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11,105
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11,105 results for “single cells”
Single-cell transcriptomics data set for plasmodium falciparum 3D7 asexual and sexual stages
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Data for "A unified model-based framework for doublet or multiplet detection in single-cell multiomics data"
<p>This repository contains all the data necessary for replicating, interpreting, and extending the COMPOSITE multiplet detection results featured in our manuscript, 'A Unified Model-Based Framework for Doublet or Multiplet Detection in Single-Cell Multiomics Data'. The data are ready to be directly used as input for the COMPOSITE cloud-based application or the Python package 'sccomposite' to replicate the results.</p>
Supplementary collection of preprocessed and analysed data objects for the paper: "Gene-level alignment of single cell trajectories"
<p>Supplementary collection of h5ad data files containing preprocessed and analysed single-cell reference and query datasets discussed in the case studies of the paper:<em><strong>"Gene-level alignment of single cell trajectories"</strong></em> (<a href="https://doi.org/10.1101/2023.03.08.531713" rel="nofollow">https://doi.org/10.1101/2023.03.08.531713</a>).</p> <p>Our artificial thymic organoid raw sequencing data are available from ArrayExpress (E-MTAB-12720).</p> <p>Below lists the original sources of the literature datasets we downloaded prior to our own preprocessing and trajectory analysis.</p> <ul> <li> <p>Original pan fetal reference data from: Suo, C. et al. (2022) "Mapping the developing human immune system across organs" (<a href="https://doi.org/10.1126/science.abo0510">https://doi.org/10.1126/science.abo0510</a><a href="http://paperpile.com/b/XTpgLG/TQtQ3">), </a>downloaded from <a href="https://developmental.cellatlas.io/fetal-immune">https://developmental.cellatlas.io/fetal-immune</a>.</p> </li> </ul> <ul> <li> <p>Original healthy/IPF data from: Adams, T. S. et al. (2020), "Single-cell RNA-seq reveals ectopic and aberrant lung-resident cell populations in idiopathic pulmonary fibrosis" (<a href="https://doi.org/10.1126/sciadv.aba1983">https://doi.org/10.1126/sciadv.aba1983</a>), downloaded from GEO (GSE136831).</p> </li> </ul> <p>We have run our scripts in <a href="https://github.com/Teichlab/G2G_notebooks">https://github.com/Teichlab/G2G_notebooks</a> to generate new latent embeddings and infer pseudotime trajectories for the above datasets, and used them to model trajectory alignments with our newly developed framework: ‘Genes2Genes’ <a href="https://github.com/Teichlab/Genes2Genes">https://github.com/Teichlab/Genes2Genes</a>. </p>
Supplementary tables for manuscript Dynamics of alternative polyadenylation in single root cells of Arabidopsis thaliana
<p>Supplementary tables for manuscript Dynamics of alternative polyadenylation in single root cells of Arabidopsis thaliana</p>
Gene expression profile at both bulk and single cell level for heterogeneous cell type mixtures of A375, HAP1, THP1, HEK239FT, K562, and A549
<p>We used poly-A bulk RNA sequencing and single-cell RNA sequencing using mixtures of well-characterized purified cell lines to better understand measurement noise between technologies. A375, HAP1, THP1, HEK239FT, K562, and A549 cells were mixed in various proportions to generate four in vivo cell mixtures that mimic tissues. Mixtures of 6, 4, 2, and 1 of the cell types are respectively denoted 6ct, 4ct, 2ct, and 1ct. In the included zip, directories are labeled using this notation, followed by a list of cell types in the mixture after an underscore. In each directory, bulk and single cell count data is included.</p>
scHolography: a computational method for single-cell spatial neighborhood reconstruction and analysis
<p>Analysis code for the paper "scHolography: a computational method for single-cell spatial neighborhood reconstruction and analysis"</p>
Seurat objects for "Single-cell multi-omic analysis of the vestibular schwannoma ecosystem uncovers a nerve injury-like state" (https://doi.org/10.1038/s41467-023-42762-w)
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Processed single cell lienage data in scLTdb
<p><span>Single-cell lineage tracing (scLT) is a powerful technique that integrates DNA barcoding systems with single-cell sequencing technologies. This approach enables the simultaneous measurement of cell lineage and transcriptome or epigenomic profiles at the single-cell level. scLT technologies have been extensively employed in studying lineage relationships and cell fate heterogeneity across various biological systems. However, there is no comprehensive database available to users for searching, analyzing, visualizing, and downloading scLT data. Therefore, we developed scLTdb </span><span>(scltdb.com)</span><span>, the first scLT database, containing </span><span>106</span><span> datasets across </span><span>36</span><span> scLT technologies. The database encompasses approximately </span><span>2.7</span><span> million cells across 13 tissues from 3 species. All datasets in scLTdb were manually curated and analyzed through a uniform workflow. scLTdb provides users with three distinct online modules designed specifically for visualizing and analyzing different modalities of scLT datasets. These modules include the Single Cell Module, which focuses on the analysis of the single cell modality; the Barcode Module, which is dedicated to the analysis of clone modality; and the Integrative Module, which facilitates the analysis of both single cell and clone modalities. Furthermore, scLTdb provides users with online tools to analyze user-provided scLT data. With these features, scLTdb aims to serve as a highly versatile platform, enabling users to conveniently explore and analyze published scLT datasets. Ultimately, scLTdb will play a vital role in promoting the advancement of modern biology.</span></p>
Single and few cell analysis for correlative light microscopy, metabolomics, and targeted proteomics (Data)
<p>Combined data for the manuscript `Single and few cell analysis for correlative light microscopy, metabolomics, and targeted proteomics` for all manuscript and supplemental information figures.</p> <p>Every folder contains the raw data and Jupyter notebook (python) for graph creation.</p> <p>Images are not enclosed but are shown in the manuscript.</p> <p> </p>
Single cell data from Imaging Mass Cytometry of mouse lung tumours treated with KRAS-G12C and immune checkpoint inhibitors (Dataset 3)
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Single-cell eQTL mapping in yeast reveals a tradeoff between growth and reproduction
<p>Expression quantitative trait loci (eQTLs) provide a key bridge between noncoding DNA sequence variants and organismal traits. The effects of eQTLs can differ among tissues, cell types, and cellular states, but these differences are obscured by gene expression measurements in bulk populations. We developed a one-pot approach to map eQTLs in <em>Saccharomyces cerevisiae</em> by single-cell RNA sequencing (scRNA-seq) and applied it to over 100,000 single cells from three crosses. We used scRNA-seq data to genotype each cell, measure gene expression, and classify the cells by cell-cycle stage. We mapped thousands of local and distant eQTLs and identified interactions between eQTL effects and cell-cycle stages. We took advantage of single-cell expression information to identify hundreds of genes with allele-specific effects on expression noise. We used cell-cycle stage classification to map 20 loci that influence cell-cycle progression. One of these loci influenced the expression of genes involved in the mating response. We showed that the effects of this locus arise from a common variant (W82R) in the gene <em>GPA1</em>, which encodes a signaling protein that negatively regulates the mating pathway. The 82R allele increases mating efficiency at the cost of slower cell-cycle progression and is associated with a higher rate of outcrossing in nature. Our results provide a more granular picture of the effects of genetic variants on gene expression and downstream traits.</p>
AsaruSim: a single-cell and spatial RNA-Seq Nanopore long-reads simulation workflow
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Single-cell TCA cycle proteomics of human embryos during early organogenesis
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Plasmid sequences for the paper "A FRET based biosensor for measuring Gα13 activation in single cells"
<p>Plasmid sequences for the paper "A FRET based biosensor for measuring Gα13 activation in single cells"</p>
Simulation data for "Conbase: a software for unsupervised discovery of clonal somatic mutations in single cells through read phasing"
<p>Simulation data used in analysis of single cell variant calling softwares in manuscript "Conbase: a software for unsupervised discovery of clonal somatic mutations in single cells through read phasing"</p> <p>Two sets of simulation experiments were performed, one with varying sequencing depth (simulation.tar.gz) and one with a fixed depth at 30 (simulation_fixedDP.tar.gz).</p> <p> </p>
Simulation of single cell read and parallel read for a 1T1R and a pseudo-crossbar memristor array
<p>Dataset: simulation of single cell read and parallel read for a 1T1R and a pseudo-crossbar memristor array (Deliverable D4.2 "Initial memristor crossbar based logic/arithmetic and memory designs and models", Fig.13, H2020 Project MNEMOSENE)</p>
S2IsoMEr - SpaceM single cell datasets
<p>Single cell datasets required for S2IsoMEr vignettes</p>
Single Cell Spatial Transcriptomics Reveals Immunotherapy-Driven Bone Marrow Niche Remodeling in AML
<p>Images utilized in the paper <em>Single Cell Spatial Transcriptomics Reveals Immunotherapy-Driven Bone Marrow Niche Remodeling in AML </em>- by Gui, Bingham et al.<em><br></em></p>
Molecular and spatial signatures of human and rat corpus cavernosum physio-pathological processes at single-cell resolution
<p><span>The composition and cellular heterogeneity of the corpus cavernosum (CC) microenvironment has been characterized, but the spatial heterogeneity at the molecular level remains unexplored. In this study, we integrate single-cell RNA sequencing (scRNA-seq) and spatial transcriptome sequencing to comprehensively chart the spatial cellular landscape of human and rat CC under normal and disease conditions. We observe differences in the proportion of cell subtypes and marker genes between humans and rats. Based on the analysis of the fibroblasts (FB) niche, we also find that the enrichment scores of mechanical force signaling vary across different regions and correlate with the spatial distribution of FB subtypes. In vitro, the soft and hard extracellular matrix (ECM) induces the differentiation of FB into APO+FB and COMP+FB, respectively. In summary, our study provides a cross-species and </span><span>physio-pathological</span><span> </span><span>transcriptomic atlas of the CC, contributing to a further understanding of the molecular anatomy and regulation of penile erection.</span></p>
Single cell RNA-seq data from: Differentiation signals induce APOBEC3A expression via GRHL3 in squamous epithelia and squamous cell carcinoma
<p>Seurat object for single cell RNA-seq of 10 head and neck squamous cell carcinoma patients, epithelial cells only. From "Differentiation signals induce APOBEC3A expression via GRHL3 in squamous epithelia and squamous cell carcinoma ". <span><span>Two APOBEC (apolipoprotein-B mRNA editing enzyme catalytic polypeptide-like) </span><span>DNA </span><span>cytosine deaminase enzymes (APOBEC3A and APOBEC3B) generate somatic mutations in cancer, driving tumour development and drug resistance. Here we used single cell RNA sequencing to study </span></span><span><span>APOBEC3A</span></span><span><span> and </span></span><span><span>AP</span><span>OB</span><span>EC3B</span></span><span><span> expression in healthy and malignant mucosal epithelia, </span><span>validating</span> <span>key</span><span> observations </span><span>with</span><span> immunohistochemistry, spatial </span><span>transcriptomics</span><span> and functional experiments. Wh</span><span>ereas</span> </span><span><span>APOBEC3B</span></span><span><span> is expressed in keratinocytes entering mitosis, we show that </span></span><span><span>APOBEC3A</span></span><span><span> expression is confined</span> <span>largely</span><span> to</span><span> terminally differentiating cells</span><span> and </span><span>requires </span><span>Grainyhead</span><span>-like transcription factor 3 (GRHL3). T</span><span>hus</span><span>, in normal tissue,</span><span> neither </span><span>deaminase</span> <span>appears to be</span><span> expressed at </span><span>high levels</span><span> during DNA replication, the c</span><span>ell cycle stage</span> <span>associated with</span><span> APOBEC-mediated mutagenesis. </span><span>In</span><span> contrast, we show that in squamous cell carcinoma, there is expansion of </span></span><span><span>GRHL3</span></span><span> <span>expression and </span><span>activity to a subset of cells undergoing DNA replication and concomitant extension of </span></span><span><span>APOBEC3A</span></span><span><span> expression to proliferating cells. </span></span><span><span>These findings </span><span>suggest</span><span> that</span> <span>APOBEC3A</span><span> may play a functional role during keratinocyte differentiation</span><span> and offer</span> </span><span><span>a mechanism for acquisition of APOBEC3A mutagenic activity in tumour</span><span>s</span><span>.</span></span><span> </span></p>
ScienceDex guides
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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.