Sirius Red-based Method for the Determination of Type I Collagen Content in New and Aged Bone

The estimation of postmortem interval (PMI)—time since death—of skeletal remains is a complex problem that has yet to be solved. Previous researchers have investigated several chemical and physical markers for bone, still none has proven to be accurate or precise enough for PMI estimation. Surprisingly, the most abundant protein in bone—type I collagen—has received very little attention. Studying the impact of chemical, physical, and environmental variables on collagen structure and content in bone is important for forensic and medical applications. While a few researchers have reported the use of a semi-quantitative stereomicroscopy-based method for the estimation of the ratio of collagen to non-collagen proteins in stained cross sections of bone; the results were deemed to lack accuracy and reproducibility for further use. The main focus of this research is to develop a quantitative spectrophotometric assay for the determination of type I collagen content in cortical bone to evaluate new and aged bone sample analysis. This method uses Sirius Red dye, which selectively binds to collagen, resulting in an absorbance decrease at 530 nm. A series of studies were performed to optimize sample preparation and extraction conditions, to address matrix and spectral interferences, and reagent stability. Several preliminary calibration studies were conducted to calibrate the method and to determine the method detection limit (MDL), accuracy (%recovery), and precision (%RSD). An increase in collagen content from 10 - 60 ug resulted in an acceptable non-linear fit, a method detection limit of 6.96 µg of collagen, an accuracy of 99.5%, and a precision of 15.6%. Subsequently, new porcine rib bone samples were analyzed to establish a baseline for collagen content. Several aged porcine bone samples with PMI values ranging from 10 - 100 years were analyzed for collagen content and these results were statistically compared to the baseline for potential application in estimating PMI.