RNA-Seq of frozen human brain tissue
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB31537 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:These are three technical replicates RNA-Seq for frozen human brain tissue. They are generated for comparison with single cell RNA sequencing data that generated by the same sample and provided elsewhere.
2019-03-15 | E-MTAB-7738 | biostudies-arrayexpress
Project description:Comparison between in vitro transcription- and cDNA-mediated annealing, selection and ligation (DASL)-based assays on brain-specific reference RNA, and postmortem frozen and formalin fixed brain tissue from autistic and control cases. Investigation of data preprocessing techniques for DASL-assayed RNA samples from frozen brain tissue.
2012-04-03 | GSE28475 | GEO
Project description:Vascular cell types are under-represented in standard single-nucleus RNA-Seq studies of human frozen post-mortem brain tissue. The dataset represents the pilot data obtained with the first version of a microvessel- enrichment protocol to obtain a higher percentage of endothelial cells as the key cellular component of the blood-brain barrier.
2023-03-08 | GSE226606 | GEO
Project description:Comparison between in vitro transcription- and cDNA-mediated annealing, selection and ligation (DASL)-based assays on brain-specific reference RNA, and postmortem frozen and formalin fixed brain tissue from autistic and control cases. Investigation of data preprocessing techniques for DASL-assayed RNA samples from frozen brain tissue. IVT- and DASL-based expression assays were performed on 10 reference RNA samples (brain and pooled artificially degraded at 0, 10, 30, and 60 min), 4 formalin-fixed tissue-extracted RNA samples with replicates, and 57 frozen tissue-extracted RNA samples with replicates. A subset of the 57 (N=33 samples) from frozen brain tissue assayed using the DASL-based platform were then used to test different dataset preprocessing strategies implemented in the lumi package in R/Bioconductor. The data from frozen-tissue extracted DASL-processed RNA samples passing exclusion criteria were log2 transformed and normalized using quantile normalisation with lumi in R (quantile-normalized data available as a supplementary file linked to the Series record).
2012-04-02 | E-GEOD-28475 | biostudies-arrayexpress
Project description:DNA Methylation data from frozen placental tissue
| PRJNA491492 | ENA
Project description:The isolation of intact single cells from frozen tissue is a challenge due to the mechanical and physical stress inflicted upon the cell during the freeze-thaw process. Ruptured cells release ambient RNA into the cell suspension, which can become encapsulated into droplets during droplet based single cell RNA-seq library preparation methods. The presence of ambient RNA in droplets has been suggested to impact data quality, however there have been limited reports on single cell RNA-seq data from frozen tissue. Here, we compare the results of single cell RNA-seq derived from disaggregated cells from frozen brain tissue with single nuclei RNA-seq derived from purified nuclei of identical tissue using the 10X Genomics Chromium 3’ gene expression assay. Our results indicated that presence of ambient RNA in the cell suspension resulted in single cell RNA-seq data with a 25-fold lower gene count, a 5-fold lower UMI count per cell and a 2-fold lower fraction of reads per cell compared with single nuclei RNA-seq data. Cell clustering with the single cell RNA-seq data was unable to resolve the heterogeneity of brain cell types. Our conclusion is that nuclei from frozen tissue are the superior substrate for single cell transcriptome analysis.
2023-02-28 | GSE225915 | GEO
Project description:Proteomic characterization of human brain tissue is increasingly utilized to identify potential novel biomarker and drug targets for a variety of neurological diseases. In whole tissue studies, results may be driven by changes in the proportion of the largest and most abundant organelles or tissue cell-type composition. Spatial proteomics approaches enhance our knowledge of disease mechanisms and changing signaling pathways at the subcellular level by taking into account the importance of cellular localization, which critically influences protein function. Density gradient-based ultracentrifugation methods allow for subcellular fractionation and have been utilized in cell lines and fresh mouse brain tissue to predict the subcellular location of thousands of proteins, but to our knowledge have not been used in frozen human brain tissue. The use of frozen human tissue for tissue fractionation faces two major challenges, the post-mortem interval, during which proteins may leach from their usual location into the cytosol, and freezing, which results in membrane breakdown. Despite these challenges, in this proof-of-concept study, we show that the majority of proteins segregate reproducibly into crude density-based centrifugation fractions, that the fractions are enriched for the appropriate organellar markers, and that significant differences in protein localization can be observed between tissue from individuals with Alzheimer’s Disease and control individuals.
2022-05-06 | PXD027456 | Pride
Project description:The surprising observation that virtually the entire human genome is transcribed means we know very little about the function of many emerging classes of RNAs, except their astounding diversity. Traditional RNA function prediction methods rely on sequence or alignment information, which are limited in their ability to classify classes of non-coding RNAs (ncRNAs). To address this, we developed CoRAL, a machine learning-based approach for classification of RNA molecules. CoRAL uses biologically interpretable features including fragment length, cleavage specificity, and antisense transcription to distinguish between different ncRNA classes. We evaluated CoRAL using genome-wide small RNA sequencing (smRNA-seq) datasets from two human tissue types (brain and skin [GSE31037]), and were able to classify six different types of RNA transcripts with 79~80% accuracy in cross-validation experiments, and with 71~73% accuracy when CoRAL uses one tissue type for training and the other as validation. Analysis by CoRAL revealed that long intergenic ncRNAs, small cytoplasmic RNAs, and small nuclear RNAs show more tissue specificity, while microRNAs, small nucleolar, and transposon-derived RNAs are highly discernible and consistent across the two tissue types. The ability to consistently annotate loci across tissue types demonstrates the potential of CoRAL to characterize ncRNAs using smRNA-seq data in less characterized organisms. Four samples were sequenced, each one coming from frozen brain tissue (frontal cortex) of a deceased female human patient with no remarkable pathology.
2013-07-07 | E-GEOD-43335 | biostudies-arrayexpress
Project description:Uhlén2015 - Human tissue-based proteome metabolic network - brain
Human brain specific proteome metabolic network
This model is described in the article:
Tissue-based map of the human proteome
Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson Å, Kampf C, Sjöstedt E, Asplund A, Olsson I, Edllund K, Lundberg E, Navani S, Szigyarto AC, Odeberg J, Djureinovic D, Takanen JO, Hober S, Alm T, Edqvist P, Berlin H, Tegel H, Mulder J, Rockberg J, Nilsson P, Schwenk JM, Hamsten M, von Feilitzen K, Forsberg M, Persson L, Johansson F, Zwahlen M, von Heijne G, Nielsen J, Pontén F.
Science 2015 January; 347(6220)
Abstract:
Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray–based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body.
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2015-01-28 | MODEL1411240032 | BioModels
Project description:Transcriptome sequencing of human brain tissue after intracerebral hemorrhage
| PRJNA705186 | ENA