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11,105 results for “single cells”
Data from: Effect of stretching on inflammation in a subcutaneous carrageenan mouse model analyzed at single-cell resolution
<p>Understanding the factors that influence the biological response to inflammation is crucial, due to its involvement in physiological and pathological processes, including tissue repair/healing, cancer, infections, and autoimmune diseases. We have previously demonstrated that in vivo stretching can reduce inflammation and increase local pro-resolving lipid mediators in rats, suggesting a direct mechanical effect on inflammation resolution. Here, we aimed to explore further the effects of stretching at the cellular/molecular level in a mouse subcutaneous carrageenan-inflammation model. Stretching for 10 minutes twice a day reduced inflammation, increased the production of pro-resolving mediator pathway intermediate 17-HDHA at 48h post carrageenan injection, and decreased both pro-resolving and pro-inflammatory mediators (e.g., PGE<sub>2</sub> and PGD<sub>2</sub>) at 96h. ScRNAseq analysis of inflammatory lesions at 96h showed that stretching increased the expression of both pro-inflammatory (<em>Nos2</em>) and pro-resolution (<em>Arg1</em>) genes in M1 and M2 macrophages at 96 hours. An intercellular communication analysis predicted specific ligand-receptor interactions orchestrated by neutrophils and M2a macrophages, suggesting a continuous neutrophil presence recruiting immune cells such as activated macrophages to contain the antigen while promoting resolution and preserving tissue homeostasis.</p>
Oxygen concentration profiles data and code from: Langet et al., 2023, Single-celled bioturbators: benthic foraminifera mediate oxygen penetration and prokaryotic diversity in intertidal sediment
<p>This archive contains the data of measured oxygen profile and the scripts used to generate the oxygen surface fluxes for the article</p><p>Langlet, D., Mermillod-Blondin, F., Deldicq, N., Bauville, A., Duong, G., Konecny, L., Hugoni, M., Denis, L., and Bouchet, V. M. P.: Single-celled bioturbators: benthic foraminifera mediate oxygen penetration and prokaryotic diversity in intertidal sediment, Biogeosciences, 20, 4875–4891, https://doi.org/10.5194/bg-20-4875-2023, 2023.</p>
High quality genomes produced from single MinION flow cells clarify polyploid and demographic histories of critically endangered Fraxinus (ash) species
<p>With populations of threatened and endangered species declining worldwide, efforts are being made to generate high-quality genomic records of these species before they are lost forever. Here, we demonstrate that data from single Oxford Nanopore Technologies (ONT) MinION flow cells can, even in the absence of highly accurate short DNA-read polishing, produce high-quality <em>de novo</em> plant genome assemblies adequate for downstream analyses, such as synteny and ploidy evaluations, paleodemographic analyses, and phylogenomics. This study focuses on three North American ash tree species in the genus <em>Fraxinus</em> (Oleaceae) that were recently added to the International Union for Conservation of Nature (IUCN) Red List as critically endangered. Our results support a whole genome triplication at the base of the Oleaceae as well as a subsequent whole genome duplication shared by <em>Syringa</em>, <em>Osmanthus</em>, <em>Olea, and Fraxinus</em>. Finally, we demonstrate the use of ONT long-read sequencing data to reveal patterns in demographic history.</p>
Single-Stranded DNA with Internal Base Modifications Mediates Highly Efficient Gene Insertion in Primary Cells
<p>The manuscript describes the use of ssDNA with chemical modifications in internal bases to improve gene insertion. The dataset contains an excel file with the data corresponding to the figures in this manuscript. In addition, it has results from next-generation sequencing used in Figure 2B. </p>
Single-cell RNA sequencing reveals dysregulated cellular programs in the inflamed epithelium of Crohn's disease patients.
<p><strong>Crohn’s disease (CD) is a complex inflammatory disorder of incompletely understood molecular aetiology. We generated a large single-cell RNA sequencing dataset from the terminal ileal biopsies of two independent cohorts comprising a total of 50 CD patients and 71 healthy controls. We performed transcriptomic analyses to reveal genes, cell types and mechanisms perturbed in CD, leveraging the power of the two cohorts to confirm our findings and assess replicability. In addition to mapping widespread alterations in cytokine signalling, we provide evidence of pan-epithelial upregulation of MHC class I genes and pathways in CD. Using non-negative matrix factorization we revealed intra- and inter-cellular upregulation of expression programs such as G-protein coupled receptor signalling and interferon signalling, respectively, in CD. We observed an enrichment of CD heritability among marker genes for various activated T cell types and myeloid cells, supporting a causal role for these cell-types in CD aetiology. Comparisons between our discovery and replication cohort revealed significant variation in differential gene-expression replicability across cell types. B, T and myeloid cells showed particularly poor replicability, suggesting caution should be exercised when interpreting unreplicated differential gene-expression result in these cell types. Overall, our results provide a rich resource for identifying cell-type specific biomarkers of Crohn’s disease and identifying genes, cell types and pathways that are causally and replicably associated with disease.</strong></p>
Spotiphy enables single-cell spatial whole transcriptomics across the entire section
<p><span>Spatial transcriptomics (ST) has advanced our understanding of tissue regionalization by enabling the visualization of gene expression within whole tissue sections, but the approach remains dogged by the challenge of achieving single-cell resolution without sacrificing whole genome coverage. Here we present Spotiphy (<u>Spot</u> <u>i</u>mager with <u>p</u>seudo single-cell resolution <u>h</u>istolog<u>y</u>), a novel computational toolkit that transforms sequencing-based ST data into single-cell-resolved whole-transcriptome images. In evaluations with Alzheimer’s disease (AD) and normal </span><span>mouse brains, </span><span>Spotiphy</span><span> delivers the most precise cellular compositions. For the first time, </span><span>Spotiphy reveals</span><span> novel astrocyte </span><span>regional specification in mouse brains. It distinguishes sub-populations of DAM (Disease-Associated Microglia) located in different AD mouse brain regions. Spotiphy also identifies multiple spatial domains as well as changes in the patterns of tumor-tumor microenvironment interactions using human breast ST data. Spotiphy enables visualization of cell localization and gene expression in tissue sections, offering key insights into the function of complex biological systems.</span></p>
Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data
<p>Hereditary diseases manifest clinically in certain tissues, however their affected cell types typically remain elusive. Single-cell expression studies showed that overexpression of disease-associated genes may point to the affected cell types. Here, we developed a method that infers disease-affected cell types from the preferential expression of disease-associated genes in cell types (PrEDiCT). We applied PrEDiCT to single-cell expression data of six human tissues, to infer the cell types affected in 1,459 hereditary diseases. Overall, we identified 114 cell types affected by 1,140 diseases. We corroborated our findings by literature text-mining and recapitulation in mouse corresponding tissues. Based on these findings, we explored features of disease-affected cell types and cell classes, highlighted cell types affected by mitochondrial diseases and heritable cancers, and identified diseases that perturb intercellular communication. This study expands our understanding of disease mechanisms and cellular vulnerability.</p>
The VSMC RNA-seq datasets for "Strategies for Arterial Grafts Optimization at Single Cell Level"
<p>This dataset collection contains the human vascular smooth muscle cell (VSMC) bulk RNA-seq data generated in the study <strong><em>Strategies for Arterial Grafts Optimization at Single Cell Level</em></strong>.</p> <p>Primary human VSMCs (CP-H167, Pricella) were cultured in DM/F12 medium supplemented with FBS, EGF, bFGF, insulin, and penicillin (CM-P167, Pricella). The media was replaced per 48 hours. VSMCs were treated with 100ng/ml MIF (HY-P7387, MCE) for 24 hours in the absence or presence of 50 µMol ISO1 (HY-16692, MCE). The VSMCs with the passage number 3 to 5 were used in the study.</p> <p>Total RNA was extracted from cultured VSMC under different treatments using RNeasy Mini Kit (74104, Qiagen). A total amount of 1 µg RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using NEBNext® UltraTM RNA Library Prep Kit for Illumina® (NEB, USA) following manufacturer’s recommendations and index codes were added to attribute sequences to each sample. The library preparations were sequenced on an Illumina Novaseq platform and 150 bp paired-end reads were generated. The index of the reference genome was built using Hisat2 v2.0.5. and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5.</p> <p>The sample info for the BAM files and their index files are listed as follows:</p> <table> <tbody> <tr> <td><strong>SampleCode</strong></td> <td><strong>Sample Description</strong></td> </tr> <tr> <td>C1</td> <td>VSMC (replicate 1)</td> </tr> <tr> <td>C2</td> <td>VSMC (replicate 2)</td> </tr> <tr> <td>C3</td> <td>VSMC (replicate 3)</td> </tr> <tr> <td>M1</td> <td>VSMC treated with MIF (replicate 1)</td> </tr> <tr> <td>M2</td> <td>VSMC treated with MIF (replicate 2)</td> </tr> <tr> <td>M3</td> <td>VSMC treated with MIF (replicate 3)</td> </tr> <tr> <td>M_I1</td> <td>VSMC treated with MIF and ISO1 (replicate 1)</td> </tr> <tr> <td>M_I2</td> <td>VSMC treated with MIF and ISO1 (replicate 2)</td> </tr> <tr> <td>M_I3</td> <td>VSMC treated with MIF and ISO1 (replicate 3)</td> </tr> </tbody> </table>
Efficient statistical method for single-cell QTL analysis
<p>This upload contains data objects associated with our paper "Efficient statistical method for single-cell QTL analysis" introducing the SAIGE-QTL method (preprint available soon!).</p>
SeuratExtend Tutorial: Curated Example Datasets for Single-Cell Analysis
<p>This repository contains example datasets specifically curated for the SeuratExtend tutorial, aimed at facilitating advanced analyses and visualization techniques in single-cell genomics. The datasets have been derived from publicly available data obtained from the 10X Genomics website and have undergone careful preprocessing to serve specific tutorial goals.</p> <p>The collection includes the following datasets:</p> <ol> <li> <p><strong>Myeloid Subset from PBMC 10k Dataset:</strong> This subset focuses on myeloid cells extracted from the larger PBMC 10k dataset, showcasing a preprocessed SeuratObject stored as an RDS file. The data serve as a primary example for demonstrating the capabilities of SeuratExtend differentiation trajectory analysis.</p> </li> <li> <p><strong>Velocyto LOOM File of Myeloid Subset from PBMC 10k Dataset:</strong> Accompanying the first dataset, this Velocyto-generated LOOM file represents a subset of the same myeloid cells, focusing on RNA velocity analyses. It provides a dynamic perspective on gene expression changes over time, enriching the tutorial with advanced single-cell transcriptomics insights.</p> </li> <li> <p><strong>SCENIC-Processed PBMC 3k Dataset:</strong> An outcome of running the SCENIC workflow on the PBMC 3k dataset, this LOOM file represents a refined dataset highlighting gene regulation networks. It serves as an advanced example for users interested in exploring gene regulatory mechanisms using SeuratExtend.</p> </li> </ol> <p>Each dataset has been subsetted and processed, making them ideal for users ranging from beginners to advanced researchers in the field of single-cell genomics. The provided data are intended for educational and tutorial purposes, allowing users to gain hands-on experience with real-world single-cell analysis scenarios.</p> <p> </p>
Data for the paper titled 'Dynamic Molecular Atlas for Cardiac Fibrosis at Single-Cell and Spatial Resolution: CD248 in Orchestrating Fibroblast-Immune Interaction'
<p>The deposited data were employed to generate the figures concerning single-cell RNA (scRNA) and spatial transcriptomic analyses in the paper titled 'Dynamic Molecular Atlas for Cardiac Fibrosis at Single-Cell and Spatial Resolution: CD248 in Orchestrating Fibroblast-Immune Interaction'.</p>
Data from: Evaluating single-cell variability in proteasomal decay
<p>Gene expression is a stochastic process that leads to variability in mRNA and protein abundances even within an isogenic population of cells grown in the same environment. <br>This variation, often called gene-expression noise, has typically been attributed to transcriptional and translational processes while ignoring the contributions of protein decay variability across cells. <br>Here we estimate the single-cell protein decay rates of two degron GFPs in \textit{Saccharomyces cerevisiae} using time-lapse microscopy. <br>We find substantial cell-to-cell variability in the decay rates of the degron GFPs.<br>We evaluate cellular features that explain the variability in the proteasomal decay and find that the amount of 20s catalytic beta subunit of the proteasome marginally explains the observed variability in the degron GFP half-lives. <br>We propose alternate hypotheses that might explain the observed variability in the decay of the two degron GFPs.<br>Overall, our study highlights the importance of studying the kinetics of the decay process at single-cell resolution and that decay rates vary at the single-cell level, and that the decay process is stochastic. <br>A complex model of decay dynamics must be included when modeling stochastic gene expression to estimate gene expression noise.</p>
Segmentation aware probabilistic phenotyping of single-cell spatial protein expression data
<p>This repository contains raw and processed data of the original datasets generated for Lee et al. "Segmentation aware probabilistic phenotyping of single-cell spatial protein expression data".</p> <p>The files Dilution_3_ROI02.ome.tiff, Dilution_3_ROI02_mask.ome.tiff, st_reclustered_expr_data.h5ad contain raw data (as ome tiff), a mesmer-generated segmentation mask, and single-cell quantification in anndata format of the cell pellet data generated for the study.</p> <p>The file tonsil-for-zenodo.tar.gz contains all raw and processed data of the IMC data of the 16 human tonsil ROIs:</p> <ul> <li>imc contains raw tiff stacks of the acquisition</li> <li>mask contains mesmer-generated masks</li> <li>channel.yml contains channel info</li> <li>exp_mat/dc/ contains single cell quantifications using mesmer segmentation</li> </ul>
Brain Transcriptome Single-cell (BTS) Atlas: Anndata, Seurat Object, CellTypist model, and Disorder Risk Geneplot
<p>Brain Transcriptome Single-cell Atlas (BTS) Anndata, Seurat object, and Celltypist model for further use of the atlas. The Celltypist model can be utilized to accurately annotate cell types in new datasets based on the atlas. Plots illustrating the expression profile for 3,380 neurological disorder risk genes across the atlas are also uploaded. Further availability for the data can be requested by the corresponding author.<br><br>This dataset is published in Kim, S., Lee, J., Koh, I.G. <em>et al.</em> An integrative single-cell atlas for exploring the cellular and temporal specificity of genes related to neurological disorders during human brain development. <em>Exp Mol Med</em> <strong>56</strong>, 2271–2282 (2024). https://doi.org/10.1038/s12276-024-01328-6</p>
Deprez et al. - 2020 - A Single-Cell Atlas of the Human Healthy Airways
<p>AnnData object of the single-cell atlas</p> <p> </p> <p>Rationale: The respiratory tract constitutes an elaborate line of defense that is based on a unique cellular ecosystem. Objectives: We aimed to investigate cell population distributions and transcriptional changes along the airways by using single-cell RNA profiling. Methods: We have explored the cellular heterogeneity of the human airway epithelium in 10 healthy living volunteers by single-cell RNA profiling. A total of 77,969 cells were collected at 35 distinct locations, from the nose to the 12th division of the airway tree. Measurements and Main Results: The resulting atlas is composed of a high percentage of epithelial cells (89.1%) but also immune (6.2%) and stromal (4.7%) cells with distinct cellular proportions in different regions of the airways. It reveals differential gene expression between identical cell types (suprabasal, secretory, and multiciliated cells) from the nose (MUC4, PI3, SIX3) and tracheobronchial (SCGB1A1, TFF3) airways. By contrast, cell-type–specific gene expression is stable across all tracheobronchial samples. Our atlas improves the description of ionocytes, pulmonary neuroendocrine cells, and brush cells and identifies a related population of NREPpositive cells. We also report the association of KRT13 with dividing cells that are reminiscent of previously described mouse “hillock” cells and with squamous cells expressing SCEL and SPRR1A/B. Conclusions: Robust characterization of a single-cell cohort in healthy airways establishes a valuable resource for future investigations. The precise description of the continuum existing from the nasal epithelium to successive divisions of the airways and the stable gene expression profile of these regions better defines conditions under which relevant tracheobronchial proxies of human respiratory diseases can be developed. Keywords: single-cell RNAseq; epithelium; nose; trachea; bronchus</p>
Exploring environmental microfungal diversity through serial single cell screening
<p>Sequences, sequence alignments, phylogenetic trees and contamination analysis data associated to the article 'Exploring environmental microfungal diversity through serial single cell screening'.</p>
Scupa: Single-cell unified polarization assessment of immune cells using the single-cell foundation model
<p>This dataset contains the processed scRNA-seq data and code for single-cell unified polarization assessment. Please refer to the article "Scupa: Single-cell unified polarization assessment of immune cells using the single-cell foundation model" for the detailed data and method description.</p> <p>*.rds: the processed scRNA-seq datasets with Universal Cell Embeddings saved as a Seurat object. The Universal Cell Embeddings are saved in the assay 'uce'.</p> <ul> <li>immune_dict_uce.rds: Immune Dictionary.</li> <li>ifnb_treatment_uce.rds: human PBMCs treated with IFN-beta.</li> <li>macrophage_stimulation_uce.rds: human macrophages treated with one or two of IFN-beta, IFN-gamma, TNF-alpha and IL-4.</li> <li>il2_treatment_uce.rds: mouse spleen CD8+ T cells treated with IL-2 or anti-PD-L1.</li> <li>pan_cancer_myeloid_uce.rds: tumor-infiltrating myeloid cells across seven cancer types.</li> </ul> <p>notebooks.zip: Jupyter notebooks containing code for training models and applications to multiple datasets.</p>
Single-cell RNA-Seq-based deconvolution of hairy cell leukemia reveals novel disease drivers and identifies DUSP1 as potential therapeutic target
<p>Microwell-based (BD Rhapsody) scRNA-seq of Hairy Cell Leukemia Patients published in </p> <blockquote> <p><strong>Single-cell RNA-Seq-based deconvolution of hairy cell leukemia reveals novel disease drivers and identifies DUSP1 as potential therapeutic target, Jan-Paul Bohn et al. Submitted.</strong></p> </blockquote> <p>The files will be made available upon publication. <br></p> <h4><strong>Description of the files</strong></h4> <ul> <li><strong>01_raw_counts: </strong>count matrices as CSV as generated by the BD Rhapsody WTA analysis pipeline</li> <li><strong>10_prepare_adata</strong>: Load BD Rhapsody WTA analysis pipeline outputs into AnnData objects and add metadata.</li> <li><strong>20_scrnaseq_qc</strong>: Use a nextflow pipeline (stored in lib/single-cell-analysis-nf) to perform threshold-based filtering of single-cell data and apply SOLO for doublet detection.</li> <li><strong>30_merge_adata</strong>: Merge samples into a single AnnData object, train a scVI model for batch effect removal, and annotate cell-types based on unsupervised clustering</li> <li><strong>40_cluster_analysis</strong>: Identify and investigate subclusters representing cell-states that go beyond the major cell-types</li> <li><strong>50_de_analysis</strong>: Generate pseudobulk and perform differential gene expression analysis using DESeq2 (based on a wrapper script stored in lib/deseq2_workflow)</li> <li><strong>70_downstream_analysis</strong>: Perform pathway analyses and generate figures for publication based on the data generated in the previous steps</li> <li><strong>containers:</strong> Conda environments used for the analysis packed up as singularity containers. </li> </ul>
Human breast cancer PDTX models bulk and single cell RNA sequencing
<p>This dataset includes information relevant to the following manuscript from the labs of Prof. Carlos Caldas (University of Cambridge), and Dr. Long V. Nguyen (Princess Margaret Cancer Centre, University Health Network):</p> <p>Nguyen LV et al. Dynamics and plasticity of human breast cancer single cell-derived clones. Under consideration for publication.</p> <p>Bulk RNA sequencing raw count matrices are provided (RawCounts.csv) along with the normalized count matrices (LogCPMNormCounts.csv).</p> <p>Single cell RNA sequencing count matrix processed from R package metacell is provided (mat.pdx_LN_v2_filt.Rda), along with the mc and mc2d files with information on metacell partitions (mc.pdx_LN_v2_filt.Rda and mc2d.pdx_LN_v2_filt.Rda).</p> <p>Single cell RNA sequencing count matrices processed using Seurat are also provided separately for each PDTX model analysed (STG139.rds, STG201.rds, AB040.rds and IC07.rds).</p> <p>Code and information on data analysis is provided for reviewers in our unpublished manuscript and on Github (https://github.com/cclab-brca/clone-dynamics).</p>
Capturing hidden regulation based on noise change of gene expression level from single cell RNA-seq in yeast
<p> </p> <p><strong>Abstract</strong></p> <p><strong>Recent progress in high throughput single cell RNA-seq (scRNA-seq) has activated the development of data-driven inferring methods of gene regulatory networks. Most network estimations assume that perturbations produce downstream effects. However, the effects of gene perturbations are sometimes compensated by a gene with redundant functionality (functional compensation). In order to avoid functional compensation, previous studies constructed double gene deletions, but its vast nature of gene combinations was not suitable for comprehensive network estimation. We hypothesized that functional compensation may emerge as a noise change without mean change (noise-only change) due to varying physical properties and strong compensation effects. Here, we show compensated interactions, which are not detected by mean change, are captured by noise-only change quantified from scRNA-seq. We investigated whether noise-only change genes caused by a single deletion of STP1 and STP2, which have strong functional compensation, are enriched in redundantly regulated genes. As a result, noise-only change genes are enriched in their redundantly regulated genes. Furthermore, novel downstream genes detected from noise change are enriched in “transport”, which is related to known downstream genes. Herein, we suggest the noise difference comparison has the potential to be applied as a new strategy for network estimation that capture even compensated interaction.</strong></p> <p><em><strong>SourceCode</strong></em></p> <p><strong><em>Main.R</em></strong><br> Please execute code along with the guide in this script</p> <p><em><strong>CreateBASiCS.R</strong></em><br> Create BASiCS chain objects, which include mean and noise data, from the raw count matrix. This step takes ~48 hours. Use of HPC is recommended</p> <p><em><strong>DifferenceTestAll.R</strong></em><br> Execute comparison with regard to the mean and noise of expression level quantified from all cells. Test enrichment to the redundantly regulated genes.</p> <p><em><strong>DifferenceTestCluster.R</strong></em><br> Execute comparison with regard to the mean and noise of expression level quantified from cells belonging to the same cluster. Test enrichment to the redundantly regulated genes.</p> <p><em><strong>STP12_detail_analysis.Rmd</strong></em><br> Output results mentioned in the discussion. Code is placed within the chunk.</p> <p><em><strong>CreateSupFig2.R</strong></em><br> Generate supplement figure 2</p> <p><em><strong>CreateFigure4AndS6.R</strong></em><br> Generate figure 4 and supplement figure 6</p> <p><strong>YeastGeneNameList.csv</strong><br> Data that convert ORF id to the common name<br> This data includes 3 columns, Name, ORF, and SGD.<br> - Name: Common name (e.g., TSC3)<br> - ORF: ORF id with Yeastract format (e.g., YBR058C.A)<br> - SGD: ORF id with SGD format (e.g., YBR058C-A)</p> <p><br> <strong>GeneticInteraction(ORF).csv</strong><br> Genetic interaction data downloaded from Yeastract. This data includes two-column, V1 and V2.<br> The interaction is described as two matrices such as V1 output to V2.</p> <p><strong>NoiseChangeGeneList</strong><br> This folder contains data required for generating tables 1 and S2.</p>
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.