GNPS - Molecular analysis of human brain tissues
Ontology highlight
ABSTRACT:
INSTRUMENT(S): maXis
ORGANISM(S): Homo Sapiens (ncbitaxon:9606)
SUBMITTER: Pieter Dorrestein
PROVIDER: MSV000079040 | GNPS | Wed Feb 18 14:33:00 GMT 2015
REPOSITORIES: GNPS
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Project description:Through non-terminal CSF extractions in C57Bl/6 mice and high-resolution liquid chromatography-mass spectrometry analysis of individual murine samples, we report the most comprehensive proteome characterization of individual murine CSF to date. Utilizing stringent protein inclusion criteria that required the identification of at least two unique peptides (1% false discovery rate at the peptide level) we identified a total of 566 unique proteins, including 128 proteins from three individual CSF samples that have been previously identified in brain tissue.
2014-04-09 | PXD000248 | Pride
Project description:Neuroepigenetics considers genetic sequences and the interplay with environmental influences to elucidate vulnerability risk for various neurological and psychiatric disorders. However, evaluating DNA methylation of brain tissue is challenging owing to the issue of tissue specificity. Consequently, peripheral surrogate tissues were used, resulting in limited progress compared with other epigenetic studies, such as cancer research. Therefore, we developed databases to establish correlations between the brain and peripheral tissues in the same individuals. Four tissues, resected brain tissue, blood, saliva, and buccal mucosa (buccal), were collected from 19 patients (aged 13–73 years) who underwent neurosurgery. Moreover, their genome-wide DNA methylation was assessed using the Infinium HumanMethylationEPIC BeadChip arrays to determine the cross-tissue correlation of each combination. These correlation analyses were conducted with all methylation sites and with variable CpGs, and with when these were adjusted for cellular proportions. For the averaged data for each CpG across individuals, the saliva–brain correlation (r = 0.90) was higher than that for blood–brain (r = 0.87) and buccal–brain (r = 0.88) comparisons. Among individual CpGs, blood had the highest proportion of CpGs correlated to the brain at nominally significant levels (19.0%), followed by saliva (14.4%) and buccal (9.8%). These results were similar to the previous IMAGE-CpG results; however, the correlation analysis between the correlation coefficients of the datasets revealed a relatively low degree of correlation (brain vs. blood: r = 0.27, saliva; r = 0.18, and buccal; r = 0.24). To the best of our knowledge, this is the fourth study in the literature initiating the development of databases for correlations between the brain and peripheral tissues in the same individuals. We present the first database developed from an Asian population, specifically Japanese samples (AMAZE-CpG), which would contribute to interpreting individual epigenetic study results from various Asian populations.
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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:Genome-wide gene expression studies may provide a comprehensive insight in gene activities and biological pathways differing between individuals and tissues (even closely related tissues building complex organs such as the brain). Our research addressed both kinds of gene expression variation – between brain regions and between individuals – by expression profiling in brain tissues derived from eight brain regions and blood from 12 vervet monkeys (Chlorocebus aethiops sabaeus). We employed the non-human primate model to assure tissue quality and to enhance the probability of precise dissection of the brain tissues, which is difficult to realize in human subjects. We characterized brain regional differences in gene expression levels which may relate to specific functions of brain tissues including disease symptoms affecting specific brain regions. We focused on inter-individual variability of brain transcript levels in different regions that correlates well between blood and brain tissues and therefore could be further reliably studied in easily accessible blood samples. Applying very stringent transcript selection criteria including 1). considerable similarities between brain and blood tissues, 2). consistent repeat measurements in blood, 3). higher inter-individual than intra-individual variability and 4). detection in all tissue samples, allowed us to identify transcripts in which inter-individual variation in brain expression profiles indicates possible genetic factors regulating gene transcript levels. High heritabilities of these transcript levels indicated that our approach focusing on transcripts showing higher inter-individual variability than intra-individual variability identifies transcripts with a strong genetic component.
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