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2,182 results for “organoid”
Spatio-temporal, optogenetic control of gene expression in organoids
<p>Organoids derived from stem cells become increasingly important to study human development and to model disease. However, methods are needed to control and study spatio-temporal patterns of gene expression in organoids. To this aim, we combined optogenetics and gene perturbation technologies to activate or knock-down RNA of target genes, at single-cell resolution and in programmable spatio-temporal patterns. To illustrate the usefulness of our approach, we locally activated Sonic Hedgehog (<em>SHH</em>) signaling in an organoid model for human neurodevelopment. High-resolution spatial transcriptomic and single-cell analyses showed that this local induction was sufficient to generate stereotypically patterned organoids in three dimensions and revealed new insights into <em>SHH</em>’s contribution to gene regulation in neurodevelopment.</p> <p>With this study, we propose optogenetic perturbations in combination with spatial transcriptomics as a powerful technology to reprogram and study cell fates and tissue patterning in organoids.</p>
Machine learning-assisted exploration of multidrug-drug administration regimens for organoid arrays
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Immunogenicity of autologous and allogeneic human primary cholangiocyte organoids
Open the record for dataset details and reuse information.
Single-cell RNAseq of Day 49 midbrain organoids from healthy and alpha-synuclein triplication iPSC lines
<p>Unbiased single-cell RNAseq of Day 49 midbrain dopaminergic organoids (10x 3' v3) of the Patikas et al. publication.</p> <p> </p> <p>The mono-unt-celltypes.h5ad refers to the object shown at Fig 2A and contains 3 cell lines:</p> <ol> <li>KOLF2 ( Control cell line)</li> <li>SNCA-3x alpha Synuclein triplication Parkinson's Disease patient-derived iPSC line</li> <li>SNCA-corr (SNCA-3x isogenic control with the triplication mutation corrected)</li> </ol> <p>The all-celltypes.h5ad refers to the object shown at Fig 4 onwards and contains the 3 cell lines that are included in model-dataset.h5ad and 7 other single-cell RNAseq samples:</p> <ol> <li>SNCA-3x+KOLF2 (chimera organoid condition of SNCA-3x and KOLF2 of iPSCs grown together in a midbrain organoid)</li> <li>SNCA-corr+KOLF2 (chimera organoid condition of SNCA-corr and KOLF2 of iPSCs grown together in a midbrain organoid)</li> <li>5 paired rotenone conditions. For each condition (KOLF2, SNCA-3x, SNCA-corr, SNCA-3x+KOLF2, SNCA-corr+KOLF2) a paired condition with acute 24h rotenone treatment in an antioxidant-free medium.</li> </ol>
Source Data and Scripts - Reconstitution of Human Brain Cell Diversity in Organoids via Four Protocols
<p>Source data and scripts associated with the manuscript "<em>Reconstitution of Human Brain Cell Diversity in Organoids via Four Protocols</em>" (Naas et al. 2024, <em>bioRxiv</em>, <a href="https://doi.org/10.1101/2024.11.15.623576" rel="nofollow">DOI: 10.1101/2024.11.15.623576</a>). Corresponding scripts to reproduce all figures and tables presented in the manuscript are also available on <a href="https://github.com/jn-goe/brain_organoids_four_protocols">https://github.com/jn-goe/brain_organoids_four_protocols</a>.</p> <div> <div> <p>The therein introduced NEST-Score is available as R package on <a href="https://github.com/jn-goe/NESTScore">https://github.com/jn-goe/NESTScore</a>.</p> <p>The interactive Shiny App data explorer is available on <a href="https://vienna-brain-organoid-explorer.vbc.ac.at/">https://vienna-brain-organoid-explorer.vbc.ac.at</a>.</p> </div> </div>
Underlying data_Survey on human brain organoids [version 2]
<p> [version 2] Data on a survey of Japanese public attitudes toward human brain organoid research in Dec. 2022.</p>
Extended data_Survey on human brain organoids [version 2]
<p>[Version 2] The detail of the questionnaire, demographic information, and correlation tables of a survey of Japanese public attitudes toward human brain organoid research in Dec. 2022.</p>
A large and diverse brain organoid dataset of 1,400 cross-laboratory images of 64 trackable brain organoids from four different clones
<p>This dataset is presented in the paper <strong><span>A large and diverse brain organoid dataset of 1,400 cross-laboratory images of 64 trackable brain organoids from four different clones</span></strong></p> <p> </p> <p>This dataset encompasses two sources of data:</p> <ol> <li>A comma-separated values ('CSV') file. This file serves as a key to our dataset with one image per row. Each image is represented by its image identifier ('img_id') with the format [org_id]_[clone]_d[imaging_day]_[imaging_lab]. For each image, the CSV file also specifies the organoid size for convenience. Alternatively, the organoid size can be calculated using the ground truth organoid segmentation (org_segGT). </li> <li>For each row of the CSV file, we provide the image and org_segGT. For Lab A, the images are in JPEG format. For lab B, the images are in TIF format. Org_segGT is a manually created binary 2D NumPy array with the same size as the image (1024 x 768 for lab A, 1388 x 1040 for lab B). A value of 1 in org_segGT at position (x, y) means that the same position (x, y) in the corresponding image is covered by the organoid. The image file and the org_segGT file have the following format: [img_id].[jpg|tif] and [img_id].npy. For day 12, organoids were imaged before and after embedding from 96-well plates in 12-well plates, allowing the investigation of well-specific optical properties.</li> </ol> <p>For segmentation and growth monitoring using this dataset, please see <a href="https://github.com/deiluca/robust_monitoring_organoid_growth">https://github.com/deiluca/robust_monitoring_organoid_growth</a>.</p>
Lineage Recording in Human Cerebral Organoids
<p>Data underlying the figures in the publication “Lineage recording in human cerebral organoids”, published in <em>Nat Methods</em>, <strong>2022</strong>, 19, 90–99.</p> <p><a href="https://doi.org/10.1038/s41592-021-01344-8">https://doi.org/10.1038/s41592-021-01344-8</a></p> <p> </p> <p>Table of contents:</p> <p><strong>Supplementary Tables 1-8</strong>: Experimental data supporting the figures of the publication.</p> <p>Data availability</p> <p>Sequence data that support the findings of this study have been deposited in ArrayExpress under the accession codes <a href="http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-10973/">E-MTAB-10973</a>, <a href="http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-10974/">E-MTAB-10974</a> (scRNA-seq data based on 10x Genomics), <a href="http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-10972/">E-MTAB-10972</a> (spatial transcriptomic RNA-seq data based on 10x Visium) and <a href="http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-10971/">E-MTAB-10971</a> (iTracer-perturb data). Bulk sequencing of barcode and scar library data as well as processed sequencing data have been deposited in Mendeley Data at <a href="https://doi.org/10.17632/nj3p3pxv6p">https://doi.org/10.17632/nj3p3pxv6p</a>.</p> <p>Code availability</p> <p>The computational code used in this study is available at GitHub (<a href="https://github.com/quadbiolab/iTracer">https://github.com/quadbiolab/iTracer</a>) or upon request.</p> <p> </p> <p> </p>
RNA-seq data of HepG2 cultured in 2D or as organoids
<p>Cancer cells cultured as organoids could better represent cancer cells grown in vivo and were shown to harbor increased stemness compared with cancer cells in 2D culture. Hep G2 cell line was used as parental cell line to establish a liver cancer organoid line. Hep G2 2D culture and organoid culture were subjected to bulk RNA-seq. </p>
Multiscale light-sheet organoid imaging framework
<p>These two files correspond to the data associated with the light-sheet recordings and time-course recordings utilized for the manuscript entitled "Multiscale light-sheet organoid imaging framework".<br> If access to the imaging data is needed, (which in total comprises of ~700 GB), please contact Prisca Liberali (prisca.liberali@fmi.ch) for more information.</p>
Tailored 3D microphantoms: an essential tool for quantitative phase tomography analysis of organoids
<p>Raw measurement data and processed results for the paper "Tailored 3D microphantoms: An essential tool for quantitative phase tomography analysis of organoids", Biocybern. Biomed. Eng. 45 (2025) 247–257. https://doi.org/10.1016/j.bbe.2025.03.003.</p> <p>Measurement data.zip - Archive contains .bmp files with raw images captured by the cameras in corresponding systems. Included .txt files contain key system parameters used for demodulation and reconstruction.</p> <p>3D refractive index reconstructions.mat - MATLAB structure file containing expected and measured 3D RI distributions for the corresponding systems and organoid samples. </p> <p> </p>
Organoid tiny dataset for stitching (SearchFirst, ome-zarr)
<p>Ome-zarr dataset that is the output of raw tifs (doi <a href="../doi/10.5281/zenodo.12795728">10.5281/zenodo.12795728</a>) processed by Fractal task (see .json file). </p> <p>Dataset acquired on Yokogawa CV8000 on 2024.06.07 by Nicole Repina, Friedrich Miescher Institute for Biomedical Research</p> <p>Dataset contains 11 fields of view (each 1000 x 1000 pix) acquired with SearchFirst*. Touching fields have 50 pix overlap. </p> <p>60x water objective, 2x2 binning, zyx spacing (6, 0.21667, 0.21667) um per pixel. Each field of view has 6 z-slices.</p> <p>Mouse small intestinal organoids (day 4) immunostained with the following dyes (2 fluorescence channels):</p> <p>Channel 1 (C01, 405nm) = DAPI nuclear stain<br>Channel 2 (C02, 488nm) = B-catenin membrane stain</p> <p>*In SearchFirst, the sample is first imaged with a first-pass low-magnification objective (e.g. 4x). An object detection algorithm is then used to identify object-containing regions. Next, the second-pass acquisition is performed with a higher magnification objective that only acquires object-containing regions. The objects are thus imaged not in a tiled grid. Further, some fields may be standalone, while others may have one or more touching fields that are acquired with the specified pixel overlap.</p>
Organoid tiny dataset for stitching (tiled, tif input)
<p>Dataset acquired on Yokogawa CV8000 on 2023.11.29 by Nicole Repina, Friedrich Miescher Institute for Biomedical Research</p> <p>Dataset contains 3 x 4 (12) tiled fields of view (each 1000 x 1000 pix) acquired with 50 pix overlap. Partial tile (gridded) acquisition.</p> <p>60x water objective, 2x2 binning, zyx spacing (10, 0.21667, 0.21667) um per pixel. Each field of view has 5 z-slices.</p> <p>Mouse small intestinal organoids immunostained with the following dyes (2 fluorescence channels):</p> <p>Channel 1 (C01, 405nm) = DAPI nuclear stain<br>Channel 3 (C03, 568nm) = B-catenin membrane stain</p>
Organoid tiny dataset for stitching (tiled, ome-zarr)
<p>Ome-zarr dataset that is the output of raw tifs (doi <a href="../doi/10.5281/zenodo.12794818">10.5281/zenodo.12794818</a>) processed by Fractal task (see .json file). </p> <p>Dataset acquired on Yokogawa CV8000 on 2023.11.29 by Nicole Repina, Friedrich Miescher Institute for Biomedical Research</p> <p>Dataset contains 3 x 4 (12) tiled fields of view (each 1000 x 1000 pix) acquired with 50 pix overlap. Partial tile (gridded) acquisition.</p> <p>60x water objective, 2x2 binning, zyx spacing (10, 0.21667, 0.21667) um per pixel. Each field of view has 5 z-slices.</p> <p>Mouse small intestinal organoids immunostained with the following dyes (2 fluorescence channels):</p> <p>Channel 1 (C01, 405nm) = DAPI nuclear stain<br>Channel 3 (C03, 568nm) = B-catenin membrane stain</p>
Modeling Lewy Body Disease with SNCA Triplication iPSC-Derived Cortical Organoids and Identifying Therapeutic Drugs
<p><span>This repository contains the source code for the single-cell and single-nuclei RNA sequencing data analysis for the study <strong><span>Modeling Lewy Body Disease with SNCA Triplication iPSC-Derived Cortical Organoids and Identifying Therapeutic Drugs</span></strong> by Yunjung Jin et al.<br></span></p>
PLayer: A Plug-and-Play Embedded Neural System to Boost Neural Organoid 3D Reconstruction
<p>This dataset supports the study titled "PLayer: A Plug-and-Play Embedded Neural System to Boost Neural Organoid 3D Reconstruction." It comprises a total of 539 high-resolution images, each with dimensions of 2048x2048 pixels. The image collection took place roughly over one month. We cultured the neural organoids ourselves based on the STEMdiff™ Cerebral Organoid Kit (STEMCELL Technologies Catalog #08570), aged 19, 34, 71, and 112 days, and the STEMdiff™ Dorsal Forebrain Organoid Differentiation Kit (STEMCELL Technologies Catalog #08620), aged 82 days. All organoids were collected on the same day, rinsed twice with PBS to remove any residual medium, and subsequently fixed with 4.0% (w/v) PFA at 4°C overnight before processing.</p>
Resolving Organoid Brain Region Identities by Mapping Single-Cell Genomic Data to Reference Atlases
<p>Data underlying the figures in the publication “Resolving organoid brain region identities by mapping single-cell genomic data to reference atlases”, published in <em>Cell Stem Cell, </em><strong>2021</strong><em>, </em>28, 1148–1159.</p> <p><a href="https://www.sciencedirect.com/science/article/pii/S1934590921000655">https://www.sciencedirect.com/science/article/pii/S1934590921000655</a></p> <p>Table of contents:</p> <p><strong>1. patscreen_srt.rds</strong>; Numerical data for <em>Figure 7</em>: RNA-seq data of a patterning screen in organoids with an array of small molecules. The dataset is in the rds data format, which can be opened in the R programming language using the function `readRDS()`. Once opened, the dataset is a Seurat object (https://satijalab.org/seurat/) and contains both the transcript counts and the metadata for all samples in the screen. The raw data used in figure 7 was also deposited in ArrayExpress (<a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-10037/">https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-10037/</a>)</p>
Annotation of a human retina organoid before and after imputation
<p>Two Seurat-objects which include the unimputed and DCA-imputed retina organoid data sets, published by Kim <em>et al. </em>, stored in RDS-format.</p>
Mouse Organoids imaged with Dual Oblique Plane Microscopy
<p>This is a dataset to demonstrate segmenting colonies of 3D cells. The primary focus is segmenting mouse organoids images using timelapse dual oblique plane microscopy. There is also a set of data mouse embryonic stem cells imaged using spinning disk confocal microscopy.</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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.