Ecafb65b-a9e1-4503-84d9-05f915ba11e9 - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001008456 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:The dataset contains metadata for all cells before scRNA-seq quality control and for cells passing quality control. It also contains a count matrix with Salmon gene counts for all cells passing quality control, and reconstructed B-cell receptor sequences using the computational tool BraCeR. The scRNA-seq data was generated using the Smart-seq2 protocol and sequenced on Illumina NextSeq500.
| EGAD00001006435 | EGA
Project description:Motivation: The B-cell receptor (BCR) performs essential functions for the adaptive immune system including recognition of pathogen-derived antigens. The vast repertoire and adaptive variation of BCR sequences due to V(D)J recombination and somatic hypermutation (SHM) necessitates single-cell characterization of BCR sequences. Single-cell RNA sequencing (scRNA-seq) presents the opportunity for simultaneous capture of paired BCR heavy and light chains and the transcriptomic signature. Results: We developed VDJPuzzle, a novel bioinformatic tool that reconstructs productive, full-length B-cell receptor sequences of both heavy and light chains. VDJPuzzle successfully reconstructed BCRs from 98.3% (n=117) human and 96.5% (n=200) murine B cells. The reconstructed BCRs were successfully validated with single-cell Sanger sequencing. Availability: VDJPuzzle is available at https://bitbucket.org/kirbyvisp/vdjpuzzle2
2018-06-22 | E-MTAB-6386 | biostudies-arrayexpress
Project description:The B-cell receptor (BCR) enables individual B cells to identify diverse antigens, including bacterial and viral proteins. While advances in RNA-seq have enabled high throughput profiling of transcript expression in single cells, the unique task of assembling the full-length heavy and light chain sequences from single cell RNA-sequencing (scRNA-seq) in B cells has been largely unstudied. We developed a new software tool, BASIC, which allows investigators to use scRNA-seq for assembling BCR sequences at single cell level. To demonstrate the utility of our software, we subjected single B cells from a human donor to scRNA-seq, assembled the full-length heavy and the light chains, and experimentally confirmed these results by using single cell primer based nested PCRs and Sanger sequencing.
2016-06-02 | E-MTAB-4745 | biostudies-arrayexpress
Project description:Single-cell gene expression analysis using sequencing (scRNA-seq) has gained increased attention in the past decades for studying cellular transcriptional programs and their heterogeneity in an unbiased manner, and novel protocols allow the simultaneous measurement of gene expression, T-cell receptor clonality and cell surface protein expression. In this article, we describe the methods to isolate scRNA/TCR-seq-compatible CD4+ T cells from murine tissues, such as skin, spleen, and lymph nodes. We describe the processing of cells and quality control parameters during library preparation, protocols for multiplexing of samples, and strategies for sequencing. Moreover, we describe a step-by-step bioinformatic analysis pipeline from sequencing data generated using these protocols. This includes quality control, preprocessing of sequencing data and demultiplexing of individual samples. We perform quantification of gene expression and extraction of T-cell receptor alpha and beta chain sequences, followed by quality control and doublet detection, and methods for harmonization and integration of datasets. Next, we describe the identification of highly variable genes and dimensionality reduction, clustering and pseudotemporal ordering of data, and we demonstrate how to visualize the results with interactive and reproducible dashboards. We will combine different analytic R-based frameworks such as Bioconductor and Seurat, illustrating how these can be interoperable to optimally analyze scRNA/TCR-seq data of CD4+ T cells from murine tissues.
2023-08-08 | GSE240041 | GEO
Project description:We combine single-cell RNA sequencing (scRNA-seq) with physiological and genetic perturbations to dissect the cellular circuit of type 2 intestinal inflammation. To uncover key responding cellular components, we profiled individual immune cells in the lamina propria (LP) and Peyer’s patches (PPs), regions of the small intestine enriched for immune cells, in homeostasis and in an intestinal type 2 inflammatory model. We obtained 36,797 and 21,270 high-quality cells passing initial filtering from PP and LP regions, respectively.
2019-10-16 | GSE124880 | GEO
Project description:Pancreatic cancer is a complex disease with a desmoplastic stroma, extreme hypoxia, and inherent resistance to therapy. Understanding the signaling and adaptive response of such an aggressive cancer is key to making advances in therapeutic efficacy and understanding disease progression. Redox factor-1 (Ref-1), a redox signaling protein, regulates the DNA binding activity of several transcription factors, including HIF-1. The conversion of HIF-1 from an oxidized to reduced state leads to enhancement of its DNA binding. In our previously published work, knockdown of Ref-1 under normoxia resulted in altered gene expression patterns on pathways including EIF2, protein kinase A, and mTOR. In this study, single cell RNA sequencing (scRNA-seq) and proteomics were used to explore the effects of Ref-1 on metabolic pathways under hypoxia.Results: We also integrated the scRNA data analysis with the proteomic analysis and found that the differentially expressed genes and pathways identified from the scRNA-seq data are highly consistent to the significant proteins observed in the proteomics data, especially for the upregulated cell cycle and transcription pathways and downregulated metabolic, apoptosis and signaling pathways under hypoxia. Conclusion: The scRNA-seq and proteomics data consistently demonstrated down-regulated central metabolism pathways in APE1/Ref-1 knockdown vs scrambled control under both normoxia and hypoxia conditions. Experimental Methods: scRNA-seq comparing pancreatic cancer cells expressing less than 20% of the Ref-1 protein was analyzed using left truncated mixture Gaussian model. Matched samples were also collected for bulk proteomic analysis of the four conditions. scRNA-seq data was validated using proteomics and qRT-PCR. Ref-1’s role in mitochondrial function was confirmed using mitochondrial function assays and qRT-PCR. Results: We also integrated the scRNA data analysis with the proteomic analysis and found that the differentially expressed genes and pathways identified from the scRNA-seq data are highly consistent to the significant proteins observed in the proteomics data, especially for the upregulated cell cycle and transcription pathways and downregulated metabolic, apoptosis and signaling pathways under hypoxia. Conclusion: The scRNA-seq and proteomics data consistently demonstrated down-regulated central metabolism pathways in APE1/Ref-1 knockdown vs scrambled control under both normoxia and hypoxia conditions.
2021-09-09 | PXD020515 | Pride
Project description:Quality control in scRNA-Seq can discriminate pacemaker cells - the mtDNA bias
| PRJNA728894 | ENA
Project description:We describe "SBARRO" a system in which synaptic networks of cells are reconstructed from scRNA-seq data using clonal infectivity paths of barcoded rabies virus RNAs
2022-10-06 | GSE214386 | GEO
Project description:Gene expression changes were compared in proliferating and senescent human IMR90 cells, with or without TSA treatment. Method: RNA was extracted, in triplicate, then sequenced with an Illumina NextSeq 500 sequencer. Sequence reads passing the quality control filters were aligned using Tophat2 and then analysed with Cufflinks.
2020-06-30 | GSE132370 | GEO
Project description:Identifying smORFs and SEPs is technically and computationally challenging. Experimentally, techniques as ribosome profiling (Ribo-Seq and mass spectroscopy (MS) are used. Ribo-Seq sequences the mRNA and does not provide the translated frame, thus identifying proteins encoded by overlapping ORFs is not feasible. Herein we have used MS to characterize smORFomes of different Mycoplasma species. This data is used to corroborate the predictions of a random forest classifier that in silico predicts all the putative SEPs encoded by different bacterial genomes.
2019-03-01 | PXD008243 | Pride