English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

A "Crossomics" Study Analysing Variability of Different Components in Peripheral Blood of Healthy Caucasoid Individuals

MPS-Authors
/persons/resource/persons97409

Selbig,  J.
BioinformaticsCRG, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

External Ressource
No external resources are shared
Fulltext (public)
Supplementary Material (public)
There is no public supplementary material available
Citation

Gruden, K., Hren, M., Herman, A., Blejec, A., Albrecht, T., Selbig, J., et al. (2012). A "Crossomics" Study Analysing Variability of Different Components in Peripheral Blood of Healthy Caucasoid Individuals. PLoS One, 7(1), e28761. doi:10.1371/journal.pone.0028761.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0014-1FB0-6
Abstract
Background: Different immunotherapy approaches for the treatment of cancer and autoimmune diseases are being developed and tested in clinical studies worldwide. Their resulting complex experimental data should be properly evaluated, therefore reliable normal healthy control baseline values are indispensable. Methodology/Principal Findings: To assess intra- and inter-individual variability of various biomarkers, peripheral blood of 16 age and gender equilibrated healthy volunteers was sampled on 3 different days within a period of one month. Complex "crossomics'' analyses of plasma metabolite profiles, antibody concentrations and lymphocyte subset counts as well as whole genome expression profiling in CD4(+)T and NK cells were performed. Some of the observed age, gender and BMI dependences are in agreement with the existing knowledge, like negative correlation between sex hormone levels and age or BMI related increase in lipids and soluble sugars. Thus we can assume that the distribution of all 39.743 analysed markers is well representing the normal Caucasoid population. All lymphocyte subsets, 20% of metabolites and less than 10% of genes, were identified as highly variable in our dataset. Conclusions/Significance: Our study shows that the intra- individual variability was at least two-fold lower compared to the inter-individual one at all investigated levels, showing the importance of personalised medicine approach from yet another perspective.