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3,508 results for “reprogramming”
Code and data for "Bordetella adenylate cyclase toxin elicits chromatin remodeling and transcriptional reprogramming that blocks differentiation of monocytes into macrophages"
<p># Code and data for "Bordetella adenylate cyclase toxin elicits chromatin remodeling and transcriptional reprogramming that blocks differentiation of monocytes into macrophages"</p> <p><br> - `pipeline` contains scripts for processing raw reads with `nf-core/rnaseq` Nextflow pipeline<br> - `setup_conda.sh` sets up a conda environment for Nextflow<br> - `samplesheet.csv` describes the samples<br> - `rna-seq-nextflow.sh` runs the analysis<br> - `data` contains the quantifications by Salmon from the pipeline and data for the CRE motif (see `analysis.Rmd` for more details)<br> - `analysis.Rmd` an R notebook containing a code to reproduce conclusions and figures in the paper and additional comments.</p> <p>The R environment (all packages) is reproducibly reconstructed via `renv` - run `renv::restore()` after loading the project.</p> <p>The generated figures and tables are stored in the `outputs` directory.</p> <p> </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>
Reprogramming Energy Homeostasis in Overweight Individuals Via Exercise, Cognitive, and Social Training
ClinicalTrials.gov study NCT06931730. IPD Sharing: YES. Countries: 1. Publications: 1.
High Fat Diet for Cardiac Metabolic Reprogramming
ClinicalTrials.gov study NCT06747429. IPD Sharing: NO. Countries: 0. Publications: 13.
Data from: Oct4 redox sensitivity potentiates reprogramming and differentiation
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Data from: Enhanced resistance to <em>Listeria</em> infection in mice surviving sepsis: The role of lipid metabolism and myeloid cell reprogramming
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Patient-derived organotypic tissue cultures as a platform to evaluate metabolic reprogramming in breast cancer patients
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TGFβRI modulates metabolic reprogramming via the aPKC-PEPCK axis during pupal diapause in the cotton Bollworm, <em>Helicoverpa armigera</em>
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Human airway macrophages are metabolically reprogrammed by IFN-γ resulting in glycolysis-dependent functional plasticity
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Lin28a induces SOX9 and chondrocyte reprogramming via HMGA2 and blunts cartilage loss in mice
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Data from: Reprograming of epigenetic mechanisms controlling host insect immunity and development in response to egg-laying by a parasitoid wasp
<p>Parasitoids are insects that use other insects as hosts. They sabotage host cellular and humoral defenses to promote the survival of their offspring by injecting viruses and venoms along with their eggs. Many pathogens and parasites disrupt host epigenetic mechanisms to overcome immune system defenses, and we hypothesized that parasitoids may utilize the same strategy. We used the ichneumon wasp Pimpla turionellae as a model idiobiont parasitoid to test this hypothesis, with pupae of the greater wax moth Galleria mellonella as the host. We found that parasitoid infestation involves the suppression of host immunity-related effector genes and the modulation of host genes involved in developmental hormone signaling. The transcriptional reprogramming of host genes following the injection of parasitoid eggs was associated with changes in host epigenetic mechanisms. The introduction of parasitoids resulted in a transient decrease in host global DNA methylation and the modulation of acetylation ratios for specific histones. Genes encoding regulators of histone acetylation and deacetylation were mostly downregulated in the parasitized pupae, suggesting that parasitoids can suppress host transcription. We also detected a strong parasitoid-specific effect on host microRNAs regulating gene expression at the post-transcriptional level. Our data therefore support the hypothesis that parasitoids may favor the survival of their offspring by interfering with host epigenetic mechanisms to suppress the immune system and disrupt development.</p>
Intracellular sodium elevation reprograms cardiac metabolism: Metabolomics data
<p><span><span><span><span><span><span><span><span><span><span><span>Intracellular Na elevation in the heart is a hallmark of pathologies where both acute and chronic metabolic remodeling occurs. We assessed whether acute (75μM ouabain 100nM blebbistatin) and chronic myocardial Na<sub>i</sub>load (PLM<sup>3SA </sup>mouse) are causally linked to metabolic remodeling and whether the hypertrophied failing heart shares a common Na-mediated metabolic 'fingerprint'. Control (PLM<sup>WT</sup>), transgenic (PLM<sup>3SA</sup>), ouabain treated and hypertrophied Langendorff-perfused mouse hearts were studied by <sup>23</sup>Na, <sup>31</sup>P, <sup>13</sup>C NMR followed by <sup>1</sup>H NMR metabolomic profiling. Elevated Na<sub>i</sub> leads to <i><u>common </u></i>adaptive metabolic alterations preceding energetic impairment: a switch from fatty acid to carbohydrate metabolism and changes in steady-state metabolite concentrations (glycolytic, anaplerotic, Krebs cycle intermediates). Inhibition of mitochondrial Na/Ca exchanger by CGP37157 ameliorated the metabolic changes. <i>In silico</i> modelling indicated altered metabolic fluxes (Krebs cycle, fatty acid, carbohydrate, amino acid metabolism). Prevention of Na<sub>i</sub> overload or inhibition of Na/Ca<sub>mito</sub>may be a new approach to ameliorate metabolic dysregulation in heart failure.</span></span></span></span></span></span></span></span></span></span></span></p>
Data from: Nanotransfection-based vasculogenic cell reprogramming drives functional recovery in a mouse model of ischemic stroke
<p>Ischemic stroke causes vascular and neuronal tissue deficiencies that could lead to significant functional impairment and/or death. Although progenitor-based vasculogenic cell therapies have shown promise as a potential rescue strategy following ischemic stroke, current approaches face major hurdles. Here we used fibroblasts nanotransfected with <em>Etv2</em>, <em>Foxc2</em>, and <em>Fli1</em> (<em>EFF</em>), to drive reprogramming-based vasculogenesis, intracranially, as a potential therapy for ischemic stroke. Perfusion analyses suggest that intracranial delivery of <em>EFF</em>-nanotransfected fibroblasts led to a dose-dependent increase in perfusion 14 days post-injection. MRI and behavioral tests revealed ~70% infarct resolution and up to ~90% motor recovery for mice treated with <em>EFF</em>-nanotransfected fibroblasts. Immunohistological analysis confirmed increases in vascularity and neuronal cellularity, as well as reduced glial scar formation in response to treatment with <em>EFF</em>-nanotransfected fibroblasts. Altogether, our results suggest that vasculogenic cell therapies based on nanotransfection-driven (i.e., non-viral) cellular reprogramming represent a promising strategy for the treatment of ischemic stroke.</p>
Data from: Integrated network analysis identifies fight-club nodes as a class of hubs encompassing key putative switch genes that induce major transcriptome reprogramming during grapevine development
We developed an approach that integrates different network-based methods to analyze the correlation network arising from large-scale gene expression data. By studying grapevine (Vitis vinifera) and tomato (Solanum lycopersicum) gene expression atlases and a grapevine berry transcriptomic data set during the transition from immature to mature growth, we identified a category named "fight-club hubs" characterized by a marked negative correlation with the expression profiles of neighboring genes in the network. A special subset named "switch genes" was identified, with the additional property of many significant negative correlations outside their own group in the network. Switch genes are involved in multiple processes and include transcription factors that may be considered master regulators of the previously reported transcriptome remodeling that marks the developmental shift from immature to mature growth. All switch genes, expressed at low levels in vegetative/green tissues, showed a significant increase in mature/woody organs, suggesting a potential regulatory role during the developmental transition. Finally, our analysis of tomato gene expression data sets showed that wild-type switch genes are downregulated in ripening-deficient mutants. The identification of known master regulators of tomato fruit maturation suggests our method is suitable for the detection of key regulators of organ development in different fleshy fruit crops.
Data from: Early life exposure to low levels of AHR agonist PCB126 (3,3',4,4',5-pentachlorobiphenyl) reprograms gene expression in adult brain
Early life exposure to environmental chemicals can have long-term consequences that are not always apparent until later in life. We recently demonstrated that developmental exposure of zebrafish to low, non-embryotoxic levels of 3,3',4,4',5-pentachlorobiphenyl (PCB126) did not affect larval behavior, but caused changes in adult behavior. The objective of this study was to investigate the underlying molecular basis for adult behavioral phenotypes resulting from early life exposure to PCB126. We exposed zebrafish embryos to PCB126 during early development and measured transcriptional profiles in whole embryos, larvae and adult male brains using RNA-sequencing. Early life exposure to 0.3 nM PCB126 induced cyp1a transcript levels in 2-dpf embryos, but not in 5-dpf larvae, suggesting transient activation of aryl hydrocarbon receptor with this treatment. No significant induction of cyp1a was observed in the brains of adults exposed as embryos to PCB126. However, a total of 2209 and 1628 genes were differentially expressed in 0.3 nM and 1.2 nM PCB126-exposed groups, respectively. KEGG pathway analyses of upregulated genes in the brain suggest enrichment of calcium signaling, MAPK and notch signaling, and lysine degradation pathways. Calcium is an important signaling molecule in the brain and altered calcium homeostasis could affect neurobehavior. The downregulated genes in the brain were enriched with oxidative phosphorylation and various metabolic pathways, suggesting that the metabolic capacity of the brain is impaired. Overall, our results suggest that PCB exposure during sensitive periods of early development alters normal development of the brain by reprogramming gene expression patterns, which may result in alterations in adult behavior.
Tri-modal single cell profiling reveals a distinct pediatric CD8αα T cell subset and broad age-related molecular reprogramming across the T cell compartment
<p>Age-associated changes in the T cell compartment are well-described. However, limitations of current single- or bi-modal single-cell assays, including flow cytometry, RNA-seq, and CITE-seq, have restricted our ability to deconvolve more complex cellular and molecular changes. Here, we profile more than 300,000 single T cells from healthy children (11–13 yrs) and older adults (55–65 yrs) using the trimodal assay, TEA-seq (protein, RNA, and chromatin accessibility), revealing that molecular programming of T cell subsets shifts toward a more activated basal state with age. Naive CD4 T cells, considered relatively resistant to aging, exhibited pronounced transcriptional and epigenetic reprogramming. Moreover, we discovered a novel CD8aa T cell subset lost with age that is epigenetically poised for rapid effector responses and displays distinct inhibitory, co-stimulatory and tissue homing properties. Together, these data reveal new insights into age-associated changes in the T cell compartment that may contribute to differential immune responses.</p>
The Data for "Improved Sendai Viral System for Reprogramming to Naive Pluripotency"
<p>These data are the original data used in the report "Improved Sendai Viral System for Reprogramming to Naive Pluripotency".</p>
Processed Seurat objects from scRNA-seq data of the aging subventricular zone (SVZ) neurogenic niche with partial reprogramming
<p>This repository contains the processed Seurat objects from the publication "Restoration of neuronal progenitors by partial reprogramming in the aged neurogenic niche" (https://doi.org/10.1038/s43587-024-00594-3).</p> <p>Raw sequencing data is available at the Gene Expression Omnibus (GEO) under accession number GSE224438. Code used to process and analyze the data is available on GitHub (https://github.com/gitlucyxu/SVZreprogramming). </p> <p>These Seurat objects are filtered to high-quality singlets for samples included in the publication. Descriptions and notable metadata:</p> <ul> <li>svz_iOSKM_cohort1_toshare.rds - SVZ after whole-body partial reprogramming, cohort 1 <ul> <li>Celltype - cell type annotation</li> <li>Treatment - condition <ul> <li>untr: old control</li> <li>2Dox0: old+OSKM</li> </ul> </li> <li>hash.ID - mouse ID (biological replicate)</li> </ul> </li> <li>svz_iOSKM_cohort2_toshare.rds - SVZ after whole-body partial reprogramming, cohort 2 <ul> <li>Celltype - cell type annotation</li> <li>Age_Treatment - condition <ul> <li>young_untr: young control</li> <li>old_untr: old control</li> <li>old_2Dox0: old+OSKM</li> </ul> </li> <li>hash.ID - mouse ID (biological replicate)</li> </ul> </li> <li>svz_ciOSKM_toshare.rds - SVZ after SVZ-targeted partial reprogramming <ul> <li>Celltype - cell type annotation</li> <li>Age_Treatment - condition <ul> <li>young_untr: young control</li> <li>old_untr: old control</li> <li>old_Dox: old+OSKM(SVZ)</li> </ul> </li> <li>MULTI_classification_rescued - mouse ID (biological replicate)</li> </ul> </li> </ul> <p> </p> <p><em>Updated 2024/07/01 (v2): replaced a corrupted file. </em></p>
Processed MERFISH datasets for brain aging (coronal, sagittal) and rejuvenation (exercise, partial reprogramming)
<p>This repository contains several AnnData ".h5ad" objects containing the processed MERFISH data and metadata for the datasets generated in our study:</p> <blockquote> <p>"aging_coronal.h5ad" - the aging coronal section dataset</p> <p>"aging_sagittal.h5ad" - the aging sagittal section dataset</p> <p>"exercise.h5ad" - exercise dataset</p> <p>"reprogramming.h5ad" - partial reprogramming dataset</p> </blockquote> <p> </p> <p>Key metadata are saved as observation annotations in each object, and include:</p> <blockquote> <p>"mouse_id" - identifier for mouse from which the cell was derived</p> <p>"slide_id" - identifier for MERSCOPE slide</p> <p>"batch" - A and B denote different experimental batches for MERFISH</p> <p>"age" - mouse age in months</p> <p>"cohort" - experimental condition / group</p> <p>"celltype" - cell type annotation</p> <p>"region" - brain region annotation</p> <p>"subregion" - brain subregion annotation (will be more variabile/noisy than region)</p> </blockquote>
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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.