Abstract/Summary

Rationale Recent advances in high-throughput molecular analyses of collagen peptides, especially ZooMS (Zooarchaeology by Mass Spectrometry), have permitted breakthroughs in the analysis of archaeological material that is highly fragmented, a factor that hinders morphological identification. Despite these advances, the challenge of successfully analysing archaeological samples with poorer collagen preservation persists. This paper examines the potential of two mass analysers, TOF (Time of Flight) and FTICR (Fourier-transform ion cyclotron resonance), and addresses how they can be used to optimise the ZooMS workflow. Methods Type 1 collagen (COL1) was extracted from 89 archaeological bones from the French Palaeolithic site of Le Piage (37–34 ka cal BP). Three ZooMS extraction protocols were applied, an acid-free buffer method (AmBic), offering rapid and less destructive analysis, and two methods of acid demineralisation (HCl and TFA) that provide higher peptide resolution. After analysing the specimens with MALDI-TOF and MALDI-FTICR, we used bottom-up and PRM (Parallel Reaction Monitoring) LC–MS/MS, and MALDI-CASI-FTICR (Continuous Accumulation of Selected Ions) to verify 26 ambiguous identifications. Results Overall, 99% of the samples could be identified to at least family level, with the rate of identification and precision varying by method. Despite challenges in detecting specific biomarkers with MALDI-FTICR—especially peptide A (COL1ɑ2 978–990), which tends to be unstable and poorly ionised—the high resolution of this method allowed the successful identification of more degraded specimens, including burnt bones. Conclusions Our work highlights the robustness of traditional MALDI-TOF ZooMS for retrieving collagen and for providing taxonomic identifications with low failure rates, features that are critical when processing large numbers of samples. MALDI-FTICR shows better potential when working with precious samples or degraded collagen. This study advances the analytical detection of peptides by optimising the ZooMS workflow and by tailoring it to specific archaeological contexts showing variation in degree of preservation.