Lipidomic Typing of Colorectal Cancer Tissue Containing Tumour-Infiltrating Lymphocytes by MALDI Mass Spectrometry Imaging

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

Ex vivo thyroid fine needle aspirations as an alternative for MALDI-MSI proteomic investigation: intra-patient comparison

Fine needle aspiration (FNA) is the reference standard for the diagnosis of thyroid nodules. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) has been successfully used to discriminate the proteomic profiles of benign and malignant thyroid FNAs within the scope of providing support to pathologists for the classification of morphologically borderline cases. However, real FNAs provide a limited amount of material due to sample collection restrictions. Ex vivo FNAs could represent a valuable alternative, increasing sample size and the power of statistical conclusions. In this study, we compared the real and ex vivo MALDI-MSI proteomic profiles, extracted from thyrocyte containing regions of interest, of 13 patients in order to verify their similarity. Statistical analysis demonstrated the mass spectra similarity of the proteomic profiles by performing intra-patient comparison, using statistical similarity systems. In conclusion, these results show that post-surgical FNAs represent a possible alternative source of material for MALDI-MSI proteomic investigations in instances where pre-surgical samples are unavailable or the number of cells is scarce

Untargeted Mass Spectrometry Approach to Study SARS-CoV-2 Proteins in Human Plasma and Saliva Proteome

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\u2013S2\u2013RBD) 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

MALDI-MSI as a Complementary Diagnostic Tool in Cytopathology: A Pilot Study for the Characterization of Thyroid Nodules

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

Analysis of Hashimoto's thyroiditis on fine needle aspiration samples by MALDI-Imaging

Matrix-Assisted Laser Desorption/Ionization (MALDI)-Mass Spectrometry imaging (MSI) has been applied in various diseases aimed to biomarkers discovery. In this study diagnosis and prognosis of Hashimoto Thyroiditis (HT) in cytopathology by MALDI-MSI has been investigated. Specimens from a routine series of subjects who underwent UltraSound-guided thyroid Fine Needle Aspirations (FNAs) were used. The molecular classifier trained in a previous study was modified to include HT as a separate entity in the group of benign lesions, in the diagnostic proteomic triage of thyroid nodules. The statistical analysis confirmed the existence of signals that HT shares with hyperplastic lesions and others that are specific and characterize this subgroup. Statistically relevant HT-related peaks were included in the model. Then, the discriminatory capability of the classifier was tested in a second validation phase, showing a good agreement with cytological diagnoses. The possibility to overlap the molecular signatures of both the lymphocytes and epithelial cells components (ROIs or pixel-by-pixel analysis) confirmed the composite proteomic background of HT. These results open the way to their possible translation as alternative serum biomarkers of this autoimmune condition

Correction to: Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device (Scientific Reports, (2024), 14, 1, (1754), 10.1038/s41598-024-51766-5)

\ua9 The Author(s) 2024.Correction to: Scientific Reportshttps://doi.org/10.1038/s41598-024-51766-5, published online 19 January 2024 The original version of this Article contained an error in Figure 7 where an incorrect reference was cited for one of the recommended algorithms for Gait Speed Detection (GSD). The original Figure 7 and accompanying legend appear below. (Figure presented.) Overview over the diferent algorithmic steps of the analytical pipeline with short explanations of the intermediate and fnal outputs of each of the algorithmic blocks; gait sequence detection (GSD), initial contact detection (ICD), cadence estimation (CAD) and stride length estimation (SL). Te algorithm column indicates the used algorithms for the two pipelines P1 (HA, COPD, CHF). (MS, PD, PFF) and P2 (MS, PD, PFF) Short citations for the algorithms are provided below the fgure. For more details see Table 1 in26. In addition, the Supplementary Information 1 file published with this Article contained errors in Tables 1 and 2. The Intraclass Correlation Coefficients (ICCs) for walking speed were incorrectly reported instead of the correct ICC values for stride length and cadence. The original Supplementary Information 1 file is provided below. The original Article and the Supplementary Information 1 file that accompanies the original Article have been corrected