Increased sensitivity of next generation sequencing-based expression profiling after globin reduction in human blood RNA

BackgroundTranscriptome analysis is of great interest in clinical research, where significant differences between individuals can be translated into biomarkers of disease. Although next generation sequencing provides robust, comparable and highly informative expression profiling data, with several million of tags per blood sample, reticulocyte globin transcripts can constitute up to 76% of total mRNA compromising the detection of low abundant transcripts. We have removed globin transcripts from 6 human whole blood RNA samples with a human globin reduction kit and compared them with the same non-reduced samples using deep Serial Analysis of Gene Expression.ResultsGlobin tags comprised 52-76% of total tags in our samples. Out of 21,633 genes only 87 genes were detected at significantly lower levels in the globin reduced samples. In contrast, 11,338 genes were detected at significantly higher levels in the globin reduced samples. Removing globin transcripts allowed us to also identify 2112 genes that could not be detected in the non-globin reduced samples, with roles in cell surface receptor signal transduction, G-protein coupled receptor protein signalling pathways and neurological processes.ConclusionsThe reduction of globin transcripts in whole blood samples constitutes a reproducible and reliable method that can enrich data obtained from next generation sequencing-based expression profiling.

[1]  Tim Hubbard Finishing the euchromatic sequence of the human genome , 2004 .

[2]  Hanlee P. Ji,et al.  Next-generation DNA sequencing , 2008, Nature Biotechnology.

[3]  Lin Feng,et al.  Power of Deep Sequencing and Agilent Microarray for Gene Expression Profiling Study , 2010, Molecular biotechnology.

[4]  J. Nurnberger,et al.  Identifying blood biomarkers for mood disorders using convergent functional genomics , 2009, Molecular Psychiatry.

[5]  J. Nurnberger,et al.  Erratum: Identifying blood biomarkers for mood disorders using convergent functional genomics (Molecular Psychiatry (2009) 14 (156-174) DOI: 10.1038/mp.2008.11) , 2009 .

[6]  A. Moorman,et al.  Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data , 2009, Nucleic acids research.

[7]  S. Lukyanov,et al.  Normalization of full-length enriched cDNA. , 2008, Molecular bioSystems.

[8]  M. van Iterson,et al.  Relative power and sample size analysis on gene expression profiling data , 2009, BMC Genomics.

[9]  R. Giegerich,et al.  Fast and effective prediction of microRNA/target duplexes. , 2004, RNA.

[10]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.

[11]  J. Bonfield,et al.  Finishing the euchromatic sequence of the human genome , 2004, Nature.

[12]  Mark D. Robinson,et al.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..

[13]  Jeppe Emmersen,et al.  DeepSAGE—digital transcriptomics with high sensitivity, simple experimental protocol and multiplexing of samples , 2006, Nucleic acids research.

[14]  International Human Genome Sequencing Consortium Finishing the euchromatic sequence of the human genome , 2004 .

[15]  R. Øvstebø,et al.  Quantification of relative changes in specific mRNAs from frozen whole blood – methodological considerations and clinical implications , 2007, Clinical chemistry and laboratory medicine.

[16]  Isaac S. Kohane,et al.  A Practical Platform for Blood Biomarker Study by Using Global Gene Expression Profiling of Peripheral Whole Blood , 2009, PloS one.

[17]  C. Shriver,et al.  Functional identity of genes detectable in expression profiling assays following globin mRNA reduction of peripheral blood samples. , 2007, Clinical Biochemistry.

[18]  M. Marton,et al.  Characterization of globin RNA interference in gene expression profiling of whole-blood samples. , 2008, Clinical chemistry.

[19]  Charles M Perou,et al.  Evaluating the comparability of gene expression in blood and brain , 2006, American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics.

[20]  D. Stenger,et al.  Effects of globin mRNA reduction methods on gene expression profiles from whole blood. , 2006, The Journal of molecular diagnostics : JMD.

[21]  R. Eils,et al.  A highly standardized, robust, and cost-effective method for genome-wide transcriptome analysis of peripheral blood applicable to large-scale clinical trials. , 2006, Genomics.

[22]  R. Vossen,et al.  Deep sequencing-based expression analysis shows major advances in robustness, resolution and inter-lab portability over five microarray platforms , 2008, Nucleic acids research.

[23]  M. Robinson,et al.  Small-sample estimation of negative binomial dispersion, with applications to SAGE data. , 2007, Biostatistics.

[24]  R. Hehlmann,et al.  Improvement of molecular monitoring of residual disease in leukemias by bedside RNA stabilization , 2002, Leukemia.

[25]  R V Jensen,et al.  Genome-wide expression profiling of human blood reveals biomarkers for Huntington's disease. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[26]  Mathew W. Wright,et al.  Naming 'junk': Human non-protein coding RNA (ncRNA) gene nomenclature , 2011, Human Genomics.

[27]  A. Pahl RETRACTED: Gene expression profiling using RNA extracted from whole blood: technologies and clinical applications , 2005 .

[28]  R. Verhaak,et al.  Prognostically useful gene-expression profiles in acute myeloid leukemia. , 2004, The New England journal of medicine.

[29]  Jun Ma,et al.  The peripheral blood transcriptome dynamically reflects system wide biology: a potential diagnostic tool. , 2006, The Journal of laboratory and clinical medicine.

[30]  A. Pahl Gene expression profiling using RNA extracted from whole blood: technologies and clinical applications. , 2005, Expert review of molecular diagnostics.

[31]  Justine R. Smith,et al.  Gene expression profiling of whole blood: Comparison of target preparation methods for accurate and reproducible microarray analysis , 2009, BMC Genomics.

[32]  J. Mattick,et al.  Non‐coding RNAs: regulators of disease , 2010, The Journal of pathology.

[33]  Uwe Oelmueller,et al.  Stabilization of mRNA expression in whole blood samples. , 2002, Clinical chemistry.

[34]  R. Tibshirani,et al.  Disease signatures are robust across tissues and experiments , 2009, Molecular systems biology.

[35]  M. Hellmich,et al.  Comparison of different isolation techniques prior gene expression profiling of blood derived cells: impact on physiological responses, on overall expression and the role of different cell types , 2004, The Pharmacogenomics Journal.