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Paleoproteomics: An Introduction to the Analysis of Ancient Proteins by Soft Ionisation Mass Spectrometry

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Paleogenomics

Part of the book series: Population Genomics ((POGE))

Abstract

The field of proteomic research, analogous to genomic research, has only recently witnessed a rapid increase in its application to the study of ancient materials. Bone has been the most commonly used archaeological and paleontological resource for recovering biological information. This has most frequently been for ancient genomic analysis, but some of the potential advantages of proteomics lie in its ability to discriminate between sources of the molecules, rather than the particular species or individual. However, proteomes could be considered more dynamic, offering different types of information than otherwise available through DNA analyses. Proteins are also considered to survive for much longer periods of time than substantial lengths of DNA and therefore the development of proteomics allows for the possibility of being able to recover information much further back in time than previously thought possible. In this chapter, the progress of this area called ‘paleoproteomics’ is reviewed, highlighting some of its greatest achievements but also some of the current limitations in the field across proteins from a range of different materials.

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Acknowledgements

The author acknowledges the support of the Royal Society in the form of a University Research Fellowship.

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  1. School of Earth and Environmental Sciences, Manchester Institute of Biotechnology, University of Manchester, Manchester, UK

    Michael Buckley

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  1. Department of Biological Sciences, University at Buffalo (SUNY), Buffalo, NY, USA

    Charlotte Lindqvist

  2. Faculty of Forestry and Environmental Management, University of New Brunswick, Fredericton, NB, Canada

    Om P. Rajora

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Buckley, M. (2018). Paleoproteomics: An Introduction to the Analysis of Ancient Proteins by Soft Ionisation Mass Spectrometry. In: Lindqvist, C., Rajora, O. (eds) Paleogenomics. Population Genomics. Springer, Cham. https://doi.org/10.1007/13836_2018_50

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