Using MALDI‐FTICR Mass Spectrometry to Enhance ZooMS Identifications of Pleistocene Bone Fragments Showing Variable Collagen Preservation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".