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The application of innovative spatial proteomics techniques, such as those based upon matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) technology, has the potential to impact research in the field of nephropathology. Notwithstanding, the possibility to apply this technology in more routine diagnostic contexts remains limited by the alternative fixatives employed by this ultraspecialized diagnostic field, where most nephropathology laboratories worldwide use bouin-fixed paraffin-embedded (BFPE) samples. Here, the feasibility of performing MALDI-MSI on BFPE renal tissue is explored, evaluating variability within the trypsin-digested proteome as a result of different preanalytical conditions and comparing them with the more standardized formalin-fixed paraffin-embedded (FFPE) counterparts. A large proportion of the features (270, 68.9%) was detected in both BFPE and FFPE renal samples, demonstrating only limited variability in signal intensity (10.22-10.06%). Samples processed with either fixative were able to discriminate the principal parenchyma regions along with diverse renal substructures, such as glomeruli, tubules, and vessels. This was observed when performing an additional "stress test", showing comparable results in both BFPE and FFPE samples when the distribution of several amyloid fingerprint proteins was mapped. These results suggest the utility of BFPE tissue specimens in MSI-based nephropathology research, further widening their application in this field.

Mass spectrometry imaging (MSI) is an emerging technology that is capable of mapping various biomolecules within their native spatial context, and performing spatial multiomics on formalin-fixed paraffin-embedded (FFPE) tissues may further increase the molecular characterization of pathological states. Here we present a novel workflow which enables the sequential MSI of lipids, N- glycans, and tryptic peptides on a single FFPE tissue section and highlight the enhanced molecular characterization that is offered by combining the multiple spatial omics data sets. In murine brain and clear cell renal cell carcinoma (ccRCC) tissue, the three molecular levels provided complementary information and characterized differ- ent histological regions. Moreover, when the spatial omics data was integrated, the different histopathological regions of the ccRCC tissue could be better discriminated with respect to the imaging data set of any single omics class. Taken together, these promising findings demonstrate the capability to more comprehensively map the molecular complexity within pathological tissue.

Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease characterized by the aberrant accumulation of extracellular matrix in the lungs. nintedanib is one of the two FDA-approved drugs for IPF treatment; however, the exact pathophysiological mechanisms of fibrosis progression and response to therapy are still poorly understood. In this work, the molecular fingerprint of fibrosis progression and response to nintedanib treatment have been investigated by mass spectrometry-based bottom-up proteomics in paraffin-embedded lung tissues from bleomycin-induced (BLM) pulmonary fibrosis mice. Our proteomics results unveiled that (i) samples clustered depending on the tissue fibrotic grade (mild, moderate, and severe) and not on the time course after BLM treatment; (ii) the dysregulation of different pathways involved in fibrosis progression such as the complement coagulation cascades, advanced glycation end products (AGEs) and their receptors (RAGEs) signaling, the extracellular matrix-receptor interaction, the regulation of actin cytoskeleton, and ribosomes; (iii) Coronin 1A (Coro1a) as the protein with the highest correlation when evaluating the progression of fibrosis, with an increased expression from mild to severe fibrosis; and (iv) a total of 10 differentially expressed proteins (padj-value ≤ 0.05 and Fold change ≤−1.5 or ≥1.5), whose abundance varied in the base of the severity of fibrosis (mild and moderate), were modulated by the antifibrotic treatment with nintedanib, reverting their trend. Notably, nintedanib significantly restored lactate dehydrogenase B (Ldhb) expression but not lactate dehydrogenase A (Ldha). Notwithstanding the need for further investigations to validate the roles of both Coro1a and Ldhb, our findings provide an extensive proteomic characterization with a strong relationship with histomorphometric measurements. These results unveil some biological processes in pulmonary fibrosis and drug-mediated fibrosis therapy.

Introduction: Despite advancements in diagnostic methods, the classification of indeterminate thyroid nodules still poses diagnostic challenges not only in pre-surgical evaluation but even after histological evaluation of surgical specimens. Proteomics, aided by mass spectrometry and integrated with artificial intelligence and machine learning algorithms, shows great promise in identifying diagnostic markers for thyroid lesions. Areas covered: This review provides in-depth exploration of how proteomics has contributed to the understanding of thyroid pathology. It discusses the technical advancements related to immunohistochemistry, genetic and proteomic techniques, such as mass spectrometry, which have greatly improved sensitivity and spatial resolution up to single-cell level. These improvements allowed the identification of specific protein signatures associated with different types of thyroid lesions. Expert commentary: Among all the proteomics approaches, spatial proteomics stands out due to its unique ability to capture the spatial context of proteins in both cytological and tissue thyroid samples. The integration of multi-layers of molecular information combining spatial proteomics, genomics, immunohistochemistry or metabolomics and the implementation of artificial intelligence and machine learning approaches, represent hugely promising steps forward toward the possibility to uncover intricate relationships and interactions among various molecular components, providing a complete picture of the biological landscape whilst fostering thyroid nodule diagnosis.

The ability of the zebrafish heart to regenerate following injury makes it a valuable model to deduce why this capability in mammals is limited to early neonatal stages. Although metabolic reprogramming and glycosylation remodeling have emerged as key aspects in many biological processes, how they may trigger a cardiac regenerative response in zebrafish is still a crucial question. Here, by using an up-to-date panel of transcriptomic, proteomic and glycomic approaches, we identify a metabolic switch from mitochondrial oxidative phosphorylation to glycolysis associated with membrane glycosylation remodeling during heart regeneration. Importantly, we establish the N- and O-linked glycan structural repertoire of the regenerating zebrafish heart, and link alterations in both sialylation and high mannose structures across the phases of regeneration. Our results show that metabolic reprogramming and glycan structural remodeling are potential drivers of tissue regeneration after cardiac injury, providing the biological rationale to develop novel therapeutics to elicit heart regeneration in mammals.

Fine-needle aspiration biopsies (FNA) represent the gold standard to exclude the malignant nature of thyroid nodules. After cytomorphology, 20–30% of cases are deemed “indeterminate for malignancy” and undergo surgery. However, after thyroidectomy, 70–80% of these nodules are benign. The identification of tools for improving FNA’s diagnostic performances is explored by matrix-assisted laser-desorption ionization mass spectrometry imaging (MALDI-MSI). A clinical study was conducted in order to build a classification model for the characterization of thyroid nodules on a large cohort of 240 samples, showing that MALDI-MSI can be effective in separating areas with benign/malignant cells. The model had optimal performances in the internal validation set (n = 70), with 100.0% (95% CI = 83.2–100.0%) sensitivity and 96.0% (95% CI = 86.3–99.5%) specificity. The external validation (n = 170) showed a specificity of 82.9% (95% CI = 74.3–89.5%) and a sensitivity of 43.1% (95% CI = 30.9–56.0%). The performance of the model was hampered in the presence of poor and/or noisy spectra. Consequently, restricting the evaluation to the subset of FNAs with adequate cellularity, sensitivity improved up to 76.5% (95% CI = 58.8–89.3). Results also suggest the putative role of MALDI-MSI in routine clinical triage, with a three levels diagnostic classification that accounts for an indeterminate gray zone of nodules requiring a strict follow-up.

What’s brewing? Mapping the distribution of bioactive compounds in green C. arabica coffee beans through MS-driven spatial metabolomics

Background Improving the prognostication of acute brain injury is a key element of critical care. Standard assessment includes pupillary light reactivity testing with a hand-held light source, but findings are interpreted subjectively; automated pupillometry might be more precise and reproducible. We aimed to assess the association of the Neurological Pupil index (NPi)—a quantitative measure of pupillary reactivity computed by automated pupillometry— with outcomes of patients with severe non-anoxic acute brain injury. Methods ORANGE is a multicentre, prospective, observational cohort study at 13 hospitals in eight countries in Europe and North America. Patients admitted to the intensive care unit after traumatic brain injury, aneurysmal subarachnoid haemorrhage, or intracerebral haemorrhage were eligible for the study. Patients underwent automated infrared pupillometry assessment every 4 h during the first 7 days after admission to compute NPi, with values ranging from 0 to 5 (with abnormal NPi being <3). The co-primary outcomes of the study were neurological outcome (assessed with the extended Glasgow Outcome Scale [GOSE]) and mortality at 6 months. We used logistic regression to model the association between NPi and poor neurological outcome (GOSE ≤4) at 6 months and Cox regression to model the relation of NPi with 6-month mortality. This study is registered with ClinicalTrials.gov, NCT04490005. Findings Between Nov 1, 2020, and May 3, 2022, 514 patients (224 with traumatic brain injury, 139 with aneurysmal subarachnoid haemorrhage, and 151 with intracerebral haemorrhage) were enrolled. The median age of patients was 61 years (IQR 46–71), and the median Glasgow Coma Scale score on admission was 8 (5–11). 40 071 NPi measurements were taken (median 40 per patient [20–50]). The 6-month outcome was assessed in 497 (97%) patients, of whom 160 (32%) patients died, and 241 (47%) patients had at least one recording of abnormal NPi, which was associated with poor neurol

Objectives: To compare intensivist-diagnosed ventilator-associated pneumonia (iVAP) with four established definitions, assessing their agreement in detecting new episodes. Methods: A multi-centric prospective study on pulmonary microbiota was carried out in patients requiring mechanical ventilation (MV). Data collected were used to compare hypothetical VAP onset according to iVAP with the study consensus criteria, the European Centre for Disease Control and Prevention definition, and two versions of the latter adjusted for leukocyte count and fever. Results: In our cohort of 186 adult patients, iVAPs were 36.6% (68/186, 95% confidence interval 30.0–44.0%), with an incidence rate of 4.64/100 patient-MV-days, and median MV-day at diagnosis of 6. Forty-seven percent of patients (87/186) were identified as VAP by at least one criterion, with a median MV-day at diagnosis of 5. Agreement between intensivist judgement (iVAP/no-iVAP) and the criteria was highest for the study consensus criteria (50/87, 57.4%), but still one-third of iVAP were not identified and 9% of patients were identified as VAP contrary to intensivist diagnosis. VAP proportion differed between criteria (25.2–30.1%). Conclusions: Caution is needed when evaluating studies describing VAP incidence. Pre-agreed criteria and definitions that capture VAP's evolving nature provide greater consistency, but new clinically driven definitions are needed to align surveillance and diagnostic criteria with clinical practice.

Idiopathic membranous nephropathy (IMN) is a pathologically defined disorder of the glomerulus, primarily responsible for nephrotic syndromes (NS) in nondiabetic adults. The underlying molecular mechanisms are still not completely clarified. To explore possible molecular and functional signatures, an optimised mass spectrometry (MS) method based on next-generation data-independent acquisition combined with ion-mobility was applied to serum of patients affected by IMN (n = 15) or by other glomerulopathies (PN) (n = 15). The statistical comparison highlighted a panel of 57 de-regulated proteins with a significant increase in lipoprotein-related proteins (APOC1, APOB, APOA1, APOL1 and LCAT) and a substantial quantitative alteration of key serpins (including A4, D1, A7, A6, F2, F1 and 1) possibly associated with IMN or NS and podocyte stress. A critical dysregulation in metabolisms of lipids (e.g., VLDL assembly and clearance) likely to be related to known hyperlipidemia in IMN, along with involvement of non-classical complement pathways and a putative enrolment of ficolin-2 in sustaining the activation of the lectin-mediated complement system have been pinpointed. Moreover, mannose receptor CD206 (MRC1-down in IMN) and biotinidase (BTD-up in IMN) are able alone to accurately distinguish IMN vs. PN. To conclude, our work provides key proteomic insights into the IMN complexity, opening the way to an efficient stratification of MN patients.

Artificial intelligence is getting a foothold in medicine for disease screening and diagnosis. While typical machine learning methods require large labeled datasets for training and validation, their application is limited in clinical fields since ground truth information can hardly be obtained on a sizeable cohort of patients. Unsupervised neural networks – such as Self-Organizing Maps (SOMs) – represent an alternative approach to identifying hidden patterns in biomedical data. Here we investigate the feasibility of SOMs for the identification of malignant and non-malignant regions in liquid biopsies of thyroid nodules, on a patient-specific basis. MALDI-ToF (Matrix Assisted Laser Desorption Ionization - Time of Flight) mass spectrometry-imaging (MSI) was used to measure the spectral profile of bioptic samples. SOMs were then applied for the analysis of MALDI-MSI data of individual patients’ samples, also testing various pre-processing and agglomerative clustering methods to investigate their impact on SOMs’ discrimination efficacy. The final clustering was compared against the sample's probability to be malignant, hyperplastic or related to Hashimoto thyroiditis as quantified by multinomial regression with LASSO. Our results show that SOMs are effective in separating the areas of a sample containing benign cells from those containing malignant cells. Moreover, they allow to overlap the different areas of cytological glass slides with the corresponding proteomic profile image, and inspect the specific weight of every cellular component in bioptic samples. We envision that this approach could represent an effective means to assist pathologists in diagnostic tasks, avoiding the need to manually annotate cytological images and the effort in creating labeled datasets.

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infection leads to a wide range of clinical manifestations and determines the need for personalized and precision medicine. To better understand the biological determinants of this heterogeneity, we explored the plasma proteome of 43 COVID-19 patients with different outcomes by an untargeted liquid chromatography-mass spectrometry approach. The comparison between asymptomatic or pauci-symptomatic subjects (MILDs), and hospitalised patients in need of oxygen support therapy (SEVEREs) highlighted 29 proteins emerged as differentially expressed: 12 overexpressed in MILDs and 17 in SEVEREs. Moreover, a supervised analysis based on a decision-tree recognised three proteins (Fetuin-A, Ig lambda-2chain-C-region, Vitronectin) that are able to robustly discriminate between the two classes independently from the infection stage. In silico functional annotation of the 29 deregulated proteins pinpointed several functions possibly related to the severity; no pathway was associated exclusively to MILDs, while several only to SEVEREs, and some associated to both MILDs and SEVEREs; SARS-CoV-2 signalling pathway was significantly enriched by proteins up-expressed in SEVEREs (SAA1/2, CRP, HP, LRG1) and in MILDs (GSN, HRG). In conclusion, our analysis could provide key information for ‘proteomically’ defining possible upstream mechanisms and mediators triggering or limiting the domino effect of the immune-related response and characterizing severe exacerbations.

Aims: Identification and characterisation of monoclonal gammopathies of renal significance (MGRS) is critical for therapeutic purposes. Amyloidosis represents one of the most common forms of MGRS, and renal biopsy remains the gold standard for their classification, although mass spectrometry has shown greater sensitivity in this area. Methods: In the present study, a new in situ proteomic technique, matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI), is investigated as an alternative to conventional laser capture microdissection MS for the characterisation of amyloids. MALDI-MSI was performed on 16 cases (3 lambda light chain amyloidosis (AL), 3 AL kappa, 3 serum amyloid A amyloidosis (SAA), 2 lambda light chain deposition disease (LCDD), 2 challenging amyloid cases and 3 controls). Analysis began with regions of interest labelled by the pathologist, and then automatic segmentation was performed. Results: MALDI-MSI correctly identified and typed cases with known amyloid type (AL kappa, AL lambda and SAA). A 'restricted fingerprint' for amyloid detection composed of apolipoprotein E, serum amyloid protein and apolipoprotein A1 showed the best automatic segmentation performance (area under the curve >0.7). Conclusions: MALDI-MSI correctly assigned minimal/challenging cases of amyloidosis to the correct type (AL lambda) and identified lambda light chains in LCDD cases, highlighting the promising role of MALDI-MSI for amyloid typing.

Ovarian cancer is a highly lethal gynecological malignancy. Drug resistance rapidly occurs, and different therapeutic approaches are needed. So far, no biomarkers have been discovered to predict early response to therapies in the case of multi-treated ovarian cancer patients. The aim of our investigation was to identify a protein panel and the molecular pathways involved in chemotherapy response through a combination of studying proteomics and network enrichment analysis by considering a subset of samples from a clinical setting. Differential mass spectrometry studies were performed on 14 serum samples from patients with heavily pretreated platinum-resistant ovarian cancer who received the FOLFOX-4 regimen as a salvage therapy. The serum was analyzed at baseline time (T0) before FOLFOX-4 treatment, and before the second cycle of treatment (T1), with the aim of understanding if it was possible, after a first treatment cycle, to detect significant proteome changes that could be associated with patients responses to therapy. A total of 291 shared expressed proteins was identified and 12 proteins were finally selected between patients who attained partial response or no-response to chemotherapy when both response to therapy and time dependence (T0, T1) were considered in the statistical analysis. The protein panel included APOL1, GSN, GFI1, LCATL, MNA, LYVE1, ROR1, SHBG, SOD3, TEC, VPS18, and ZNF573. Using a bioinformatics network enrichment approach and metanalysis study, relationships between serum and cellular proteins were identified. An analysis of protein networks was conducted and identified at least three biological processes with functional and therapeutic significance in ovarian cancer, including lipoproteins metabolic process, structural component modulation in relation to cellular apoptosis and autophagy, and cellular oxidative stress response. Five proteins were almost independent from the network (LYVE1, ROR1, TEC, GFI1, and ZNF573). All proteins were asso

Noninvasive follicular thyroid neoplasms with papillary-like nuclear features (NIFTP) are low-risk thyroid lesions most often characterised by RAS-type mutations. The histological diagnosis may be challenging, and even immunohistochemistry and molecular approaches have not yet provided conclusive solutions. This study characterises a set of NIFTPs by Matrix-Assisted Laser Desorption/Ionisation (MALDI)-Mass Spectrometry Imaging (MSI) to highlight the proteomic signatures capable of overcoming histological challenges. Archived formalin-fixed paraffin-embedded samples from 10 NIFTPs (n = 6 RAS-mutated and n = 4 RAS-wild type) were trypsin-digested and analysed by MALDI-MSI, comparing their profiles to normal tissue and synchronous benign nodules. This allowed the definition of a four-peptide signature able to distinguish RAS-mutant from wild-type cases, the latter showing proteomic similarities to hyperplastic nodules. Moreover, among the differentially expressed signals, Peptidylprolyl Isomerase A (PPIA, 1505.8 m/z), which has already demonstrated a role in the development of cancer, was found overexpressed in NIFTP RAS-mutated nodules compared to wild-type lesions. These results underlined that high-throughput proteomic approaches may add a further level of biological comprehension for NIFTPs. In the future, thanks to the powerful single-cell detail achieved by new instruments, the complementary NGS-MALDI imaging sequence might be the correct methodological approach to confirm that the current NIFTP definition encompasses heterogeneous lesions that must be further characterised.

In cancer microenvironment, aberrant glycosylation events of ECM proteins and cell surface receptors occur. We developed a protocol to generate 3D bioprinted models of colorectal cancer (CRC) crosslinking hyaluronic acid and gelatin functionalized with three signalling glycans characterized in CRC, 3′-Sialylgalactose, 6′-Sialylgalactose and 2′-Fucosylgalactose. The crosslinking, performed exploiting azide functionalized gelatin and hyaluronic acid and 4arm-PEG-dibenzocyclooctyne, resulted in biocompatible hydrogels that were 3D bioprinted with commercial CRC cells HT-29 and patient derived CRC tumoroids. The glycosylated hydrogels showed good 3D printability, biocompatibility and stability over the time. SEM and synchrotron radiation SAXS/WAXS analysis revealed the influence of glycosylation in the construct morphology, whereas MALDI-MS imaging showed that protein profiles of tumoroid cells vary with glycosylation, indicating that sialylation and fucosylation of ECM proteins induce diverse alterations to the proteome of the tumoroid and surrounding cells.

Background: The role of upper airways microbiota and its association with ventilator-associated pneumonia (VAP) development in mechanically ventilated (MV) patients is unclear. Taking advantage of data collected in a prospective study aimed to assess the composition and over-time variation of upper airway microbiota in patients MV for non-pulmonary reasons, we describe upper airway microbiota characteristics among VAP and NO-VAP patients. Methods: Exploratory analysis of data collected in a prospective observational study on patients intubated for non-pulmonary conditions. Microbiota analysis (trough 16S-rRNA gene profiling) was performed on endotracheal aspirates (at intubation, T0, and after 72 h, T3) of patients with VAP (cases cohort) and a subgroup of NO-VAP patients (control cohort, matched according to total intubation time). Results: Samples from 13 VAP patients and 22 NO-VAP matched controls were analyzed. At intubation (T0), patients with VAP revealed a significantly lower microbial complexity of the microbiota of the upper airways compared to NO-VAP controls (alpha diversity index of 84 ± 37 and 160 ± 102, in VAP and NO_VAP group, respectively, p-value < 0.012). Furthermore, an overall decrease in microbial diversity was observed in both groups at T3 as compared to T0. At T3, a loss of some genera (Prevotella 7, Fusobacterium, Neisseria, Escherichia–Shigella and Haemophilus) was found in VAP patients. In contrast, eight genera belonging to the Bacteroidetes, Firmicutes and Fusobacteria phyla was predominant in this group. However, it is unclear whether VAP caused dysbiosis or dysbiosis caused VAP. Conclusions: In a small sample size of intubated patients, microbial diversity at intubation was less in patients with VAP compared to patients without VAP.

The association between lipid metabolism and long-term outcomes is relevant for tumor diagnosis and therapy. Archival material such as formalin-fixed and paraffin embedded (FFPE) tissues is a highly valuable resource for this aim as it is linked to long-term clinical follow-up. There-fore, there is a need to develop robust methodologies able to detect lipids in FFPE material and correlate them with clinical outcomes. In this work, lipidic alterations were investigated in patient-derived xenograft of breast cancer by using a matrix-assisted laser desorption ionization mass spec-trometry (MALDI MSI) based workflow that included antigen retrieval as a sample preparation step. We evaluated technical reproducibility, spatial metabolic differentiation within tissue com-partments, and treatment response induced by a glutaminase inhibitor (CB-839). This protocol shows a good inter-day robustness (CV = 26 ± 12%). Several lipids could reliably distinguish necrotic and tumor regions across the technical replicates. Moreover, this protocol identified distinct alterations in the tissue lipidome of xenograft treated with glutaminase inhibitors. In conclusion, lipidic alterations in FFPE tissue of breast cancer xenograft observed in this study are a step-forward to a robust and reproducible MALDI-MSI based workflow for pre-clinical and clinical applications.

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.

Nephropathy represents a major complication of Fabry Disease and its accurate characterization is of paramount importance in predicting the disease progression and assessing the therapeutic responses. The diagnostic process still relies on performing renal biopsy, nevertheless many efforts have been made to discover early reliable biomarkers allowing us to avoid invasive procedures. In this field, proteomics offers a sensitive and fast method leading to an accurate detection of specific pathological proteins and the discovery of diagnostic and prognostic biomarkers that reflect disease progression and facilitate the evaluation of therapeutic responses. Here, we report a review of selected literature focusing on the investigation of several proteomic techniques highlighting their advantages, limitations and future perspectives in their application in the routine study of Fabry Nephropathy.

Due its ability to provide a global snapshot of kidney physiology, urine has emerged as a highly promising, non-invasive source in the search for new molecular indicators of disease diagnosis, prognosis, and surveillance. In particular, proteomics represents an ideal strategy for the identification of urinary protein markers; thus, a urinomic approach could also represent a powerful tool in the investigation of the most common kidney cancer, which is clear cell Renal Cell Carcinoma (ccRCC). Currently, these tumors are classified after surgical removal using the TNM and nuclear grading systems and prognosis is usually predicted based upon staging. However, the aggressiveness and clinical outcomes of ccRCC remain heterogeneous within each stratified group, highlighting the need for novel molecular indicators that can predict the progression of these tumors. In our study, we explored the association between the urinary proteome and the ccRCC staging and grading classification. The urine proteome of 44 ccRCC patients with lesions of varying severity was analyzed via label-free proteomics. MS data revealed several proteins with altered abundance according to clinicopathological stratification. Specifically, we determined a panel of dysregulated proteins strictly related to stage and grade, suggesting the potential utility of MS-based urinomics as a complementary tool in the staging process of ccRCC.

Introduction: Diabetic nephropathy (DN) and hypertensive nephrosclerosis (HN) represent the most common causes of chronic kidney disease (CKD) and many patients progress to -end-stage renal disease. Patients are treated primarily through the management of cardiovas-cular risk factors and hypertension; however patients with HN have a more favorable outcome. A noninvasive clinical approach to separate these two entities, especially in hypertensive patients who also have diabetes, would allow for targeted treatment and more appropriate resource allocation to those patients at the highest risk of CKD progression. Meth-ods: In this preliminary study, high-spatial-resolution matrix-assisted laser desorption/ion-ization (MALDI) mass spectrometry imaging (MSI) was integrated with high-mass accuracy MALDI-FTICR-MS and nLC-ESI-MS/MS analysis in order to detect tissue proteins within kidney biopsies to discriminate cases of DN (n = 9) from cases of HN (n = 9). Results: Differences in the tryptic peptide profiles of the 2 groups could clearly be detected, with these becoming even more evident in the more severe histological classes, even if this was not evident with routine histology. In particular, 4 putative proteins were detected and had a higher signal intensity within regions of DN tissue with extensive sclerosis or fibrosis. Among these, 2 proteins (PGRMC1 and CO3) had a signal intensity that increased at the latter stages of the disease and may be associated with progression. Discussion/Conclusion: This preliminary study represents a valuable starting point for a future study employing a larger cohort of patients to develop sensitive and specific protein biomarkers that could reliably differentiate between diabetic and hypertensive causes of CKD to allow for improved diagnosis, fewer biopsy procedures, and refined treatment approaches for clinicians.

Recurrent somatic mutations in ETNK1 (Ethanolamine-Kinase-1) were identified in several myeloid malignancies and are responsible for a reduced enzymatic activity. Here, we demonstrate in primary leukemic cells and in cell lines that mutated ETNK1 causes a significant increase in mitochondrial activity, ROS production, and Histone H2AX phosphorylation, ultimately driving the increased accumulation of new mutations. We also show that phosphoethanolamine, the metabolic product of ETNK1, negatively controls mitochondrial activity through a direct competition with succinate at mitochondrial complex II. Hence, reduced intracellular phosphoethanolamine causes mitochondria hyperactivation, ROS production, and DNA damage. Treatment with phosphoethanolamine is able to counteract complex II hyperactivation and to restore a normal phenotype.

This study presents the matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) as a unique technology for the in-situ detection of N-glycans and proteins from the same formalin-fixed paraffin-embedded (FFPE) tissue section.

This study presents the implementation of a MALDI-MSI statistical tool that could be used to assist pathologists in the diagnosis of indeterminate cases.

Mass spectrometry imaging (MSI) is an emerging technology that is capable of mapping various biomolecules within their native spatial context, and performing spatial multiomics on formalin-fixed paraffin-embedded (FFPE) tissues may further increase the molecular characterization of pathological states. Here we present a novel workflow which enables the sequential MSI of lipids, N-glycans, and tryptic peptides on a single FFPE tissue section and highlight the enhanced molecular characterization that is offered by combining the multiple spatial omics data sets. In murine brain and clear cell renal cell carcinoma (ccRCC) tissue, the three molecular levels provided complementary information and characterized different histological regions. Moreover, when the spatial omics data was integrated, the different histopathological regions of the ccRCC tissue could be better discriminated with respect to the imaging data set of any single omics class. Taken together, these promising findings demonstrate the capability to more comprehensively map the molecular complexity within pathological tissue.

The podocyte injury, and consequent proteinuria, that characterize the pathology of idiopathic membranous nephropathy (IMN) is mediated by an autoimmune reaction against podocyte antigens. In particular, the activation of pathways leading to abundant renal deposits of complement is likely to involve the binding of mannose-binding lectin (MBL) to aberrant glycans on immunoglobulins. To obtain a landscape of circulatory IgG Fc glycosylation characterizing this disease, we conducted a systematic N-glycan profiling study of IgG1, 2, and 4 by mass spectrometry. The cohort included 57 IMN patients, a pathological control group with nephrotic syndrome (PN) (n = 20), and 88 healthy control subjects. The effect of sex and age was assessed in all groups and controlled by rigorous matching. Several IgG Fc glycan traits were found to be associated with IMN. Interestingly, among them, only IgG4-related results were specific for IMN and not for PN. Hypogalactosylation of IgG4, already shown for IMN, was observed to occur in the absence of core fucose, in line with a probable increase of pro-inflammatory IgG. In addition, elevated levels of fucosylated IgG4, along with low levels of hybrid-type glycans, were detected. Some of these IgG4 alterations are likely to be more pronounced in high PLA2R (phospholipase A2 receptor) patients. IgG Fc glycosylation patterns associated with IMN warrant further studies of their role in disease mechanisms and may eventually enrich the diagnostic spectrum regarding patient stratification.