Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Transcription profiling of human healthy peripheral blood from different individuals

ABSTRACT: We carried out a survey of the variation in gene expression patterns in the blood of healthy individuals, by using cDNA microarrays. Our results revealed a surprising consistency in these patterns, but also evidence of distinct patterns of interindividual and temporal variation. Some features of variation in the expression patterns were associated with differences in the cellular composition of the blood sample, with gender, age, and time of day. These results expand our understanding of human variation and hematological physiology and provide a genome-wide molecular portrait of a healthy human tissue.

ORGANISM(S): Homo sapiens

SUBMITTER: Janos Demeter

PROVIDER: E-SMDB-1819 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Individuality and variation in gene expression patterns in human blood.

Whitney Adeline R AR Diehn Maximilian M Popper Stephen J SJ Alizadeh Ash A AA Boldrick Jennifer C JC Relman David A DA Brown Patrick O PO

Proceedings of the National Academy of Sciences of the United States of America 20030210 4

The nature and extent of interindividual and temporal variation in gene expression patterns in specific cells and tissues is an important and relatively unexplored issue in human biology. We surveyed variation in gene expression patterns in peripheral blood from 75 healthy volunteers by using cDNA microarrays. Characterization of the variation in gene expression in healthy tissue is an essential foundation for the recognition and interpretation of the changes in these patterns associated with in ...[more]

PMID: 12578971

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Project description:We describe a simple method, Digital Restriction Enzyme Analysis of Methylation (DREAM), based on sequential DNA digestion with a pair of methylation-blocked and methylation-tolerant neoschizomeric restriction enzymes SmaI/XmaI followed by end repair and ultra-deep sequencing. DREAM provides information on 160,000 unique CpG sites of which 39,000 are in CpG islands, and 33,000 are at transcription start sites (-1 kb to +1 kb) of 13,139 RefSeq genes. We compared DNA methylation values in white blood cells from 4 healthy individuals and found them to be remarkably uniform. Interindividual differences >30% were observed only at 227 of 28,331 (0.8%) of autosomal CCCGGG sites covered by 100+ sequencing reads. Similarly, differences at only 59 sites were observed between the cord and adult blood. Conserved methylation patterns in healthy blood cells contrasted with extensive changes affecting 18-40% of CpG sites in leukemia. The method is cost effective, quantitative (r2=0.93 when compared to bisulfite pyrosequencing), reproducible (r2=0.997), and can detect differences >25% with false positive rate <0.001. Accurate analysis of changes in DNA methylation will be useful in quantifying epigenetic effects of environment and nutrition, correlating developmental epigenetic variation with phenotypes, understanding epigenetics of cancer and chronic diseases, measuring the effects of drugs on DNA methylation or deriving new biological insights into mammalian genomes. Digital restriction enzyme analysis of methylation (DREAM) was performed to determine the DNA methylation profiles of healthy white blood cells from cord blood and adult blood, acute myeloid leukemia bone marrow, and two leukemia cell lines (HEL and K562). In this approach, genomic DNA is sequentially cut at CCCGGG sites with the methylation-sensitive enzyme SmaI (blunt ends) and its methylation-tolerant neoschizomer XmaI (5'CCGG overhangs), creating different end sequences that represent methylation status of the CCCGGG sites. These end sequences are analyzed by next-generation sequencing, and thereafter the methylation status at individual CCCGGG sites across the genome can be determined.

Dataset Information

Transcription profiling of human healthy peripheral blood from different individuals

Publications

Individuality and variation in gene expression patterns in human blood.

Similar Datasets

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