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5,412 results for “Cell differentiation”
LRRK2 G2019S mutation suppresses differentiation of Th9 and Treg cells via JAK/STAT3
<p>Flow cytometry (Immune profiling) Tidy Data: Figure 1d,e, 2d</p> <p>ELISA Tidy Data: Figure 2c, 4b</p> <p>qRT-PCR Tidy Data: Figure 1b, 3a, 4c,d</p> <p>Cell count Tidy Data: Figure 2b</p> <p> </p> <p> </p>
Differential response of α-synuclein expression to bacterial ligands and metabolites in mouse enteroendocrine cells
<p>Dataset for manuscript <em>"<strong> </strong></em><strong>α</strong><strong>-synuclein expression in response to bacterial ligands and metabolites in gut enteroendocrine cells</strong><em>". </em>Tabs in excel file are title with the figure number. </p>
Single-Cell Profiling of CD8+ T Cells in Acute Myeloid Leukemia Reveals a Continuous Spectrum of Differentiation and Clonal Hyperexpansion
<p>Data for the publication <strong>Single-Cell Profiling of CD8<sup>+</sup> T Cells in Acute Myeloid Leukemia Reveals a Continuous Spectrum of Differentiation and Clonal Hyperexpansion</strong></p>
Generation of beta-like cell subtypes from differentiated human induced pluripotent stem cells in 3D spheroids
<p>This repository contains single-cell RNA-sequencing data files (raw FASTQ files generated from Illumina HiSeq sequencing) related to the article entitled "Generation of beta-like cell subtypes from differentiated human induced pluripotent stem cells in 3D spheroids" by Lisa Morisseau et al. (2023) published in the Molecular Omics journal (DOI: 10.1039/d3mo00050h).</p> <p> </p>
Single-cell RNA sequencing identifies shared differentiation paths of mouse thymic innate T cells
<p>scRNA sequencing datasets used in the paper titled 'Single-cell RNA sequencing identifies shared differentiation paths of mouse thymic innate T cells' published in Nature Communications<br> <br> https://www.nature.com/articles/s41467-020-18155-8</p>
Raw differential gene expression data, data S1, from: Molecular cascades and cell type-specific signatures in ASD revealed by single cell genomics
<p>Genomic profiling in post-mortem brain from autistic individuals has consistently revealed convergent molecular changes. What drives these changes and how they relate to genetic susceptibility in this complex condition is not understood. We performed deep single nuclear RNA sequencing (snRNAseq) to examine cell composition and transcriptomics, identifying dysregulation of cell type-specific gene regulatory networks (GRNs) in autism, which we corroborated using snATAC-seq and spatial transcriptomics. Transcriptomic changes were primarily cell type-specific, involving multiple cell types, most prominently interhemispheric and callosal-projecting neurons, interneurons within superficial laminae, and distinct glial reactive states involving oligodendrocytes, microglia, and astrocytes. Autism-associated GRN drivers and their targets were enriched in rare and common genetic risk variants, connecting autism genetic susceptibility and cellular and circuit alterations in the human brain. This data is the raw differential gene expression comparing ASD versus CTL subjects for each cell cluster. </p>
Dataset for: "Parameter identifiability and model selection for partial differential equation models of cell invasion"
<p>This is the dataset accompanying the paper "Parameter identifiability and model selection for partial differential equation models of cell invasion" (https://arxiv.org/abs/2309.01476). It consists of a series of images taken of a barrier assay experiment to study tissue expansion of MDCK cells, along with cell density data in MATLAB format.</p> <p>File structure: the contents of the four zip files should be combined (they were split into four files for practical reasons regarding file size). The data corresponds to eight experiments, four with circular initial conditions, and four with triangular initial conditions, the associated data are located in 04-05-22 exp1/Circle and 04-05-22 exp1/Triangle respectively, each labeled "xy<n>", where <n> from 1 to 8 is an identifier for the experiment. The images under the xy<n>_Phase folders are the raw images taken of the experiment, those under the xy<n>_mask folder are processed images indicating the extend of the spread of the cell population. The DensityCellcyleFraction folder contain data files in MATLAB format. The most relevant is the "density" variable, which is a rank-3 tensor of size 150x150x77 such that density(i,j,k) corresponds to the cell density at location (x_i,y_j) and time t_k. The process for calculating the cell density is described in the paper.</p> <p>Alternatively, the density data is also provided in csv format. In the csv_data folder, xy<n>/t<k>.csv encodes a matrix representing cell density for experiment <n> at time t_k.</p> <p>The code for processing and analysing these data are provided in the "code" folder. It is also available at https://github.com/liuyue002/woundhealing .</p> <p>Abstract of the paper:</p> <p>When employing a mechanistic model to study biological systems, practical parameter identifiability is important for making predictions in a wide range of scenarios, as well as for understanding the mechanisms driving the system behaviour. We argue that parameter identifiability should be considered alongside goodness-of-fit and model complexity as criteria for model selection. To demonstrate, we use a profile likelihood approach to investigate parameter identifiability for four extensions of the Fisher--KPP model, given experimental data from a cell invasion assay. We show that more complicated models tend to be less identifiable, with parameter estimates being more sensitive to subtle differences in experimental procedures, and require more data to be practically identifiable. The results from identifiability analysis can inform model selection, as well as data collection and experimental design.</p>
Datasets for 'Label-free imaging of 3D pluripotent stem cell differentiation dynamics on chip'
<p>Here we publish the datasets associated with our publication ‘Label-free imaging of 3D pluripotent stem cell differentiation dynamics<br> on chip’. 3D cultures of human induced pluripotent stem cells (hiPSCs) were imaged while undergoing definitive endoderm (DE) differentiation.<br> Data were acquired either on the live 3D cultures at various time points during the 3 days DE differentiation, or after fixation and immunostaining, with the 3D cultures being fixed at regular 24 h intervals during differentiation, as to form a timeline. Images were recorded with a standard confocal microscope using a xy-resolution of 0.25 μm/px and a z-resolution of 1 μm/plane.</p>
Figure 4 in Investigation the effects of vitreous humor on proliferation and dedifferentiation of differentiated NTERA2 cells
Figure 4. Western blot analysis show that the NT2 cells are known as perfectly positive cells for OCT4 protein expression.
Figure 2 in Investigation the effects of vitreous humor on proliferation and dedifferentiation of differentiated NTERA2 cells
Figure 2. Cell cycle analysis of NT2-RA cells after treatment with different amounts of VH at (A) 3 days and (B) 6 days post-treatment. After treatment with RA the number of cells at S phase decreased. These numbers were recovered after three and six days treatment with different amounts of VH.
Epigenome-wide analysis reveals specific DNA hypermethylation of T cells during human hematopoietic differentiation
<p>Epigenetic regulation plays an important role in cellular development and differentiation. A detailed map of the DNA methylation dynamics that occur during cell differentiation would contribute to decipher the molecular networks governing cell fate commitment. In this study we used the most recent Illumina MethylationEPIC Beadchip platform to describe the genome-wide DNA methylation changes observed throughout hematopoietic maturation by analyzing multiple hematopoietic cell types at different developmental stages.</p>
Simulation data for "Phylogenetic analysis of migration, differentiation, and class switching in B cells"
<p>Simulation data for https://doi.org/10.1101/2020.05.30.124446.</p> <p>Scripts available at: https://bitbucket.org/kleinstein/projects/src/master/Hoehn2020/</p> <p>data_twostate.tsv: AIRR TSV file of data used for simulations</p> <p>trees_twostate.RData: Tree topologies used to simulate migration</p> <p>simulations.tar.gz: Files used in simulation analyses. File names show:</p> <p><rate (r)>_<rate A to B (r_ab)>_<pi_A>_<repetition></p> <p>laddersims.tar.gz: Files used in ladder tree simulation analyses. File names show:</p> <p><rate (r)>_<rate A to B (r_ab)>_<pi_A>_<number of tips>_<number of trees>_<repetition></p> <p> </p>
RNA-seq - Definitive endoderm differentiation of human pluripotent stem cells in G1 phase
<p>Backup copy of the processed RNA-seq data at http://ngs.sanger.ac.uk/production/endoderm/</p>
Oncogenic signalling is coupled to colorectal cancer cell differentiation state
<p>Mass cytometry and single-cell RNA-sequencing data as well as R Markdown reports to reproduce the figures of our publication.</p> <p>Raw MC data were saved post de-convolution, spillover-compensation, and removal of calibration bead events. Gates for singlets and non-dead cells (low_Pt) are included as logical columns and should be applied prior to usage.</p> <p>As we performed random sampling to equalise cell numbers across conditions, batch normalisation, and used non-linear dimensionality reduction techniques (UMAP and Diffusion Maps), resulting plots may differ slightly from the published figures, yet still support the drawn conclusions. Already normalised and/or sampled data as well as pre-computed UMAP and Diffusion Map coordinates are included in this data set to reproduce the manuscript figures exactly, as shown in the included report “figures_only”. For all details on the batch normalisation and data analysis steps performed, please consult the report “data_analysis” instead.</p>
Bulk RNA-seq dataset of human embryonic stem cells (hESCs) differentiation
<p>The dataset consists of 31 bulk samples obtained from human embryonic stem cells undergoing undirected differentiation. The samples were collected at 16 time points on days 0 and 7-21 of differentiation. The file contains the raw read counts.</p> <p>More details can be found in:</p> <p>SPIRAL: Significant Process InfeRence ALgorithm for single cell RNA-sequencing and spatial transcriptomics\<br> Hadas Biran, Tamar Hashimshony, Tamar Lahav, Or Efrat, Yael Mandel-Gutfreund, and Zohar Yakhini</p> <p> <a href="https://doi.org/10.1101/2022.05.24.493189">https://doi.org/10.1101/2022.05.24.493189</a></p>
CyTOF and Flow Cytometry dataset assocaited with "Early-to-mid stage idiopathic Parkinson's disease shows enhanced cytotoxicity and differentiation in CD8 T-cells in females"
<p>This dataset contains all the raw mass cytometry (CyTOF) and flow cytometry fcs files associated with Capelle <i>et al</i>. '<i>Early-to-mid stage idiopathic Parkinson's disease shows enhanced cytotoxicity and differentiation in CD8 T-cells in females',</i> <i><strong>Nature Communications</strong>, <strong>2023</strong>,</i> In Press.</p><p>The dataset contains the following information:</p><p>1, The folder " CoPImmunoPD Flow Zenodo V2.zip " contains all the raw fcs files of flow cytometry analysis and the excel table with marker information of five staining panels in the initial discovery analysis using fresh blood samples. The folder also includes the fcs files of analyzing cytotoxicity potential within CD8 T cells and of validation analyses using cryopreserved samples. Single-color/fluorochrome staining files have also been provided for the relevant experiments in the given subfolders for compensation.</p><p>2, The folder "<a href="https://zenodo.org/api/files/75c910aa-3615-4eff-a201-9d2d46e33ea0/CoPImmunoPD_CyTOF_Zenodo.zip">CoPImmunoPD_CyTOF_Zenodo.zip</a>" contains all the raw fcs files generated from the CyTOF measurements in the initial discovery analysis.</p><p><strong>To reproduce our published Figures, please be assure to first read all the accompanied readme/excel information annotation files deposited in the corresponding folders within the zip files, all the Source Data files of different main and supplementary Figure subpanels, Methods and/or any other relevant sections in our manuscript.</strong></p>
Tailoring Maintenance Therapy to Cluster of Differentiation 5 Positive (CD5+) Regulatory B Cell Recovery in ANCA Vasculitis
ClinicalTrials.gov study NCT03906227. IPD Sharing: YES. Countries: 1. Publications: 1.
Raw differential gene expression data, data S1, from: Molecular cascades and cell type-specific signatures in ASD revealed by single cell genomics
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Intravital quantification reveals dynamic calcium concentration changes across B cell differentiation stages
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Wolbachia endosymbionts manipulate the self-renewal and differentiation of germline stem cells to reinforce fertility of their fruit fly host
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.