The Functional Genomics Core (fgc) is a Core Facility for the Institute for Research in Biomedicine (IRB Barcelona, Catalunya, Spain), for Columbus Children's Hospital and a Satellite Facility for the Comprehensive Cancer Center at the Ohio State University (both Columbus, Ohio, USA). It offers a complete solution including consultation, RNA characterization, microarray processing and data analysis..
» Genome wide expression profiles
Molecular consequences of treatment with new compounds and prediction of clinical outcome
» Genome wide SNP analysis 100,000 polymorphisms analyzed in a single experiment
Design of experiments
Analysis of ten-thousands of genes is a new challenge in molecular biology and needs
a very careful design of experiments when researchers need high quality results. Basically
any kind of technical variation will be observed in the results. When samples shall be
compared for differential gene expression, every technical step must be done as similar
as possible. Some examples of how results can be compromised:
Cell cultures are treated slightly different (cell density, change of medium, different batches of FCS). Induction of stress response can be observed, changes of cell cycle or apoptosis.
Samples are treated different before RNA isolation (storage, freezing). Heat shock or cold shock can be induced, apoptosis or RNA degradation can appear.
Alterations in RNA isolation protocols (thawing of samples, usage of different methods of RNA isolation within a project, usage of mRNA and total RNA within a project). Differential gene expression can be observed in hundreds of genes
Differences in RNA integrity due to technical variations (we can quantify RNA integrity of your samples, see RNA analysis) Stable mRNAs behave like upregulated, unstable RNAs downregulated
Variations in chip processing (different batches of enzymes, several people doing the experiments). When minor alterations in gene expression shall be interogated, these differences can completely camouflage biological differences
More generally spoken for physicians:
You want to study differences in blood pressure after application of a new drug. You take care of
keeping everything as similar as possible for the placebo-group and the treatment-group
(age, gender, nutrition, etc). Studying 20.000 parameters in a microarray experiment needs
at least the same careful planning!
More generally spoken for molecular biologists:
You want to study consequences of a point-mutation on signaling. You check the sequence of
wildetype and mutant for each nucleotide, you treat both cells as identical as possible,
etc. You do all this while you usually measure a handful of parameters, influenced by the
point-mutation. Studying 20.000 parameters in a microarray experiment needs at least the
same careful planning!
Based on extensive research on quality control, results of microarray experiments performed
at fgc are highly reproducible and reliable.
