one image per organoid 2) to assign the correct organoid numbers (1-9) across all images and 3) to reproduce the results for organoid segmentation, global cysticity classification and local cyst segmentation.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.