1
|
Ligneul C, Najac C, Döring A, Beaulieu C, Branzoli F, Clarke WT, Cudalbu C, Genovese G, Jbabdi S, Jelescu I, Karampinos D, Kreis R, Lundell H, Marjańska M, Möller HE, Mosso J, Mougel E, Posse S, Ruschke S, Simsek K, Szczepankiewicz F, Tal A, Tax C, Oeltzschner G, Palombo M, Ronen I, Valette J. Diffusion-weighted MR spectroscopy: Consensus, recommendations, and resources from acquisition to modeling. Magn Reson Med 2024; 91:860-885. [PMID: 37946584 DOI: 10.1002/mrm.29877] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 07/18/2023] [Accepted: 09/08/2023] [Indexed: 11/12/2023]
Abstract
Brain cell structure and function reflect neurodevelopment, plasticity, and aging; and changes can help flag pathological processes such as neurodegeneration and neuroinflammation. Accurate and quantitative methods to noninvasively disentangle cellular structural features are needed and are a substantial focus of brain research. Diffusion-weighted MRS (dMRS) gives access to diffusion properties of endogenous intracellular brain metabolites that are preferentially located inside specific brain cell populations. Despite its great potential, dMRS remains a challenging technique on all levels: from the data acquisition to the analysis, quantification, modeling, and interpretation of results. These challenges were the motivation behind the organization of the Lorentz Center workshop on "Best Practices & Tools for Diffusion MR Spectroscopy" held in Leiden, the Netherlands, in September 2021. During the workshop, the dMRS community established a set of recommendations to execute robust dMRS studies. This paper provides a description of the steps needed for acquiring, processing, fitting, and modeling dMRS data, and provides links to useful resources.
Collapse
Affiliation(s)
- Clémence Ligneul
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Chloé Najac
- C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - André Döring
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Christian Beaulieu
- Departments of Biomedical Engineering and Radiology, University of Alberta, Alberta, Edmonton, Canada
| | - Francesca Branzoli
- Paris Brain Institute-ICM, Sorbonne University, UMR S 1127, Inserm U 1127, CNRS UMR 7225, Paris, France
| | - William T Clarke
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Cristina Cudalbu
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Guglielmo Genovese
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minnesota, Minneapolis, USA
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Ileana Jelescu
- Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Dimitrios Karampinos
- Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany
| | - Roland Kreis
- MR Methodology, Department for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
- Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Henrik Lundell
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager anf Hvidovre, Hvidovre, Denmark
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Małgorzata Marjańska
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minnesota, Minneapolis, USA
| | - Harald E Möller
- NMR Methods & Development Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Jessie Mosso
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- LIFMET, EPFL, Lausanne, Switzerland
| | - Eloïse Mougel
- Université Paris-Saclay, CEA, CNRS, MIRCen, Laboratoires des Maladies Neurodégénératives, Fontenay-aux-Roses, France
| | - Stefan Posse
- Department of Neurology, University of New Mexico School of Medicine, New Mexico, Albuquerque, USA
- Department of Physics and Astronomy, University of New Mexico School of Medicine, New Mexico, Albuquerque, USA
| | - Stefan Ruschke
- Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany
| | - Kadir Simsek
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK
| | | | - Assaf Tal
- Department of Chemical and Biological Physics, The Weizmann Institute of Science, Rehovot, Israel
| | - Chantal Tax
- University Medical Center Utrecht, Utrecht, The Netherlands
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Maryland, Baltimore, USA
- F. M. Kirby Center for Functional Brain Imaging, Kennedy Krieger Institute, Maryland, Baltimore, USA
| | - Marco Palombo
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK
| | - Itamar Ronen
- Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton, UK
| | - Julien Valette
- Université Paris-Saclay, CEA, CNRS, MIRCen, Laboratoires des Maladies Neurodégénératives, Fontenay-aux-Roses, France
| |
Collapse
|
2
|
Chen M, Zhou X, Cai H, Li D, Song C, You H, Ma R, Dong Z, Peng Z, Feng ST. Evaluation of Hypoxia in Hepatocellular Carcinoma Using Quantitative MRI: Significances, Challenges, and Advances. J Magn Reson Imaging 2023; 58:12-25. [PMID: 36971442 DOI: 10.1002/jmri.28694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 03/09/2023] [Accepted: 03/10/2023] [Indexed: 03/29/2023] Open
Abstract
This review aimed to perform a scoping review of promising MRI methods in assessing tumor hypoxia in hepatocellular carcinoma (HCC). The hypoxic microenvironment and upregulated hypoxic metabolism in HCC are determining factors of poor prognosis, increased metastatic potential, and resistance to chemotherapy and radiotherapy. Assessing hypoxia in HCC is essential for personalized therapy and predicting prognoses. Oxygen electrodes, protein markers, optical imaging, and positron emission tomography can evaluate tumor hypoxia. These methods lack clinical applicability because of invasiveness, tissue depth, and radiation exposure. MRI methods, including blood oxygenation level-dependent, dynamic contrast-enhanced MRI, diffusion-weighted imaging, MRI spectroscopy, chemical exchange saturation transfer MRI, and multinuclear MRI, are promising noninvasive methods that evaluate the hypoxic microenvironment by observing biochemical processes in vivo, which may inform on therapeutic options. This review summarizes the recent challenges and advances in MRI techniques for assessing hypoxia in HCC and highlights the potential of MRI methods for examining the hypoxic microenvironment via specific metabolic substrates and pathways. Although the utilization of MRI methods for evaluating hypoxia in patients with HCC is increasing, rigorous validation is needed in order to translate these MRI methods into clinical use. Due to the limited sensitivity and specificity of current quantitative MRI methods, their acquisition and analysis protocols require further improvement. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 4.
Collapse
Affiliation(s)
- Meicheng Chen
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Province Guangdong, People's Republic of China
| | - Xiaoqi Zhou
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Province Guangdong, People's Republic of China
| | - Huasong Cai
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Province Guangdong, People's Republic of China
| | - Di Li
- Department of Medical Ultrasonics, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Province Guangdong, People's Republic of China
| | - Chenyu Song
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Province Guangdong, People's Republic of China
| | - Huayu You
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Province Guangdong, People's Republic of China
| | - Ruixia Ma
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Province Guangdong, People's Republic of China
| | - Zhi Dong
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Province Guangdong, People's Republic of China
| | - Zhenpeng Peng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Province Guangdong, People's Republic of China
| | - Shi-Ting Feng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Province Guangdong, People's Republic of China
| |
Collapse
|
3
|
Abdul Rashid K, Ibrahim K, Wong JHD, Mohd Ramli N. Lipid Alterations in Glioma: A Systematic Review. Metabolites 2022; 12:metabo12121280. [PMID: 36557318 PMCID: PMC9783089 DOI: 10.3390/metabo12121280] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/08/2022] [Accepted: 11/29/2022] [Indexed: 12/23/2022] Open
Abstract
Gliomas are highly lethal tumours characterised by heterogeneous molecular features, producing various metabolic phenotypes leading to therapeutic resistance. Lipid metabolism reprogramming is predominant and has contributed to the metabolic plasticity in glioma. This systematic review aims to discover lipids alteration and their biological roles in glioma and the identification of potential lipids biomarker. This systematic review was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Extensive research articles search for the last 10 years, from 2011 to 2021, were conducted using four electronic databases, including PubMed, Web of Science, CINAHL and ScienceDirect. A total of 158 research articles were included in this study. All studies reported significant lipid alteration between glioma and control groups, impacting glioma cell growth, proliferation, drug resistance, patients' survival and metastasis. Different lipids demonstrated different biological roles, either beneficial or detrimental effects on glioma. Notably, prostaglandin (PGE2), triacylglycerol (TG), phosphatidylcholine (PC), and sphingosine-1-phosphate play significant roles in glioma development. Conversely, the most prominent anti-carcinogenic lipids include docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), and vitamin D3 have been reported to have detrimental effects on glioma cells. Furthermore, high lipid signals were detected at 0.9 and 1.3 ppm in high-grade glioma relative to low-grade glioma. This evidence shows that lipid metabolisms were significantly dysregulated in glioma. Concurrent with this knowledge, the discovery of specific lipid classes altered in glioma will accelerate the development of potential lipid biomarkers and enhance future glioma therapeutics.
Collapse
Affiliation(s)
- Khairunnisa Abdul Rashid
- Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Kamariah Ibrahim
- Department of Biomedical Science, Faculty of Medicine, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Jeannie Hsiu Ding Wong
- Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Norlisah Mohd Ramli
- Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur 50603, Malaysia
- Correspondence: ; Tel.: +60-379673238
| |
Collapse
|
4
|
Suh CH, Kim HS, Park JE, Jung SC, Choi CG, Woo DC, Lee HB, Kim SJ. Comparative Value of 2-Hydroxyglutarate-to-Lipid and Lactate Ratio versus 2-Hydroxyglutarate Concentration on MR Spectroscopic Images for Predicting Isocitrate Dehydrogenase Mutation Status in Gliomas. Radiol Imaging Cancer 2020; 2:e190083. [PMID: 33778723 DOI: 10.1148/rycan.2020190083] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 04/17/2020] [Accepted: 05/05/2020] [Indexed: 11/11/2022]
Abstract
Purpose To compare the ability of 2-hydroxyglutarate (2HG)-to-lipid and lactate (2HG/[lipid + lactate]) ratio with the ability of 2HG concentration alone to predict the isocitrate dehydrogenase (IDH) mutation status in patients with glioma. Materials and Methods In this retrospective study, consecutive patients with histopathologically proven glioma were enrolled between July 2016 and February 2019. A total of 79 patients were enrolled (mean age, 44 years; 49 men). The 2HG concentration and other MR spectroscopic parameters were measured by single-voxel point-resolved spectroscopy before surgery. The diagnostic performance of the 2HG concentration and 2HG/(lipid + lactate) ratio were calculated. Internal validation was assessed by the bootstrap approach with 1000 bootstrap resamples. Differences in the predictive accuracy of 2HG/(lipid + lactate) ratio and 2HG concentration were determined by calculating the integrated discrimination improvement. The diagnostic accuracy (sensitivity, specificity, and area under the receiver operating characteristic curve [AUC]) of these measures was also compared separately in patients with glioblastomas and patients with lower-grade gliomas. Results Of the 79 enrolled patients, 28 had IDH mutations and 51 had wild-type IDH. The sensitivity, specificity, and AUC of 2HG concentration for predicting IDH-mutant gliomas were 89% (25 of 28), 67% (34 of 51), and 0.80 (95% confidence interval [CI]: 0.70, 0.88; C statistic, 0.80), respectively. The sensitivity, specificity, and AUC of the 2HG/(lipid + lactate) ratio for predicting IDH-mutant gliomas were 79% (22 of 28), 92% (47 of 51), and 0.90 (95% CI: 0.81, 0.96; C statistics, 0.90), respectively. The optimal cutoff value for the 2HG/(lipid + lactate) ratio was 0.63. The 2HG/(lipid + lactate) ratio was significantly better for predicting IDH mutation status than the 2HG concentration alone (P < .01). In glioblastoma, the 2HG/(lipid + lactate) ratio was also better for predicting IDH mutations than the 2HG concentration alone, with borderline significance (P = .052). In lower-grade glioma, the 2HG/(lipid + lactate) ratio and the 2HG concentration showed comparable diagnostic performance (P = .72). Conclusion The 2HG/(lipid + lactate) ratio is more accurate for predicting IDH mutation status in patients with glioma than the 2HG concentration alone.Keywords: Brain/Brain Stem, CNS, MR-Imaging, MR-Spectroscopy, Neoplasms-Primary, Neuro-Oncology© RSNA, 2020.
Collapse
Affiliation(s)
- Chong Hyun Suh
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Olympic-ro 33, Seoul 05505, Republic of Korea (C.H.S., H.S.K., J.E.P., S.C.J., C.G.C., H.B.L., S.J.K.), and Bioimaging Center, Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea (D.C.W.)
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Olympic-ro 33, Seoul 05505, Republic of Korea (C.H.S., H.S.K., J.E.P., S.C.J., C.G.C., H.B.L., S.J.K.), and Bioimaging Center, Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea (D.C.W.)
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Olympic-ro 33, Seoul 05505, Republic of Korea (C.H.S., H.S.K., J.E.P., S.C.J., C.G.C., H.B.L., S.J.K.), and Bioimaging Center, Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea (D.C.W.)
| | - Seung Chai Jung
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Olympic-ro 33, Seoul 05505, Republic of Korea (C.H.S., H.S.K., J.E.P., S.C.J., C.G.C., H.B.L., S.J.K.), and Bioimaging Center, Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea (D.C.W.)
| | - Choong Gon Choi
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Olympic-ro 33, Seoul 05505, Republic of Korea (C.H.S., H.S.K., J.E.P., S.C.J., C.G.C., H.B.L., S.J.K.), and Bioimaging Center, Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea (D.C.W.)
| | - Dong-Cheol Woo
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Olympic-ro 33, Seoul 05505, Republic of Korea (C.H.S., H.S.K., J.E.P., S.C.J., C.G.C., H.B.L., S.J.K.), and Bioimaging Center, Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea (D.C.W.)
| | - Ho Beom Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Olympic-ro 33, Seoul 05505, Republic of Korea (C.H.S., H.S.K., J.E.P., S.C.J., C.G.C., H.B.L., S.J.K.), and Bioimaging Center, Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea (D.C.W.)
| | - Sang Joon Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Olympic-ro 33, Seoul 05505, Republic of Korea (C.H.S., H.S.K., J.E.P., S.C.J., C.G.C., H.B.L., S.J.K.), and Bioimaging Center, Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea (D.C.W.)
| |
Collapse
|
5
|
Singh J, Suh EH, Sharma G, Khemtong C, Sherry AD, Kovacs Z. Probing carbohydrate metabolism using hyperpolarized 13 C-labeled molecules. NMR IN BIOMEDICINE 2019; 32:e4018. [PMID: 30474153 PMCID: PMC6579721 DOI: 10.1002/nbm.4018] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Revised: 08/03/2018] [Accepted: 08/11/2018] [Indexed: 05/05/2023]
Abstract
Glycolysis is a fundamental metabolic process in all organisms. Anomalies in glucose metabolism are linked to various pathological conditions. In particular, elevated aerobic glycolysis is a characteristic feature of rapidly growing cells. Glycolysis and the closely related pentose phosphate pathway can be monitored in real time by hyperpolarized 13 C-labeled metabolic substrates such as 13 C-enriched, deuterated D-glucose derivatives, [2-13 C]-D-fructose, [2-13 C] dihydroxyacetone, [1-13 C]-D-glycerate, [1-13 C]-D-glucono-δ-lactone and [1-13 C] pyruvate in healthy and diseased tissues. Elevated glycolysis in tumors (the Warburg effect) was also successfully imaged using hyperpolarized [U-13 C6 , U-2 H7 ]-D-glucose, while the size of the preexisting lactate pool can be measured by 13 C MRS and/or MRI with hyperpolarized [1-13 C]pyruvate. This review summarizes the application of various hyperpolarized 13 C-labeled metabolites to the real-time monitoring of glycolysis and related metabolic processes in normal and diseased tissues.
Collapse
Affiliation(s)
- Jaspal Singh
- Advanced Imaging Research Center, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Eul Hyun Suh
- Advanced Imaging Research Center, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Gaurav Sharma
- Advanced Imaging Research Center, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Chalermchai Khemtong
- Advanced Imaging Research Center, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - A. Dean Sherry
- Advanced Imaging Research Center, The University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Chemistry and Biochemistry, The University of Texas at Dallas, Richardson, TX, USA
| | - Zoltan Kovacs
- Advanced Imaging Research Center, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| |
Collapse
|
6
|
Landheer K, Schulte RF, Treacy MS, Swanberg KM, Juchem C. Theoretical description of modern1H in Vivo magnetic resonance spectroscopic pulse sequences. J Magn Reson Imaging 2019; 51:1008-1029. [DOI: 10.1002/jmri.26846] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Revised: 06/14/2019] [Accepted: 06/17/2019] [Indexed: 01/20/2023] Open
Affiliation(s)
- Karl Landheer
- Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science New York New York USA
| | | | - Michael S. Treacy
- Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science New York New York USA
| | - Kelley M. Swanberg
- Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science New York New York USA
| | - Christoph Juchem
- Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science New York New York USA
- Radiology, Columbia University College of Physicians and Surgeons New York New York USA
| |
Collapse
|
7
|
Martano G, Borroni EM, Lopci E, Cattaneo MG, Mattioli M, Bachi A, Decimo I, Bifari F. Metabolism of Stem and Progenitor Cells: Proper Methods to Answer Specific Questions. Front Mol Neurosci 2019; 12:151. [PMID: 31249511 PMCID: PMC6584756 DOI: 10.3389/fnmol.2019.00151] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Accepted: 05/28/2019] [Indexed: 01/01/2023] Open
Abstract
Stem cells can stay quiescent for a long period of time or proliferate and differentiate into multiple lineages. The activity of stage-specific metabolic programs allows stem cells to best adapt their functions in different microenvironments. Specific cellular phenotypes can be, therefore, defined by precise metabolic signatures. Notably, not only cellular metabolism describes a defined cellular phenotype, but experimental evidence now clearly indicate that also rewiring cells towards a particular cellular metabolism can drive their cellular phenotype and function accordingly. Cellular metabolism can be studied by both targeted and untargeted approaches. Targeted analyses focus on a subset of identified metabolites and on their metabolic fluxes. In addition, the overall assessment of the oxygen consumption rate (OCR) gives a measure of the overall cellular oxidative metabolism and mitochondrial function. Untargeted approach provides a large-scale identification and quantification of the whole metabolome with the aim to describe a metabolic fingerprinting. In this review article, we overview the methodologies currently available for the study of invitro stem cell metabolism, including metabolic fluxes, fingerprint analyses, and single-cell metabolomics. Moreover, we summarize available approaches for the study of in vivo stem cell metabolism. For all of the described methods, we highlight their specificities and limitations. In addition, we discuss practical concerns about the most threatening steps, including metabolic quenching, sample preparation and extraction. A better knowledge of the precise metabolic signature defining specific cell population is instrumental to the design of novel therapeutic strategies able to drive undifferentiated stem cells towards a selective and valuable cellular phenotype.
Collapse
Affiliation(s)
| | - Elena Monica Borroni
- Humanitas Clinical and Research Center, Rozzano, Italy.,Department of Medical Biotechnology and Translational Medicine, University of Milan, Milan, Italy
| | - Egesta Lopci
- Nuclear Medicine Unit, Humanitas Clinical and Research Hospital-IRCCS, Rozzano, Italy
| | - Maria Grazia Cattaneo
- Department of Medical Biotechnology and Translational Medicine, University of Milan, Milan, Italy
| | - Milena Mattioli
- Laboratory of Cell Metabolism and Regenerative Medicine, Department of Medical Biotechnology and Translational Medicine, University of Milan, Milan, Italy
| | - Angela Bachi
- IFOM-FIRC Institute of Molecular Oncology, Milan, Italy
| | - Ilaria Decimo
- Laboratory of Pharmacology, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
| | - Francesco Bifari
- Laboratory of Cell Metabolism and Regenerative Medicine, Department of Medical Biotechnology and Translational Medicine, University of Milan, Milan, Italy
| |
Collapse
|
8
|
Moss HG, Jenkins DD, Yazdani M, Brown TR. Identifying the translational complexity of magnetic resonance spectroscopy in neonates and infants. NMR IN BIOMEDICINE 2019; 32:e4089. [PMID: 30924565 PMCID: PMC6593752 DOI: 10.1002/nbm.4089] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Revised: 02/04/2019] [Accepted: 02/07/2019] [Indexed: 06/09/2023]
Abstract
Little attention has been paid to relating MRS outputs of vendor-supplied platforms to those from research software. This comparison is crucial to advance MRS as a clinical prognostic tool for disease or injury, recovery, and outcome. The work presented here investigates the agreement between metabolic ratios reported from vendor-provided and LCModel fitting algorithms using MRS data obtained on Siemens 3 T TIM Trio and 3 T Skyra MRI scanners in a total of 55 premature infants and term neonates with hypoxic ischemic encephalopathy (HIE). We compared peak area ratios in single voxels placed in basal ganglia (BG) and frontal white matter (WM) using standard PRESS (TE = 30 ms and 270 ms) and STEAM (TE = 20 ms) MRS sequences at multiple times after birth from 5 to 60 days. A total of 74 scans met quality standards for inclusion, reflecting a spectrum of neonatal disease and several months of early infant development. For the long TE PRESS sequence, N-acetylaspartate (NAA) and Choline (Cho) ratios to Creatine (Cr) correlated strongly between LCModel and vendor-supplied software in the BG. For shorter TEs, the ratios of NAA/Cr and Cho/Cr were more closely related using STEAM at TE = 20 ms in BG and WM, which was significantly better than using PRESS at TE = 30 ms in the BG of HIE infants. At short TEs, however, it is still unclear which MRS sequence, STEAM or PRESS, is superior and thus more work is required in this regard for translating research-generated MRS ratios to clinical diagnosis and prognostication, and unlocking the potential of MRS for in vivo metabolomics. MRS at both long and short TEs is desirable for standard metabolites such as NAA, Cho and Cr, along with important lower concentration metabolites such as myo-inositol and glutathione.
Collapse
Affiliation(s)
- Hunter G. Moss
- Department of RadiologyMedical University of South CarolinaCharlestonSouth Carolina
| | - Dorothea D. Jenkins
- Department of PediatricsMedical University of South CarolinaCharlestonSouth Carolina
| | - Milad Yazdani
- Department of RadiologyMedical University of South CarolinaCharlestonSouth Carolina
| | - Truman R. Brown
- Department of RadiologyMedical University of South CarolinaCharlestonSouth Carolina
| |
Collapse
|
9
|
Landheer K, Schulte R, Geraghty B, Hanstock C, Chen AP, Cunningham CH, Graham SJ. Diffusion-weighted J-resolved spectroscopy. Magn Reson Med 2016; 78:1235-1245. [PMID: 27797114 DOI: 10.1002/mrm.26514] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2016] [Revised: 09/26/2016] [Accepted: 09/26/2016] [Indexed: 12/19/2022]
Abstract
PURPOSE To develop a novel diffusion-weighted magnetic resonance spectroscopy (DW-MRS) technique in conjunction with J-resolved spatially localized spectroscopy (JPRESS) to measure the apparent diffusion coefficients (ADCs) of brain metabolites beyond N-acetylaspartic acid (NAA), creatine (Cr), and choline (Cho) at 3T. This technique will be useful to probe tissue microstructures in vivo, as the various metabolites have different physiological characteristics. METHODS Two JPRESS spectra were collected (high b-value and low b-value), and the ADCs of 16 different metabolites were estimated. Two analysis pipelines were developed: 1) a 2D pipeline that uses ProFit software to extract ADCs from metabolites not typically accessible at 3T and 2) a 1D pipeline that uses TARQUIN software to extract the metabolite concentrations from each line in the 2D dataset, allowing for scaling as well as validation. RESULTS The ADCs of 16 different metabolites were estimated from within six subjects in parietal white matter. There was excellent agreement between the results obtained from the 1D and 2D pipelines for NAA, Cr, and Cho. CONCLUSION The proposed technique provided consistent estimates for the ADCs of NAA, Cr, Cho, glutamate + glutamine, and myo-inositol in all subjects and additionally glutathione and scyllo-inositol in all but one subject. Magn Reson Med 78:1235-1245, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
Collapse
Affiliation(s)
- Karl Landheer
- Department of Medical Biophysics, University of Toronto, Ontario, Canada.,Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | | | - Ben Geraghty
- Department of Medical Biophysics, University of Toronto, Ontario, Canada.,Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Christopher Hanstock
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | | | - Charles H Cunningham
- Department of Medical Biophysics, University of Toronto, Ontario, Canada.,Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Simon J Graham
- Department of Medical Biophysics, University of Toronto, Ontario, Canada.,Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
| |
Collapse
|