Manuel Galli

Proteomic profiles of thyroid tumors by mass spectrometry-imaging on tissue microarrays.

The current study proposes the successful use of a mass spectrometry-imaging technology that explores the composition of biomolecules and their spatial distribution directly on-tissue to differentially classify benign and malignant cases, as well as different histotypes. To identify new specific markers, we investigated with this technology a wide histological Tissue Microarray (TMA)-based thyroid lesion series. Results showed specific protein signatures for malignant and benign specimens and allowed to build clusters comprising several proteins with discriminant capabilities. Among them, FINC, ACTB1, LMNA, HSP7C and KAD1 were identified by LC-ESI-MS/MS and found up-expressed in malignant lesions. These findings represent the opening of further investigations for their translation into clinical practice, e.g. for setting up new immunohistochemical stainings, and for a better understanding of thyroid lesions. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Machine learning approaches in MALDI-MSI: clinical applications

ABSTRACT Introduction: Despite the unquestionable advantages of Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging in visualizing the spatial distribution and the relative abundance of biomolecules directly on-tissue, the yielded data is complex and high dimensional. Therefore, analysis and interpretation of this huge amount of information is mathematically, statistically and computationally challenging. Areas covered: This article reviews some of the challenges in data elaboration with particular emphasis on machine learning techniques employed in clinical applications, and can be useful in general as an entry point for those who want to study the computational aspects. Several characteristics of data processing are described, enlightening advantages and disadvantages. Different approaches for data elaboration focused on clinical applications are also provided. Practical tutorial based upon Orange Canvas and Weka software is included, helping familiarization with the data processing. Expert commentary: Recently, MALDI-MSI has gained considerable attention and has been employed for research and diagnostic purposes, with successful results. Data dimensionality constitutes an important issue and statistical methods for information-preserving data reduction represent one of the most challenging aspects. The most common data reduction methods are characterized by collecting independent observations into a single table. However, the incorporation of relational information can improve the discriminatory capability of the data.

A Support Vector Machine Classification of Thyroid Bioptic Specimens Using MALDI-MSI Data

Biomarkers able to characterise and predict multifactorial diseases are still one of the most important targets for all the “omics” investigations. In this context, Matrix-Assisted Laser Desorption/Ionisation-Mass Spectrometry Imaging (MALDI-MSI) has gained considerable attention in recent years, but it also led to a huge amount of complex data to be elaborated and interpreted. For this reason, computational and machine learning procedures for biomarker discovery are important tools to consider, both to reduce data dimension and to provide predictive markers for specific diseases. For instance, the availability of protein and genetic markers to support thyroid lesion diagnoses would impact deeply on society due to the high presence of undetermined reports (THY3) that are generally treated as malignant patients. In this paper we show how an accurate classification of thyroid bioptic specimens can be obtained through the application of a state-of-the-art machine learning approach (i.e., Support Vector Machines) on MALDI-MSI data, together with a particular wrapper feature selection algorithm (i.e., recursive feature elimination). The model is able to provide an accurate discriminatory capability using only 20 out of 144 features, resulting in an increase of the model performances, reliability, and computational efficiency. Finally, tissue areas rather than average proteomic profiles are classified, highlighting potential discriminating areas of clinical interest.

Matrix-Assisted Laser Desorption/Ionisation Mass Spectrometry Imaging in the Study of Gastric Cancer: A Mini Review

Gastric cancer (GC) is one of the leading causes of cancer-related deaths worldwide and the disease outcome commonly depends upon the tumour stage at the time of diagnosis. However, this cancer can often be asymptomatic during the early stages and remain undetected until the later stages of tumour development, having a significant impact on patient prognosis. However, our comprehension of the mechanisms underlying the development of gastric malignancies is still lacking. For these reasons, the search for new diagnostic and prognostic markers for gastric cancer is an ongoing pursuit. Modern mass spectrometry imaging (MSI) techniques, in particular matrix-assisted laser desorption/ionisation (MALDI), have emerged as a plausible tool in clinical pathology as a whole. More specifically, MALDI-MSI is being increasingly employed in the study of gastric cancer and has already elucidated some important disease checkpoints that may help us to better understand the molecular mechanisms underpinning this aggressive cancer. Here we report the state of the art of MALDI-MSI approaches, ranging from sample preparation to statistical analysis, and provide a complete review of the key findings that have been reported in the literature thus far.

Molecular signatures of medullary thyroid carcinoma by matrix-assisted laser desorption/ionisation mass spectrometry imaging

The main aim of the study was to assess the feasibility of matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) in the pathological investigation of Medullary Thyroid Carcinoma (MTC). Formalin-fixed paraffin-embedded (FFPE) samples from seven MTC patients were analysed by MALDI-MSI in order to detect proteomic alterations within tumour lesions and to define the molecular profiles of specific findings, such as amyloid deposition and C cell hyperplasia (CCH). nLC-ESI MS/MS was employed for the identification of amyloid components and to select alternative proteomic markers of MTC pathogenesis. Results highlighted the potential of MALDI-MSI to confirm the classic immunohistochemical methods employed for the diagnosis of MTC, with good sensitivity and specificity. Intratumoural amyloid components were also detected and identified, and were characterised by calcitonin, apolipoprotein E, apolipoprotein IV, and vitronectin. The tryptic peptide profiles representative of MTC and CCH were distinctly different, with four alternative markers for MTC being detected; K1C18, and three histones (H2A, H3C, and H4). Finally, a further 115 proteins were identified through the nLC-ESI-MS/MS analysis alone, with moesin, veriscan, and lumican being selected due to their potential involvement in MTC pathogenesis. This approach represents a complimentary strategy that could be employed to detect new proteomic markers of MTC. STATEMENT OF SIGNIFICANCE: Medullary thyroid carcinoma (MTC) is a rare endocrine malignancy that originates from the parafollicular C-cells of the thyroid. The diagnosis is typically established using a combination of fine-needle aspiration biopsy (FNAB) of a suspicious nodule along with the demonstrable elevation of serum biomarkers, such as calcitonin and carcinoembryonic antigen (CEA). Unfortunately, this combination is often associated with a high degree of false-positive results and this can lead to misdiagnosis and avoidable total thyroidectomy. The current study presents the potential role of MALDI-MSI in the search for new proteomic markers of MTC with diagnostic and prognostic significance. MALDI-MSI was capable of detecting the classic immunohistochemical markers employed for the diagnosis of MTC, with good sensitivity and specificity. Furthermore, the complementary combination of MALDI-MSI and nLC-ESI-MS/MS analysis, using a single tissue section, enabled further potential markers to be identified and their spatial localisation visualised within tumoural regions. Such findings could be a valuable starting point for further studies focused on confirming the data presented here using thyroid FNABs, with the final objective being to provide complimentary assistance for the detection of MTC during the pre-operative phase.

Proteomics and Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging as a Modern Diagnostic Tool in Kidney Diseases

Внаслідок швидкого розвитку сучасної науки ми постійно, день у день, удосконалюємо наші знання про патогенез і морфологію захворювань. У зв’язку з настанням ери наукомік, таких як геноміка, транскріптоміка та протеоміка, є велика необхідність у розумінні молекулярних механізмів хвороб і організмів. Кінцевими цілями повинні бути такі: більш успішні діагностика та прогноз, виявлення потенційних терапевтичних мішеней і прогнозування відповіді на лікування. Хронічна хвороба нирок (ХХН) є всесвітньою проблемою охорони здоров’я, що характеризується швидкозростаючою захворюваністю, a сам термін «ХХН» охоплює велику підмножину захворювань. Останнім часом для вивчення ХХН використовуються сучасні технології протеоміки, такі як мас-спектрометрія з матрично-активованою лазерною десорбцією/іонізацією. Незважаючи на ранній етап розвитку подібних методик, уже існує значна кількість досліджень і публікацій, присвячених цій темі.

The putative role of MALDI-MSI in the study of Membranous Nephropathy

Membranous Nephropathy (MN) is an immunocomplex mediated renal disease that represents one of the most frequent glomerulopathies worldwide. This glomerular disease can manifest as primary (idiopathic) or secondary and this distinction is crucial when choosing the most appropriate course of treatment. In secondary cases, the best strategy involves treating the underlying disease, whereas in primary forms, the identification of confirmatory markers of the idiopathic etiology underlining the process is requested by clinicians. Among those currently reported, the positivity to circulating antigens (PLA2R, IgG4 and THSD7A) was demonstrated in approximately 75% of iMN patients, while approximately 1 in 4 patients with iMN still lack a putative diagnostic marker. Ultimately, the discovery of biomarkers to help further stratify these two different forms of glomerulopathy seems mandatory. Here, MALDI-MSI was applied to FFPE renal biopsies from histologically diagnosed primary and secondary MN patients (n=20) in order to detect alterations in their tissue proteome. MALDI-MSI was able to generate molecular signatures of primary and secondary MN, with one particular signal (m/z 1459), identified as Serine/threonine-protein kinase MRCK gamma, being over-expressed in the glomeruli of primary MN patients with respect to secondary MN. Furthermore, a number of signals that could differentiate the different forms of iMN that were positive to PLA2R or IgG4 were detected, as well as a further set of signals (m/z 1094, 1116, 1381 and 1459) that could distinguish these patients from those who were negative to both. These signals could potentially represent future targets for the further stratification of iMN patients. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

MALDI-MS Imaging in the Study of Glomerulonephritis

Glomerulonephritis (GNs) are one of the most frequent causes of chronic kidney disease (CKD), a renal condition that often leads to end-stage renal failure, and a careful assessment of these diseases is essential for prognostic and therapeutic purposes. The application of MALDI-MSI directly on bioptic renal tissue represents a new stimulating perspective and facilitates the detection of specific proteomic indicators that are directly correlated with the pathological alterations that occur within the glomeruli during the development of glomerulonephritis. Here, we describe the standard workflow for the MALDI-MSI analysis of clinical fresh-frozen and FFPE renal biopsies and highlight how the obtained molecular information, when combined with histology, can be used to detect specific protein markers of GNs.