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Guan C, Kong L. Mass spectrometry imaging in pulmonary disorders. Clin Chim Acta 2024; 561:119835. [PMID: 38936534 DOI: 10.1016/j.cca.2024.119835] [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: 03/26/2024] [Revised: 06/24/2024] [Accepted: 06/24/2024] [Indexed: 06/29/2024]
Abstract
Mass Spectrometry Imaging (MSI) represents a novel and advancing technology that offers unparalleled in situ characterization of tissues. It provides comprehensive insights into the chemical structures, relative abundances, and spatial distributions of a vast array of both identified and unidentified endogenous and exogenous compounds, a capability not paralleled by existing analytical methodologies. Recent scholarly endeavors have increasingly explored the utility of MSI in the adjunct diagnosis and biomarker research of pulmonary disorders, including but not limited to lung cancer. Concurrently, MSI has proven instrumental in elucidating the spatiotemporal dynamics of various pharmacological agents. This review concisely delineates the fundamental principles underpinning MSI, its applications in pulmonary disease diagnosis, biomarker discovery, and drug distribution investigations. Additionally, it presents a forward-looking perspective on the prospective trajectories of MSI technological advancements.
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Affiliation(s)
- Chunliu Guan
- Key Laboratory of Environment Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu 210009, China
| | - Lu Kong
- Key Laboratory of Environment Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu 210009, China.
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Mondal S, Nandy A, Dande G, Prabhu K, Valmiki RR, Koner D, Banerjee S. Mass Spectrometric Imaging of Anionic Phospholipids Desorbed from Human Hippocampal Sections: Discrimination between Temporal and Nontemporal Lobe Epilepsies. ACS Chem Neurosci 2024; 15:983-993. [PMID: 38355427 DOI: 10.1021/acschemneuro.3c00693] [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] [Indexed: 02/16/2024] Open
Abstract
Temporal lobe epilepsy (TLE) is one of the most common neurological disorders, often accompanied by hippocampal sclerosis. The molecular processes underlying this epileptogenesis are poorly understood. To examine the lipid profile, 39 fresh frozen sections of the human hippocampus obtained from epilepsy surgery for TLE (n = 14) and non-TLE (control group; n = 25) patients were subjected to desorption electrospray ionization mass spectrometry imaging in the negative ion mode. In contrast to our earlier report that showed striking downregulation of positively charged phospholipids (e.g., phosphatidylcholine and phosphatidylethanolamine, etc.) in the TLE hippocampus, this study finds complementary upregulation of negatively charged phospholipids, notably, phosphatidylserine and phosphatidylglycerol. This result may point to an active metabolic pool in the TLE hippocampus that produces these anionic phospholipids at the expense of the cationic phospholipids. This metabolic shift could be due to the dysregulation of the Kennedy and CDP-DG pathways responsible for biosynthesizing these lipids. Thus, this study further opens up opportunities to investigate the molecular hallmarks and potential therapeutic targets for TLE.
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Affiliation(s)
- Supratim Mondal
- Department of Chemistry, Indian Institute of Science Education and Research Tirupati, Tirupati 517507, India
| | - Abhijit Nandy
- Department of Chemistry, Indian Institute of Science Education and Research Tirupati, Tirupati 517507, India
| | - Geetha Dande
- Department of Chemistry, Indian Institute of Science Education and Research Tirupati, Tirupati 517507, India
| | - Krishna Prabhu
- Department of Neurological Sciences, Christian Medical College, Vellore 632004, India
| | | | - Debasish Koner
- Department of Chemistry, Indian Institute of Technology Hyderabad, Kandi 502284, India
| | - Shibdas Banerjee
- Department of Chemistry, Indian Institute of Science Education and Research Tirupati, Tirupati 517507, India
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Holbrook JH, Kemper GE, Hummon AB. Quantitative mass spectrometry imaging: therapeutics & biomolecules. Chem Commun (Camb) 2024; 60:2137-2151. [PMID: 38284765 PMCID: PMC10878071 DOI: 10.1039/d3cc05988j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 01/22/2024] [Indexed: 01/30/2024]
Abstract
Mass spectrometry imaging (MSI) has become increasingly utilized in the analysis of biological molecules. MSI grants the ability to spatially map thousands of molecules within one experimental run in a label-free manner. While MSI is considered by most to be a qualitative method, recent advancements in instrumentation, sample preparation, and development of standards has made quantitative MSI (qMSI) more common. In this feature article, we present a tailored review of recent advancements in qMSI of therapeutics and biomolecules such as lipids and peptides/proteins. We also provide detailed experimental considerations for conducting qMSI studies on biological samples, aiming to advance the methodology.
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Affiliation(s)
- Joseph H Holbrook
- Ohio State Biochemistry Program, The Ohio State University, Columbus, Ohio 43210, USA.
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
| | - Gabrielle E Kemper
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
| | - Amanda B Hummon
- Ohio State Biochemistry Program, The Ohio State University, Columbus, Ohio 43210, USA.
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
- Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio 43210, USA
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Chen KA, Kirchoff KE, Butler LR, Holloway AD, Kapadia MR, Kuzmiak CM, Downs-Canner SM, Spanheimer PM, Gallagher KK, Gomez SM. Analysis of Specimen Mammography with Artificial Intelligence to Predict Margin Status. Ann Surg Oncol 2023; 30:7107-7115. [PMID: 37563337 PMCID: PMC10592216 DOI: 10.1245/s10434-023-14083-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 07/17/2023] [Indexed: 08/12/2023]
Abstract
BACKGROUND Intraoperative specimen mammography is a valuable tool in breast cancer surgery, providing immediate assessment of margins for a resected tumor. However, the accuracy of specimen mammography in detecting microscopic margin positivity is low. We sought to develop an artificial intelligence model to predict the pathologic margin status of resected breast tumors using specimen mammography. METHODS A dataset of specimen mammography images matched with pathologic margin status was collected from our institution from 2017 to 2020. The dataset was randomly split into training, validation, and test sets. Specimen mammography models pretrained on radiologic images were developed and compared with models pretrained on nonmedical images. Model performance was assessed using sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). RESULTS The dataset included 821 images, and 53% had positive margins. For three out of four model architectures tested, models pretrained on radiologic images outperformed nonmedical models. The highest performing model, InceptionV3, showed sensitivity of 84%, specificity of 42%, and AUROC of 0.71. Model performance was better among patients with invasive cancers, less dense breasts, and non-white race. CONCLUSIONS This study developed and internally validated artificial intelligence models that predict pathologic margins status for partial mastectomy from specimen mammograms. The models' accuracy compares favorably with published literature on surgeon and radiologist interpretation of specimen mammography. With further development, these models could more precisely guide the extent of resection, potentially improving cosmesis and reducing reoperations.
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Affiliation(s)
- Kevin A Chen
- Division of Surgical Oncology, Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kathryn E Kirchoff
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Logan R Butler
- Division of Surgical Oncology, Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alexa D Holloway
- Division of Surgical Oncology, Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Muneera R Kapadia
- Division of Surgical Oncology, Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Cherie M Kuzmiak
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Stephanie M Downs-Canner
- Department of Surgery, Breast Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Phillip M Spanheimer
- Division of Surgical Oncology, Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kristalyn K Gallagher
- Division of Surgical Oncology, Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Shawn M Gomez
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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Kumar BS. Recent Advances and Applications of Ambient Mass Spectrometry Imaging in Cancer Research: An Overview. Mass Spectrom (Tokyo) 2023; 12:A0129. [PMID: 37789912 PMCID: PMC10542858 DOI: 10.5702/massspectrometry.a0129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 08/25/2023] [Indexed: 10/05/2023] Open
Abstract
Cancer metabolic variability has a significant impact on both diagnosis and treatment outcomes. The discovery of novel biological indicators and metabolic dysregulation, can significantly rely on comprehension of the modified metabolism in cancer, is a research focus. Tissue histology is a critical feature in the diagnostic testing of many ailments, such as cancer. To assess the surgical margin of the tumour on patients, frozen section histology is a tedious, laborious, and typically arbitrary method. Concurrent monitoring of ion images in tissues facilitated by the latest advancements in mass spectrometry imaging (MSI) is far more efficient than optical tissue image analysis utilized in conventional histopathology examination. This article focuses on the "desorption electrospray ionization (DESI)-MSI" technique's most recent advancements and uses in cancer research. DESI-MSI can provide wealthy information based on the variances in metabolites and lipids in normal and cancerous tissues by acquiring ion images of the lipid and metabolite variances on biopsy samples. As opposed to a systematic review, this article offers a synopsis of the most widely employed cutting-edge DESI-MSI techniques in cancer research.
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Affiliation(s)
- Bharath S. Kumar
- Correspondence to: Bharath S. Kumar, 21, B2, 27th Street, Nanganallur, Chennai, India, e-mail:
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Kumar BS. Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) in disease diagnosis: an overview. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:3768-3784. [PMID: 37503728 DOI: 10.1039/d3ay00867c] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Tissue analysis, which is essential to histology and is considered the benchmark for the diagnosis and prognosis of many illnesses, including cancer, is significant. During surgery, the surgical margin of the tumor is assessed using the labor-intensive, challenging, and commonly subjective technique known as frozen section histopathology. In the biopsy section, large numbers of molecules can now be visualized at once (ion images) following recent developments in [MSI] mass spectrometry imaging under atmospheric conditions. This is vastly superior to and different from the single optical tissue image processing used in traditional histopathology. This review article will focus on the advancement of desorption electrospray ionization mass spectrometry imaging [DESI-MSI] technique, which is label-free and requires little to no sample preparation. Since the proportion of molecular species in normal and abnormal tissues is different, DESI-MSI can capture ion images of the distributions of lipids and metabolites on biopsy sections, which can provide rich diagnostic information. This is not a systematic review but a summary of well-known, cutting-edge and recent DESI-MSI applications in cancer research between 2018 and 2023.
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Affiliation(s)
- Bharath Sampath Kumar
- Independent Researcher, 21, B2, 27th Street, Nanganallur, Chennai 61, TamilNadu, India.
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