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Dataset results
95 results for “Epidermal differentiation”
Insights into metabolic changes during epidermal differentiation as revealed by multiphoton microscopy with fluorescence lifetime imaging
<p>Rapid developments in the field of organotypic cultures has generated a growing need for effective quality control measures during tissue development. In this study, we correlate metabolic changes with epidermal differentiation and demonstrate that multiphoton microscopy with fluorescence lifetime imaging (MPM-FLIM) can be applied as a non-invasive approach to monitor epidermal differentiation of keratinocytes with respect to proliferative and differentiated states. Keratinocytes grown at 1.5 mM Ca2+ exhibited increased expression of differentiation markers KRT1 and KRT10 compared to 60 μM Ca2+, and a metabolic shift from glycolysis to mitochondrial respiration. Fitting the fluorescence decay with a biexponential model revealed a decreased relative fraction of intracellular NADH and FAD after high calcium treatment, consistent with increased oxidative phosphorylation. Using these two parameters, the epidermal differentiation process could be monitored over a 96 h period. Implementing discriminating analysis based on k-means clustering generated clusters that correlated well with culturing time, suggesting that this methodology can be employed as part of an automated pipeline for monitoring keratinocyte differentiation.</p>
Convolutional Neural Net (CNN) models for epigenomic landscapes in epidermal differentiation - Basset architecture, classification and regression
<p>Deep learning models trained on epigenomic landscapes in keratinocyte differentiation. The models are Basset convolutional neural networks (Kelley, et al 2016). The dataset used to train these models can be found at https://doi.org/10.5281/zenodo.4062509. The file `nn.ggr.models.basset.clf.tar.gz` contains 10 cross-validated models that were pretrained using ENCODE-Roadmap trained model weights as initialization weights and also 10 cross-validated models that were initialized with random weights. Similarly, the file `nn.ggr.models.basset.regr.tar.gz` contains 10 cross-validated models that were pretrained using the classification model weights as initialization weights and also 10 cross-validated models that were initialized with random weights.</p>
Histological Characterization and Differentiation of Rash From Other Epidermal Growth Factor Receptor (EGFR) Inhibitors
ClinicalTrials.gov study NCT00709878. IPD Sharing: Not stated. Countries: 1. Publications: 24.
Machine learning datasets for epigenomic landscapes in epidermal differentiation
<p>Datasets for training classification and regression models on sequence and epigenomic features. The data used here is generated from an integrative analysis of the Genomics of Gene Regulation dataset (https://www.encodeproject.org/awards/U01HG007919). For classification models, the peak files used to label genomic regions as positives or negatives can be found in `ggr.label_files.tar.gz`. For regression models, the bigwig files used for target signals can be found at the ENCODE portal. he processed dataset stored in hdf5 format files along with processing details are in the file `nn.ggr.hdf5_files.tar.gz`.</p>
CASZ1 is essential for activation of epidermal terminal differentiation
GEO Series GSE241873. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
AHR-TFAP2A regulates epidermal differentiation in response to environmental cues
GEO Series GSE226047. Homo sapiens. 22 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Identification of bone morphogenetic protein (BMP)-7 as a key instructive factor for human epidermal Langerhans cell differentiation and proliferation
GEO Series GSE49085. Homo sapiens. 6 samples. Type: Expression profiling by array.
Long-term expansion and differentiation of adult murine epidermal stem cells in three-dimensional organoid cultures
GEO Series GSE104521. Mus musculus. 32 samples. Type: Expression profiling by high throughput sequencing.
Differential cell composition and split epidermal differentiation in human palm, sole, and hip skin
GEO Series GSE193101. Homo sapiens. 30 samples. Type: Expression profiling by high throughput sequencing.
SPT6 Promotes Epidermal Differentiation and Blockade of an Intestinal Phenotype through Control of Transcriptional Elongation
GEO Series GSE153129. Homo sapiens. 24 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Transcription factor p63 bookmarks genomic loci in epithelial cells and regulates a subset of target genes during epidermal differentiation through dynamic enhancers (ChIP-Seq)
GEO Series GSE59824. Homo sapiens. 13 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Human epidermal neural crest stem cells (hEPI-NCSC) - expanded versus pre-differentiated into neural stem cell-like cells
GEO Series GSE42678. Homo sapiens. 2 samples. Type: Expression profiling by array.
Epidermal RORα maintains barrier integrity and prevents allergic inflammation by regulating late differentiation and lipid metabolism
GEO Series GSE275323. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
5C analysis of the Epidermal Differentiation Complex locus reveals distinct chromatin interaction networks between gene-rich and gene-poor TADs in skin epithelial cells
GEO Series GSE102519. Mus musculus. 4 samples. Type: Other.
Isosorbide di-(linoleate/oleate) stimulates pro-differentiation gene expression to restore the epidermal barrier and improve skin hydration
GEO Series GSE157932. Homo sapiens. 6 samples. Type: Expression profiling by array.
The Underlying Dermal Scaffold Affects Molecular Differentiation Markers in Three-Dimensional Human Epidermal Equivalents
GEO Series GSE310114. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
The long non-coding NEAT1 is a DNp63 target gene able to modulate epidermal differentiation [microarray]
GEO Series GSE232654. Homo sapiens. 20 samples. Type: Expression profiling by array.
Transcription factor p63 bookmarks genomic loci in epithelial cells and regulates a subset of target genes during epidermal differentiation through dynamic enhancers
GEO Series GSE59827. Homo sapiens. 21 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
The volume-regulated anion channel LRRC8 is involved in initiation of epidermal differentiation and is deregulated in psoriasis
GEO Series GSE289090. Homo sapiens. 16 samples. Type: Expression profiling by high throughput sequencing.
ΔNp63-Senataxin circuit controls keratinocyte differentiation by promoting the transcriptional termination of epidermal genes
GEO Series GSE193205. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing.
ScienceDex guides
Understand access before you commit
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.