Dataset Information

Mass spectrometry imaging of ancient teeth proteomes

ABSTRACT: In order to research the variation in protein distribution in teeth, proteins were extracted from archaeological (15-18th century, Netherlands) and modern teeth and identified using LC-MS/MS. Of the recovered proteins we then visualised the distribution of collagen type I (both the alpha-1 and -2 chains), alpha-2-HS-glycoprotein, haemoglobin subunit alpha and myosin light polypeptide 6 using MALDI-MSI. We found distinct differences in the spatial distributions of different proteins as well as between some peptides of the same protein. The reason for these differences in protein spatial distribution remain unclear, yet this study highlights the ability of MALDI-MSI for visualisng the spatial distribution of proteins in archaeological biomineralised tissues. Therefore MALDI-MSI might prove a useful tool to improve our understanding of protein preservation as well as aid in deciding sampling strategies.

INSTRUMENT(S): Orbitrap Fusion Lumos, SYNAPT G2-Si

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Tooth

SUBMITTER: Jan Dekker

LAB HEAD: Jessica Hendy

PROVIDER: PXD038114 | Pride | 2023-03-11

REPOSITORIES: Pride

ACCESS DATA

Publications

Spatial analysis of the ancient proteome of archeological teeth using mass spectrometry imaging.

Dekker Joannes J Larson Tony T Tzvetkov Jordan J Harvey Virginia L VL Dowle Adam A Hagan Richard R Genever Paul P Schrader Sarah S Soressi Marie M Hendy Jessica J

Rapid communications in mass spectrometry : RCM 20230401 8

<h4>Rationale</h4>Proteins extracted from archaeological bone and teeth are utilised for investigating the phylogeny of extinct and extant species, the biological sex and age of past individuals, as well as ancient health and physiology. However, variable preservation of proteins in archaeological materials represents a major challenge.<h4>Methods</h4>To better understand the spatial distribution of ancient proteins preserved within teeth, we applied matrix assisted laser desorption/ionisation m ...[more]

PMID: 36735645

Similar Datasets

Project description:We developed cell2location, a principled and versatile Bayesian model that is designed to resolve fine-grained cell types in spatial transcriptomic data and create comprehensive cellular maps of diverse tissues. To validate cell2location in real tissue, we applied the model to data from the mouse brain, which features diverse neural cell types organised in a well characterised spatial architecture across brain areas, thus presenting a canonical use case to test spatial genomics. We generated matched single nucleus (sn, this submission) and Visium spatial RNA-seq (10X Genomics) profiles of adjacent mouse brain sections that contain multiple regions from the telencephalon and diencephalon. To assess the biological and intra-organ technical variation in spatial mapping, we assayed two mouse brains and serial tissue sections from each brain (total of 3 and 2 matched sections from two animals, respectively, and an extra section for snRNA-seq), creating a rich multi-modal and replicated transcriptomic dataset. Tissue processing. Brains of wild-type adult C57BL/6 mice (postnatal day 56, 1 female and 1 male) were dissected, snap frozen, embedded in optimal cutting temperature compound (Tissue-Tek) and stored at -80oC. Brain hemispheres were cryosectioned at -20oC using a cryostat (Leica, CM3050S). To assess tissue quality, RNA was extracted from test tissue sections using the RNeasy Pico Kit (Qiagen) and yielded high RIN values (9.6 and 9.7) on an Agilent Bioanalyser, indicating high RNA quality. For matched single nuclei and Visium RNA-seq experiments, brain hemispheres were cryosectioned to adjacent thick (200 µm) and thin (10 µm) coronal sections, respectively, and processed the same day. In total, four consecutive sets of thick and thin tissue sections were collected from each brain. Five sets of tissue sections yielded both good quality single nuclei and Visium data (three adjacent sections from mouse 1 and two sections from mouse 2) while one additional section from mouse 2 yielded good single nuclei; these were considered for analysis in this study. Visium spatial transcriptomics. Thin (10 µm) mouse brain sections were cryosectioned and mounted directly onto separate capture areas on 10X Visium Spatial Gene Expression slides (beta product version). Processing was done per manufacturer’s protocols. Briefly, sections were methanol-fixed, hematoxylin and eosin (H&E)-stained, and imaged on a NanoZoomer 2.0 slide scanner (Hamamatsu). Sections were then permeabilized and further processed to obtain cDNA libraries that were quality controlled using the Agilent Bioanalyser. The cDNA libraries were sequenced on the Illumina HiSeq 4000 system, aiming at 300 million raw reads per section with read lengths 28cy R1, 8cy i7 index, 0cy i5 index, 91cy read 2. 10X Visium spatial sequencing data was aligned to mouse pre-mRNA genome reference version mm10 using 10X SpaceRanger and mRNA count matrices were generated by adding intronic and exonic reads for each gene in each location. The paired histology H&E images were processed using 10X SpaceRanger to select locations covered by tissue by aligning pre-recorded spot locations with fiducial border spots in the histology image. This allows evaluating the correspondence between cell maps produced using our method and the known brain anatomy. This also allows identifying the number of nuclei in each spot using nuclear segmentation as described in Suppl. Methods and reported in Fig S8A-D. The histology image was used to manually annotate cortical layers in the primary somatosensory cortex (SSp) region using the lasso tool in the 10X Loupe browser.

Project description:Gliomas represent 80% of all malignant cerebral tumors and are classified within different malignity grade. Glioblastoma, the most aggressive group, represent more than half of all gliomas but remain a heterogeneous group. Indeed, patient survival can reach from several months to a few years after surgery and chemotherapy. To study gliomas, MALDI mass spectrometry imaging is an interesting technique, allowing the analysis of tumor heterogeneity. In this study, MALDI-MSI is coupled with spatially-resolved microproteomic within the objective to identify subgroups of glioblastomas patients in order to help diagnosis and prognostic. Molecular images are generated from thin tissue section in order to determine the spatial localization of digested peptides. Thanks to unsupervised statistical analysis, we then generated hierarchical clustering of homogeneous molecular regions. According to these regions, spatially resolved proteomic gave access to large scale protein identification and relative quantification. Hierarchical clustering reveal three molecular region within all the tumors sample : region red which represent 33% of surface of all sample, region yellow (25.7%) and region blue (40.8%). Consequently, 3 groups of patient can be determined: group A composed mostly of region red (13/50 patients), group B composed mostly of region yellow (9/50 patients) and group C composed mostly of region blue (24/50 patients). Spatially resolved proteomic was performed and the 147 extractions were analyzed by nano-LC MS/MS. Two-way ANOVA test reveal panel of proteins specifically overexpressed in each region. Protein overexpressed in region red are associated with neurogenesis; protein overexpressed in region yellow are associated with an activation of the immune system and protein overexpressed in region blue are involved with viral processes. To conclude, the combination of MALDI-MSI and microproteomic provides new information on these tumors, allowing us to differentiate three group of patient. In the future, these data will permit to build a more precise classification, a better medical care for patients and the identification of potential new therapeutic targets.

Dataset Information

Mass spectrometry imaging of ancient teeth proteomes

Publications

Spatial analysis of the ancient proteome of archeological teeth using mass spectrometry imaging.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure