bioRxivBioinformaticsPreprint
Molecular biointerface characterisation for an implanted medical device using cryogenic orbitrap secondary ion mass spectrometry (Cryo-OrbiSIMS)
Akmal Hidayat Bin SabriiD, Kei F. Carver Wong, Anna M. KotowskaiD, Leanne E. FisheriD, Jeni LuckettiD, Jimiama Mafeni MaseiD, Lisa KämmerlingiD, Grazziela Patrocinio FigueredoiD, David J. ScurriD, Amir M. GhaemmaghamiiD, Morgan R. AlexanderiD
Abstract
Implanted medical devices often fail due to foreign body reaction (FBR), a process still not fully understood. This work presents a new depth profiling approach to provide insight into the spatial metabolomics of the biointerface of implants, revealing biomolecular strata representative of the host response. This study examines silicone rubber poly(dimethyl siloxane) catheters implanted in mice for 1 and 28 days. Cryo-OrbiSIMS was used in combination with ToF-SIMS to identify metabolite profiles from the biological deposit found on the implants after removal from the tissue which were previously unattainable using tissue sectioning. Machine learning and statistical analysis of the profiles were used to help identify early biointerface responses to the implant, including at 1 day of implantation the observation of elevated sugars and itaconate an immunomodulatory metabolite that modulates FBR. At day 28 inflammation associated markers were observed such as urate and palmitic acid (FA 16:0). Depth profiling revealed two distinct molecular layers in the deposits: amino acids, and nucleic acids were preferentially seen towards the host tissue, consistent with the observation of a cell monolayer in the tissue sections, whereas certain lipids and fatty acids where either at the catheter-deposit interface or towards the host tissue after 28 days. The stratification was less well developed at 1-day implantation, but common lipids were seen at the deposit-implant interface across both time points. These insights advance understanding of FBR and support the development of improved implant materials.
- organism: mouse
- assay: metagenomics
- assay: metabolomics
- research focus: cell-cell communication
- approach: statistical model
- Citations
- 0
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