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Stopka SA, van der Reest J, Abdelmoula WM, Ruiz DF, Joshi S, Ringel AE, Haigis MC, Agar NYR. Spatially resolved characterization of tissue metabolic compartments in fasted and high-fat diet livers. PLoS One 2022; 17:e0261803. [PMID: 36067168 PMCID: PMC9447892 DOI: 10.1371/journal.pone.0261803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 08/12/2022] [Indexed: 11/18/2022] Open
Abstract
Cells adapt their metabolism to physiological stimuli, and metabolic heterogeneity exists between cell types, within tissues, and subcellular compartments. The liver plays an essential role in maintaining whole-body metabolic homeostasis and is structurally defined by metabolic zones. These zones are well-understood on the transcriptomic level, but have not been comprehensively characterized on the metabolomic level. Mass spectrometry imaging (MSI) can be used to map hundreds of metabolites directly from a tissue section, offering an important advance to investigate metabolic heterogeneity in tissues compared to extraction-based metabolomics methods that analyze tissue metabolite profiles in bulk. We established a workflow for the preparation of tissue specimens for matrix-assisted laser desorption/ionization (MALDI) MSI that can be implemented to achieve broad coverage of central carbon, nucleotide, and lipid metabolism pathways. Herein, we used this approach to visualize the effect of nutrient stress and excess on liver metabolism. Our data revealed a highly organized metabolic tissue compartmentalization in livers, which becomes disrupted under high fat diet. Fasting caused changes in the abundance of several metabolites, including increased levels of fatty acids and TCA intermediates while fatty livers had higher levels of purine and pentose phosphate-related metabolites, which generate reducing equivalents to counteract oxidative stress. This spatially conserved approach allowed the visualization of liver metabolic compartmentalization at 30 μm pixel resolution and can be applied more broadly to yield new insights into metabolic heterogeneity in vivo.
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Affiliation(s)
- Sylwia A. Stopka
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United Statees of America
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United Statees of America
| | - Jiska van der Reest
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United Statees of America
- Department of Cell Biology, Blavatnik Institute, Ludwig Center, Harvard Medical School, Boston, MA, United Statees of America
| | - Walid M. Abdelmoula
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United Statees of America
| | - Daniela F. Ruiz
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United Statees of America
- Bouvé College of Health Sciences, Northeastern University, Boston, MA, United Statees of America
| | - Shakchhi Joshi
- Department of Cell Biology, Blavatnik Institute, Ludwig Center, Harvard Medical School, Boston, MA, United Statees of America
| | - Alison E. Ringel
- Department of Cell Biology, Blavatnik Institute, Ludwig Center, Harvard Medical School, Boston, MA, United Statees of America
| | - Marcia C. Haigis
- Department of Cell Biology, Blavatnik Institute, Ludwig Center, Harvard Medical School, Boston, MA, United Statees of America
- * E-mail: (MCH); (NYRA)
| | - Nathalie Y. R. Agar
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United Statees of America
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United Statees of America
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, United Statees of America
- * E-mail: (MCH); (NYRA)
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2
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Abdelmoula WM, Stopka SA, Randall EC, Regan M, Agar JN, Sarkaria JN, Wells WM, Kapur T, Agar NYR. massNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation. Bioinformatics 2022; 38:2015-2021. [PMID: 35040929 PMCID: PMC8963284 DOI: 10.1093/bioinformatics/btac032] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 01/04/2022] [Accepted: 01/13/2022] [Indexed: 01/21/2023] Open
Abstract
MOTIVATION Mass spectrometry imaging (MSI) provides rich biochemical information in a label-free manner and therefore holds promise to substantially impact current practice in disease diagnosis. However, the complex nature of MSI data poses computational challenges in its analysis. The complexity of the data arises from its large size, high-dimensionality and spectral nonlinearity. Preprocessing, including peak picking, has been used to reduce raw data complexity; however, peak picking is sensitive to parameter selection that, perhaps prematurely, shapes the downstream analysis for tissue classification and ensuing biological interpretation. RESULTS We propose a deep learning model, massNet, that provides the desired qualities of scalability, nonlinearity and speed in MSI data analysis. This deep learning model was used, without prior preprocessing and peak picking, to classify MSI data from a mouse brain harboring a patient-derived tumor. The massNet architecture established automatically learning of predictive features, and automated methods were incorporated to identify peaks with potential for tumor delineation. The model's performance was assessed using cross-validation, and the results demonstrate higher accuracy and a substantial gain in speed compared to the established classical machine learning method, support vector machine. AVAILABILITY AND IMPLEMENTATION https://github.com/wabdelmoula/massNet. The data underlying this article are available in the NIH Common Fund's National Metabolomics Data Repository (NMDR) Metabolomics Workbench under project id (PR001292) with http://dx.doi.org/10.21228/M8Q70T. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Walid M Abdelmoula
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA,Invicro LLC, Boston, MA 02210, USA
| | - Sylwia A Stopka
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA,Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Elizabeth C Randall
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Michael Regan
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Jeffrey N Agar
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA 02111, USA
| | - Jann N Sarkaria
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN 55902, USA
| | - William M Wells
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA,Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA
| | - Tina Kapur
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Nathalie Y R Agar
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA,Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA,Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA,To whom correspondence should be addressed.
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3
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Lopez BGC, Kohale IN, Du Z, Korsunsky I, Abdelmoula WM, Dai Y, Stopka SA, Gaglia G, Randall EC, Regan MS, Basu SS, Clark AR, Marin BM, Mladek AC, Burgenske DM, Agar JN, Supko JG, Grossman SA, Nabors LB, Raychaudhuri S, Ligon KL, Wen PY, Alexander B, Lee EQ, Santagata S, Sarkaria J, White FM, Agar NYR. Multimodal platform for assessing drug distribution and response in clinical trials. Neuro Oncol 2022; 24:64-77. [PMID: 34383057 PMCID: PMC8730776 DOI: 10.1093/neuonc/noab197] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Response to targeted therapy varies between patients for largely unknown reasons. Here, we developed and applied an integrative platform using mass spectrometry imaging (MSI), phosphoproteomics, and multiplexed tissue imaging for mapping drug distribution, target engagement, and adaptive response to gain insights into heterogeneous response to therapy. METHODS Patient-derived xenograft (PDX) lines of glioblastoma were treated with adavosertib, a Wee1 inhibitor, and tissue drug distribution was measured with MALDI-MSI. Phosphoproteomics was measured in the same tumors to identify biomarkers of drug target engagement and cellular adaptive response. Multiplexed tissue imaging was performed on sister sections to evaluate spatial co-localization of drug and cellular response. The integrated platform was then applied on clinical specimens from glioblastoma patients enrolled in the phase 1 clinical trial. RESULTS PDX tumors exposed to different doses of adavosertib revealed intra- and inter-tumoral heterogeneity of drug distribution and integration of the heterogeneous drug distribution with phosphoproteomics and multiplexed tissue imaging revealed new markers of molecular response to adavosertib. Analysis of paired clinical specimens from patients enrolled in the phase 1 clinical trial informed the translational potential of the identified biomarkers in studying patient's response to adavosertib. CONCLUSIONS The multimodal platform identified a signature of drug efficacy and patient-specific adaptive responses applicable to preclinical and clinical drug development. The information generated by the approach may inform mechanisms of success and failure in future early phase clinical trials, providing information for optimizing clinical trial design and guiding future application into clinical practice.
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Affiliation(s)
- Begoña G C Lopez
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ishwar N Kohale
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Ziming Du
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ilya Korsunsky
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Walid M Abdelmoula
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Yang Dai
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Sylwia A Stopka
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Giorgio Gaglia
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Elizabeth C Randall
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Michael S Regan
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Sankha S Basu
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Amanda R Clark
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Bianca-Maria Marin
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota, USA
| | - Ann C Mladek
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Jeffrey N Agar
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts, USA
| | - Jeffrey G Supko
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Stuart A Grossman
- Brain Cancer Program, Johns Hopkins Hospital, Baltimore, Maryland, USA
| | - Louis B Nabors
- University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Keith L Ligon
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Patrick Y Wen
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Brian Alexander
- Department of Radiation Oncology, Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Eudocia Q Lee
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Sandro Santagata
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jann Sarkaria
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota, USA
| | - Forest M White
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Center for Precision Cancer Medicine, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Nathalie Y R Agar
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
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4
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Basu SS, Stopka SA, Abdelmoula WM, Randall EC, Gimenez-Cassina Lopez B, Regan MS, Calligaris D, Lu FF, Norton I, Mallory MA, Santagata S, Dillon DA, Golshan M, Agar NYR. Interim clinical trial analysis of intraoperative mass spectrometry for breast cancer surgery. NPJ Breast Cancer 2021; 7:116. [PMID: 34504095 PMCID: PMC8429658 DOI: 10.1038/s41523-021-00318-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 07/26/2021] [Indexed: 12/03/2022] Open
Abstract
Optimal resection of breast tumors requires removing cancer with a rim of normal tissue while preserving uninvolved regions of the breast. Surgical and pathological techniques that permit rapid molecular characterization of tissue could facilitate such resections. Mass spectrometry (MS) is increasingly used in the research setting to detect and classify tumors and has the potential to detect cancer at surgical margins. Here, we describe the ex vivo intraoperative clinical application of MS using a liquid micro-junction surface sample probe (LMJ-SSP) to assess breast cancer margins. In a midpoint analysis of a registered clinical trial, surgical specimens from 21 women with treatment naïve invasive breast cancer were prospectively collected and analyzed at the time of surgery with subsequent histopathological determination. Normal and tumor breast specimens from the lumpectomy resected by the surgeon were smeared onto glass slides for rapid analysis. Lipidomic profiles were acquired from these specimens using LMJ-SSP MS in negative ionization mode within the operating suite and post-surgery analysis of the data revealed five candidate ions separating tumor from healthy tissue in this limited dataset. More data is required before considering the ions as candidate markers. Here, we present an application of ambient MS within the operating room to analyze breast cancer tissue and surgical margins. Lessons learned from these initial promising studies are being used to further evaluate the five candidate biomarkers and to further refine and optimize intraoperative MS as a tool for surgical guidance in breast cancer.
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Affiliation(s)
- Sankha S Basu
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sylwia A Stopka
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Walid M Abdelmoula
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Elizabeth C Randall
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Michael S Regan
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - David Calligaris
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Fake F Lu
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Isaiah Norton
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Melissa A Mallory
- Department of Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sandro Santagata
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Deborah A Dillon
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Mehra Golshan
- Department of Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Yale Cancer Center, Department of Surgery, New Haven, CT, USA
| | - Nathalie Y R Agar
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
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5
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Stopka SA, Wood EA, Khattar R, Agtuca BJ, Abdelmoula WM, Agar NYR, Stacey G, Vertes A. High-Throughput Analysis of Tissue-Embedded Single Cells by Mass Spectrometry with Bimodal Imaging and Object Recognition. Anal Chem 2021; 93:9677-9687. [PMID: 34236164 DOI: 10.1021/acs.analchem.1c00569] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
In biological tissues, cell-to-cell variations stem from the stochastic and modulated expression of genes and the varying abundances of corresponding proteins. These variations are then propagated to downstream metabolite products and result in cellular heterogeneity. Mass spectrometry imaging (MSI) is a promising tool to simultaneously provide spatial distributions for hundreds of biomolecules without the need for labels or stains. Technological advances in MSI instrumentation for the direct analysis of tissue-embedded single cells are dominated by improvements in sensitivity, sample pretreatment, and increased spatial resolution but are limited by low throughput. Herein, we introduce a bimodal microscopy imaging system combined with fiber-based laser ablation electrospray ionization (f-LAESI) MSI with improved throughput ambient analysis of tissue-embedded single cells (n > 1000) to provide insight into cellular heterogeneity. Based on automated image analysis, accurate single-cell sampling is achieved by f-LAESI leading to the discovery of cellular phenotypes characterized by differing metabolite levels.
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Affiliation(s)
- Sylwia A Stopka
- Department of Chemistry, The George Washington University, Washington, District of Columbia 20052, United States.,Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, United States.,Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Ellen A Wood
- Department of Chemistry, The George Washington University, Washington, District of Columbia 20052, United States
| | - Rikkita Khattar
- Department of Chemistry, The George Washington University, Washington, District of Columbia 20052, United States
| | - Beverly J Agtuca
- Divisions of Plant Sciences and Biochemistry, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri 65211, United States
| | - Walid M Abdelmoula
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Nathalie Y R Agar
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, United States.,Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, United States.,Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Gary Stacey
- Divisions of Plant Sciences and Biochemistry, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri 65211, United States
| | - Akos Vertes
- Department of Chemistry, The George Washington University, Washington, District of Columbia 20052, United States
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6
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Choi JE, Sebastian C, Ferrer CM, Lewis CA, Sade-Feldman M, LaSalle T, Gonye A, Lopez BGC, Abdelmoula WM, Regan MS, Cetinbas M, Pascual G, Wojtkiewicz GR, Silveira GG, Boon R, Ross KN, Tirosh I, Saladi SV, Ellisen LW, Sadreyev RI, Benitah SA, Agar NYR, Hacohen N, Mostoslavsky R. A unique subset of glycolytic tumour-propagating cells drives squamous cell carcinoma. Nat Metab 2021; 3:182-195. [PMID: 33619381 PMCID: PMC7954080 DOI: 10.1038/s42255-021-00350-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 01/20/2021] [Indexed: 12/11/2022]
Abstract
Head and neck squamous cell carcinoma (SCC) remains among the most aggressive human cancers. Tumour progression and aggressiveness in SCC are largely driven by tumour-propagating cells (TPCs). Aerobic glycolysis, also known as the Warburg effect, is a characteristic of many cancers; however, whether this adaptation is functionally important in SCC, and at which stage, remains poorly understood. Here, we show that the NAD+-dependent histone deacetylase sirtuin 6 is a robust tumour suppressor in SCC, acting as a modulator of glycolysis in these tumours. Remarkably, rather than a late adaptation, we find enhanced glycolysis specifically in TPCs. More importantly, using single-cell RNA sequencing of TPCs, we identify a subset of TPCs with higher glycolysis and enhanced pentose phosphate pathway and glutathione metabolism, characteristics that are strongly associated with a better antioxidant response. Together, our studies uncover enhanced glycolysis as a main driver in SCC, and, more importantly, identify a subset of TPCs as the cell of origin for the Warburg effect, defining metabolism as a key feature of intra-tumour heterogeneity.
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Affiliation(s)
- Jee-Eun Choi
- The Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
- The MGH Center for Regenerative Medicine, Harvard Medical School, Boston, MA, USA
| | - Carlos Sebastian
- The Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
- The MGH Center for Regenerative Medicine, Harvard Medical School, Boston, MA, USA
- Candiolo Cancer Institute-FPO, IRCCS, Candiolo, Italy
| | - Christina M Ferrer
- The Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
- The MGH Center for Regenerative Medicine, Harvard Medical School, Boston, MA, USA
| | - Caroline A Lewis
- The Whitehead Institute for Biomedical Research, Cambridge, MA, USA
| | - Moshe Sade-Feldman
- The Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
- The Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Thomas LaSalle
- The Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
- The Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Anna Gonye
- The Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
- The Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Begona G C Lopez
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Walid M Abdelmoula
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael S Regan
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Murat Cetinbas
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Gloria Pascual
- Institute for Research and Biomedicine (IRB) Barcelona, The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
| | - Gregory R Wojtkiewicz
- Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Giorgia G Silveira
- The Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
- The MGH Center for Regenerative Medicine, Harvard Medical School, Boston, MA, USA
| | - Ruben Boon
- The Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
- The MGH Center for Regenerative Medicine, Harvard Medical School, Boston, MA, USA
| | - Kenneth N Ross
- The Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Itay Tirosh
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Srinivas V Saladi
- The Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
- The Massachusetts Eye and Ear Infirmary, Boston, MA, USA
| | - Leif W Ellisen
- The Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
| | - Ruslan I Sadreyev
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Salvador Aznar Benitah
- Institute for Research and Biomedicine (IRB) Barcelona, The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
| | - Nathalie Y R Agar
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Nir Hacohen
- The Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
- The Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Raul Mostoslavsky
- The Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA.
- The MGH Center for Regenerative Medicine, Harvard Medical School, Boston, MA, USA.
- The Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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7
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Bulk M, Abdelmoula WM, Geut H, Wiarda W, Ronen I, Dijkstra J, van der Weerd L. Quantitative MRI and laser ablation-inductively coupled plasma-mass spectrometry imaging of iron in the frontal cortex of healthy controls and Alzheimer’s disease patients. Neuroimage 2020; 215:116808. [DOI: 10.1016/j.neuroimage.2020.116808] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Accepted: 03/20/2020] [Indexed: 12/27/2022] Open
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8
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Randall EC, Lopez BGC, Peng S, Regan MS, Abdelmoula WM, Basu SS, Santagata S, Yoon H, Haigis MC, Agar JN, Tran NL, Elmquist WF, White FM, Sarkaria JN, Agar NYR. Localized Metabolomic Gradients in Patient-Derived Xenograft Models of Glioblastoma. Cancer Res 2019; 80:1258-1267. [PMID: 31767628 DOI: 10.1158/0008-5472.can-19-0638] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 07/12/2019] [Accepted: 11/13/2019] [Indexed: 12/17/2022]
Abstract
Glioblastoma (GBM) is increasingly recognized as a disease involving dysfunctional cellular metabolism. GBMs are known to be complex heterogeneous systems containing multiple distinct cell populations and are supported by an aberrant network of blood vessels. A better understanding of GBM metabolism, its variation with respect to the tumor microenvironment, and resulting regional changes in chemical composition is required. This may shed light on the observed heterogeneous drug distribution, which cannot be fully described by limited or uneven disruption of the blood-brain barrier. In this work, we used mass spectrometry imaging (MSI) to map metabolites and lipids in patient-derived xenograft models of GBM. A data analysis workflow revealed that distinctive spectral signatures were detected from different regions of the intracranial tumor model. A series of long-chain acylcarnitines were identified and detected with increased intensity at the tumor edge. A 3D MSI dataset demonstrated that these molecules were observed throughout the entire tumor/normal interface and were not confined to a single plane. mRNA sequencing demonstrated that hallmark genes related to fatty acid metabolism were highly expressed in samples with higher acylcarnitine content. These data suggest that cells in the core and the edge of the tumor undergo different fatty acid metabolism, resulting in different chemical environments within the tumor. This may influence drug distribution through changes in tissue drug affinity or transport and constitute an important consideration for therapeutic strategies in the treatment of GBM. SIGNIFICANCE: GBM tumors exhibit a metabolic gradient that should be taken into consideration when designing therapeutic strategies for treatment.See related commentary by Tan and Weljie, p. 1231.
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Affiliation(s)
- Elizabeth C Randall
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Begoña G C Lopez
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Sen Peng
- Division of Cancer and Cell Biology, Translational Genomics Research Institute - Affiliate of City of Hope, Phoenix, Arizona
| | - Michael S Regan
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Walid M Abdelmoula
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Sankha S Basu
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Sandro Santagata
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Haejin Yoon
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts
| | - Marcia C Haigis
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts
| | - Jeffrey N Agar
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts
| | - Nhan L Tran
- Department of Cancer Biology, Mayo Clinic, Scottsdale, Arizona
| | - William F Elmquist
- Department of Pharmaceutics, College of Pharmacy, University of Minnesota, Minneapolis, Minnesota
| | - Forest M White
- Department of Biological Engineering, Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, 500 Main St, Cambridge, Massachusetts
| | - Jann N Sarkaria
- Department of Radiation Oncology, Mayo Clinic, 200 First St SW, Rochester, Minnesota
| | - Nathalie Y R Agar
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts. .,Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.,Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
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9
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Micoogullari Y, Basu SS, Ang J, Weisshaar N, Schmitt ND, Abdelmoula WM, Lopez B, Agar JN, Agar N, Hanna J. Dysregulation of very-long-chain fatty acid metabolism causes membrane saturation and induction of the unfolded protein response. Mol Biol Cell 2019; 31:7-17. [PMID: 31746669 PMCID: PMC6938273 DOI: 10.1091/mbc.e19-07-0392] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
The unfolded protein response (UPR) senses defects in the endoplasmic reticulum (ER) and orchestrates a complex program of adaptive cellular remodeling. Increasing evidence suggests an important relationship between lipid homeostasis and the UPR. Defects in the ER membrane induce the UPR, and the UPR in turn controls the expression of some lipid metabolic genes. Among lipid species, the very-long-chain fatty acids (VLCFAs) are relatively rare and poorly understood. Here, we show that loss of the VLCFA-coenzyme A synthetase Fat1, which is essential for VLCFA utilization, results in ER stress with compensatory UPR induction. Comprehensive lipidomic analyses revealed a dramatic increase in membrane saturation in the fat1Δ mutant, likely accounting for UPR induction. In principle, this increased membrane saturation could reflect adaptive membrane remodeling or an adverse effect of VLCFA dysfunction. We provide evidence supporting the latter, as the fat1Δ mutant showed defects in the function of Ole1, the sole fatty acyl desaturase in yeast. These results indicate that VLCFAs play essential roles in protein quality control and membrane homeostasis and suggest an unexpected requirement for VLCFAs in Ole1 function.
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Affiliation(s)
| | - Sankha S Basu
- Department of Neurosurgery, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115
| | | | | | | | - Walid M Abdelmoula
- Department of Neurosurgery, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115
| | - Begona Lopez
- Department of Neurosurgery, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115
| | - Jeffrey N Agar
- Department of Chemistry and Chemical Biology and.,Department of Pharmacological Sciences, Northeastern University, Boston, MA 02111
| | - Nathalie Agar
- Department of Neurosurgery, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115
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10
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Abdelmoula WM, Regan MS, Lopez BGC, Randall EC, Lawler S, Mladek AC, Nowicki MO, Marin BM, Agar JN, Swanson KR, Kapur T, Sarkaria JN, Wells W, Agar NYR. Automatic 3D Nonlinear Registration of Mass Spectrometry Imaging and Magnetic Resonance Imaging Data. Anal Chem 2019; 91:6206-6216. [PMID: 30932478 DOI: 10.1021/acs.analchem.9b00854] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Multimodal integration between mass spectrometry imaging (MSI) and radiology-established modalities such as magnetic resonance imaging (MRI) would allow the investigations of key questions in complex biological systems such as the central nervous system. Such integration would provide complementary multiscale data to bridge the gap between molecular and anatomical phenotypes, potentially revealing new insights into molecular mechanisms underlying anatomical pathologies presented on MRI. Automatic coregistration between 3D MSI/MRI is a computationally challenging process due to dimensional complexity, MSI data sparsity, lack of direct spatial-correspondences, and nonlinear tissue deformation. Here, we present a new computational approach based on stochastic neighbor embedding to nonlinearly align 3D MSI to MRI data, identify and reconstruct biologically relevant molecular patterns in 3D, and fuse the MSI datacube to the MRI space. We demonstrate our method using multimodal high-spectral resolution matrix-assisted laser desorption ionization (MALDI) 9.4 T MSI and 7 T in vivo MRI data, acquired from a patient-derived, xenograft mouse brain model of glioblastoma following administration of the EGFR inhibitor drug of Erlotinib. Results show the distribution of some identified molecular ions of the EGFR inhibitor erlotinib, a phosphatidylcholine lipid, and cholesterol, which were reconstructed in 3D and mapped to the MRI space. The registration quality was evaluated on two normal mouse brains using the Dice coefficient for the regions of brainstem, hippocampus, and cortex. The method is generic and can therefore be applied to hyperspectral images from different mass spectrometers and integrated with other established in vivo imaging modalities such as computed tomography (CT) and positron emission tomography (PET).
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Affiliation(s)
- Walid M Abdelmoula
- Department of Neurosurgery, Brigham and Women's Hospital , Harvard Medical School , Boston , Massachusetts 02115 , United States
| | - Michael S Regan
- Department of Neurosurgery, Brigham and Women's Hospital , Harvard Medical School , Boston , Massachusetts 02115 , United States
| | - Begona G C Lopez
- Department of Neurosurgery, Brigham and Women's Hospital , Harvard Medical School , Boston , Massachusetts 02115 , United States
| | - Elizabeth C Randall
- Department of Radiology, Brigham and Women's Hospital , Harvard Medical School , Boston , Massachusetts 02115 , United States
| | - Sean Lawler
- Department of Neurosurgery, Brigham and Women's Hospital , Harvard Medical School , Boston , Massachusetts 02115 , United States
| | - Ann C Mladek
- Department of Radiation Oncology , Mayo Clinic , 200 First Street SW , Rochester , Minnesota 55902 , United States
| | - Michal O Nowicki
- Department of Neurosurgery, Brigham and Women's Hospital , Harvard Medical School , Boston , Massachusetts 02115 , United States
| | - Bianca M Marin
- Department of Radiation Oncology , Mayo Clinic , 200 First Street SW , Rochester , Minnesota 55902 , United States
| | - Jeffrey N Agar
- Department of Chemistry and Chemical Biology , Northeastern University , 412 TF (140 The Fenway) , Boston , Massachusetts 02111 , United States
| | - Kristin R Swanson
- Mathematical NeuroOncology Lab, Department of Neurosurgery , Mayo Clinic , 5777 East Mayo Boulevard , Phoenix , Arizona 85054 , United States
| | - Tina Kapur
- Department of Radiology, Brigham and Women's Hospital , Harvard Medical School , Boston , Massachusetts 02115 , United States
| | - Jann N Sarkaria
- Department of Radiation Oncology , Mayo Clinic , 200 First Street SW , Rochester , Minnesota 55902 , United States
| | - William Wells
- Department of Radiology, Brigham and Women's Hospital , Harvard Medical School , Boston , Massachusetts 02115 , United States.,Computer Science and Artificial Intelligence Laboratory , Massachusetts Institute of Technology , Cambridge , Massachusetts 02139 , United States
| | - Nathalie Y R Agar
- Department of Neurosurgery, Brigham and Women's Hospital , Harvard Medical School , Boston , Massachusetts 02115 , United States.,Department of Radiology, Brigham and Women's Hospital , Harvard Medical School , Boston , Massachusetts 02115 , United States.,Department of Cancer Biology, Dana-Farber Cancer Institute , Harvard Medical School , Boston , Massachusetts 02115 , United States
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11
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Randall EC, Emdal KB, Laramy JK, Kim M, Roos A, Calligaris D, Regan MS, Gupta SK, Mladek AC, Carlson BL, Johnson AJ, Lu FK, Xie XS, Joughin BA, Reddy RJ, Peng S, Abdelmoula WM, Jackson PR, Kolluri A, Kellersberger KA, Agar JN, Lauffenburger DA, Swanson KR, Tran NL, Elmquist WF, White FM, Sarkaria JN, Agar NYR. Integrated mapping of pharmacokinetics and pharmacodynamics in a patient-derived xenograft model of glioblastoma. Nat Commun 2018; 9:4904. [PMID: 30464169 PMCID: PMC6249307 DOI: 10.1038/s41467-018-07334-3] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Accepted: 10/23/2018] [Indexed: 12/13/2022] Open
Abstract
Therapeutic options for the treatment of glioblastoma remain inadequate despite concerted research efforts in drug development. Therapeutic failure can result from poor permeability of the blood-brain barrier, heterogeneous drug distribution, and development of resistance. Elucidation of relationships among such parameters could enable the development of predictive models of drug response in patients and inform drug development. Complementary analyses were applied to a glioblastoma patient-derived xenograft model in order to quantitatively map distribution and resulting cellular response to the EGFR inhibitor erlotinib. Mass spectrometry images of erlotinib were registered to histology and magnetic resonance images in order to correlate drug distribution with tumor characteristics. Phosphoproteomics and immunohistochemistry were used to assess protein signaling in response to drug, and integrated with transcriptional response using mRNA sequencing. This comprehensive dataset provides simultaneous insight into pharmacokinetics and pharmacodynamics and indicates that erlotinib delivery to intracranial tumors is insufficient to inhibit EGFR tyrosine kinase signaling.
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Affiliation(s)
- Elizabeth C Randall
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Kristina B Emdal
- Department of Biological Engineering, Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, 500 Main St, Cambridge, MA, 02142, USA
| | - Janice K Laramy
- Department of Pharmaceutics, College of Pharmacy, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Minjee Kim
- Department of Pharmaceutics, College of Pharmacy, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Alison Roos
- Department of Cancer Biology, Mayo Clinic, 13400 E. Shea Blvd.MCCRB 03-055, Scottsdale, AZ, 85259, USA
| | - David Calligaris
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Michael S Regan
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Shiv K Gupta
- Department of Radiation Oncology, Mayo Clinic, 200 First St SW, Rochester, MN, 55902, USA
| | - Ann C Mladek
- Department of Radiation Oncology, Mayo Clinic, 200 First St SW, Rochester, MN, 55902, USA
| | - Brett L Carlson
- Department of Radiation Oncology, Mayo Clinic, 200 First St SW, Rochester, MN, 55902, USA
| | - Aaron J Johnson
- Department of Immunology, Mayo Clinic, 200 First St SW, Rochester, MN, 55902, USA
| | - Fa-Ke Lu
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, 02138, USA
- Department of Biomedical Engineering, Binghamton University, State University of New York, Binghamton, NY, 13902, USA
| | - X Sunney Xie
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Brian A Joughin
- Department of Biological Engineering, Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, 500 Main St, Cambridge, MA, 02142, USA
| | - Raven J Reddy
- Department of Biological Engineering, Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, 500 Main St, Cambridge, MA, 02142, USA
| | - Sen Peng
- Cancer and Cell Biology Division, Translational Genomics Research Institute, Phoenix, AZ, 85004, USA
| | - Walid M Abdelmoula
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Pamela R Jackson
- Mathematical NeuroOncology Lab, Department of Neurosurgery, Mayo Clinic, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA
| | - Aarti Kolluri
- Mathematical NeuroOncology Lab, Department of Neurosurgery, Mayo Clinic, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA
| | | | - Jeffrey N Agar
- Department of Chemistry and Chemical Biology, Northeastern University, 412 TF (140 The Fenway), Boston, MA, 02111, USA
| | - Douglas A Lauffenburger
- Department of Biological Engineering, Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, 500 Main St, Cambridge, MA, 02142, USA
| | - Kristin R Swanson
- Mathematical NeuroOncology Lab, Department of Neurosurgery, Mayo Clinic, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA
| | - Nhan L Tran
- Department of Cancer Biology, Mayo Clinic, 13400 E. Shea Blvd.MCCRB 03-055, Scottsdale, AZ, 85259, USA
| | - William F Elmquist
- Department of Pharmaceutics, College of Pharmacy, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Forest M White
- Department of Biological Engineering, Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, 500 Main St, Cambridge, MA, 02142, USA
| | - Jann N Sarkaria
- Department of Radiation Oncology, Mayo Clinic, 200 First St SW, Rochester, MN, 55902, USA
| | - Nathalie Y R Agar
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
- Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, 02115, USA.
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12
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Abdelmoula WM, Pezzotti N, Hölt T, Dijkstra J, Vilanova A, McDonnell LA, Lelieveldt BPF. Interactive Visual Exploration of 3D Mass Spectrometry Imaging Data Using Hierarchical Stochastic Neighbor Embedding Reveals Spatiomolecular Structures at Full Data Resolution. J Proteome Res 2018; 17:1054-1064. [PMID: 29430923 PMCID: PMC5838640 DOI: 10.1021/acs.jproteome.7b00725] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
![]()
Technological
advances in mass spectrometry imaging (MSI) have
contributed to growing interest in 3D MSI. However, the large size
of 3D MSI data sets has made their efficient analysis and visualization
and the identification of informative molecular patterns computationally
challenging. Hierarchical stochastic neighbor embedding (HSNE), a
nonlinear dimensionality reduction technique that aims at finding
hierarchical and multiscale representations of large data sets, is
a recent development that enables the analysis of millions of data
points, with manageable time and memory complexities. We demonstrate
that HSNE can be used to analyze large 3D MSI data sets at full mass
spectral and spatial resolution. To benchmark the technique as well
as demonstrate its broad applicability, we have analyzed a number
of publicly available 3D MSI data sets, recorded from various biological
systems and spanning different mass-spectrometry ionization techniques.
We demonstrate that HSNE is able to rapidly identify regions of interest
within these large high-dimensionality data sets as well as aid the
identification of molecular ions that characterize these regions of
interest; furthermore, through clearly separating measurement artifacts,
the HSNE analysis exhibits a degree of robustness to measurement batch
effects, spatially correlated noise, and mass spectral misalignment.
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Affiliation(s)
- Walid M Abdelmoula
- Division of Image Processing, Department of Radiology, Leiden University Medical Center , 2333 ZA Leiden, The Netherlands.,Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School , Boston, Massachusetts 02115, United States
| | - Nicola Pezzotti
- Computer Graphics and Visualization Group, Faculty of EEMCS, Delft University of Technology , 2628 CN Delft, The Netherlands
| | - Thomas Hölt
- Computer Graphics and Visualization Group, Faculty of EEMCS, Delft University of Technology , 2628 CN Delft, The Netherlands
| | - Jouke Dijkstra
- Division of Image Processing, Department of Radiology, Leiden University Medical Center , 2333 ZA Leiden, The Netherlands
| | - Anna Vilanova
- Computer Graphics and Visualization Group, Faculty of EEMCS, Delft University of Technology , 2628 CN Delft, The Netherlands
| | | | - Boudewijn P F Lelieveldt
- Division of Image Processing, Department of Radiology, Leiden University Medical Center , 2333 ZA Leiden, The Netherlands.,Computer Graphics and Visualization Group, Faculty of EEMCS, Delft University of Technology , 2628 CN Delft, The Netherlands
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13
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Bulk M, Abdelmoula WM, Nabuurs RJA, van der Graaf LM, Mulders CWH, Mulder AA, Jost CR, Koster AJ, van Buchem MA, Natté R, Dijkstra J, van der Weerd L. Postmortem MRI and histology demonstrate differential iron accumulation and cortical myelin organization in early- and late-onset Alzheimer's disease. Neurobiol Aging 2017; 62:231-242. [PMID: 29195086 DOI: 10.1016/j.neurobiolaging.2017.10.017] [Citation(s) in RCA: 79] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2017] [Revised: 10/18/2017] [Accepted: 10/18/2017] [Indexed: 11/15/2022]
Abstract
Previous MRI studies reported cortical iron accumulation in early-onset (EOAD) compared to late-onset (LOAD) Alzheimer disease patients. However, the pattern and origin of iron accumulation is poorly understood. This study investigated the histopathological correlates of MRI contrast in both EOAD and LOAD. T2*-weighted MRI was performed on postmortem frontal cortex of controls, EOAD, and LOAD. Images were ordinally scored using predefined criteria followed by histology. Nonlinear histology-MRI registration was used to calculate pixel-wise spatial correlations based on the signal intensity. EOAD and LOAD were distinguishable based on 7T MRI from controls and from each other. Histology-MRI correlation analysis of the pixel intensities showed that the MRI contrast is best explained by increased iron accumulation and changes in cortical myelin, whereas amyloid and tau showed less spatial correspondence with T2*-weighted MRI. Neuropathologically, subtypes of Alzheimer's disease showed different patterns of iron accumulation and cortical myelin changes independent of amyloid and tau that may be detected by high-field susceptibility-based MRI.
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Affiliation(s)
- Marjolein Bulk
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands; Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands; Percuros BV, Leiden, the Netherlands.
| | - Walid M Abdelmoula
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Rob J A Nabuurs
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Linda M van der Graaf
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands; Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Coen W H Mulders
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Aat A Mulder
- Department of Molecular Cell Biology, Electron Microscopy Section, Leiden University Medical Center, Leiden, the Netherlands
| | - Carolina R Jost
- Department of Molecular Cell Biology, Electron Microscopy Section, Leiden University Medical Center, Leiden, the Netherlands
| | - Abraham J Koster
- Department of Molecular Cell Biology, Electron Microscopy Section, Leiden University Medical Center, Leiden, the Netherlands
| | - Mark A van Buchem
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Remco Natté
- Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands
| | - Jouke Dijkstra
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Louise van der Weerd
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands; Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
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14
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Bulk M, Graaf L, Abdelmoula WM, Nabuurs R, Dijkstra J, Natte R, Buchem M, Weerd L. [O1–08–04]: IRON AND MYELIN AS SOURCES OF MRI CONTRAST IN PATIENTS WITH ALZHEIMER's DISEASE. Alzheimers Dement 2017. [DOI: 10.1016/j.jalz.2017.07.079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Marjolein Bulk
- Leiden University Medical CenterLeidenNetherlands
- Percuros BVLeidenNetherlands
| | - Linda Graaf
- Leiden University Medical CenterLeidenNetherlands
| | | | - Rob Nabuurs
- Leiden University Medical CenterLeidenNetherlands
| | | | - Remco Natte
- Leiden University Medical CenterLeidenNetherlands
| | - Mark Buchem
- Leiden University Medical CenterLeidenNetherlands
| | - Louise Weerd
- Leiden University Medical CenterLeidenNetherlands
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15
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Heijs B, Abdelmoula WM, Lou S, Briaire-de Bruijn IH, Dijkstra J, Bovée JVMG, McDonnell LA. Histology-Guided High-Resolution Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging. Anal Chem 2015; 87:11978-83. [DOI: 10.1021/acs.analchem.5b03610] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Affiliation(s)
- Bram Heijs
- Center
for Proteomics and Metabolomics, Leiden University Medical Center, Einthovenweg 20, 2333ZC Leiden, The Netherlands
| | - Walid M. Abdelmoula
- Division
of Image Processing, Department of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333ZA Leiden, The Netherlands
| | - Sha Lou
- Center
for Proteomics and Metabolomics, Leiden University Medical Center, Einthovenweg 20, 2333ZC Leiden, The Netherlands
| | - Inge H. Briaire-de Bruijn
- Department
of Pathology, Leiden University Medical Center, Albinusdreef
2, 2333ZA Leiden, The Netherlands
| | - Jouke Dijkstra
- Division
of Image Processing, Department of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333ZA Leiden, The Netherlands
| | - Judith V. M. G. Bovée
- Department
of Pathology, Leiden University Medical Center, Albinusdreef
2, 2333ZA Leiden, The Netherlands
| | - Liam A. McDonnell
- Center
for Proteomics and Metabolomics, Leiden University Medical Center, Einthovenweg 20, 2333ZC Leiden, The Netherlands
- Department
of Pathology, Leiden University Medical Center, Albinusdreef
2, 2333ZA Leiden, The Netherlands
- Fondazione Pisana per la Scienza ONLUS, 56125 Pisa, Italy
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16
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Carreira RJ, Shyti R, Balluff B, Abdelmoula WM, van Heiningen SH, van Zeijl RJ, Dijkstra J, Ferrari MD, Tolner EA, McDonnell LA, van den Maagdenberg AMJM. Large-scale mass spectrometry imaging investigation of consequences of cortical spreading depression in a transgenic mouse model of migraine. J Am Soc Mass Spectrom 2015; 26:853-61. [PMID: 25877011 PMCID: PMC4422864 DOI: 10.1007/s13361-015-1136-8] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Revised: 03/10/2015] [Accepted: 03/10/2015] [Indexed: 05/04/2023]
Abstract
Cortical spreading depression (CSD) is the electrophysiological correlate of migraine aura. Transgenic mice carrying the R192Q missense mutation in the Cacna1a gene, which in patients causes familial hemiplegic migraine type 1 (FHM1), exhibit increased propensity to CSD. Herein, mass spectrometry imaging (MSI) was applied for the first time to an animal cohort of transgenic and wild type mice to study the biomolecular changes following CSD in the brain. Ninety-six coronal brain sections from 32 mice were analyzed by MALDI-MSI. All MSI datasets were registered to the Allen Brain Atlas reference atlas of the mouse brain so that the molecular signatures of distinct brain regions could be compared. A number of metabolites and peptides showed substantial changes in the brain associated with CSD. Among those, different mass spectral features showed significant (t-test, P < 0.05) changes in the cortex, 146 and 377 Da, and in the thalamus, 1820 and 1834 Da, of the CSD-affected hemisphere of FHM1 R192Q mice. Our findings reveal CSD- and genotype-specific molecular changes in the brain of FHM1 transgenic mice that may further our understanding about the role of CSD in migraine pathophysiology. The results also demonstrate the utility of aligning MSI datasets to a common reference atlas for large-scale MSI investigations.
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Affiliation(s)
- Ricardo J. Carreira
- />Center for Proteomics and Metabolomics, Leiden University Medical Center, Einthovenweg 20, 2333 ZC Leiden, The Netherlands
| | - Reinald Shyti
- />Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Benjamin Balluff
- />Center for Proteomics and Metabolomics, Leiden University Medical Center, Einthovenweg 20, 2333 ZC Leiden, The Netherlands
| | - Walid M. Abdelmoula
- />Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Rene J. van Zeijl
- />Center for Proteomics and Metabolomics, Leiden University Medical Center, Einthovenweg 20, 2333 ZC Leiden, The Netherlands
| | - Jouke Dijkstra
- />Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Michel D. Ferrari
- />Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
| | - Else A. Tolner
- />Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
| | - Liam A. McDonnell
- />Center for Proteomics and Metabolomics, Leiden University Medical Center, Einthovenweg 20, 2333 ZC Leiden, The Netherlands
- />Fondazione Pisana per la Scienza ONLUS, Pisa, Italy
| | - Arn M. J. M. van den Maagdenberg
- />Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- />Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
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Škrášková K, Khmelinskii A, Abdelmoula WM, De Munter S, Baes M, McDonnell L, Dijkstra J, Heeren RMA. Precise Anatomic Localization of Accumulated Lipids in Mfp2 Deficient Murine Brains Through Automated Registration of SIMS Images to the Allen Brain Atlas. J Am Soc Mass Spectrom 2015; 26:948-57. [PMID: 25916600 PMCID: PMC4422856 DOI: 10.1007/s13361-015-1146-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2015] [Revised: 03/19/2015] [Accepted: 03/19/2015] [Indexed: 05/04/2023]
Abstract
Mass spectrometry imaging (MSI) is a powerful tool for the molecular characterization of specific tissue regions. Histochemical staining provides anatomic information complementary to MSI data. The combination of both modalities has been proven to be beneficial. However, direct comparison of histology based and mass spectrometry-based molecular images can become problematic because of potential tissue damages or changes caused by different sample preparation. Curated atlases such as the Allen Brain Atlas (ABA) offer a collection of highly detailed and standardized anatomic information. Direct comparison of MSI brain data to the ABA allows for conclusions to be drawn on precise anatomic localization of the molecular signal. Here we applied secondary ion mass spectrometry imaging at high spatial resolution to study brains of knock-out mouse models with impaired peroxisomal β-oxidation. Murine models were lacking D-multifunctional protein (MFP2), which is involved in degradation of very long chain fatty acids. SIMS imaging revealed deposits of fatty acids within distinct brain regions. Manual comparison of the MSI data with the histologic stains did not allow for an unequivocal anatomic identification of the fatty acids rich regions. We further employed an automated pipeline for co-registration of the SIMS data to the ABA. The registration enabled precise anatomic annotation of the brain structures with the revealed lipid deposits. The precise anatomic localization allowed for a deeper insight into the pathology of Mfp2 deficient mouse models.
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Affiliation(s)
- Karolina Škrášková
- />FOM-Institute AMOLF, Amsterdam, The Netherlands
- />TI-COAST, Amsterdam, The Netherlands
| | - Artem Khmelinskii
- />FOM-Institute AMOLF, Amsterdam, The Netherlands
- />Percuros B.V., Enschede, The Netherlands
- />Division of Image Processing, Department of Radiology, LUMC, Leiden, The Netherlands
| | - Walid M. Abdelmoula
- />Division of Image Processing, Department of Radiology, LUMC, Leiden, The Netherlands
| | | | - Myriam Baes
- />Laboratory of Cellular Metabolism, KU Leuven, Leuven, Belgium
| | - Liam McDonnell
- />Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
- />Fondazione Pisana per la Scienza ONLUS, Pisa, Italy
| | - Jouke Dijkstra
- />Division of Image Processing, Department of Radiology, LUMC, Leiden, The Netherlands
| | - Ron M. A. Heeren
- />FOM-Institute AMOLF, Amsterdam, The Netherlands
- />TI-COAST, Amsterdam, The Netherlands
- />M4I, The Maastricht MultiModal Molecular Imaging Institute, University of Maastricht, Maastricht, The Netherlands
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18
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Abdelmoula WM, Škrášková K, Balluff B, Carreira RJ, Tolner EA, Lelieveldt BPF, van der Maaten L, Morreau H, van den Maagdenberg AMJM, Heeren RMA, McDonnell LA, Dijkstra J. Automatic generic registration of mass spectrometry imaging data to histology using nonlinear stochastic embedding. Anal Chem 2014; 86:9204-11. [PMID: 25133861 DOI: 10.1021/ac502170f] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The combination of mass spectrometry imaging and histology has proven a powerful approach for obtaining molecular signatures from specific cells/tissues of interest, whether to identify biomolecular changes associated with specific histopathological entities or to determine the amount of a drug in specific organs/compartments. Currently there is no software that is able to explicitly register mass spectrometry imaging data spanning different ionization techniques or mass analyzers. Accordingly, the full capabilities of mass spectrometry imaging are at present underexploited. Here we present a fully automated generic approach for registering mass spectrometry imaging data to histology and demonstrate its capabilities for multiple mass analyzers, multiple ionization sources, and multiple tissue types.
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Affiliation(s)
- Walid M Abdelmoula
- Division of Image Processing, Department of Radiology, ‡Center for Proteomics and Metabolomics, §Department of Human Genetics, ∥Department of Neurology, and ⊥Department of Pathology, Leiden University Medical Center , 2333 ZA Leiden, The Netherlands
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19
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Abdelmoula WM, Carreira RJ, Shyti R, Balluff B, van Zeijl RJM, Tolner EA, Lelieveldt BFP, van den Maagdenberg AMJM, McDonnell LA, Dijkstra J. Automatic registration of mass spectrometry imaging data sets to the Allen brain atlas. Anal Chem 2014; 86:3947-54. [PMID: 24661141 DOI: 10.1021/ac500148a] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Mass spectrometry imaging holds great potential for understanding the molecular basis of neurological disease. Several key studies have demonstrated its ability to uncover disease-related biomolecular changes in rodent models of disease, even if highly localized or invisible to established histological methods. The high analytical reproducibility necessary for the biomedical application of mass spectrometry imaging means it is widely developed in mass spectrometry laboratories. However, many lack the expertise to correctly annotate the complex anatomy of brain tissue, or have the capacity to analyze the number of animals required in preclinical studies, especially considering the significant variability in sizes of brain regions. To address this issue, we have developed a pipeline to automatically map mass spectrometry imaging data sets of mouse brains to the Allen Brain Reference Atlas, which contains publically available data combining gene expression with brain anatomical locations. Our pipeline enables facile and rapid interanimal comparisons by first testing if each animal's tissue section was sampled at a similar location and enabling the extraction of the biomolecular signatures from specific brain regions.
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Affiliation(s)
- Walid M Abdelmoula
- Division of Image Processing, Department of Radiology, ‡Center for Proteomics and Metabolomics, §Department of Human Genetics, and ∥Department of Neurology, Leiden University Medical Center , 2333 ZA Leiden, the Netherlands
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20
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Abstract
Choroidal neovascularization (CNV) is a common manifestation of age-related macular degeneration (AMD). It is characterized by the growth of abnormal blood vessels in the choroidal layer causing blurring and deterioration of the vision. In late stages, these abnormal vessels can rupture the retinal layers causing complete loss of vision at the affected regions. Determining the CNV size and type in fluorescein angiograms is required for proper treatment and prognosis of the disease. Computer-aided methods for CNV segmentation is needed not only to reduce the burden of manual segmentation but also to reduce inter- and intraobserver variability. In this paper, we present a framework for segmenting CNV lesions based on parametric modeling of the intensity variation in fundus fluorescein angiograms. First, a novel model is proposed to describe the temporal intensity variation at each pixel in image sequences acquired by fluorescein angiography. The set of model parameters at each pixel are used to segment the image into regions of homogeneous parameters. Preliminary results on datasets from 21 patients with Wet-AMD show the potential of the method to segment CNV lesions in close agreement with the manual segmentation.
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Eldeeb SM, Abdelmoula WM, Shah SM, Fahmy AS. Quantitative assessment of age-related macular degeneration using parametric modeling of the leakage transfer function: preliminary results. Annu Int Conf IEEE Eng Med Biol Soc 2012; 2012:5967-5970. [PMID: 23367288 DOI: 10.1109/embc.2012.6347353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Age-related macular degeneration (AMD) is a major cause of blindness and visual impairment in older adults. The wet form of the disease is characterized by abnormal blood vessels forming a choroidal neovascular membrane (CNV), that result in destruction of normal architecture of the retina. Current evaluation and follow up of wet AMD include subjective evaluation of Fluorescein Angiograms (FA) to determine the activity of the lesion and monitor the progression or regression of the disease. However, this subjective evaluation prevents accurate monitoring of the disease progression or regression in response to a pharmacologic agent. In this work, we present a method that allows objective assessment of the activity of a CNV lesion which can be statistically compared across different patient and time points. The method is based on a hypothesis that the discrepancy in the time-intensity signals among the diseased and normal retinal areas are due to an implicit transfer function whose parameters can be used to characterize the retina. The method begins with parametric modeling of the temporal variation of the lesion and background intensities. Then, the values of the model parameters are used to evaluate the change in the activity of the disease. Preliminary results on five datasets show that the calculated parameters are highly correlated with the Visual Acuity (VA) of the patients.
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Affiliation(s)
- Safaa M Eldeeb
- Center for Informatics Science, Nile University, Cairo Egypt
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