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1,350 results for “iPSC”
Unveiling Genomic Rearrangements in Engineered iPSC Lines
<p>Bionano smap files of Dual Analysis of each detected unique SV not present in parental cells. Only SVs within 12kbp of a canonical gene are presented for clarity.</p>
Aberrant development of pancreatic beta cells derived from human iPSCs with FOXA2 deficiency
<p><strong>Project manager(s)</strong><strong>: </strong>Essam M. Abdelalim</p> <p>Induced pluripotent stem cells (iPSCs) were generated from a patient with a heterozygous deletion of the short arm of chromosome 20 at bands p11.22 to p11.21 (~969 kb deletion), which contains only one gene, <em>FOXA2 </em>(<em>FOXA2<sup>+/-</sup></em>iPSCs) as well as healthy controls (Ctr1 iPSCs and Ctr2 iPSCs). <em>FOXA2<sup>+/-</sup></em>iPSCs were differentiated into different stages of beta cell development to understand the role of FOXA2 during pancreatic beta cell development as described in the article entitled "<strong>Aberrant development of pancreatic beta cells derived from human iPSCs with <em>FOXA2</em> deficiency" by Elsayed et al</strong>. The dataset represents RNA-seq data generated from pancreatic progenitors (PP2) and endocrine progenitors (EPs) derived from Ctr1 iPSCs, Ctr2 iPSCs, and three clones of <em>FOXA2<sup>+/-</sup></em>iPSCs. </p> <p>The file name is: Sample name _overall sample number_read direction_001 where:</p> <p>- PP2-Ctr 1: pancreatic progenitors (PP2) derived from Ctr1 iPSCs (healthy control 1)</p> <p>- PP2-Ctr 2: pancreatic progenitors (PP2) derived from Ctr2 iPSCs (healthy control 2)</p> <p>- PP2-FOX1, PP2-FOX2, and PP2-FOX3: pancreatic progenitors (PP2) derived from three different clones of patient-derived <em>FOXA2<sup>+/-</sup></em>iPSCs.</p> <p>- EP-Ctr1: endocrine progenitors (Eps) derived from Ctr1 iPSCs (healthy control 1)</p> <p>- EP-Ctr2: endocrine progenitors (Eps) derived from Ctr2 iPSCs (healthy control 2)</p> <p>- EP-FOX R1, EP-FOX R2, and EP-FOX R3: endocrine progenitors (EPs) derived from three different clones of patient-derived <em>FOXA2<sup>+/-</sup></em>iPSCs.</p> <p>- The RNA-Seq data were generated from two Ctr-iPSC lines and three FOXA2<sup>+/-</sup>iPSC lines.</p> <p>- Read direction: R1 (Forward), R2 (Reverse).</p>
Flow cytometry data from human iPSC-derived macrophages
<p>Human induced pluripotent cells (iPSCs) were obtained from the HipSci project (http://www.hipsci.org) and differentiated into macrophages using an established protocol (van Wilgenburg, 2013). The genotype_id column of the flow_sample_metadata.txt file contains the canonical HipSci iPSC line name from which the macrophages were differentiated.</p> <p><strong>Data acquisition</strong></p> <p>We used flow cytometry to measure the cell surface expression of three canonical macrophage markers: CD14, CD16 (FCGR3A/FCGR3B) and CD206 (MRC1). Macrophages were cultured in 10 cm tissue-culture treated plates and detached from the plates by incubation in 6 mg/ml lidocaine-PBS solution (Sigma L5647) for 30 minutes followed by gentle scraping. From each cell line we harvested between 300,000-500,000 cells. Detached cells were washed in media, centrifuged at 1200 rpm for 5 minutes and resuspended in flow cytometry buffer (2% BSA, 0.001% EDTA in D-PBS) and split into two wells of a 96-well plate. Nonspecific antibody binding sites were blocked by incubating cells with Human TruStain FcX (Biolegend) for 45 minutes and washing with flow cytometry buffer. Half of the cells were stained for 1 hour with the PE-isotype control (BD 555749) antibody. The other half of the cells were co-stained for 1 hour with following three antibodies: CD14-Pacific Blue (BD 558121), CD16-PE (BD 555407), CD206-APC (BD 550889). After staining, the cells were washed three times. Resuspended cells were filtered through cell-strainer cap tubes (BD 352235) and measured on the BD LSRFortessa Cell Analyzer.</p>
Differential gene expression in iPSC-derived macrophages after IFNg stimulation and Salmonella infection
<p>We used likelihood ratio test implemented in DESeq2 v1.10.0 (test = “LRT”) to test if a model that allowed different mean expression in each condition explained the data better than a null model assuming the same mean expression across conditions. See the manuscript for more details: http://www.biorxiv.org/content/early/2017/05/18/102392 .</p> <p>We used the following commands in DESeq2:<br> #Run DESeq2<br> dds = DESeq2::DESeqDataSetFromMatrix(combined_expression_data_filtered$counts, design, ~condition_name) <br> dds = DESeq2::DESeq(dds, test = "LRT", reduced = ~ 1)</p> <p>#Extract differentially expressed genes in each condition<br> ifng_genes = results(dds, contrast=c("condition_name","IFNg","naive")) <br> sl1344_genes = results(dds, contrast=c("condition_name","SL1344","naive")) <br> ifng_sl1344_genes = results(dds, contrast=c("condition_name","IFNg_SL1344","naive"))</p>
Characterization of a loss-of-function NAPB mutation in monozygotic triplets affected with epilepsy and autism using cortical neurons from proband-derived and CRISPR-corrected iPSC lines. Author names and affiliations
<p>RNA-seq data of matured cortical neurons (8-weeks old) derived from induced pluripoent stem cells (iPSC). There are three replicates (Rep1, Rep2, Rep3) for each sample with Forwad read (R1_001.fastq.gz) and reverse read (R2_001.fastq.gz).</p> <p>CtrlF: Control Father sample</p> <p>CtrlM: Control mother sample</p> <p>NDD_01: Proband sample</p> <p>NDD_04: Proband sample</p> <p>NDD_05: Proband sample</p>
Data from: Multiscale chromatin dynamics and high entropy in plant iPSC ancestors
<p>Plant protoplasts constitute the starting material to induce pluripotent cell masses <em>in vitro</em> competent for tissue regeneration. Dedifferentiation is associated with large-scale chromatin reorganisation and massive transcriptome reprogramming, characterized by stochastic gene expression. How this cellular variability reflects on chromatin organisation in individual cells and what are the factors influencing chromatin transitions during culturing is largely unknown. High-throughput imaging and a custom, supervised image analysis protocol extracting over 100 chromatin features unravelled a rapid, multiscale dynamics of chromatin patterns which trajectory strongly depends on nutrients availability. Decreased abundance in H1 (linker histones) is hallmark of chromatin transitions. We measured a high heterogeneity of chromatin patterns indicating an intrinsic entropy as hallmark of the initial cultures. We further measured an entropy decline over time, and an antagonistic influence by external and intrinsic factors, such as phytohormones and epigenetic modifiers, respectively. Collectively, our study benchmarks an approach to understand the variability and evolution of chromatin patterns underlying plant cell reprogramming <em>in vitro</em>.</p>
Gene expression QTL mapping in stimulated iPSC-derived macrophages provides insights into common complex diseases.
<p>Many disease-associated variants are thought to be regulatory but are not present in existing catalogues of expression quantitative trait loci (eQTL). We hypothesise that these variants may regulate expression in specific biological contexts, such as stimulated immune cells. Here, we used human iPSC-derived macrophages to map eQTLs across 24 cellular conditions. We found that 76% of eQTLs detected in at least one stimulated condition were also found in naive cells. The percentage of response eQTLs (reQTLs) varied widely across conditions (3.7% - 28.4%), with reQTLs specific to a single condition being rare (1.11%). Despite their relative rarity, reQTLs were overrepresented (p=0.05, Fisher's exact test) among disease-colocalizing eQTLs. We nominated an additional 21.7% of disease effector genes at GWAS loci via colocalization of reQTLs, with 38.6% of these not found in the Genotype–Tissue Expression (GTEx) catalogue. Our study highlights the diversity of genetic effects on expression and demonstrates how condition-specific regulatory variation can enhance our understanding of common disease risk alleles.</p>
Dataset: Century Therapeutics, Inc. (IPSC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Molecular and functional variation in iPSC-derived sensory neurons
<p>These files contain summary statistics for gene expression QTLs and splicing QTLs from an RNA-sequencing study of iPSC-derived sensory neurons from 97 individuals.</p>
Data from: Multiscale chromatin dynamics and high entropy in plant iPSC ancestors
Open the record for dataset details and reuse information.
Computationally-informed point of departure evaluation for proarrhythmic cardiotoxicity assessment using 3D engineered cardiac microtissues from human iPSC-derived cardiomyocytes
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>
Dataset related to manuscript: Rapid iPSC inclusionopathy models shed light on formation, consequence and molecular subtype of a-synuclein inclusions
<p>Key Resources Table and tabular data related to Lam, Ndayisaba et al 2024.</p> <p>Code for generating graphs can be found at: doi:10.5281/zenodo.12574231 (version 1), doi:10.5281/zenodo.12574230 (all versions)</p>
A novel UPLC-MS metabolomic analysis-based strategy to monitor the course and extent of iPSC differentiation to hepatocytes
<p>ms2 raw data, peak tables generated in Quantitative Analysis Software from Agilent and Matlab functions for QC-SVRC, data clean-up and analysis for the publication with title "Monitoring the differentiation of iPSC to hepatocytes by means of UPLC-MS metabolomics".</p>
Somatic mutations alter the differentiation outcomes of iPSC-derived neurons (Metadata and AnnData/H5AD files)
<p><strong>Data S1:</strong> Metadata information for the 828,937 processed cells from the DN dataset: donor identity, cell type annotation, pool identifier, 10x sample, time point and replicate information. Related to STAR Methods: Reanalysis of pooled single-cell data (DA).</p> <p><strong>Data S2-S4:</strong> AnnData/H5AD files containing the single-cell gene expression matrices and the metadata for day 11, day 30 and day 52, respectively. The gene expression is normalised and log-transformed, but not scaled. md5 files are also included. Related to STAR Methods: DE analysis between failed and successful lines.</p> <p>#File names:</p> <p><strong>File-Data S1: </strong>suppData1.RDS</p> <p><strong>File-Data S2: </strong>allpools.scanpy.D11.wMetaClustUmapGraph.exprLogNormNotScaled_notKO.h5ad</p> <p><strong>File-Data S3: </strong>allpools.scanpy.D30.wMetaClustUmapGraph.exprLogNormNotScaled_notKO.h5ad</p> <p><strong>File-Data S4: </strong>allpools.scanpy.D52.wMetaClustUmapGraph.exprLogNormNotScaled_notKO.h5ad</p>
Closed loop recordings from IPSC cell culture using planar MEA
<p>Recordings from IPSC cell culture using planar MEA in closed loop with the AlzModel. </p> <p>The script 'alzgraphs.py' is used to display summary statistics. </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>
Keratinocytes derived from patient-specific iPSCs recapitulate the genetic signature of psoriasis disease
<p>Induced pluripotent stem cells (iPSCs) were generated from control (Ctrl) and two psoriasis patients (PsO1 and PsO2) having genetic background of psoriasis. The iPSCs were differentiated into epidermal keratinocytes as described in the article entitled "Keratinocytes derived from patient-specific iPSCs recapitulate the genetic signature of psoriasis disease" by Gowher et al. The dataset represent RNA-seq data generated from keratiocytes derived from ctrl-iPSCs and psoriasis patients iPSCs (PsO1 and PsO2).</p> <p>The file name is : Sample name with replicate_overall sample number_read direction_001 where:</p> <p>- Ctrl Rep1 and Ctrl Rep2: Keratinocytes derived from control (Ctrl-iPSCs). </p> <p>- PSO1 Rep1 and PSO1 Rep2: Keratinocytes derived from psoriasis patient iPSCs (PSO1-iPSCs)</p> <p>- PSO2 Rep1 and PSO2 Rep2: Keratinocytes derived from psoriasis patient iPSCs (PSO2-iPSCs).</p> <p>- RNA-seq data was generated from two biological repliicates (Rep1 and Rep2).</p> <p>- Read direction: R1 (Forward), R2 (Reverse).</p> <p> </p>
QC and WGS around the breakpoints of deletions in the compound heterozygous PRKN-deficient PD iPSC line FINi006-A (FI.CS.PRKNDex2/Dex5-7.@40)
<p>BAM files from WGS on the regions of deletions in both <em>PRKN</em> gene alleles of the iPSC line (clone 18) derived using Sendai virus from PRKN 09/090 patient's fibroblasts </p>
Safety and Early Efficacy of iPSC-Derived Motor Neuron Progenitor Cells (XS228) in Subacute Spinal Cord Injury: A Phase I Trial
ClinicalTrials.gov study NCT06976229. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
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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.