Find research datasets worth reusing

opencc-by-4.0Aug 2022View details →

Single-cell transcriptome analysis of the in vivo response to viral infection in the cave nectar bat Eonycteris spelaea

Bats are reservoir hosts of many zoonotic viruses with pandemic potential in humans. Here, we utilized single-cell transcriptome sequencing (scRNA-seq) to provide detailed comparative analyses of the immune repertoire and the transcriptional responses in the bat lungs upon in vivo infection with a double-stranded RNA virus, Pteropine orthoreovirus PRV3M. Neutrophils were observed to have basally high IDO1 expression, uniquely amongst mammals currently profiled by scRNA-seq. NK/T cells were the most abundant immune cell type in lung tissue, and included three distinct CD8 + effector T cell populations delineated by the differential expression of KLRB1, GFRA2 and DPP4. We identified NK/T clusters which up-regulated genes involved in T-cell activation and effector function early after viral infection. Alveolar macrophages and classical monocytes were key drivers of antiviral interferon signaling. Infection also resulted in the expansion of a CSF1R + population expressing collagen-like genes, which became the predominant myeloid cell type after infection. This work uncovers novel features relevant to viral disease tolerance in bats, lays a foundation for future in vivo and in vitro experimental investigations, and serves as a key resource for comparative immunology studies across bats and other mammals.   This upload is the transcriptome fasta file used for alignment for the dataset.

Figure 4 in Transcriptomic analysis of Bursaphelenchus xylophilus treated by a potential phytonematicide, punicalagin

Figure 4: (CONtiNUed)

Figure 2 in Transcriptomic analysis of Bursaphelenchus xylophilus treated by a potential phytonematicide, punicalagin

Figure 2: Functional annotation statistics of unigenes.

Figure 1 in Transcriptomic analysis of Bursaphelenchus xylophilus treated by a potential phytonematicide, punicalagin

Figure 1: Sequence length distribution of assembled unigenes.

Figure A2 in Transcriptomic analysis of Bursaphelenchus xylophilus treated by a potential phytonematicide, punicalagin

Figure A2: (CONtiNUed)

opencc-by-4.0Jul 2024View details →

Single-cell transcriptomic analysis of B cells reveals new insights into atypical memory B cells in COVID-19

Here, we performed single-cell RNA sequencing of S1 and RBD protein-specific B cells from convalescent COVID-19 patients with different clinical manifestations. This study aimed to evaluate the role and developmental pathway of atypical memory B cells in response to SARS-CoV-2 infection. The results revealed a proinflammatory signature across B cell subsets associated with disease severity, as evidenced by the upregulation of genes such as GADD45B, MAP3K8, and NFKBIA in critical and severe individuals. Furthermore, the analysis of atypical memory B cells suggested a developmental pathway similar to that of conventional memory B cells through germinal centers, as indicated by the expression of several genes involved in germinal center processes, including CXCR4, CXCR5, BCL2, and MYC. Additionally, the upregulation of genes characteristic of the immune response in COVID-19, such as ZFP36 and DUSP1, suggested that the differentiation and activation of atypical memory B cells may be influenced by exposure to SARS-CoV-2 and that these genes may contribute to the immune response for COVID-19 recovery. Our study contributes to a better understanding of atypical memory B cells in COVID-19 and the role of other B cell subsets across different clinical manifestations.

opencc-by-4.0Nov 2018View details →

Recovery and analysis of transcriptome subsets from pooled single-cell RNA-seq libraries

Processed data files for manuscript: "Recovery and analysis of transcriptome subsets from pooled single-cell RNA-seq libraries" <a href="https://doi.org/10.1093/nar/gky1204">https://doi.org/10.1093/nar/gky1204</a> . Scripts for generating figures are found here: https://github.com/rnabioco/scrna-subsets

opencc-by-4.0Aug 2024View details →

Transcriptome analysis of dark-induced bleaching octocoral

<h3>Transcriptome of  dark-induced bleaching soft coral, Lobophytum hsiehi</h3> The raw transcriptomic data has been archived in NCBI BioProject under accession number PRJNA1037697. The transcript reads (transcript_sequence.fasta) deposited here were processed using Trinity, TransDecoder, and CD-HIT. The dataset (transcript_and_deg_information.xlsx) provided transcript annotations and results from the differential expression analysis.

opencc-by-4.0Dec 2023View details →

Analysis and visualization of the Fasciola hepatica spatial transcriptomics dataset

This repository contains various files related to the analysis of the paper: Spatial transcriptomics of a parasitic flatworm provides a molecular map of drug targets and drug-resistance genes.

opencc-by-4.0Apr 2024View details →

Data and Analysis Files Repository: Repurposing Large-Format Microarrays for Scalable Spatial Transcriptomics

Data and Analysis Files from "Repurposing Large-Format Microarrays for Scalable Spatial Transcriptomics" ArraySeq_Method.zip contains the following folder and contents: <ul> <li>STARSolo: All code and count matrix output from fastq spatial barcode demultiplexing. </li> <li>Images: All resolution-downsampled H&E image scans from analyzed tissues</li> <li>Space_Ranger: All 10x Space Ranger output from Visium datasets generated in the paper. </li> <li>Analysis: All scripts for analyzing and plotting Array-seq and Visium datasets generated in this paper. Also contains output h5ad files. </li> </ul> ArraySeq_Barcode_generation_n12.rmd: The script used to generate the Array-seq probes with 12-mer spatial barcodes.

opencc-by-4.0Nov 2024View details →

Comprehensive Transcriptomic Analysis of Spodoptera Frugi-perda Reveals Stage-Specific Gene Expression and P450-Mediated Adaptation Mechanisms

Spodoptera frugiperda is a highly adaptable agricultural pest with a complex life cycle, posing significant challenges to pest control. This study conducted a transcriptomic analysis across nine developmental stages, identifying 6,834 differentially expressed genes (DEGs) and 3,072 stage-specific genes (SSGs) critical for their development and adaptability. Significant gene expression shifts were observed during the transition from late larval stages to adulthood. Notably, a high number of SSGs in 6th instar larvae and adult males were enriched in pathways related to oxidative phosphorylation and neural signaling, indicating high metabolic and reproductive demands. Widely shared DEGs associated with ecdysone signaling and detoxification processes underscore robust adaptation mechanisms.

opencc-by-4.0Sep 2021View details →

Transcriptome analysis of T47D cells and H2A.J-KO derivatives for the paper entitled: The histone variant H2A.J is enriched in luminal epithelial gland cells

H2A.J is a poorly studied mammalian-specific variant of histone H2A. We used immunohistochemistry to study its localization in various human and mouse tissues. H2A.J showed cell-type specific expression with a striking enrichment in luminal epithelial cells of multiple glands including those of breast, prostate, pancreas, thyroid, stomach, and salivary glands. H2A.J was also highly expressed in many carcinoma cell lines and in particular, those derived from luminal breast and prostate cancer. H2A.J thus appears to be a novel marker for luminal epithelial cancers. Knocking-out the H2AFJ gene in T47D luminal breast cancer cells reduced the expression of several estrogen-responsive genes which may explain its putative tumorigenic role in luminal-B breast cancer.

opencc-by-4.0Oct 2021View details →

Dynamic prostate cancer transcriptome analysis delineates the trajectory to disease progression.

This file contains vst-normalized gene expression data along with annotations which can be used to reproduce our findings.

opencc-by-nc-4.0Nov 2022View details →

datset of "Networks and genes modulated by posterior hypothalamic stimulation in patients with aggressive behaviours: Analysis of probabilistic mapping, normative connectomics, and atlas-derived transcriptomics of the largest international multi-centre dataset"

This dataset accompanies the manuscript: "Networks and genes modulated by posterior hypothalamic stimulation in patients with aggressive behaviours: Analysis of probabilistic mapping, normative connectomics, and atlas-derived transcriptomics of the largest international multi-centre dataset." DOI: (https://doi.org/10.1101/2022.10.29.22281666) by Flavia Venetucci Gouveia1,2,3*&dagger;,Jürgen Germann4,5&dagger;, Gavin JB Elias4,5, Alexandre Boutet4,6, Aaron Loh4,5, Adriana Lucia Lopez Rios7,8, Cristina V Torres Diaz9, William Omar Contreras Lopez10,11, Raquel CR Martinez3,12, Erich T Fonoff13, Juan C Benedetti-Isaac14,  Peter Giacobbe 2,15,16, Pablo M Arango Pava17, Han Yan5,18, George M Ibrahim5, 18,19,20, Nir Lipsman2,5,15, Andres M Lozano4,5, Clement Hamani2,5,15* 1. Neuroscience and Mental Health, Hospital for Sick Children Research Institute; Toronto, Canada  2. Sunnybrook Research Institute; Toronto, Canada 3. Division of Neuroscience, Sírio-Libanês Hospital; São Paulo, Brazil 4. Division of Neurosurgery, Department of Surgery, University Health Network, Toronto, Canada 5. Division of Neurosurgery, Department of Surgery, University of Toronto; Toronto, Canada 6. Joint Department of Medical Imaging, University of Toronto; Toronto, Canada 7. Department of Functional and Stereotactic Neurosurgery, University Hospital San Vicente Fundación,     Medellín, Colombia 8. Department of Functional and Stereotactic Neurosurgery, San Vicente Fundación, Rionegro, Colombia 9. Department of Neurosurgery, University Hospital La Princesa; Madrid, Spain 10. Nemod Research Group, Universidad Autónoma de Bucaramanga; Bucaramanga, Colombia 11. Division of Functional Neurosurgery, Department of Neurosurgery, FOSCAL Clinic; Bucaramanga,       Colombia 12. LIM 23, Institute of Psychiatry, School of Medicine, University of São Paulo; São Paulo, Brazil 13. Department of Neurology, Integrated Clinic of Neuroscience, School of Medicine, University of São Paulo;       São Paulo, Brazil. 14. Stereotactic and Functional Neurosurgery Division of the International Misericordia Clinic; Barranquilla,      Colombia 15. Harquail Centre for Neuromodulation, Sunnybrook Health Sciences Centre; Toronto, Canada 16. Department of Psychiatry, University of Toronto; Toronto, Canada 17. Servicio de Neuocirugia Funcional y Esterotaxia, Clinica Comuneros Bucaramanga, Clinica Desa y Clinica       Dime Neurocardiovascular de Cali; Clinica Nueva del Lago, Bogota, Colombia. 18. Division of Neurosurgery, The Hospital for Sick Children; Toronto, Canada 19. Institute of Biomedical Engineering, University of Toronto; Toronto, Canada 20. Institute of Medical Science, University of Toronto; Toronto, Canada &dagger; Flavia Venetucci Gouveia and Jürgen Germann contributed equally to this work and share first authorship. * Corresponding Author: Dr. Flavia Venetucci Gouveia. Neuroscience and Mental Health, Hospital for Sick Children Research Institute. 686, Bay Street, Toronto, ON, M5G 0A4, Canada. flavia.venetuccigouveia@sickkids.ca * Corresponding Author: Dr. Clement Hamani. Sunnybrook Research Institute. 2075 Bayview Ave, S126. Toronto, ON, M4N3M5, Canada. clement.hamani@sunnybrook.ca It contains a zip folder ("estimated_binary_Volume_of_Tissue_Activated.zip") with one file (in nii.gz format) per patient estimating the Volume of Activated Tissue for that patient (the estimated 'reach' of the active DBS stimulation) and a demographics file. The case numbers are identical to Table 1 in the manuscript.

opencc-by-4.0Dec 2022View details →

Meta-analysis of diurnal transcriptomics in mouse liver reveals low repeatability of rhythm analyses

The accumulation of public transcriptomic timeseries data enables robust meta-analyses that were not possible until recently. To assess the consistency of biological rhythms across studies, 57 public mouse liver tissue timeseries totaling 1096 RNA-seq samples were obtained and analyzed. Only the control groups of each study were included, to create comparable data. Technical factors in RNA-seq library preparation were the largest contributors to transcriptome-level differences, beyond biological or experiment-specific factors such as lighting conditions. Core clock genes were remarkably consistent in phase across all studies. Overlap of genes identified as rhythmic across studies was generally low, with no pair of studies having over 60% overlap. Distributions of phases of significant genes were remarkably inconsistent across studies, but the genes that consistently identified as rhythmic had acrophase clustering near ZT0 and ZT12. Despite the discrepancies between single-study analyses, cross-study analyses found substantial consistency. Running compareRhythms on each pair of studies identified a median of only 11% of the identified rhythmic genes as rhythmic in only one of the two studies. Data was integrated across studies in a JIVE analysis, which showed that the top two components of joint within-study variation are determined by time of day. A shape-invariant model with random effects was fit to the genes to identify the underlying shape of the rhythms, consistent across all studies, including identifying 72 genes with consistently multiple peaks. This dataset accumulates the quantified values from the 1096 samples along with the sample and study meta-data, and the results of JTK and BooteJTK methods run on each of the individual studies. It also includes the spline-fit curves results from the Shape Invarient Models (SIM).

opencc-by-4.0Feb 2023View details →

Supporting data and analysis for," A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease", main figures PART 1

This deposit contains the supporting records of images and image analysis  presented in, " A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease".  doi: https://doi.org/10.1101/2022.06.22.497218 Associated Zenodo repositories: <table> <thead> <tr> <th scope="col">Description</th> <th scope="col">DOI</th> </tr> </thead> <tbody> <tr> <td>Main figures PART 1, Figure 1,2,3,5</td> <td>10.5281/zenodo.7653239</td> </tr> <tr> <td>Main figures PART 2, Figure 6</td> <td>10.5281/zenodo.7900973</td> </tr> <tr> <td>Supplemental 3DTC figures: S1, S4, S5, S7, S8, S9</td> <td>10.5281/zenodo.7894632</td> </tr> </tbody> </table> Contents:1) a collection of .zip files contains the 3D tissue cytometry files for tissue analyzed in the manuscript doi: https://doi.org/10.1101/2022.06.22.497218.  This collection includes the individual analyses for figures 2, 3 and 5.  Figure 6 analyses are included in a compansion repository: 10.5281/zenodo.7900973.  Contents of zip files by figure contain at a minimum the .obx and a .tif file which includes the segmented objects and associated measurements for use by VTEA (https://vtea.wiki/).  Additional files may include gate files (.vtg) or max projections (.tif). 2) a collection of zip files containing the RNAScope image files shown in: Figure 1 P,Q. The supplemental figure data for RNAScope. Figures S1,S4 and S5 are found in: 10.5281/zenodo.7894633. Please address any concerns or questions to the authors listed in the deposit or manuscript, doi: https://doi.org/10.1101/2022.06.22.497218

Supporting data and analysis for," A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease", Supplemental 3DTC figures

This deposit contains the supporting records of analysis for 3D cytometry presented in, " A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease".  doi: https://doi.org/10.1101/2022.06.22.497218 found in supplemental figures. Contents: 1) a collection of .zip files contains the 3D tissue cytometry files for tissue analyzed in the manuscript doi: https://doi.org/10.1101/2022.06.22.497218.  This collection includes the individual analyses by figures in the supplemental figure data for 3D tissue cytometry.  The main figure data is found at: 10.5281/zenodo.7653239 and 10.5281/zenodo.7900973. 2) a collection of zip files containing the RNAScope image files shown in:  Figures S1,S4 and S5. The main RNAScope figure data is found at: 10.5281/zenodo.7653239 Please address any concerns or questions to the authors listed in the deposit or manuscript, doi: https://doi.org/10.1101/2022.06.22.497218

Comparative transcriptomic analysis reveals coordinated mechanisms of different genotypes of common vetch in response to Al stress

Supplementary Table

Supporting data and analysis for," A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease", main figures PART 2

This deposit contains the supporting records of analysis for 3D cytometry presented in, " A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease".  doi: https://doi.org/10.1101/2022.06.22.497218 Contents: 1) a collection of .zip files contains the 3D tissue cytometry files for tissue analyzed in the manuscript doi: https://doi.org/10.1101/2022.06.22.497218.  This collection includes the individual analyses for figure 6 analyses. Contents of zip files by figure contain at a minimum the .obx and a .tif file which includes the segmented objects and associated measurements for use by VTEA (https://vtea.wiki/).  Additional files may include gate files (.vtg) or max projections (.tif).   Please address any concerns or questions to the authors listed in the deposit or manuscript, doi: https://doi.org/10.1101/2022.06.22.497218

Proteomics analysis for: The platelet transcriptome and proteome in Alzheimer's disease and aging: an exploratory cross-sectional study

Alzheimer’s disease (AD) and aging are associated with platelet hyperactivity. However, the mechanisms underlying abnormal platelet function in AD and aging are yet poorly understood. To explore the molecular profile of AD and aged platelets, we investigated platelet activation (i.e., CD62P expression), proteome and transcriptome in AD patients, non-demented elderly, and young individuals as controls. AD, aged and young individuals showed similar levels of platelet activation based on CD62P expression. However, AD and aged individuals had a proteomic signature suggestive of increased platelet activation compared with young controls. Transcriptomic profiling suggested the dysregulation proteolytic machinery involved in the regulation of platelet function, particularly in the ubiquitin-proteasome system in AD and autophagy in aging. The functional implication of these transcriptomic alterations remains unclear and requires further investigations.