Novel profiling methodologies are redefining the diagnostic capabilities and therapeutic approaches towards more precise and personalized healthcare. Complementary information can be obtained from different omic approaches in combination with the traditional macro- and microscopic analysis of the tissue, providing a more complete assessment of the disease. Mass spectrometry imaging, as a tissue typing approach, provides information on the molecular level directly measured from the tissue. Lipids, metabolites, glycans, and proteins can be used for better understanding imbalances in the DNA to RNA to protein translation, which leads to aberrant cellular behavior. Several studies have explored the capabilities of this technology to be applied to tumor subtyping, patient prognosis, and tissue profiling for intraoperative tissue evaluation. In the future, intercenter studies may provide the needed confirmation on the reproducibility, robustness, and applicability of the developed classification models for tissue characterization to assist in disease management.

Currently, pathological evaluation of stage I/II colon cancer, following the Union Internationale Contre Le Cancer (UICC) guidelines, is insufficient to identify patients that would benefit from adjuvant treatment. In our study, we analyzed tissue samples from 276 patients with colon cancer utilizing mass spectrometry imaging. Two distinct approaches are herein presented for data processing and analysis. In one approach, four different machine learning algorithms were applied to predict the tendency to develop metastasis, which yielded accuracies over 90% for three of the models. In the other approach, 1007 m/z features were evaluated with regards to their prognostic capabilities, yielding two m/z features as promising prognostic markers. One feature was identified as a fragment from collagen (collagen 3A1), hinting that a higher collagen content within the tumor is associated with poorer outcomes. Identification of proteins that reflect changes in the tumor and its microenvironment could give a very much-needed prediction of a patient’s prognosis, and subsequently assist in the choice of a more adequate treatment.

Cancer-related deaths are very commonly attributed to complications from metastases to neighboring as well as distant organs. Dissociate response in the treatment of pancreatic adenocarcinoma is one of the main causes of low treatment success and low survival rates. This behavior could not be explained by transcriptomics or genomics; however, differences in the composition at the protein level could be observed. We have characterized the proteomic composition of primary pancreatic adenocarcinoma and distant metastasis directly in human tissue samples, utilizing mass spectrometry imaging. The mass spectrometry data was used to train and validate machine learning models that could distinguish both tissue entities with an accuracy above 90%. Model validation on samples from another collection yielded a correct classification of both entities. Tentative identification of the discriminative molecular features showed that collagen fragments (COL1A1, COL1A2, and COL3A1) play a fundamental role in tumor development. From the analysis of the receiver operating characteristic, we could further advance some potential targets, such as histone and histone variations, that could provide a better understanding of tumor development, and consequently, more effective treatments.

Due to their outstanding properties nanodiamonds are a promising nanoscale material in various applications such as microelectronics, polishing, optical monitoring, medicine and biotechnology. Beyond the typical diamond characteristics like extreme hardness or high thermal conductivity, they have additional benefits as intrinsic fluorescence due to lattice defects without photobleaching, obtained during the high pressure high temperature process. Further the carbon surface and its various functional groups in consequence of the synthesis, facilitate additional chemical and biological modification. In this work we present our recent results on chemical modification of the nanodiamond surface with phosphate groups and their electrochemically assisted immobilization on titanium-based materials to increase adhesion at biomaterial surfaces. The starting material is detonation nanodiamond, which exhibits a heterogeneous surface due to the functional groups resulting from the nitrogen-rich explosives and the subsequent purification steps after detonation synthesis. Nanodiamond surfaces are chemically homogenized before proceeding with further functionalization. Suspensions of resulting surface-modified nanodiamonds are applied to the titanium alloy surfaces and the nanodiamonds subsequently fixed by electrochemical immobilization. Titanium and its alloys have been widely used in bone and dental implants for being a metal that is biocompatible with body tissues and able to bind with adjacent bone during healing. In order to improve titanium material properties towards biomedical applications the authors aim to increase adhesion to bone material by incorporating nanodiamonds into the implant surface, namely the anodically grown titanium dioxide layer. Differently functionalized nanodiamonds are characterized by infrared spectroscopy and the modified titanium alloys surfaces by scanning and transmission electron microscopy. The process described shows an adsorption and immobilization of modified nanodiamonds on titanium; where aminosilanized nanodiamonds coupled with O-phosphorylethanolamine show a homogeneous interaction with the titanium substrate.