Tissue compartment analysis for biomarker discovery by gene expression profiling.
BACKGROUND
Although high throughput technologies for gene profiling are reliable tools, sample/tissue heterogeneity limits their outcomes when applied to identify molecular markers. Indeed, inter-sample differences in cell composition contribute to scatter the data, preventing detection of small but relevant changes in gene expression level. To date, attempts to circumvent this difficulty were based on isolation of the different cell structures constituting biological samples. As an alternate approach, we developed a tissue compartment analysis (TCA) method to assess the cell composition of tissue samples, and applied it to standardize data and to identify biomarkers.


RESULTS
TCA is based on the comparison of mRNA expression levels of specific markers of the different constitutive structures in pure isolated structures, on the one hand, and in the whole sample on the other. TCA method was here developed with human kidney samples, as an example of highly heterogeneous organ. It was validated by comparison of the data with those obtained by histo-morphometry. TCA demonstrated the extreme variety of composition of kidney samples, with abundance of specific structures varying from 5 to 95% of the whole sample. TCA permitted to accurately standardize gene expression level amongst >100 kidney biopsies, and to identify otherwise imperceptible molecular disease markers.


CONCLUSIONS
Because TCA does not require specific preparation of sample, it can be applied to all existing tissue or cDNA libraries or to published data sets, inasmuch specific operational compartments markers are available. In human, where the small size of tissue samples collected in clinical practice accounts for high structural diversity, TCA is well suited for the identification of molecular markers of diseases, and the follow up of identified markers in single patients for diagnosis/prognosis and evaluation of therapy efficiency. In laboratory animals, TCA will interestingly be applied to central nervous system where tissue heterogeneity is a limiting factor.
Disset
Antoine
A
UPMC Univ Paris 06, UMRS 872, Laboratoire de Génomique, Physiologie et Physiopathologie Rénales, Paris, France.
Cheval
Lydie
L
Soutourina
Olga
O
Duong Van Huyen
Jean-Paul
JP
Li
Guorong
G
Genin
Christian
C
Tostain
Jacques
J
Loupy
Alexandre
A
Doucet
Alain
A
Rajerison
Rabary
R
D015415:Biomarkers / Q000032:analysis / Q000378:metabolism*; D001706:Biopsy; D019295:Computational Biology / Q000379:methods; D018076:DNA, Complementary / Q000378:metabolism; D020869:Gene Expression Profiling / Q000379:methods*; D023281:Genomics / Q000379:methods; D006801:Humans; D007668:Kidney / Q000378:metabolism; D015233:Models, Statistical; D040901:Proteomics / Q000379:methods; D012333:RNA, Messenger / Q000378:metabolism; D015203:Reproducibility of Results; D020133:Reverse Transcriptase Polymerase Chain Reaction
D015415:Biomarkers; D018076:DNA, Complementary; D012333:RNA, Messenger