Skip to main content

The identification of archaeological eggshell using peptide markers.

Presslee, S., Wilson, J., Woolley, J., Best, J., Russell, D., Radini, A., Fischer, R., Kessler, B., Boano, R., Collins, M. and Demarchi, B., 2017. The identification of archaeological eggshell using peptide markers. STAR: Science & Technology of Archaeological Research, 3 (1), 89 - 99.

Full text available as:

[img]
Preview
PDF (OPEN ACCESS ARTICLE)
The identification of archaeological eggshell using peptide markers.pdf - Published Version
Available under License Creative Commons Attribution.

3MB

DOI: 10.1080/20548923.2018.1424300

Abstract

Avian eggshell survives well in alkaline and neutral soils, but its potential as an archaeological resource remains largely unexplored, mainly due to difficulties in its identification. Here we exploit the release of novel bird genomes and, for the first time on eggshell, use MALDI-ToF (matrix-assisted laser desorption ionisation-time of flight) mass spectrometry in combination with peptide sequencing by LC-MS/MS. The eggshell proteome is revealed as unexpectedly complex, with 5755 proteins identified for a reference collection comprising 23 bird species. We determined 782 m/z markers useful for eggshell identification, 583 of which could be assigned to known eggshell peptide sequences. These were used to identify eggshell fragments recovered from a medieval site at Freeschool Lane, Leicester. We discuss the specificity of the peptide markers and highlight the importance of assessing the level of taxonomic identification achievable for archaeological interpretation.

Item Type:Article
ISSN:2054-8923
Additional Information:This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Uncontrolled Keywords:Eggshell; birds; zooarchaeology; proteomics; mass spectrometry (ZooMS)
Group:Faculty of Science & Technology
ID Code:31170
Deposited By: Symplectic RT2
Deposited On:29 Aug 2018 13:41
Last Modified:14 Mar 2022 14:12

Downloads

Downloads per month over past year

More statistics for this item...
Repository Staff Only -