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2,063 results for “single cell transcriptomics”
Single-cell and spatial transcriptomics delineate molecular traits and immunosuppressive landscape during histological progression of lung adenocarcinoma
<p>Two specimens of lung adenocarcinoma, each corresponding to the lepidic and solid histologic patterns as confirmed through histologic scrutiny, were procured in accordance with standard surgical protocols. These specimens underwent a process of formalin fixation and were subsequently encapsulated within paraffin-embedded tissue blocks. The specimens were then sectioned and subjected to hematoxylin and eosin (H&E) staining to facilitate subsequent imaging at a resolution of 40x (equivalent to 0.25 micron/pixel) via the use of Aperio GT450 scanners. The tissue slides were then conveyed to the Genomics core, where following the decoverslipping of the tissue, the Visium CytAssist device was employed to transfer transcriptomic probes from the original glass slides to capture areas on Visium slides measuring 11mm x 11mm. Comprehensive transcriptomic profiling was achieved post mRNA permeabilization, through poly(A) capture and probe hybridization. The resultant libraries were sequenced utilizing the Illumina Novaseq 6000, using paired-end sequencing with a read length of 150 base pairs.</p>
Code and data of "Uncovering disease-related multicellular pathway modules on large-scale single-cell transcriptomes with scPAFA"
<p>Code and data to reproduce the analyses and figures presented in "Uncovering disease-related multicellular pathway modules on large-scale single-cell transcriptomes with scPAFA"</p>
Single-Cell Transcriptomics Reveals a Heterogeneous Cellular Response to BK Virus Infection
<p>The files are the Indrops count matrices for BKV and Mock samples corresponding to the 8 experiments and samples described in the Bioproject https://www.ncbi.nlm.nih.gov/bioproject/PRJNA715178</p> <p> </p>
Raw data to: "Persistent RNA virus infection is short-lived at the single cell level but leaves transcriptomic footprints"
<p>Raw data underlying the publication by Reuther and Martin et al. entitled "Persistent RNA virus infection is short-lived at the single cell level but leaves transcriptomic footprints"</p>
Processed data and scripts supporting the manuscript "Single-cell transcriptomics reveals immune suppression and cell states predictive of patient outcomes in rhabdomyosarcoma"
<p>This submission contains the compiled count table, processed R objects and various scripts and output files accompanying our manuscript "Single-cell transcriptomics reveals immune suppression and cell states predictive of patient outcomes in rhabdomyosarcoma" (Nature Communications, 2023, https://doi.org/10.1038/s41467-023-38886-8)</p>
Supporting data for "Dissecting the cellular architecture of neuroblastoma bone marrow metastasis using single-cell transcriptomics and epigenomics unravels the role of monocytes at the metastatic niche"
<p>This data repository contains several datasets supplementing the paper “Dissecting the cellular architecture of neuroblastoma bone marrow metastasis using single-cell transcriptomics and epigenomics unravels the role of monocytes at the metastatic niche” by Fetahu, Esser-Skala, Dnyansagar et al. (2023).</p> <ul> <li>HOMER_Results.zip: detailed results of the HOMER analysis</li> <li>nblast_scopen_gene_activity_normalized_motifs_added.rds: Seurat object with scATAC-seq data</li> <li>snp_array.tgz: SNP array data</li> <li>R_data_generated.tgz: Files generated by the scRNA-seq analysis scripts in the GitHub repository associated with the publication.</li> </ul>
Comparative Analysis of Droplet- vs. Microwell-based Whole Transcriptome Single-Cell Sequencing Technologies in Complex Human Tissues
<p>In the past decade, high-dimensional single-cell omics tools have enabled scientists to study the tumor microenvironment (TME) in unprecedented detail. However, recent investigations suggest that each technique has its unique strengths but also technology-inherent limitations. Here we directly compared two commercially available high-throughput single-cell RNA sequencing (scRNA-seq) technologies - droplet-based 10X Chromium <em>vs.</em> microwell-based BD Rhapsody - using paired samples from patients with localized prostate cancer (PCa) undergoing a radical prostatectomy.</p> <p>Although high technical consistency was observed in unraveling the whole transcriptome, the relative abundance of detectable cell populations differed. This could in part be ascribed to differences in the performance to recover cells with low-mRNA content. Hence, immune cells such as neutrophils are underrepresented in data generated with the widely used droplet-based scRNA-seq protocol, highlighting the importance of considering platform limitations in low mRNA content cell recovery. In contrast, droplet-based scRNA-seq demonstrated superiority in terms of recovering cells of epithelial origin. Moreover, we discovered platform-dependent variabilities in mRNA quantification and cell-type marker annotation, affecting the composition of identified tissue profiles and the exploratory value of the generated datasets. Overall, our study emphasizes the importance of carefully selecting the appropriate scRNA-seq platform to improve cell type representation and obtain a more comprehensive and accurate understanding of the TME.</p>
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)
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Data from: Single cell transcriptomics shows dose-dependent disruption of hepatic zonation by TCDD in mice
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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.
Single-cell repertoire and transcriptome sequencing reveals clonally expanded and transcriptionally distinct lymphocytes in aged CNS
<p>Single-cell repertoire and transcriptome sequencing reveals clonally expanded and transcriptionally distinct lymphocytes in aged CNS. Gene expression and immune receptor repertoire sequencing was performing for both B and T cells. This dataset contains the VDJ sequencing information for the four samples. Each B cell and T cell library was sequenced across four lanes. </p> <p> </p> <p>Files with _WT_ in their name correspond to the young (4-6 week B6 mice) </p> <p>Files with _12_ in their name before the BDJ or VDJ text correspond to the 12-month-old cohort.</p> <p>Files with _18_ in their name before the BDJ or VDJ text correspond to the 18-month-old cohort in which four brains were pooled.</p> <p>Files with 4_18_ in their name before the BDJ or VDJ text correspond to the 18-month-old mouse that was processed and sequenced alone. </p> <p> </p> <p>The L001 - L004 in the file names indicates the sequencing lane. Samples with BDJ correspond to the B cell repertoire library (B cell VDJ). Samples with TDJ correspond to the T cell repertoire library (T cell VDJ). </p>
Input data of manuscript "CACTUS: integrating clonal architecture with genomic clustering and transcriptome profiling of single tumor cells"
<p>This is the directory containing input data necessary to reproduce analyses presented in the manuscript:</p> <blockquote> <p><strong>CACTUS: integrating clonal architecture with genomic clustering and transcriptome profiling of single tumor cells</strong><br> Shadi Darvish Shafighi, Szymon M Kiełbasa, Julieta Sepúlveda Yáñez, Ramin Monajemi, Davy Cats, Hailiang Mei, Roberta Menafra, Susan Kloet, Hendrik Veelken, Cornelis A.M. van Bergen, Ewa Szczurek</p> </blockquote>
Sequence data for the article "Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol" - single cells dataset
<p>Sequence data (Illumina MiSeq runs) for the article "Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol". dataset of 2300 single cells. File names indicate unique sequencing runs. In the manuscripts, the informations about cell lines are found in the Supplemental Table 1. </p>
Sequence data for the article "Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol" - Protocol optimization
<p>Sequence data (Illumina MiSeq runs) for the article "Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol". Optimization of the protocol. Files names indicate unique run identifiers. In the manuscript, the link between unique run identifiers, cells and purpose of the experiment is found in the Supplemental Table 1. </p>
Comparison of Fixed Single Cell RNA-seq Methods to Enable Transcriptome Profiling of Neutrophils in Clinical Samples
<p>Monitoring neutrophil gene expression is a powerful tool for understanding disease mechanisms, developing new diagnostics, therapies and optimizing clinical trials. Neutrophils are sensitive to the processing, storage and transportation steps that are involved in clinical sample analysis. This study is the first to evaluate the capabilities of technologies from 10X Genomics, PARSE Biosciences, and HIVE (Honeycomb Biotechnologies) to generate high-quality RNA data from human blood-derived neutrophils. Our comparative analysis shows that all methods produced high quality data, importantly capturing the transcriptomes of neutrophils. 10X FLEX cell populations in particular showed a close concordance with the flow cytometry data. Here, we establish a reliable single-cell RNA sequencing workflow for neutrophils in clinical trials: we offer guidelines on sample collection to preserve RNA quality and demonstrate how each method performs in capturing sensitive cell populations in clinical practice.</p> <p><strong>This dataset includes the FACS, 10X 3', Parse, 10X Flex, and Hive data and analysis.</strong></p>
Single Cell RNA Transcriptomics of Mantle Cell Lymphoma Reveals the Presence of Treatment-Resistant Subclones at the Time of Diagnosis - Supplementary Data
<p><strong>Supplementary Table 4.</strong> Results of optical genomic mapping for patient P009. The table lists genomic aberrations shared for diagnosis and relapse and those exclusive at both timepoints.</p> <p><strong>Supplementary Table 5.</strong> <strong>Sheet A:</strong> Results of differential gene expression analysis between relapse and diagnosis MCL cells of four patients (P009, P022, P027 and P087) performed using Wilcoxon rank-sum test. Only genes with adjusted p-value < 0.01, absolute log2 fold change > 1, and expression in at least 25% of cells in the upregulated group are included. <strong>Sheet B: </strong>Results of GO biological process enrichment analysis performed using clusterProfiler on upregulated and downregulated genes from each patient comparison. Multiple testing correction was performed using the Benjamini-Hochberg method. Only terms with q-value < 0.01 are shown.</p> <p><strong>Supplementary Table 6.</strong> Proportions of cells at four cell cycle phases (G0, G1, S, and G2M) at diagnosis (DG) and their changes after relapse (REL) in four aggressive MCL patients. Statistical analysis was performed using chi-square test.</p> <p><strong>Supplementary Table 7. </strong>Characterization of lymphoma sub-clones detected using CNV inference. <strong>Sheet A: </strong>Cell counts and proportions (prop) relative to total MCL cells in each subclone at diagnosis (DG) and relapse (REL). prop_FC – proportion fold change at relapse compared to diagnosis (calculated as prop_REL/prop_DG for expanding clones, or prop_DG/prop_REL for contracting clones). Subclone labels indicate putative treatment sensitivity: "Sens" denotes subclones that contracted or disappeared at relapse (suggesting chemotherapy sensitivity), while "Res" denotes subclones that expanded at relapse (suggesting chemotherapy resistance). <strong>Sheet B:</strong> CNV profiles of MCL subclones identified through inferCNV analysis. Each row specifies chromosomal coordinates (chr, start, end), copy number state (cn), cytogenetic location (cytobands), and genes within altered regions. Copy number states: cn=1 (deletion), cn=2 (normal), cn=3+ (amplification).</p> <p><strong>Supplementary Table 8.</strong> Differential gene expression analysis between resistant and sensitive MCL subclones. <strong>Sheet A:</strong> Summary of pairwise subclone comparisons. For each comparison between resistant (Res) and sensitive (Sens) subclones, the table reports counts of total, upregulated and downregulated differentially expressed genes and their ratio, enriched Gene Ontology (GO) terms and Hallmark gene sets in each direction. <strong>Sheet B:</strong> Complete list of differentially expressed genes from all pairwise subclone comparisons. Each row represents a single gene with statistical metrics including p-value, adjusted p-value, average log2 fold change (avg_log2FC), expression percentages in each subclone (pct.1, pct.2), regulatory direction (up/down), and gene description. <strong>Sheet C:</strong> Gene Ontology (GO) enrichment analysis results for differentially expressed genes. Only significantly enriched (q-value < 0.05) biological processes are shown with enrichment statistics including gene ratio, background ratio, fold enrichment, z-score, adjusted p-values, and lists of genes contributing to each enriched term. <strong>Sheet D: </strong>Hallmark gene set enrichment analysis (GSEA) results identifying coordinated expression programs differentially active between resistant and sensitive subclones. Results include normalized enrichment scores (NES), statistical significance, leading edge metrics, and core enrichment genes for each pathway.</p> <p><strong>Supplementary Table 9.</strong> Somatic variants in P069 samples. The variants were identified using a consensus approach integrating three variant callers (Mutect2, VarScan2, and Strelka2) and filtered using SomaticSeq with a PASS threshold score of 0.5. The table includes sample identifiers, genomic coordinates (hg38), variant allele frequencies (VAF) and genotypes (GT) for matched normal and tumor samples, sequencing depth (DP), and comprehensive functional annotations generated by Funcotator using GENCODE and HGNC databases. Key annotation fields include gene symbols, variant classifications, transcript information, protein changes, and gene ontology details. Only variants passing consensus filtering criteria with VAF ≥1% for heterozygous and ≥85% for homozygous calls are included.</p> <p><strong>Supplementary Data Object 1. </strong>SingleCellExperiment object with 57751 cells post-QC and 36601 genes. Data columns include sample, patient, timepoint, compartment, cell type annotation (cell_type_manual), cell cycle phase (tricyclePhase), tumor subclones (subclone_label).</p>
Data from: Mouse gingival single cell transcriptomic atlas identified a novel fibroblast subpopulation activated to guide oral barrier immunity in periodontitis
<p>Periodontitis, one of the most common non-communicable diseases, is characterized by chronic oral inflammation and uncontrolled tooth supporting alveolar bone resorption. Its underlying mechanism to initiate aberrant oral barrier immunity has yet to be delineated. Here, we report a unique fibroblast subpopulation activated to guide oral inflammation (AG fibroblasts) identified in a single-cell RNA sequencing gingival cell atlas constructed from the mouse periodontitis models. AG fibroblasts localized beneath the gingival epithelium and in the cervical periodontal ligament responded to the ligature placement and to the discrete topical application of Toll-like receptor stimulants to mouse maxillary tissue. The upregulated chemokines and ligands of AG fibroblasts linked to the putative receptors of neutrophils in the early stages of periodontitis. In the established chronic inflammation, neutrophils together with AG fibroblasts appeared to induce type 3 innate lymphoid cells (ILC3s) that were the primary source of interleukin-17 cytokines. The comparative analysis of <em>Rag2-/-</em> and <em>Rag2gc-/-</em> mice suggested that ILC3 contributed to the cervical alveolar bone resorption interfacing the gingival inflammation. We propose that the AG fibroblast–neutrophil–ILC3 axis as a previously unrecognized mechanism which could be involved in the complex interplay between oral barrier immune cells contributing to pathological inflammation in periodontitis.</p>
Gleaning Euglenozoa-specific DNA polymerases in public single-cell transcriptome data
<p><span>Multiple genes encoding family A DNA polymerases (famA DNAPs), which are evolutionary relatives of DNA polymerase </span><span>I</span><span> (Pol</span><span>I</span><span>) in bacteria and phages, have been found in eukaryotic genomes, and many of these proteins are used mainly in organelles. Among members of the phylum Euglenozoa, distinct types of famA DNAP, Pol</span><span>I</span><span>A, Pol</span><span>I</span><span>BCD+, POP, and eugPolA, have been found. It is intriguing how the suite of famA DNAPs had been established during the evolution of Euglenozoa, but the DNAP data have not been sampled from the taxa that sufficiently represent the diversity of this phylum. In particular, little sequence data were available for basal branching species in Euglenozoa until recently. Thanks to the single-cell transcriptome data from symbiontids and phagotrophic euglenids, we have an opportunity to cover the "hole" in the repertory of famA DNAPs in the deep branches in Euglenozoa. The current study identified 16 new famA DNAP sequences in the transcriptome data from 33 phagotrophic euglenids and two symbiontids, respectively. Based on the new famA DNAP sequences, the updated diversity and evolution of famA DNAPs in Euglenozoa are discussed.</span></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>
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>
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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.