Find research datasets worth reusing

opencc-by-4.0Aug 2020View details →

Single cell RNA-seq Data - Dissecting the functional reprogramming of the microenvironment in bone marrow fibrosis at the single cell level

We provide results regarding the bioinformatic analysis of scRNA-seq from distinct bone marrow fibrosis mouse models and human samples.   These include: Robjects&Markdown - R markdown and R objects with QC statistics, UMAP and final scRNA-seq data sets. Markers - Excel tables with cluster specific marker genes. DE Genes - Excel table with DE genes when comparing cells in control vs. disease condition per cluster. GO Analysis - Gene enrichment analysis of either DE genes. These are divided by either UP or down regulated genes.

opencc-zeroMay 2024View details →

Data from: Dissecting gene activation and chromatin remodeling dynamics in single human cells undergoing reprogramming

During cell fate transitions, cells remodel their transcriptome, chromatin, and epigenome; however, it has been difficult to determine the temporal dynamics and cause-effect relationship between these changes at the single-cell level. Here, we employ the heterokaryon-mediated reprogramming system as a single-cell model to dissect key temporal events during early stages of pluripotency conversion using super-resolution imaging. We reveal that, following heterokaryon formation, the somatic nucleus undergoes global chromatin decompaction and removal of repressive histone modifications H3K9me3 and H3K27me3 without acquisition of active modifications H3K4me3 and H3K9ac. The pluripotency gene OCT4 (POU5F1) shows nascent and mature RNA transcription within the first 24 h after cell fusion without requiring an initial open chromatin configuration at its locus. NANOG, conversely, has significant nascent RNA transcription only at 48 h after cell fusion but, strikingly, exhibits genomic reopening early on. These findings suggest that the temporal relationship between chromatin compaction and gene activation during cellular reprogramming is gene context dependent.

opencc-by-4.0Mar 2023View details →

Supplementary material for "Marker and source-marker reprogramming of Most Permissive Boolean networks and ensembles with BoNesis"

This archive contains supplementary material for the paper <a href="https://doi.org/10.48550/arXiv.2207.13307">Marker and source-marker reprogramming of Most Permissive Boolean networks and ensembles with BoNesis</a> by Loïc Paulevé (2023): <ul> <li>sources of the executable paper in Markdown and Jupyter notebook formats</li> <li>benchmark dataset</li> <li>scripts for converting the executable paper in LaTeX for arXiv deposit</li> </ul>

opencc-by-4.0Aug 2023View details →

Analysis Products: Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency

This record contains analysis products for the paper "Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency" by Nair, Ameen et al. Please refer to the READMEs in the directories, which are summarized below. The record contains the following files: `clusters.tsv`:  contains the cluster id, name and colour of clusters in the paper scATAC.zip Analysis products for the single-cell ATAC-seq data. Contains: - `cells.tsv`: list of barcodes that pass QC. Columns include:     - `barcode`     - `sample`: (time point)     - `umap1`     - `umap2`     - `cluster`     - `dpt_pseudotime_fibr_root`: pseudotime values treating a fibroblast cell as root     - `dpt_pseudotime_xOSK_root`: pseudotime values treating xOSK cell as root - `peaks.bed`: list of peaks of 500bp across all cell states. 4th column contains the peak set label. Note that ~5000 peaks are not assigned to any peak set and are marked as NA. - `features.tsv`: 50 dimensional representation of each cell  - `cell_x_peak.mtx.gz`: sparse matrix of fragment counts within peaks. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (combine sample + barcode). Rows correspond to peaks in `peaks.bed`  scATAC_clusters.zip Analysis products corresponding to cluster pseudo-bulks of the single-cell ATAC-seq data.  - `clusters.tsv`: contains the cluster id, name and colour used in the paper - `peaks`: contains `overlap_reproducibilty/overlap.optimal_peak` peaks called using ENCODE bulk ATAC-seq pipeline in the narrowPeak format. - `fragments`: contains per cluster fragment files  scATAC_scRNA_integration.zip Analysis products from the integration of scATAC with scRNA. Contains: - `peak_gene_links_fdr1e-4.tsv`: file with peak gene links passing FDR 1e-4. For analyses in the paper, we filter to peaks with absolute correlation >0.45. - `harmony.cca.30.feat.tsv`: 30 dimensional co-embedding for scATAC and scRNA cells obtained by CCA followed by applying Harmony over assay type. - `harmony.cca.metadata.tsv`: UMAP coordinates for scATAC and scRNA cells derived from the Harmony CCA embedding. First column contains barcode. scRNA.zip Analysis products for the single-cell RNA-seq data. Contains: - `seurat.rds`: seurat object that contains expression data (raw counts, normalized, and scaled), reductions (umap, pca), knn graphs, all associated metadata. Note that barcode suffix (1-9 corresponds to samples D0, D2, ..., D14, iPSC) - `genes.txt`: list of all genes - `cells.tsv`: list of barcodes that pass QC across samples. Contains:     - `barcode_sample`: barcode with index of sample (1-9 corresponding to D0, D2, ..., D14, iPSC)      - `sample`: sample name (D0, D2, .., D14, iPSC)     - `umap1`     - `umap2`     - `nCount_RNA`     - `nFeature_RNA`     - `cluster`     - `percent.mt`: percent of mitochondrial transcripts in cell     - `percent.oskm`: percent of OSKM transcripts in cell - `gene_x_cell.mtx.gz`: sparse matrix of gene counts. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (barcode suffix contains sample information). Rows correspond to genes in `genes.txt`  - `pca.tsv`: first 50 PC of each cell - `oskm_endo_sendai.tsv`: estimated raw counts (cts, may not be integers) and log(1+ tp10k) normalized expression (norm) for endogenous and exogenous (Sendai derived) counts of POU5F1 (OCT4), SOX2, KLF4 and MYC genes. Rows are consistent with `seurat.rds` and `cells.tsv` multiome.zip multiome/snATAC: These files are derived from the integration of nuclei from multiome (D1M and D2M), with cells from day 2 of scATAC-seq (labeled D2).  - `cells.tsv`: This is the list of nuclei barcodes that pass QC from multiome AND also cell barcodes from D2 of scATAC-seq. Includes:     - `barcode`     - `umap1`: These are the coordinates used for the figures involving multiome in the paper.     - `umap2`: ^^^      - `sample`: D1M and D2M correspond to multiome, D2 corresponds to day 2 of scATAC-seq     - `cluster`: For multiome barcodes, these are labels transfered from scATAC-seq. For D2 scATAC-seq, it is the original cluster labels.  - `peaks.bed`: This is the same file as scATAC/peaks.bed. List of peaks of 500bp. 4th column contains the peak set label. Note that ~5000 peaks are not assigned to any peak set and are marked as NA. - `cell_x_peak.mtx.gz`: sparse matrix of fragment counts within peaks. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (combine sample + barcode). Rows correspond to peaks in `peaks.bed`. - `features.no.harmony.50d.tsv`: 50 dimensional representation of each cell prior to running Harmony (to correct for batch effect between D2 scATAC and D1M,D2M snMultiome). Rows correspond to cells from `cells.tsv`. - `features.harmony.10d.tsv`: 10 dimensional representation of each cell after running Harmony. Rows correspond to cells from `cells.tsv`. multiome/snRNA: - `seurat.rds`: seurat object that contains expression data (raw counts, normalized, and scaled), reductions (umap, pca),associated metadata. Note that barcode suffix (1,2 corresponds to samples D1M, D2M). Please use the UMAP/features from snATAC/ for consistency. - `genes.txt`: list of all genes (this is different from the list in scRNA analysis) - `cells.tsv`: list of barcodes that pass QC across samples. Contains:     - `barcode_sample`: barcode with index of sample (1,2 corresponding to D1M, D2M respectively)      - `sample`: sample name (D1M, D2M)     - `nCount_RNA`     - `nFeature_RNA`     - `percent.oskm`: percent of OSKM genes in cell - `gene_x_cell.mtx.gz`: sparse matrix of gene counts. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (barcode suffix contains sample information). Rows correspond to genes in `genes.txt`

opencc-by-3.0-usJul 2023View details →

Grandmaternal allergen sensitization reprograms epigenetic and airway responses to allergen in second generation offspring

SUPPLEMENTARY FIGURE LEGEND Supplemental Figure 1. Grandmaternal allergen sensitization during pregnancy regulates differentially methylated cytosines in airway epithelium of second-generation offspring at baseline and after allergen sensitization. Grandmaternal HDM exposure resulted in 1,390 DMCs in airway epithelium of F2 offspring at baseline (F0 HDM•F2 Base vs F0 Veh•F2 Base). F2 HDM sensitization alone resulted in 1,467 DMCs (F0Veh•F2Sens vs F0 Veh•F2 Base). In contrast, HDM sensitization in F2 mice from HDM-exposed founders resulted in 3,756 total DMCs (F0 HDM•F2 Sens vs F0 HDM•F2 Base). N=8 total for each group (n=4 males and n=4 females).   Supplemental Figure 2. Gene ontology analysis of DMCs in airway epithelium of second-generation offspring. Gene ontology analysis of DMCs in airway epithelium of F2 mice identified enriched biological processes (individual small circles), which were then grouped based on functional annotation using Cytoscape Enrichment Map (larger, labeled, black circles). Each small circle represents an individual biological process and size is proportional to number of involved genes, while connecting edges represent shared genes between individual biological processes. (A) Gene ontology analysis showing enriched biological processes due to grandmaternal HDM exposure in F2 offspring at baseline (F0 HDM•F2 Base versus F0 VEH•F2 Base). (B) Gene ontology analysis showing effects of HDM sensitization alone on enriched biological processes in F2 offspring from vehicle-exposed F0 mice (F0 Veh•F2 Sens vs F0 VEH•F2 Base). (C) Gene ontology analysis showing potentiating effects of F0 grandmaternal HDM exposure on enriched biological processes after HDM sensitization in F2 mice (F0 HDM•F2 Sens vs F0 HDM•F2 Base). N=8 total for each group (n=4 males and n=4 females).   Supplementary Figure 3. Differential methylation is linked to altered gene expression in airway epithelium of second-generation offspring. A subset of F2 airway epithelial genes identified during differentially-methylated pathway analyses were analyzed by real-time RT-PCR to assess for expression. Grandmaternal HDM exposure significantly increased Smad3 expression at baseline and potentiated its response after F2 HDM sensitization. Paxillin expression was also increased after HDM sensitization compared to non-sensitized F2 descendants from HDM exposed founders, although this difference did not meet statistical significance (p = 0.06). No differences were observed between Ets3, Cdh3, or Cacna1d expression between groups. *p < 0.05. Delta CT values were analyzed with two-way ANOVA and Tukey’s post-test. N=8 total for each group (n=4 males “open circles” and n=4 females “closed circles”), except for F0 HDM•F2 Base, n=5.   Supplementary Figure 4. Grandmaternal allergen exposure during pregnancy regulates vagal ganglia cytosine methylation in second-generation offspring at baseline and after allergen sensitization. Grandmaternal HDM sensitization during pregnancy resulted in 901 DMCs in F2 mice at baseline (F0 HDM•F2 Base vs F0 Veh•F2 Base). F2 HDM sensitization alone resulted in 1,467 DMCs (F0Veh•F2Sens vs F0 Veh•F2 Base). In contrast, HDM sensitization in F2 mice from HDM-sensitized F0 grandmaternal founders resulted in 1,787 total DMCs (F0 HDM•F2 Sens vs F0 HDM•F2 Base). N=8 total for each group (n=4 males and n=4 females).   Supplementary Figure 5. Grandmaternal allergen sensitization during pregnancy modifies epigenetic signatures in second-generation offspring in a tissue-specific manner. Only rarely were differentially methylated genes shared between vagal ganglia neurons and airway epithelium in all groups. Specifically, neurons and epithelium in non-sensitized F2 mice from grandmaternal HDM-sensitized F0 founders shared only 15 genes in common (light blue circles). Neurons and epithelium in HDM-sensitized F2 mice from Vehicle-exposed grandmaternal founders shared only 9 genes in common (grey circles). Neurons and epithelium in HDM-sensitized F2 mice from HDM-sensitized grandmaternal F0 founders shared only 41 genes in common. N=8 total for each group (n=4 males and n=4 females).   Supplementary Figure 6. Gene ontology analysis of biological process enrichment in vagal ganglia from second-generation offspring. Gene ontology analysis of DMCs in vagal ganglia of F2 mice identified enriched biological processes (individual small circles), which were then grouped based on functional annotation using Cytoscape Enrichment Map (larger, labeled, black circles). Each small circle represents an individual biological process and size is proportional to number of involved genes, while connecting edges represent shared genes between individual biological processes. (A) Gene ontology analysis showing effects of HDM sensitization on biological process enrichment in F2 offspring from vehicle-exposed F0 mice (F0 Veh•F2 Sens vs F0 VEH•F2 Base). (B) Gene ontology analysis showing potentiating effects of F0 grandmaternal HDM sensitization on biological process enrichment in F2 mice after HDM sensitization (F0 HDM•F2 Sens vs F0 HDM•F2 Base). N=8 total for each group (n=4 males and n=4 females).   Supplemental Figure 7. Quantification of airway sensory nerve density using 3D confocal microscopy. Airway epithelial substance P-positive sensory nerves were analyzed in whole mount specimens using immunofluorescence and 3-dimensional confocal microscopy. Total nerve length and the number of branch points were quantified from nerve models generated based on substance P-positive voxels. Top) Representative images of substance P-expressing nerves (purple, top left) and nerve modeling (white filaments, top right). Bottom) Nerve length (left) and the number of branch points (right) were similar between untreated F2 mice from HDM exposed grandmothers and vehicle exposed grandmothers. N=6 both groups (n=3 males “open circles” and n=3 females “closed circles”).   SUPPLEMENTAL TABLES Supplemental Table S1. Pathways identified by DMC analysis that were not present on DMR analysis in airway epithelium of second-generation F2 mice. All DMR-associated pathways were also identified by DMC analysis, as indicated in Table 1 in main text.   Supplemental Table S2. Over-represented transcription factor binding sites in DMRs of airway epithelium and vagal ganglia from F2 mice before and after allergen sensitization.   Supplemental Table S3. Pathway identified by DMC analysis in vagal ganglia of second-generation F2 mice. All DMR-associated pathways were also identified by DMC analysis, as indicated in Table 3 in main text.   SUPPLEMENTARY DATA FILES Lebold&Cook_DMC_Gene List.xlsx.  Lebold&Cook_DMR Gene List.xlsx. Differentially methylated cytosines (DMC) and regions (DMR) source data files.

publicApr 2024View details →

Single cell transcriptomics unveiled that early life BDE-99 exposure reprogrammed the gut-liver axis to promote a pro-inflammatory metabolic signature in male mice at late adulthood (Part 1/2)

Open the record for dataset details and reuse information.

publicOct 2025View details →

Reprogramming feedback strength in gibberellin biosynthesis highlights conditional regulation by the circadian clock and carbon dioxide

Open the record for dataset details and reuse information.

publicApr 2024View details →

Single cell transcriptomics unveiled that early life BDE-99 exposure reprogrammed the gut-liver axis to promote a pro-inflammatory metabolic signature in male mice at late adulthood (Part 2/2)

Open the record for dataset details and reuse information.

publicMay 2024View details →

Data from: Dissecting gene activation and chromatin remodeling dynamics in single human cells undergoing reprogramming

Open the record for dataset details and reuse information.

opencc-by-4.0Jan 2022View details →

List of publicly available cellular reprogramming HTS datasets

These datasets were obtained by manual curation via the following procedure: First, I queried GEO using keywords in the list below.  <ol> <li>Yamanaka</li> <li>Yamanaka AND ("Genome binding/occupancy profiling by high throughput sequen"[Filter]) </li> <li>Reprogramming AND ("Genome binding/occupancy profiling by high throughput sequen"[Filter]) </li> <li>OSKM AND ("Genome binding/occupancy profiling by high throughput sequen"[Filter]) </li> <li>Sox2 AND ("Genome binding/occupancy profiling by high throughput sequen"[Filter]) </li> <li>Sox2 reprogramming AND ("Genome binding/occupancy profiling by high throughput sequen"[Filter]) </li> <li>somatic reprogramming AND ("Genome binding/occupancy profiling by high throughput sequen"[Filter]) </li> <li>somatic cell reprogramming AND ("Genome binding/occupancy profiling by high throughput sequen"[Filter]) </li> <li>Reprogramming pluripotency AND ("Genome binding/occupancy profiling by high throughput sequen"[Filter]) </li> <li>Reprogramming pluripotency </li> <li>Reprogramming pluripotency AND ("Methylation profiling by high throughput sequencing"[Filter]) </li> <li>OSKM AND ("Methylation profiling by high throughput sequencing"[Filter]) </li> <li>Sox2 Reprogramming AND ("Methylation profiling by high throughput sequencing"[Filter]) </li> </ol> Results were selected for manual input if they contained either transcriptomic (e.g., RNA-Sequencing) or epigenomics (e.g., RRBS) data and had at least one intermediate cellular reprogramming timepoint.

opencc-by-4.0Jan 2022View details →

Supplemental Data for "Embryonic Exposure to Tryptophan Yields Bullying Victimization via Reprogramming the Microbiota-Gut-Brain Axis in a Chicken Model"

This is data set associated with an article "Embryonic Exposure to Tryptophan Yields Bullying Victimization via Reprogramming the Microbiota-Gut-Brain Axis in a Chicken Model" by Xiaohong Huang, Jiaying Hu, Haining Peng, and Heng-wei Cheng, including Supplemental Table S1, S2, S3, S4, S5, S6.

opencc-by-4.0Apr 2022View details →

Metabolic data: Inhibition of mitochondrial complex I reverses NOTCH1-driven metabolic reprogramming in T-cell acute lymphoblastic leukemia

T-cell acute lymphoblastic leukemia (T-ALL) is commonly driven by activating mutations in NOTCH1 that facilitate glutamine oxidation. Here we identify oxidative phosphorylation (OxPhos) as a critical pathway for leukemia cell survival and demonstrate a direct relationship between NOTCH1, elevated OxPhos gene expression, and acquired chemoresistance in pre-leukemic and leukemic models. Disrupting OxPhos with IACS-010759, an inhibitor of mitochondrial complex I, causes potent growth inhibition through induction of metabolic shut-down and redox imbalance in NOTCH1-mutated and less so in NOTCH1-wt T-ALL cells. Mechanistically, inhibition of OxPhos induces metabolic reprogramming into glutaminolysis. We show that pharmacological blockade of OxPhos combined with inducible knock-down of glutaminase, the key glutamine enzyme, confers synthetic lethality in mice harboring NOTCH1-mutated T-ALL. We leverage this synthetic lethal interaction to demonstrate that IACS-010759 in combination with chemotherapy containing L-asparaginase, an enzyme that uncovers the glutamine dependency of leukemic cells, causes reduced glutaminolysis and profound tumor reduction in pre-clinical models of human T-ALL. In summary, this metabolic dependency of T-ALL on OxPhos provides a rational therapeutic target.

opencc-by-4.0Dec 2021View details →

Plasmid Sequences for Brophy et al., 2022: Synthetic genetic circuits as a means of reprogramming plant roots

Plasmid Sequences for Brophy et. al. 2022: Synthetic genetic circuits as a means of reprogramming plant roots

opencc-by-4.0Dec 2017View details →

Data from: Extensive epigenetic reprogramming during the life cycle of Marchantia polymorpha

This GFF contains M. polymorpha genes (taken from the official release v3.1, Phytozome 11, phytozome.jgi.doe.gov) and transposable elements and repeats identified in the study (see Materials and Methods).

dryad36/100

Data from: Oct4 redox sensitivity potentiates reprogramming and differentiation

The transcription factor Oct4/Pou5f1 is a component of the regulatory circuitry governing pluripotency and is widely used to induce pluripotency from somatic cells. Here we use domain swapping and mutagenesis to study Oct4's reprogramming ability, identifying a redox-sensitive DNA binding domain cysteine residue (Cys48) as a key determinant of reprogramming and differentiation. Oct4 Cys48 sensitizes the protein to oxidative inhibition of DNA binding activity and promotes oxidation-mediated protein ubiquitylation. Pou5f1C48S point mutation has little effect on undifferentiated embryonic stem cells (ESCs), but upon retinoic acid (RA) treatment causes retention of Oct4 expression, deregulated gene expression and aberrant differentiation. Pou5f1C48S ESCs also form less differentiated teratomas and contribute poorly to adult somatic tissues. Finally, we describe Pou5f1C48S(Janky) mice, which in the homozygous condition are severely developmentally restricted after E4.5. Rare animals bypassing this restriction appear normal at birth but are sterile. Collectively, these findings uncover a novel Oct4 redox mechanism involved in both entry into and exit from pluripotency.

opencc-zeroMay 2024View details →

opencc-by-4.0Jul 2018View details →

Global signaling profiling in a human model of tumorigenic progression indicates a role for alternative RNA splicing in cellular reprogramming.

Data was collected using a LTQ-XL mass spectrometer (Thermo).  Phosphopeptides were enriched from cell extracts from 3 independent biological replicates, and each replicate was analyzed as 3 technical replicates for a total of 9 LC/MS/MS runs per cell line. Cell lines are based on the MCF-10A lineage of human mammary epithelial cells, and include MCF-10A (10A), MCF-10AT (AT), MCF-10ATG3B (TG) and MCF-10ACA1a (CA).