Testing the efficacy and comparability of ZooMS protocols on archaeological bone

Collagen peptide mass fingerprinting, best known as Zooarchaeology by Mass Spectrometry (or ZooMS) when applied to archaeology, has become invaluable for the taxonomic identification of archaeological collagenous materials, in particular fragmentary and modified bone remains. Prior to MALDI-based spectrometric analysis, collagen needs to be extracted from the bone's inorganic matrix, isolated and purified. Several protocols are currently employed for ZooMS analysis, however their efficacy and comparability has not been directly tested. Here, we use four different ZooMS protocols to analyze 400 bone samples from seven archaeological sites, dating to between ~500,000–2000 years ago. One of them, single-pot solid-phase-enhance sample preparation (SP3), is used for the first time as a ZooMS protocol. Our results indicate that the least-destructive ZooMS protocol which uses an ammonium bicarbonate buffer as a means of extracting collagen is most suitable for bones with good collagen preservation, whereas the acid-based methodologies can improve success rates for bones with low-to-medium collagen preservation. Since preservation of biomolecules in archaeological bones is highly variable due to age and environmental conditions, we use the percent nitrogen by weight (%N) value as an independent semi-quantitative proxy for assessing collagen content and for predicting which bones will likely result in a successful ZooMS-based identification. We find that 0.26%N as a threshold for screening material could optimize the number of spectra which produce identifications using ZooMS. Significance statement: We present a direct comparison of three previously published ZooMS protocols for the analyses of archaeological bones, and the first use of an SP3-based approach to ZooMS analysis. Our results show that the acid-based ZooMS protocols increase the success rate for bones with low-medium collagen preservation. We identify 0.26%N as a threshold for optimizing the number of samples with enough collagen for successful peptide mass fingerprinting.