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11,105 results for “single cells”
Data and code for: Stochastic bacterial population dynamics restrict the establishment of antibiotic resistance from single cells
<p>This dataset contains experimental data and custom R code for likelihood-based model-fitting associated with the manuscript "Stochastic bacterial population dynamics restrict the establishment of antibiotic resistance from single cells". In particular, we estimate the per-cell establishment probability (i.e. probability that a single cell gives rise to a large population) of a resistant strain, in the presence of antibiotics at concentrations below its standard minimum inhibitory concentration. The experiments are conducted here with <em>Pseudomonas aeruginosa</em>, while the model and methods can be applied more generally.</p>
Data from: Division of functional roles for termite gut protists revealed by single-cell transcriptomes
<p>The microbiome in the hindgut of wood-feeding termites comprises various species of bacteria, archaea, and protists. This gut community is indispensable for the termite, which thrives solely on recalcitrant and nitrogen-poor wood. However, the difficulty in culturing these microorganisms has hindered our understanding of the function of each species in the gut. Although protists predominate in the termite gut microbiome and play a major role in wood digestion, very few culture-independent studies have explored the contribution of each species to digestion. Here, we report single-cell transcriptomes of four protists species comprising the protist population in worldwide pest <em>Coptotermes formosanus</em>. Comparative transcriptomic analysis revealed that the expression patterns of the genes involved in wood digestion were different among species, reinforcing their division of roles in wood degradation. Transcriptomes, together with enzyme assays, also suggested that one of the protists, <em>Cononympha leidyi</em>, actively degrades chitin and assimilates it into amino acids. We propose that C. leidyi contributes to nitrogen recycling and inhibiting infection from entomopathogenic fungi through chitin degradation. Two of the genes for chitin degradation were further revealed to be acquired via lateral gene transfer (LGT) implying the importance of LGT in the evolution of symbiosis. Our single-cell-based approach successfully characterized the function of each protist in termite hindgut and explained why the gut community includes multiple species.</p>
Single cell transcriptomics of of Abedinium reveals a new early-branching dinoflagellate lineage
<p>Dinoflagellates possess many unique cellular characteristics with unresolved evolutionary histories including nuclei with greatly expanded genomes and chromatin packaged using histone-like proteins and dinoflagellate-viral nucleoproteins instead of histones, highly reduced mitochondrial genomes with extensive RNA editing, a mix of photosynthetic and cryptic secondary plastids, and tertiary plastids. Resolving the evolutionary origin of these traits requires understanding their ancestral states and early intermediates. Several deep-branching dinoflagellate lineages are good candidates for such reconstruction, however they tend to be delicate and environmentally sparse, so such analyses are not always simple. Here, we employ transcriptome sequencing from manually-isolated and microscopically documented cells to resolve the placement of two cells of one such genus, <i>Abedinium</i>,<i> </i>collected by ROV in deep waters off the coast of Monterey Bay. One cell corresponds to the only described species, <i>A. dasypus</i>, while the second cell is distinct and formally described as<i> Abedinium folium, </i>sp. nov. <i>Abedinium</i> has classically been assigned to the deep-branching dinoflagellates subgroup Noctilucea, which is weakly supported by phylogenetic analyses of the single characterized gene from any member of the genus, small subunit ribosomal RNA (SSU rRNA). However, a phylogenetic analysis based on 221 proteins from the transcriptome places <i>Abedinium </i>in a distinct lineage, separate from and basal to the Noctilucea and the rest of the core dinoflagellates. The transcriptome also contains evidence of a cryptic plastid functioning in the biosynthesis of isoprenoids, iron-sulfur clusters, and heme, a mitochondrial genome with all three expected protein-coding genes (<i>cob</i>, <i>cox1</i>, and cox3), and the presence of some but not all dinoflagellate-specific chromatin packaging proteins.</p>
Multi-Domain Translation between Single-Cell Imaging and Sequencing Data using Autoencoders
<p>This record contains raw data related to the article "Multi-Domain Translation between Single-Cell Imaging and Sequencing Data using Autoencoders".</p>
Single-cell profiling identifies pre-existing CD19-negative subclones in a B-ALL patient with CD19-negative relapse after CAR-T therapy
<p>This repository contains necessary files for reproducing the analysis in Rabilloud, Potier et al. (2020). The instructions for reproducing the analysis are given in github (https://github.com/Delphine-Potier/B-ALL-CAR-T) and extra files are available in GEO/SRA (GSE153697 ; SRP269742).</p>
Raw single cell mass cytometry data from breast cancer patient derived xenografts
<p>Raw single cell mass cytometry data from breast cancer patient derived xenografts.</p> <p>Data is organised in an expressioset R object</p>
Single molecule microscopy reveals key physical features of repair foci in living cells
<p>Single Particle Tracking data of repair proteins in budding yeast, described in the manuscript entitled 'Single molecule microscopy reveals key physical features of repair foci in living cells'.</p>
Accurate SNV detection in single cells by transposon-based whole-genome amplification of complementary strands
<p>Common SNPs from gnomAD</p>
Data from: Morphological identification and single-cell genomics of marine diplonemids
Recent global surveys of marine biodiversity have revealed that a group of organisms known as "marine diplonemids" constitutes one of the most abundant and diverse planktonic lineages [1]. Though discovered over a decade ago [2 and 3], their potential importance was unrecognized, and our knowledge remains restricted to a single gene amplified from environmental DNA, the 18S rRNA gene (small subunit [SSU]). Here, we use single-cell genomics (SCG) and microscopy to characterize ten marine diplonemids, isolated from a range of depths in the eastern North Pacific Ocean. Phylogenetic analysis confirms that the isolates reflect the entire range of marine diplonemid diversity, and comparisons to environmental SSU surveys show that sequences from the isolates range from rare to superabundant, including the single most common marine diplonemid known. SCG generated a total of ∼915 Mbp of assembled sequence across all ten cells and ∼4,000 protein-coding genes with homologs in the Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology database, distributed across categories expected for heterotrophic protists. Models of highly conserved genes indicate a high density of non-canonical introns, lacking conventional GT-AG splice sites. Mapping metagenomic datasets [4] to SCG assemblies reveals virtually no overlap, suggesting that nuclear genomic diversity is too great for representative SCG data to provide meaningful phylogenetic context to metagenomic datasets. This work provides an entry point to the future identification, isolation, and cultivation of these elusive yet ecologically important cells. The high density of nonconventional introns, however, also portends difficulty in generating accurate gene models and highlights the need for the establishment of stable cultures and transcriptomic analyses.
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</p>
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</p>
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</p>
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</p>
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</p>
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</p>
scooby: Modeling multi-modal genomic profiles from DNA sequence at single-cell resolution - Supplementary data and code
<p>Data and code to reproduce the analyses from the study: "scooby: Modeling multi-modal genomic profiles from DNA sequence at single-cell resolution". </p>
Summary statistics of eQTLs obtained from single-nuclei RNA-seq in 8 major brain cell-types for mendelian randomisation
<p>This dataset contains <em>cis</em>-eQTL summary statistics for 8 brain cell-types, generated on a snRNA-seq dataset on post-mortem brains from 391 individuals (full), as well as a controls-only (subset of full, 183 individuals).</p> <p>Genotype dosage matrices were obtained with <code>SeqArray</code>, where 0 = homozygous alt, 1 = heterozygous, 2 = homozygous ref https://bioconductor.org/packages/release/bioc/manuals/SeqArray/man/SeqArray.pdf . The eQTL models as applied by <code>MatrixEQTL</code> therefore use "ref" as the effect allele (as implemented in their additive model).</p> <p>The eQTL summary statistics for within the "full" and "controls-only" dataset have been packed into <code>.tar.gz</code> files for each cell-type, where unpacking will yield summary statistics by chromosome. Each file contains the following columns;</p> <p>1. <code>SNP</code> (in rsid format)</p> <p>2. <code>gene</code> (in symbol format)</p> <p>3. <code>t.stat</code> (t-statistic as determined by MatrixEQTL)</p> <p>4. <code>p.value</code> (linear model association p-value)</p> <p>5. <code>FDR</code> (false discovery rate as determined by MatrixEQTL)</p> <p>6. <code>beta</code> (effect size / slope of the linear model)</p> <p>7. <code>chrom</code> (chromosome in "chrN" format)</p> <p>8. <code>position</code> (SNP position, hg38 build)</p> <p>9. <code>effect_allele</code> (this is the "ref" allele as described above)</p> <p>10. <code>other_allele</code> (alternate allele)</p> <p>11. <code>maf</code> (minor allele frequency, as determined by SeqArray on this dataset)</p> <p> </p> <p>In addition, single-cell expression matrices in count format are available in the <code>single-cell_data.tar</code> archive for the full 391 individuals (2,348,438 cells). This archive contains processed single-cell counts as described in our manuscript for the 4 datasets included; "BRYOIS_192" (separated into "MS" and "AD" as per their publication), "MATTHEWS", "ROCHE_MPD92" and "MRC_60". In addition, a cell-level metadata file containing covariates and cell-type labels across all datasets is included (<code>cell_level_metadata.rds</code>). Cell barcodes and individual IDs have been renamed to preserve anonymity.</p> <p> </p> <p><strong>January 2025 update: </strong>Now published at <strong><em>Nature Genetics</em></strong>. <strong>https://www.nature.com/articles/s41588-024-02050-9</strong></p> <p><strong>May 2025 update: </strong>Added the aggregated pseudo "Bulk" eQTLs as seen in Fig 1. d. </p>
Datasets for MambaCpG: Accurate Imputation of Single-cell DNA Methylation Status Using Mamba
Open the record for dataset details and reuse information.
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>
A single-cell atlas of Drosophila trachea reveals glycosylation-mediated Notch signaling in cell fate specification
<p>Processed data files for the following article:</p><p>Li Y, Lu T, Dong P, Chen J, Zhao Q, Wang Y, Xiao T, Wu H, Zhao Q and Huang H. A single-cell atlas of Drosophila trachea reveals glycosylation-mediated Notch signaling in cell fate specification.</p><p>Please also refer to code at https://github.com/Tianfeng-Lu/single-cell-atlas-of-fly-trachea</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.