Predicting the prognosis of colorectal cancer (CRC) patients remains challenging and a characterisation of the tumour immune environment represents one of the most crucial avenues when attempting to do so. For this reason, molecular approaches which are capable of classifying the immune environments associated with tumour infiltrating lymphocytes (TILs) are being readily investigated. In this proof of concept study, we aim to explore the feasibility of using spatial lipidomics by MALDI-MSI to distinguish CRC tissue based upon their TIL content. Formalin-fixed paraffin-embedded tissue from human thymus and tonsil was first analysed by MALDI-MSI to obtain a curated mass list from a pool of single positive T lymphocytes, whose putative identities were annotated using an LC-MS-based lipidomic approach. A CRC tissue microarray (TMA, n = 30) was then investigated to determine whether these cases could be distinguished based upon their TIL content in the tumour and its microenvironment. MALDI-MSI from the pool of mature T lymphocytes resulted in the generation of a curated mass list containing 18 annotated m/z features. Initially, subsets of T lymphocytes were then distinguished based on their state of maturation and differentiation in the human thymus and tonsil tissue. Then, when applied to a CRC TMA containing differing amounts of T lymphocyte infiltration, those cases with a high TIL content were distinguishable from those with a lower TIL content, especially within the tumour microenvironment, with three lipid signals being shown to have the greatest impact on this separation (p < 0.05). On the whole, this preliminary study represents a promising starting point and suggests that a lipidomics MALDI-MSI approach could be a promising tool for subtyping the diverse immune environments in CRC.

The present study applies for the first time as Matrix-Assisted Laser Desorption/Ionization (MALDI) Mass Spectrometry Imaging (MSI) on real thyroid Fine Needle Aspirations (FNAs) to test its possible complementary role in routine cytology in the diagnosis of thyroid nodules. The primary aim is to evaluate the potential employment of MALDI-MSI in cytopathology, using challenging samples such as needle washes. Firstly, we designed a statistical model based on the analysis of Regions of Interest (ROIs), according to the morphological triage performed by the pathologist. Successively, the capability of the model to predict the classification of the FNAs was validated in a different group of patients on ROI and pixel-by-pixel approach. Results are very promising and highlight the possibility to introduce MALDI-MSI as a complementary tool for the diagnostic characterization of thyroid nodules.

Gelatin–dextran hydrogel scaffolds (G-PEG-Dx) were evaluated for their ability to activate the bone marrow human mesenchymal stromal cells (BM-hMSCs) towards mineralization. G-PEG-Dx1 and G-PEG-Dx2, with identical composition but different architecture, were seeded with BM-hMSCs in presence of fetal bovine serum or human platelet lysate (hPL) with or without osteogenic medium. G-PEG-Dx1, characterized by a lower degree of crosslinking and larger pores, was able to induce a better cell colonization than G-PEG-Dx2. At day 28, G-PEG-Dx2, with hPL and osteogenic factors, was more efficient than G-PEG-Dx1 in inducing mineralization. Scanning electron microscopy (SEM) and Raman spectroscopy showed that extracellular matrix produced by BM-hMSCs and calcium-positive mineralization were present along the backbone of the G-PEG-Dx2, even though it was colonized to a lesser degree by hMSCs than G-PEG-Dx1. These findings were confirmed by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), detecting distinct lipidomic signatures that were associated with the different degree of scaffold mineralization. Our data show that the architecture and morphology of G-PEG-Dx2 is determinant and better than that of G-PEG-Dx1 in promoting a faster mineralization, suggesting a more favorable and active role for improving bone repair.

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Since the start of the COVID-19 outbreak, more than four million people have died of this disease. Given its ability to provide a precise response, mass spectrometry-based proteomics could represent a useful tool to study this pathology. To this end, an untargeted nLC-ESI-MS/MS-based method to characterise SARS-CoV-2 proteins, including possible variants, and investigate human saliva and plasma proteome in a single analysis was developed for further application in patients. Four SARS-CoV-2 recombinant proteins, three (S1–S2–RBD) belonging to the spike glycoprotein (S) and one corresponding to the nucleoprotein (N), were prepared and analysed with nLC-UHRTOF by injecting decreasing amounts to establish the limit of detection (LOD) of the method. This was determined as 10 pg for all the components of the S protein and for N (71 amol and 213 amol, respectively). Various viral inactivation strategies plus deglycosylation and digestion approaches were then tested in saliva and plasma spiked with different quantities of SARS-CoV-2 recombinant proteins. The limit of characterisation (LOC) in saliva for the N and S proteins was observed at 100 pg (coverage of 20% and 3%, respectively); instead, in plasma, it was 33 pg for N and 330 pg for the S protein, with a coverage of 4% for both. About 300 and 800 human proteins were identified in plasma and saliva, respectively, including several key effectors and pathways that are known to be altered in COVID-19 patients. In conclusion, this approach allows SARS-CoV-2 proteins and the human proteome to be simultaneously explored, both for plasma and saliva, showing a high relevant potential for retrospective studies aimed at investigating possible virus variants and for patient stratification.