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3,584 results for “Fibroblasts”
Human pancreatic cancer single cell atlas reveals association of CXCL10+ fibroblasts and basal subtype tumor cells
Open the record for dataset details and reuse information.
Fibroblasts reaction to 1 and 3 µM ML-7
<p>Healthy (HF), scar (SF) and Dupuytren (DF) fibroblasts videos showing how cells reacted to 1 and 3 µM ML-7 addition.</p>
h5ad files of secondary fibroblast analysis
<p>h5ad files of secondary fibroblast analysis. For each dataset the nomenclature is</p> <p>NAME_YEAR_CONDITION_ORGANISM_X.h5</p> <p>The X parameter can be of 4 types:</p> <p>- Nothing: it is the raw adata file with all cells.</p> <p>- processed: it contains all cell types, and it has been processed.</p> <p>- fb_processed: same file, but with fibroblast cells only.</p> <p>- fb_robust: same as fb_processed, but with the definitive cell type annotation.</p>
Cancer-Associated Fibroblast Classification in Single-Cell and Spatial Proteomics Data
<p>ometiff: Imaging Data</p> <p>Cell Masks: Masks generated with cellprofiler from ilastik segmentation training</p> <p>cp-output_config: All relevant cellprofiler output and additional configuration files (for example clinical data) necessary to generate the single cell experiments.</p> <p>IMC Data Objects: Single cell experiment RDS files.</p> <p> </p> <p>scRNA-seq_dataobjects: .Rds files containing the clustered breast cancer, colon cancer, HNSCC, NSCLC and PDAC datasets as well as the integrated validation dataset.</p>
h5ad files of secondary fibroblast analysis [HUMAN DATASETS]
<p>h5ad files of secondary fibroblast analysis [HUMAN DATASETS]. For each dataset the nomenclature is</p> <p>NAME_YEAR_CONDITION_ORGANISM_X.h5</p> <p>The X parameter can be of 4 types:</p> <p>- Nothing: it is the raw adata file with all cells.</p> <p>- processed: it contains all cell types, and it has been processed.</p> <p>- fb_processed: same file, but with fibroblast cells only.</p> <p>- fb_robust: same as fb_processed, but with the definitive cell type annotation.</p> <p>It also includes the adata file associated with the cellxgene object: https://cellxgene.cziscience.com/collections/3c4f0970-7614-43de-beb7-6128b3cb74ed</p>
Lipidomics of mitochondria isolated from human fibroblasts
<p>Mitochondrial trifunctional protein (TFP) has a monolysocardiolipin-acyltransferase (MLCL-AT) activity and therefore establishes a link between fatty acid oxidation and cardiolipin remodelling. We hypothesized that TFP deficiency produced changes in cardiolipin and other phospholipid content and composition in mitochondria of TFP cultured fibroblasts. The data showed that phospholipid profiles varied among patient fibroblasts. There was a correlation between genotype and the phospholipid profiles. Two profiles were found when cardiolipin, monolysocardiolipin, and oxidized cardiolipin and other phospholipids were considered, one of them similar to Barth syndrome. We concluded that cardiolipin remodeling may play a role in the pathogenesis of at least some patients with TFP/LCHAD deficiency.</p> <p>A previously described protocol was employed for the identification and quantification of mitochondrial phospholipids (including CL) and oxidized phospholipids by LC-MS/MS [1]. Briefly, lipids were extracted using the Folch method, total phosphate content was quantified, and samples were then analyzed using a LC-MS/MS system. The identification and quantification of the lipid species were achieved with an optimized workflow using SIEVE 2.2 software, and an in-house database.<br> [1]. Chao H, Anthonymuthu TS, Kenny EM, Amoscato AA, Cole LK, Hatch GM et al. Disentangling oxidation/hydrolysis reactions of brain mitochondrial cardiolipins in pathogenesis of traumatic injury. JCI Insight 2018;3(21).</p>
TKS5 mediates the formation of podosome rosettes in pulmonary fibroblasts and promotes fibrosis.
<p>Data and code for recreation of computationally-created figures of the paper: Barbayianni I., Kanellopoulou P. et al. <strong><em>SRC and TKS5-mediated podosome formation promotes Extracellular matrix invasion and pulmonary fibrosis</em></strong>. 2023</p> <p>Raw QuantSeq and re-analyzed single cell RNA-seq data can be found at GSE220982 and GSE122960, respectively.</p>
Mapped data: Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency
<p>This record contains mapped sequencing data for the paper "Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency" by Nair, Ameen <em>et al</em>. It contains single-cell RNA-seq (scRNA) and single-cell ATAC-seq (scATAC) data from a time course of human dermal fibroblasts induced with Yamanaka factors OSKM using a Sendai virus based delivery system. The scRNA and scATAC data is performed at days 0, 2, 4, 6, 8, 10, 12, 14 and the final iPSCs. The experiment was re-performed and single-nucleus multiome (ATAC+RNA) was collected on days 1 and 2. </p> <p>The data is as follows:</p> <p><strong>scATAC</strong>: We used Chromap (commit <a href="https://github.com/haowenz/chromap/tree/6e97125b9">https://github.com/haowenz/chromap/tree/6e97125b9</a>, <a href="https://doi.org/10.1038/s41467-021-26865-w">https://doi.org/10.1038/s41467-021-26865-w</a>) to perform barcode correction, alignment and filtering for each of our samples. The corresponding fragment files (tab separated file containing mapped fragments with columns: chr, start, end, barcode, number of reads) and their tabix indices are available for each sample.</p> <p><strong>scRNA</strong>: We used cellranger v6.0.2 for read mapping and quantification to obtain the counts matrix. We used the GRCh38 2020-A reference. For each sample, the raw and filtered counts matrices are provided. E.g. `D0/raw_feature_bc_matrix.h5` contains an HDF5 object containing gene counts for each barcode and associated metadata for the Day 0 sample. Similarly, the files in `D0/raw_feature_bc_matrix/` contain the same gene x barcode matrix, with the counts matrix in Matrix Market format (`matrix.mtx.gz`), and gene (`features.tsv.gz`) and barcode names (`barcodes.tsv.gz`). </p> <p><strong>multiome</strong>: The ATAC and RNA components are separately processed using the same tools as mentioned above for scATAC and scRNA. Outputs are in the `snATAC` and `snRNA` subdirectories respectively. In addition, the `ATAC.RNA.bc.map.tsv` file contains a map to link snATAC barcodes to snRNA barcodes. </p>
ChromBPNet models and data: Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency
<p>This record contains ChromBPNet models and data used to train the models for the paper "Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency" by Nair, Ameen <em>et al</em>.</p> <p>`data` contains bigwigs and regions (peaks + non-peaks) used for training each of the models. See `data/README.txt` for more details.</p> <p><strong>Models:</strong></p> <p><em>Loading the model:</em></p> <p>The models were trained using tf1.14. The models are provided in h5 format for tf1.14 (py3.7) and SavedModel format for tf2.X. tf2.X tested only for py3.8-11, tf2.8-13.</p> <p>To load the models in tf1.14:</p> <pre><code class="language-python">model = tf.keras.models.load_model("path/to/model.h5")</code></pre> <p>In tf2:</p> <pre><code class="language-python">model = tf.keras.models.load_model("path/to/model_dir")</code></pre> <p>If all fails, you can load the architecture as provided in `model_arch.py` with default parameters (`bpnet_seq` for bias model and `chrombpnet` for chrombpnet model), and then load the weights using `model.load_weights` from the weights provided in the `weights` directory.</p> <p> </p> <p><em>Usage:</em></p> <p>The bias models take as input one-hot sequence of length 2000. It has 2 outputs, a vector of logits of length 2000, and 1 logcounts scalar:</p> <pre><code class="language-python"># seq_one_hot of length B x 2000 x 4 out_bias_logits, out_bias_logcounts = bias_model.predict(seq_one_hot) # out_bias_logits: B x 2000 # out_bias_logcounts: B x 1</code></pre> <p>The ChromBPNet model takes as input a one-hot sequence of length 2000, bias logits of length 2000 and bias log-counts scalar. It has the same output types as the bias model. To run the chrombpnet model to obtain predictions:</p> <pre><code class="language-python">pred_profile, pred_logcounts = chrombpnet_model.predict([seq_one_hot, out_bias_logits, out_bias_logcounts]) # pred_profile: B x 2000 # pred_logcounts: B x 1 </code></pre> <p>If you wish to obtain the "de-biased" predictions (see Methods), simply pass in zeros instead of the bias model predictions as:</p> <pre><code class="language-python">pred_profile_debiased, pred_logcounts_debiased = chrombpnet_model.predict([seq_one_hot, np.zeros((seq_one_hot.shape[0], 2000)), np.zeros((seq_one_hot.shape[0], 1))])</code></pre> <p>To obtain predicted per-base predicted counts (with or without bias):</p> <pre><code class="language-python">pred_per_base_counts = scipy.special.softmax(pred_profile, axis=-1) * (np.exp(pred_logcounts)-1) # pred_per_base_counts: B x 2000 </code></pre> <p>Note that in general predicted counts can't be compared across models as they are not corrected for sequencing depth.</p> <p> </p> <p><em>Note:</em></p> <p>All bias models used across folds are identical, except for the final intercept term in the counts output (see Methods), that is specific to each cell state, fold combination.</p> <p> </p> <p><em>Folds:</em></p> <p>The splits used for training the different folds are as below:</p> Fold Test Chromosomes Validation Chromosomes 0 chr1 chr8, chr10 1 chr2, chr19 chr1 2 chr3, chr20 chr2, chr19 3 chr6, chr13, chr22 chr3, chr20 4 chr5, chr16, chrY chr6, chr13, chr22 5 chr4, chr15, chr21 chr5, chr16, chrY 6 chr7, chr18, chr14 chr4, chr15, chr21 7 chr11, chr17, chrX chr7, chr18, chr14 8 chr9, chr12 chr11, chr17, chrX 9 chr8, chr10 chr9, chr12 <p>Remaining chromosomes were used as the training chromosome for each fold.</p>
Molecular Dynamics Analysis of Fibroblast Activation Protein (FAP) and Dipeptidyl Peptidase IV (DPP-IV) Complexes with Known Inhibitors
<p>MD Input and Trajectories associated with the Manuscript entitled “Solving the Fibroblast Activation Protein (FAP) Conundrum: An in Depth In Silico Analysis Suggests Key Roles for Ala657 and Tyr541 in Endopeptidase Activity and Ligand Selectivity” </p>
Patient-Derived Tumor Organoid and Fibroblast Assembloid Models for interrogation of the tumor microenvironment in Esophageal Adenocarcinoma
<p>This repository contains original microscopy data from the Sharpe et al. paper, "Patient-Derived Tumor Organoid and Fibroblast Assembloid Models for interrogation of the tumor microenvironment in Esophageal Adenocarcinoma" in Cell Reports Methods 2024.</p> <p>All whole slide images were obtained using an LM dotSlide slide scanning microscope in Olympus .vsi format and can be opened using the BioFormats library (for example, in QuPath). Wholemount immunofluorescent stains were imaged on a Leica SP8 laser-scanning confocal microscope and are presented as .IMS files, which allows for visualization and further analysis of the 3D data in Imaris (Oxford Instruments).</p> <p>Data are arranged in subfolders based on the figure they came from (Figures 1-4).</p>
Study of Dietary Additive Phosphorus on Proteinuria and Fibroblast Growth Factor-23
ClinicalTrials.gov study NCT02020785. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Fibroblast Specific Inhibition of LOXL2 and TGFbeta1 Signaling in Patients With Pulmonary Fibrosis.
ClinicalTrials.gov study NCT03928847. IPD Sharing: NO. Countries: 1. Publications: 2.
Dose Escalation Pan-FGFR (Fibroblast Growth Factor Receptor) Inhibitor (Rogaratinib)
ClinicalTrials.gov study NCT01976741. IPD Sharing: Not stated. Countries: 7. Publications: 2.
Study of KRN23 (Burosumab), a Recombinant Fully Human Monoclonal Antibody Against Fibroblast Growth Factor 23 (FGF23), in Pediatric Subjects With X-linked Hypophosphatemia (XLH)
ClinicalTrials.gov study NCT02163577. IPD Sharing: Not stated. Countries: 4. Publications: 3.
Study of Rogaratinib (BAY1163877) vs Chemotherapy in Patients With FGFR (Fibroblast Growth Factor Receptor)-Positive Locally Advanced or Metastatic Urothelial Carcinoma
ClinicalTrials.gov study NCT03410693. IPD Sharing: Not stated. Countries: 29. Publications: 2.
A Study of Erdafitinib Compared With Vinflunine or Docetaxel or Pembrolizumab in Participants With Advanced Urothelial Cancer and Selected Fibroblast Growth Factor Receptor (FGFR) Gene Aberrations
ClinicalTrials.gov study NCT03390504. IPD Sharing: Not stated. Countries: 27. Publications: 1.
Fibroblast Growth Factor-23 (FGF23) Reduction in Predialysis Chronic Kidney Disease (CKD)
ClinicalTrials.gov study NCT00843349. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Changes in Serum Fibroblast Growth Factor 21 (FGF21) Levels Correlated With Exercise
ClinicalTrials.gov study NCT01512368. IPD Sharing: Not stated. Countries: 1. Publications: 3.
The Effect of Lanthanum Carbonate on Fibroblast Growth Factor 23 ( FGF23) in Chronic Kidney Disease
ClinicalTrials.gov study NCT01002872. IPD Sharing: YES. Countries: 1. Publications: 6.
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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.