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Piga I, Magni F, Smith A. The journey towards clinical adoption of MALDI-MS-based imaging proteomics: from current challenges to future expectations. FEBS Lett 2024; 598:621-634. [PMID: 38140823 DOI: 10.1002/1873-3468.14795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/06/2023] [Accepted: 12/09/2023] [Indexed: 12/24/2023]
Abstract
Among the spatial omics techniques available, mass spectrometry imaging (MSI) represents one of the most promising owing to its capability to map the distribution of hundreds of peptides and proteins, as well as other classes of biomolecules, within a complex sample background in a multiplexed and relatively high-throughput manner. In particular, matrix-assisted laser desorption/ionisation (MALDI-MSI) has come to the fore and established itself as the most widely used technique in clinical research. However, the march of this technique towards clinical utility has been hindered by issues related to method reproducibility, appropriate biocomputational tools, and data storage. Notwithstanding these challenges, significant progress has been achieved in recent years regarding multiple facets of the technology and has rendered it more suitable for a possible clinical role. As such, there is now more robust and extensive evidence to suggest that the technology has the potential to support clinical decision-making processes under appropriate circumstances. In this review, we will discuss some of the recent developments that have facilitated this progress and outline some of the more promising clinical proteomics applications which have been developed with a clear goal towards implementation in mind.
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Affiliation(s)
- Isabella Piga
- Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca, Vedano al Lambro, Italy
| | - Fulvio Magni
- Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca, Vedano al Lambro, Italy
| | - Andrew Smith
- Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca, Vedano al Lambro, Italy
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Bastos DCDS, Chiamolera MI, Silva RE, Souza MDCBD, Antunes RA, Souza MM, Mancebo ACA, Arêas PCF, Reis FM, Lo Turco EG, Bloise FF, Ortiga-Carvalho TM. Metabolomic analysis of follicular fluid from women with Hashimoto thyroiditis. Sci Rep 2023; 13:12497. [PMID: 37532758 PMCID: PMC10397241 DOI: 10.1038/s41598-023-39514-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 07/26/2023] [Indexed: 08/04/2023] Open
Abstract
Hashimoto thyroiditis is an autoimmune disease characterized by hypothyroidism and a high level of anti-thyroid autoantibodies. It has shown to negatively impact female fertility; however, the mechanisms are unclear. Ovarian follicular fluid appears to be the key to understanding how Hashimoto thyroiditis affecst fertility. Thus, we aimed to evaluated the metabolic profile of follicular fluid and antithyroid autoantibody levels in the context of Hashimoto thyroiditis. We collected follicular fluid from 61 patients, namely 38 women with thyroid autoantibody positivity and 23 women as negative controls, undergoing in vitro fertilization treatment. Follicular fluid samples were analyzed using metabolomics, and thyroid autoantibodies were measured. Fifteen metabolites with higher concentrations in the follicular fluid samples from Hashimoto thyroiditis were identified, comprising five possible affected pathways: the glycerophospholipid, arachidonic acid, linoleic acid, alpha-linolenic acid, and sphingolipid metabolism pathways. These pathways are known to regulate ovarian functions. In addition, antithyroglobulin antibody concentrations in both serum and follicular fluid were more than tenfold higher in women with Hashimoto thyroiditis than in controls. Our data showed that the metabolic profile of follicular fluid is altered in women with Hashimoto thyroiditis, suggesting a potential mechanistic explanation for the association of this disease with female infertility.
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Affiliation(s)
- Diana Caroline da Silva Bastos
- Laboratório de Endocrinologia Translacional, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brasil
| | - Maria Izabel Chiamolera
- Laboratório de Endocrinologia Molecular e Translacional, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brasil
| | - Renata Elen Silva
- Laboratório de Endocrinologia Molecular e Translacional, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brasil
| | | | - Roberto Azevedo Antunes
- Fertipraxis Centro de Reproducao Humana, Rio de Janeiro, Brasil
- Maternidade Escola, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brasil
| | | | | | | | - Fernando M Reis
- Departamento de Ginecologia e Obstetrícia, Universidade Federal de Minas Gerais, Belo Horizonte, Brasil
| | - Edson Guimarães Lo Turco
- Ion Medicine, São Paulo, Brasil
- Departamento de Cirurgia, Disciplina de Urologia, Setor de Reprodução Assistida Universidade Federal de São Paulo, Universidade Federal de São Paulo, São Paulo, Brasil
| | - Flavia Fonseca Bloise
- Laboratório de Endocrinologia Translacional, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brasil
| | - Tania M Ortiga-Carvalho
- Laboratório de Endocrinologia Translacional, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brasil.
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Piga I, L'Imperio V, Capitoli G, Denti V, Smith A, Magni F, Pagni F. Paving the path toward multi-omics approaches in the diagnostic challenges faced in thyroid pathology. Expert Rev Proteomics 2023; 20:419-437. [PMID: 38000782 DOI: 10.1080/14789450.2023.2288222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 11/22/2023] [Indexed: 11/26/2023]
Abstract
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.
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Affiliation(s)
- Isabella Piga
- Department of Medicine and Surgery, Clinical Proteomics and Metabolomics Unit, University of Milano - Bicocca, Monza, Italy
| | - Vincenzo L'Imperio
- Department of Medicine and Surgery, Pathology, Fondazione IRCCS San Gerardo dei Tintori, University of Milan-Bicocca, Monza, Italy
| | - Giulia Capitoli
- Department of Medicine and Surgery, Bicocca Bioinformatics Biostatistics and Bioimaging B4 Center, University of Milan - Bicocca (UNIMIB), Monza, Italy
| | - Vanna Denti
- Department of Medicine and Surgery, Clinical Proteomics and Metabolomics Unit, University of Milano - Bicocca, Monza, Italy
| | - Andrew Smith
- Department of Medicine and Surgery, Clinical Proteomics and Metabolomics Unit, University of Milano - Bicocca, Monza, Italy
| | - Fulvio Magni
- Department of Medicine and Surgery, Clinical Proteomics and Metabolomics Unit, University of Milano - Bicocca, Monza, Italy
| | - Fabio Pagni
- Department of Medicine and Surgery, Pathology, Fondazione IRCCS San Gerardo dei Tintori, University of Milan-Bicocca, Monza, Italy
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Gong L, Zhou P, Li JL, Liu WG. Investigating the diagnostic efficiency of a computer-aided diagnosis system for thyroid nodules in the context of Hashimoto's thyroiditis. Front Oncol 2023; 12:941673. [PMID: 36686823 PMCID: PMC9850089 DOI: 10.3389/fonc.2022.941673] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 12/09/2022] [Indexed: 01/07/2023] Open
Abstract
Objectives This study aims to investigate the efficacy of a computer-aided diagnosis (CAD) system in distinguishing between benign and malignant thyroid nodules in the context of Hashimoto's thyroiditis (HT) and to evaluate the role of the CAD system in reducing unnecessary biopsies of benign lesions. Methods We included a total of 137 nodules from 137 consecutive patients (mean age, 43.5 ± 11.8 years) who were histopathologically diagnosed with HT. The two-dimensional ultrasound images and videos of all thyroid nodules were analyzed by the CAD system and two radiologists with different experiences according to ACR TI-RADS. The diagnostic cutoff values of ACR TI-RADS were divided into two categories (TR4 and TR5), and then the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of the CAD system and the junior and senior radiologists were compared in both cases. Moreover, ACR TI-RADS classification was revised according to the results of the CAD system, and the efficacy of recommended fine-needle aspiration (FNA) was evaluated by comparing the unnecessary biopsy rate and the malignant rate of punctured nodules. Results The accuracy, sensitivity, specificity, PPV, and NPV of the CAD system were 0.876, 0.905, 0.830, 0.894, and 0.846, respectively. With TR4 as the cutoff value, the AUCs of the CAD system and the junior and senior radiologists were 0.867, 0.628, and 0.722, respectively, and the CAD system had the highest AUC (P < 0.0001). With TR5 as the cutoff value, the AUCs of the CAD system and the junior and senior radiologists were 0.867, 0.654, and 0.812, respectively, and the CAD system had a higher AUC than the junior radiologist (P < 0.0001) but comparable to the senior radiologist (P = 0.0709). With the assistance of the CAD system, the number of TR4 nodules was decreased by both junior and senior radiologists, the malignant rate of punctured nodules increased by 30% and 22%, and the unnecessary biopsies of benign lesions were both reduced by nearly half. Conclusions The CAD system based on deep learning can improve the diagnostic performance of radiologists in identifying benign and malignant thyroid nodules in the context of Hashimoto's thyroiditis and can play a role in FNA recommendations to reduce unnecessary biopsy rates.
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Capitoli G, Piga I, L’Imperio V, Clerici F, Leni D, Garancini M, Casati G, Galimberti S, Magni F, Pagni F. Cytomolecular Classification of Thyroid Nodules Using Fine-Needle Washes Aspiration Biopsies. Int J Mol Sci 2022; 23:ijms23084156. [PMID: 35456973 PMCID: PMC9028391 DOI: 10.3390/ijms23084156] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 03/31/2022] [Accepted: 04/05/2022] [Indexed: 12/13/2022] Open
Abstract
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.
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Affiliation(s)
- Giulia Capitoli
- Bicocca Bioinformatics Biostatistics and Bioimaging B4 Center, Department of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy; (G.C.); (S.G.)
| | - Isabella Piga
- Proteomics and Metabolomics Unit, Department of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy; (I.P.); (F.C.); (F.M.)
| | - Vincenzo L’Imperio
- Department of Medicine and Surgery, Pathology, University of Milano-Bicocca, 20900 Monza, Italy;
| | - Francesca Clerici
- Proteomics and Metabolomics Unit, Department of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy; (I.P.); (F.C.); (F.M.)
| | - Davide Leni
- Department of Radiology, San Gerardo Hospital, ASST Monza, 20900 Monza, Italy;
| | - Mattia Garancini
- Department of Surgery, San Gerardo Hospital, ASST Monza, 20900 Monza, Italy;
| | - Gabriele Casati
- Department of Clinical Pathology, San Gerardo Hospital, ASST Monza, 20900 Monza, Italy;
| | - Stefania Galimberti
- Bicocca Bioinformatics Biostatistics and Bioimaging B4 Center, Department of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy; (G.C.); (S.G.)
| | - Fulvio Magni
- Proteomics and Metabolomics Unit, Department of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy; (I.P.); (F.C.); (F.M.)
| | - Fabio Pagni
- Department of Medicine and Surgery, Pathology, University of Milano-Bicocca, 20900 Monza, Italy;
- Correspondence: ; Tel.: +39-03-9233-2090
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Thakur SS. Proteomics and its application in endocrine disorders. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2021; 1869:140701. [PMID: 34329747 DOI: 10.1016/j.bbapap.2021.140701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Affiliation(s)
- Suman S Thakur
- Proteomics and Cell Signaling, Lab W110, Centre for Cellular & Molecular Biology, Habsiguda, Uppal Road, Hyderabad 500 007, Telangana, India.
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