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Zhvansky ES, Ivanov DG, Sorokin AA, Bugrova AE, Nikolaev EN, Popov IA. Interactive Estimation of Heterogeneity from Mass Spectrometry Imaging. Anal Chem 2021; 93:3706-3709. [PMID: 33591173 DOI: 10.1021/acs.analchem.1c00437] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
In this work, we demonstrate a new approach for interactively assessing hyperspectral data spatial structures for heterogeneity using mass spectrometry imaging. This approach is based on the visualization of the cosine distance as the similarity levels between mass spectra of a chosen region and the rest of the image (sample). The applicability of the method is demonstrated on a set of mass spectrometry images of frontal mouse brain slices. Selection of the reference pixel of the mass spectrometric image and a further view of the corresponding cosine distance map helps to prepare supporting vectors for further analysis, select features, and carry out biological interpretation of different tissues in the mass spectrometry context with or without histological annotation. Visual inspection of the similarity maps reveals the spatial distribution of features in tissue samples, which can serve as the molecular histological annotation of a slide.
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Affiliation(s)
- Evgeny S Zhvansky
- Moscow Institute of Physics and Technology, Institutskij bystr. 9, 141700 Dolgoprudnyi, Moscow Region, Russia
| | - Daniil G Ivanov
- Moscow Institute of Physics and Technology, Institutskij bystr. 9, 141700 Dolgoprudnyi, Moscow Region, Russia.,Emanuel Institute for Biochemical Physics of the Russian Academy of Sciences, Kosygina st. 4, 119334 Moscow, Russia
| | - Anatoly A Sorokin
- Moscow Institute of Physics and Technology, Institutskij bystr. 9, 141700 Dolgoprudnyi, Moscow Region, Russia.,Institute of Cell Biophysics RAS, Institutskaya st., 3, 142290 Pushchino, Russia.,Institute of Systems, Molecular and Integrative Biology, Biosciences Building, University of Liverpool, Crown Street, Liverpool L69 7ZB, U.K
| | - Anna E Bugrova
- Emanuel Institute for Biochemical Physics of the Russian Academy of Sciences, Kosygina st. 4, 119334 Moscow, Russia
| | - Evgeny N Nikolaev
- Skolkovo Institute of Science and Technology, Novaya Street, 100, 143025 Skolkovo, Russia
| | - Igor A Popov
- Moscow Institute of Physics and Technology, Institutskij bystr. 9, 141700 Dolgoprudnyi, Moscow Region, Russia
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Verbeeck N, Caprioli RM, Van de Plas R. Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry. MASS SPECTROMETRY REVIEWS 2020; 39:245-291. [PMID: 31602691 PMCID: PMC7187435 DOI: 10.1002/mas.21602] [Citation(s) in RCA: 115] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Accepted: 08/27/2018] [Indexed: 05/20/2023]
Abstract
Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experiment. While this makes it particularly suited for exploratory analysis, the large amount and high-dimensional nature of data generated by IMS techniques make automated computational analysis indispensable. Research into computational methods for IMS data has touched upon different aspects, including spectral preprocessing, data formats, dimensionality reduction, spatial registration, sample classification, differential analysis between IMS experiments, and data-driven fusion methods to extract patterns corroborated by both IMS and other imaging modalities. In this work, we review unsupervised machine learning methods for exploratory analysis of IMS data, with particular focus on (a) factorization, (b) clustering, and (c) manifold learning. To provide a view across the various IMS modalities, we have attempted to include examples from a range of approaches including matrix assisted laser desorption/ionization, desorption electrospray ionization, and secondary ion mass spectrometry-based IMS. This review aims to be an entry point for both (i) analytical chemists and mass spectrometry experts who want to explore computational techniques; and (ii) computer scientists and data mining specialists who want to enter the IMS field. © 2019 The Authors. Mass Spectrometry Reviews published by Wiley Periodicals, Inc. Mass SpecRev 00:1-47, 2019.
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Affiliation(s)
- Nico Verbeeck
- Delft Center for Systems and ControlDelft University of Technology ‐ TU DelftDelftThe Netherlands
- Aspect Analytics NVGenkBelgium
- STADIUS Center for Dynamical Systems, Signal Processing, and Data Analytics, Department of Electrical Engineering (ESAT)KU LeuvenLeuvenBelgium
| | - Richard M. Caprioli
- Mass Spectrometry Research CenterVanderbilt UniversityNashvilleTN
- Department of BiochemistryVanderbilt UniversityNashvilleTN
- Department of ChemistryVanderbilt UniversityNashvilleTN
- Department of PharmacologyVanderbilt UniversityNashvilleTN
- Department of MedicineVanderbilt UniversityNashvilleTN
| | - Raf Van de Plas
- Delft Center for Systems and ControlDelft University of Technology ‐ TU DelftDelftThe Netherlands
- Mass Spectrometry Research CenterVanderbilt UniversityNashvilleTN
- Department of BiochemistryVanderbilt UniversityNashvilleTN
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3
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Affiliation(s)
- Yu Song
- Department of Statistics, George Washington University, Washington, DC, USA
| | - Reza Modarres
- Department of Statistics, George Washington University, Washington, DC, USA
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Phenotype-loci associations in networks of patients with rare disorders: application to assist in the diagnosis of novel clinical cases. Eur J Hum Genet 2018; 26:1451-1461. [PMID: 29946186 PMCID: PMC6138686 DOI: 10.1038/s41431-018-0139-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2017] [Revised: 02/06/2018] [Accepted: 03/06/2018] [Indexed: 12/29/2022] Open
Abstract
Copy number variations (CNVs) are genomic structural variations (deletions, duplications, or translocations) that represent the 4.8-9.5% of human genome variation in healthy individuals. In some cases, CNVs can also lead to disease, being the etiology of many known rare genetic/genomic disorders. Despite the last advances in genomic sequencing and diagnosis, the pathological effects of many rare genetic variations remain unresolved, largely due to the low number of patients available for these cases, making it difficult to identify consistent patterns of genotype-phenotype relationships. We aimed to improve the identification of statistically consistent genotype-phenotype relationships by integrating all the genetic and clinical data of thousands of patients with rare genomic disorders (obtained from the DECIPHER database) into a phenotype-patient-genotype tripartite network. Then we assessed how our network approach could help in the characterization and diagnosis of novel cases in clinical genetics. The systematic approach implemented in this work is able to better define the relationships between phenotypes and specific loci, by exploiting large-scale association networks of phenotypes and genotypes in thousands of rare disease patients. The application of the described methodology facilitated the diagnosis of novel clinical cases, ranking phenotypes by locus specificity and reporting putative new clinical features that may suggest additional clinical follow-ups. In this work, the proof of concept developed over a set of novel clinical cases demonstrates that this network-based methodology might help improve the precision of patient clinical records and the characterization of rare syndromes.
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Ludwik KA, McDonald OG, Brenin DR, Lannigan DA. ERα-Mediated Nuclear Sequestration of RSK2 Is Required for ER + Breast Cancer Tumorigenesis. Cancer Res 2018; 78:2014-2025. [PMID: 29351904 DOI: 10.1158/0008-5472.can-17-2063] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Revised: 11/02/2017] [Accepted: 01/16/2018] [Indexed: 11/16/2022]
Abstract
Although ribosomal protein S6 kinase A3 (RSK2) activation status positively correlates with patient responses to antiestrogen hormonal therapies, the mechanistic basis for these observations is unknown. Using multiple in vitro and in vivo models of estrogen receptor-positive (ER+) breast cancer, we report that ERα sequesters active RSK2 into the nucleus to promote neoplastic transformation and facilitate metastatic tumor growth. RSK2 physically interacted with ERα through its N terminus to activate a proneoplastic transcriptional network critical to the ER+ lineage in the mammary gland, thereby providing a gene signature that effectively stratified patient tumors according to ERα status. ER+ tumor growth was strongly dependent on nuclear RSK2, and transgenic mice engineered to stably express nuclear RSK2 in the mammary gland developed high-grade ductal carcinoma in situ Mammary cells isolated from the transgenic model and introduced systemically successfully disseminated and established metastatic lesions. Antiestrogens disrupted the interaction between RSK2 and ERα, driving RSK2 into the cytoplasm and impairing tumor formation. These findings establish RSK2 as an obligate participant of ERα-mediated transcriptional programs, tumorigenesis, and divergent patient responses to antiestrogen therapies.Significance: Nuclear accumulation of active RSK drives a protumorigenic transcriptional program and renders ER+ breast cancer susceptible to endocrine-based therapies. Cancer Res; 78(8); 2014-25. ©2018 AACR.
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Affiliation(s)
- Katarzyna A Ludwik
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Oliver G McDonald
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - David R Brenin
- Department of Surgery, University of Virginia, Charlottesville, Virginia
| | - Deborah A Lannigan
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee. .,Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee
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Pattern recognition for predictive, preventive, and personalized medicine in cancer. EPMA J 2017; 8:51-60. [PMID: 28620443 DOI: 10.1007/s13167-017-0083-9] [Citation(s) in RCA: 80] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2016] [Accepted: 02/05/2017] [Indexed: 12/18/2022]
Abstract
Predictive, preventive, and personalized medicine (PPPM) is the hot spot and future direction in the field of cancer. Cancer is a complex, whole-body disease that involved multi-factors, multi-processes, and multi-consequences. A series of molecular alterations at different levels of genes (genome), RNAs (transcriptome), proteins (proteome), peptides (peptidome), metabolites (metabolome), and imaging characteristics (radiome) that resulted from exogenous and endogenous carcinogens are involved in tumorigenesis and mutually associate and function in a network system, thus determines the difficulty in the use of a single molecule as biomarker for personalized prediction, prevention, diagnosis, and treatment for cancer. A key molecule-panel is necessary for accurate PPPM practice. Pattern recognition is an effective methodology to discover key molecule-panel for cancer. The modern omics, computation biology, and systems biology technologies lead to the possibility in recognizing really reliable molecular pattern for PPPM practice in cancer. The present article reviewed the pathophysiological basis, methodology, and perspective usages of pattern recognition for PPPM in cancer so that our previous opinion on multi-parameter strategies for PPPM in cancer is translated into real research and development of PPPM or precision medicine (PM) in cancer.
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Kaddi CD, Bennett RV, Paine MRL, Banks MD, Weber AL, Fernández FM, Wang MD. DetectTLC: Automated Reaction Mixture Screening Utilizing Quantitative Mass Spectrometry Image Features. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2016; 27:359-65. [PMID: 26508443 PMCID: PMC5003040 DOI: 10.1007/s13361-015-1293-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2015] [Revised: 10/05/2015] [Accepted: 10/07/2015] [Indexed: 05/25/2023]
Abstract
Full characterization of complex reaction mixtures is necessary to understand mechanisms, optimize yields, and elucidate secondary reaction pathways. Molecular-level information for species in such mixtures can be readily obtained by coupling mass spectrometry imaging (MSI) with thin layer chromatography (TLC) separations. User-guided investigation of imaging data for mixture components with known m/z values is generally straightforward; however, spot detection for unknowns is highly tedious, and limits the applicability of MSI in conjunction with TLC. To accelerate imaging data mining, we developed DetectTLC, an approach that automatically identifies m/z values exhibiting TLC spot-like regions in MS molecular images. Furthermore, DetectTLC can also spatially match m/z values for spots acquired during alternating high and low collision-energy scans, pairing product ions with precursors to enhance structural identification. As an example, DetectTLC is applied to the identification and structural confirmation of unknown, yet significant, products of abiotic pyrazinone and aminopyrazine nucleoside analog synthesis. Graphical Abstract ᅟ.
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Affiliation(s)
- Chanchala D Kaddi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Rachel V Bennett
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, 30332, USA
- The Coca-Cola Company, 1 Coca-Cola Plaza, Atlanta, GA, 30313, USA
| | - Martin R L Paine
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Mitchel D Banks
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Arthur L Weber
- SETI Institute, Ames Research Center, Moffett Field, CA, 94035, USA
| | - Facundo M Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
| | - May D Wang
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
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