Degraded RNA from human anterior cruciate ligaments yields valid gene expression profiles

ABSTRACT Background Correlating gene expression patterns with biomechanical properties of connective tissues provides insight into molecular processes underlying development and maintenance of the tissues’ physical characteristics. Cadaveric tissues such as human knees are widely considered suitable for biomechanical studies, but their usefulness for gene expression experiments is potentially limited by unavoidable, nuclease-mediated degradation of RNA. Methods We quantified mRNAs encoding 84 extracellular matrix components and five housekeeping proteins in intact and partially degraded RNA from human anterior cruciate ligaments (ACLs), and compared relative amounts of the individual mRNAs by regression analysis. Results Human ACL RNA degraded in vitro by limited ribonuclease digestion (N=6) resembled degraded RNA isolated from cadaveric tissue. PCR threshold cycle (Ct) values from degraded RNAs ranged variably higher than values obtained from their corresponding non-degraded RNAs, reflecting both the expected loss of target templates in the degraded preparations as well as differences in the extent of degradation. Relative Ct values obtained for mRNA targets in degraded preparations correlated strongly with the corresponding target levels in non-degraded RNA, both for each ACL as well as for pooled results from all six ACLs. Nuclease-mediated degradation produced similar, strongly correlated losses of housekeeping and non-housekeeping gene mRNAs. Expression profiling of RNA degraded in situ yielded comparable results, confirming that in vitro digestion suitably mimicked degradation by endogenous ribonucleases in frozen and thawed ACL. Conclusion PCR-based expression analyses can yield valid mRNA profiles from partially degraded RNA preparations such as those obtained from cadaveric knees and other skeletal motion segments used in biomechanical studies. Legitimate comparisons between variably degraded tissues can be made by normalizing quantitative data to an appropriate housekeeping transcript.