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Luchicchi A, Muñoz‐Gonzalez G, Halperin ST, Strijbis E, van Dijk LHM, Foutiadou C, Uriac F, Bouman PM, Schouten MAN, Plemel J, 't Hart BA, Geurts JJG, Schenk GJ. Micro-diffusely abnormal white matter: An early multiple sclerosis lesion phase with intensified myelin blistering. Ann Clin Transl Neurol 2024; 11:973-988. [PMID: 38425098 PMCID: PMC11021636 DOI: 10.1002/acn3.52015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 01/03/2024] [Accepted: 01/25/2024] [Indexed: 03/02/2024] Open
Abstract
OBJECTIVE Multiple sclerosis (MS) is a chronic central nervous system disease whose white matter lesion origin remains debated. Recently, we reported subtle changes in the MS normal appearing white matter (NAWM), presenting with an increase in myelin blisters and myelin protein citrullination, which may recapitulate some of the prodromal degenerative processes involved in MS pathogenesis. Here, to clarify the relevance of these changes for subsequent MS myelin degeneration we explored their prevalence in WM regions characterized by subtly reduced myelination (dubbed as micro-diffusely abnormal white matter, mDAWM). METHODS We used an in-depth (immuno)histochemistry approach in 27 MS donors with histological presence of mDAWM and 5 controls. An antibody panel against degenerative markers was combined and the presence of myelin/axonal aberrations was analyzed and compared with the NAWM from the same cases/slices/regions. RESULTS mDAWM-defined areas exhibit ill-defined borders, no signs of Wallerian degeneration, and they associate with visible veins. Remarkably, such areas present with augmented myelin blister frequency, enhanced prevalence of polar myelin phospholipids, citrullination, and degradation of myelin basic protein (MBP) when compared with the NAWM. Furthermore, enhanced reactivity of microglia/macrophages against citrullinated MBP was also observed in this tissue. INTERPRETATION We report a new histologically defined early phase in MS lesion formation, namely mDAWM, which lacks signs of Wallerian pathology. These results support the prelesional nature of the mDAWM. We conceptualize that evolution to pathologically evident lesions comprises the previously documented imbalance of axo-myelinic units (myelin blistering) leading to their degeneration and immune system activation by released myelin components.
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Affiliation(s)
- Antonio Luchicchi
- Department of Anatomy and NeurosciencesAmsterdam University Medical Centers, location VU Medical Center, Amsterdam NeuroscienceAmsterdamthe Netherlands
- MS Centrum Amsterdam, Amsterdam University Medical Centers, location VU Medical CenterAmsterdamthe Netherlands
| | - Gema Muñoz‐Gonzalez
- Department of Anatomy and NeurosciencesAmsterdam University Medical Centers, location VU Medical Center, Amsterdam NeuroscienceAmsterdamthe Netherlands
- MS Centrum Amsterdam, Amsterdam University Medical Centers, location VU Medical CenterAmsterdamthe Netherlands
| | - Saar T. Halperin
- Department of Anatomy and NeurosciencesAmsterdam University Medical Centers, location VU Medical Center, Amsterdam NeuroscienceAmsterdamthe Netherlands
- MS Centrum Amsterdam, Amsterdam University Medical Centers, location VU Medical CenterAmsterdamthe Netherlands
| | - Eva Strijbis
- MS Centrum Amsterdam, Amsterdam University Medical Centers, location VU Medical CenterAmsterdamthe Netherlands
- Department of NeurologyAmsterdam University Medical Centers, location VU Medical CenterAmsterdamthe Netherlands
| | - Laura H. M. van Dijk
- Department of Anatomy and NeurosciencesAmsterdam University Medical Centers, location VU Medical Center, Amsterdam NeuroscienceAmsterdamthe Netherlands
| | - Chrisa Foutiadou
- Department of Anatomy and NeurosciencesAmsterdam University Medical Centers, location VU Medical Center, Amsterdam NeuroscienceAmsterdamthe Netherlands
| | - Florence Uriac
- Department of Anatomy and NeurosciencesAmsterdam University Medical Centers, location VU Medical Center, Amsterdam NeuroscienceAmsterdamthe Netherlands
| | - Piet M. Bouman
- Department of Anatomy and NeurosciencesAmsterdam University Medical Centers, location VU Medical Center, Amsterdam NeuroscienceAmsterdamthe Netherlands
- MS Centrum Amsterdam, Amsterdam University Medical Centers, location VU Medical CenterAmsterdamthe Netherlands
| | - Maxime A. N. Schouten
- Department of Anatomy and NeurosciencesAmsterdam University Medical Centers, location VU Medical Center, Amsterdam NeuroscienceAmsterdamthe Netherlands
- MS Centrum Amsterdam, Amsterdam University Medical Centers, location VU Medical CenterAmsterdamthe Netherlands
| | - Jason Plemel
- Department of NeuroscienceUniversity of AlbertaEdmontonAlbertaCanada
| | - Bert A. 't Hart
- Department of Anatomy and NeurosciencesAmsterdam University Medical Centers, location VU Medical Center, Amsterdam NeuroscienceAmsterdamthe Netherlands
- MS Centrum Amsterdam, Amsterdam University Medical Centers, location VU Medical CenterAmsterdamthe Netherlands
| | - Jeroen J. G. Geurts
- Department of Anatomy and NeurosciencesAmsterdam University Medical Centers, location VU Medical Center, Amsterdam NeuroscienceAmsterdamthe Netherlands
- MS Centrum Amsterdam, Amsterdam University Medical Centers, location VU Medical CenterAmsterdamthe Netherlands
| | - Geert J. Schenk
- Department of Anatomy and NeurosciencesAmsterdam University Medical Centers, location VU Medical Center, Amsterdam NeuroscienceAmsterdamthe Netherlands
- MS Centrum Amsterdam, Amsterdam University Medical Centers, location VU Medical CenterAmsterdamthe Netherlands
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Ancău M, Tanti GK, Butenschoen VM, Gempt J, Yakushev I, Nekolla S, Mühlau M, Scheunemann C, Heininger S, Löwe B, Löwe E, Baer S, Fischer J, Reiser J, Ayachit SS, Liesche-Starnecker F, Schlegel J, Matiasek K, Schifferer M, Kirschke JS, Misgeld T, Lueth T, Hemmer B. Validating a minipig model of reversible cerebral demyelination using human diagnostic modalities and electron microscopy. EBioMedicine 2024; 100:104982. [PMID: 38306899 PMCID: PMC10850420 DOI: 10.1016/j.ebiom.2024.104982] [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: 12/10/2022] [Revised: 01/11/2024] [Accepted: 01/12/2024] [Indexed: 02/04/2024] Open
Abstract
BACKGROUND Inflammatory demyelinating diseases of the central nervous system, such as multiple sclerosis, are significant sources of morbidity in young adults despite therapeutic advances. Current murine models of remyelination have limited applicability due to the low white matter content of their brains, which restricts the spatial resolution of diagnostic imaging. Large animal models might be more suitable but pose significant technological, ethical and logistical challenges. METHODS We induced targeted cerebral demyelinating lesions by serially repeated injections of lysophosphatidylcholine in the minipig brain. Lesions were amenable to follow-up using the same clinical imaging modalities (3T magnetic resonance imaging, 11C-PIB positron emission tomography) and standard histopathology protocols as for human diagnostics (myelin, glia and neuronal cell markers), as well as electron microscopy (EM), to compare against biopsy data from two patients. FINDINGS We demonstrate controlled, clinically unapparent, reversible and multimodally trackable brain white matter demyelination in a large animal model. De-/remyelination dynamics were slower than reported for rodent models and paralleled by a degree of secondary axonal pathology. Regression modelling of ultrastructural parameters (g-ratio, axon thickness) predicted EM features of cerebral de- and remyelination in human data. INTERPRETATION We validated our minipig model of demyelinating brain diseases by employing human diagnostic tools and comparing it with biopsy data from patients with cerebral demyelination. FUNDING This work was supported by the DFG under Germany's Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy, ID 390857198) and TRR 274/1 2020, 408885537 (projects B03 and Z01).
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Affiliation(s)
- Mihai Ancău
- Department of Neurology, Klinikum Rechts der Isar, School of Medicine and Health, Technical University of Munich, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; Institute of Neuronal Cell Biology, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Goutam Kumar Tanti
- Department of Neurology, Klinikum Rechts der Isar, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Vicki Marie Butenschoen
- Department of Neurosurgery, Klinikum Rechts der Isar, School of Medicine and Health, Technical University of Munich, Germany
| | - Jens Gempt
- Department of Neurosurgery, Klinikum Rechts der Isar, School of Medicine and Health, Technical University of Munich, Germany; Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Igor Yakushev
- Department of Nuclear Medicine, Klinikum Rechts der Isar, School of Medicine and Health, Technical University of Munich, Germany
| | - Stephan Nekolla
- Department of Nuclear Medicine, Klinikum Rechts der Isar, School of Medicine and Health, Technical University of Munich, Germany
| | - Mark Mühlau
- Department of Neurology, Klinikum Rechts der Isar, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Christian Scheunemann
- Institute of Micro Technology and Medical Device Technology, Technical University of Munich, Garching, Germany; Ergosurg GmbH, Ismaning, Germany
| | - Sebastian Heininger
- Institute of Micro Technology and Medical Device Technology, Technical University of Munich, Garching, Germany; Ergosurg GmbH, Ismaning, Germany
| | - Benjamin Löwe
- Institute of Micro Technology and Medical Device Technology, Technical University of Munich, Garching, Germany; Ergosurg GmbH, Ismaning, Germany
| | - Erik Löwe
- Institute of Micro Technology and Medical Device Technology, Technical University of Munich, Garching, Germany; Ergosurg GmbH, Ismaning, Germany
| | - Silke Baer
- Centre for Preclinical Research, Department of Veterinary Medicine, Technical University of Munich, Munich, Germany
| | - Johannes Fischer
- Centre for Preclinical Research, Department of Veterinary Medicine, Technical University of Munich, Munich, Germany
| | - Judith Reiser
- Centre for Preclinical Research, Department of Veterinary Medicine, Technical University of Munich, Munich, Germany
| | - Sai S Ayachit
- Department of Neurology, Klinikum Rechts der Isar, School of Medicine and Health, Technical University of Munich, Munich, Germany; Graduate School of Systemic Neurosciences, Ludwig Maximilian University of Munich, Germany
| | - Friederike Liesche-Starnecker
- Department of Neuropathology, Institute of Pathology, Technical University of Munich School of Medicine, Munich, Germany; Medical Faculty, Institute of Pathology and Molecular Diagnostics, University of Augsburg, Augsburg, Germany
| | - Jürgen Schlegel
- Department of Neuropathology, Institute of Pathology, Technical University of Munich School of Medicine, Munich, Germany
| | - Kaspar Matiasek
- Clinical and Comparative Neuropathology, Centre for Clinical Veterinary Medicine, Ludwig-Maximilians-University, Munich, Germany
| | - Martina Schifferer
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Jan S Kirschke
- Department of Neuroradiology, Klinikum Rechts der Isar, School of Medicine and Health, Technical University of Munich, Germany
| | - Thomas Misgeld
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; Institute of Neuronal Cell Biology, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Tim Lueth
- Institute of Micro Technology and Medical Device Technology, Technical University of Munich, Garching, Germany; Ergosurg GmbH, Ismaning, Germany
| | - Bernhard Hemmer
- Department of Neurology, Klinikum Rechts der Isar, School of Medicine and Health, Technical University of Munich, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.
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Li Z, Feng R, Liu Q, Feng J, Lao G, Zhang M, Li J, Zhang Y, Wei H. APART-QSM: an improved sub-voxel quantitative susceptibility mapping for susceptibility source separation using an iterative data fitting method. Neuroimage 2023; 274:120148. [PMID: 37127191 DOI: 10.1016/j.neuroimage.2023.120148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 02/06/2023] [Accepted: 04/28/2023] [Indexed: 05/03/2023] Open
Abstract
The brain tissue phase contrast in MRI sequences reflects the spatial distributions of multiple substances, such as iron, myelin, calcium, and proteins. These substances with paramagnetic and diamagnetic susceptibilities often colocalize in one voxel in brain regions. Both opposing susceptibilities play vital roles in brain development and neurodegenerative diseases. Conventional QSM methods only provide voxel-averaged susceptibility value and cannot disentangle intravoxel susceptibilities with opposite signs. Advanced susceptibility imaging methods have been recently developed to distinguish the contributions of opposing susceptibility sources for QSM. The basic concept of separating paramagnetic and diamagnetic susceptibility proportions is to include the relaxation rate R2* with R2' in QSM. The magnitude decay kernel, describing the proportionality coefficient between R2' and susceptibility, is an essential reconstruction coefficient for QSM separation methods. In this study, we proposed a more comprehensive complex signal model that describes the relationship between 3D GRE signal and the contributions of paramagnetic and diamagnetic susceptibility to the frequency shift and R2* relaxation. The algorithm is implemented as a constrained minimization problem in which the voxel-wise magnitude decay kernel and sub-voxel susceptibilities are determined alternately in each iteration until convergence. The calculated voxel-wise magnitude decay kernel could realistically model the relationship between the R2' relaxation and the volume susceptibility. Thus, the proposed method effectively prevents the errors of the magnitude decay kernel from propagating to the final susceptibility separation reconstruction. Phantom studies, ex vivo macaque brain experiments, and in vivo human brain imaging studies were conducted to evaluate the ability of the proposed method to distinguish paramagnetic and diamagnetic susceptibility sources. The results demonstrate that the proposed method provides state-of-the-art performances for quantifying brain iron and myelin compared to previous QSM separation methods. Our results show that the proposed method has the potential to simultaneously quantify whole brain iron and myelin during brain development and aging. The proposed model was also deployed with multiple-orientation complex GRE data input measurements, resulting in high-quality QSM separation maps with more faithful tissue delineation between brain structures compared to those reconstructed by single-orientation QSM separation methods.
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Affiliation(s)
- Zhenghao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ruimin Feng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Qiangqiang Liu
- Department of Neurosurgery, Clinical Neuroscience Center Comprehensive Epilepsy Unit, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jie Feng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Guoyan Lao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ming Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jun Li
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Yuyao Zhang
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Hongjiang Wei
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
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