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A Dataset Examining Technical Factors on Fixed Whole Blood Single-Cell RNA-Seq

ABSTRACT: A Dataset Examining Technical Factors on Fixed Whole Blood Single-Cell RNA-Seq

PROVIDER: PRJNA1107957 | ENA |

REPOSITORIES: ENA

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Dataset's files

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			Action	DRS
	SRR28902813_1.fastq.gz	Fastqsanger.gz
	SRR28902813_2.fastq.gz	Fastqsanger.gz
	SRR28902814_1.fastq.gz	Fastqsanger.gz
	SRR28902814_2.fastq.gz	Fastqsanger.gz
	SRR28902815_1.fastq.gz	Fastqsanger.gz

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A Dataset Examining Technical Factors on Fixed Whole Blood Single-Cell RNA-Seq

Project description:High-throughput sequencing technologies have enabled many biological discoveries; nevertheless, the resolution of bulk RNA sequencing is insufficient to elucidate the heterogeneity at the single-cell level. Single-cell RNA sequencing (scRNA-Seq) has emerged as a powerful tool for disentangling this complexity.. A major challenge in scRNA-Seq of blood is the analysis of granulocytes. These cells are pivotal to immunological responses but are recalcitrant to isolation from blood without inducing activation artifacts, thereby perturbing their transcriptomic integrity.. The difficulty of granulocyte handling for scRNA-Seq necessitate immediate processing of samples, a workflow incompatible with the logistical demands of large-scale, multi-centric studies. Innovations in commercial fixed scRNA-Seq methodologies promise to ameliorate these limitations by preserving RNA integrity post-fixation. Our investigation evaluates two such commercial kits for fixed scRNA-Seq. Our overall goal was to determine feasibility for distributed collection and centralised processing of fixed whole blood samples.

2024-05-06 | GSE266615 | GEO

Technical controls MiCA

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A deep learning framework for the reconstruction of gene expression profiles derived from formalin-fixed paraffin-embedded samples

Project description:This dataset was utilized to investigate the impact of key preanalytical factors on the preparation of formalin-fixed, paraffin-embedded (FFPE) tissue samples and subsequent library construction for RNA-sequencing analysis. The dataset consists eight RNA-Seq data, derived from two colorectal cancer (CRC) patient samples obtained from Prince of Wales Hospital in Hong Kong.

2025-06-04 | GSE298630 | GEO

Examining preharvest genetic and morphological factors contributing to lettuce (Lactuca sativa L.) shelf life

Project description:Examining preharvest genetic and morphological factors contributing to lettuce (Lactuca sativa L.) shelf life

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EMPV1 technical repeat

Project description:2-4-8 cell porcine embryo technical repeat dye-swap for Agilent EMPV1 (4x44k Format)

2013-02-25 | GSE34157 | GEO

RNA-seq: technical variability and sampling

Project description:RNA-seq: technical variability and sampling

| PRJNA234515 | ENA

PAT-ChIP-Seq: a novel tool for the epigenetic profiling of formaldehyde-fixed and paraffin-embedded (FFPE) pathology samples

Project description:Epigenetic alterations in the pattern of DNA and histone modifications play a crucial role in cancer development. Analysis of patient samples, however, is hampered by technical limitations in the study of chromatin structure from pathology archives that typically consist of heavily fixed, paraffin-embedded material.

2010-12-02 | GSE21449 | GEO

RNA-seq: technical variability and sampling (male)

Project description:Background: RNA-seq is revolutionizing the way we study transcriptomes. mRNA can be surveyed without prior knowledge of gene transcripts. Alternative splicing of transcript isoforms and the identification of previously unknown exons are being reported. Initial reports of differences in exon usage, and splicing between samples as well as quantitative differences among samples are beginning to surface. Biological variation has been reported to be larger than technical variation. In addition, technical variation has been reported to be in line with expectations due to random sampling. However, strategies for dealing with technical variation will differ depending on the magnitude. The size of technical variance, and the role of sampling are examined in this manuscript. Results: Independent Solexa/Illumina experiments containing technical replicates are analyzed. When coverage is low, large disagreements between technical replicates are apparent. Exon detection between technical replicates is highly variable when the coverage is less than 5 reads per nucleotide and estimates of gene expression are more likely to disagree when coverage is low. Although large disagreements in the estimates of expression are observed at all levels of coverage. Conclusions: Technical variability is too high to ignore. Technical variability results in inconsistent detection of exons at low levels of coverage. Further, the estimate of the relative abundance of a transcript can substantially disagree, even when coverage levels are high. This may be due to the low sampling fraction and if so, it will persist as an issue needing to be addressed in experimental design even as the next wave of technology produces larger numbers of reads. We provide practical recommendations for dealing with the technical variability, without dramatic cost increases.

2014-01-15 | GSE54068 | GEO

RNA-seq: technical variability and sampling (female)

2014-01-15 | GSE54067 | GEO

Confirmation dataset of Transcriptome Sequencing (RNAseq) data utilizing Formalin-Fixed, Paraffin-Embedded Biopsies with Clear Cell Renal Cell Carcinoma for Exploration of Disease Biology and Biomarker Development

Project description:This dataset complements GSE76207. Formalin-fixed, paraffin-embedded (FFPE) tissues are an underused resource for molecular analyses. This proof of concept study aimed to compare RNAseq results from FFPE biopsies (Eikrem et al., PLoS One. 2016 Feb 22;11(2):e0149743. doi: 10.1371/journal.pone.0149743. eCollection 2016. PMID: 26901863) with FFPE confirmation dataset from clear cell renal cell carcinoma (ccRCC) patients. Core biopsies were obtained with a 16g needle from 16 patients undergoing partial or full nephrectomy. The RNA sequencing libraries were generated applying the new Illumina TruSeq® Access library preparation protocol. The goal of the study is to compare the discovery and the confirmation data and to assess the utilization of the data for ccRCC biomarker development.

2016-11-21 | GSE82122 | GEO

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