Multiple sclerosis (MS) is a debilitating inflammatory demyelinating disorder affecting millions worldwide (Reich et al., 2018). MS causes dynamic changes to myelin in the central nervous system, including the quintessential focal inflammatory destruction of myelin, as well as the phenomenon of remyelination that can follow the demyelination (Lubetzki et al., 2020; Brown et al., 2014; Chari, 2007; Lassmann et al., 1997). Demyelinated axons are susceptible to persistent damage and neurological dysfunction in MS; therefore, remyelination is a crucial aspect of tissue repair, and as such represents an important therapeutic target (Kremer et al., 2019). Most knowledge about remyelination in MS derives from postmortem studies using histochemistry and electron microscopy. This is because investigating remyelination in vivo in real time is limited by imperfect discrimination on neuroimaging modalities such as magnetic resonance imaging (MRI). Furthermore, in human beings, where collecting serial short-interval scans is challenging, it is difficult to detect and track the dynamic occurrence of remyelination. Therefore, to investigate the pathobiology of remyelination in the context of focal inflammatory demyelination, a reliable preclinical model is needed to develop techniques that can then be applied clinically.

Rodent models have been widely used to investigate various aspects of the pathobiology of demyelination. However, while toxin models in mice, including the lysolecithin and cuprizone models, display demyelination and even remyelination, they do not recapitulate the recruitment of peripherally derived adaptive immune cells that occurs in MS, which can potentially confound the MRI signal (Kipp et al., 2012; Kipp et al., 2017). Conversely, rodent experimental autoimmune encephalomyelitis (EAE) models, though driven by an immune response, are often neither focally nor profoundly demyelinating and mostly impair the spinal cord. There is no known rodent model that is characterized by multifocal inflammatory demyelination in the brain that resembles MS and is disseminated in both space and time.

However, EAE in the common marmoset (Callithrix jacchus) is a well-recognized translational model that serves as a bridge between the rodent EAE and human MS (Jagessar et al., 2016; Kap et al., 2010; Kap et al., 2016). Not only does EAE resemble MS white matter lesions (WML) both radiologically and pathologically (Sati et al., 2012; Maggi et al., 2017), but marmoset WML spontaneously remyelinate (Lee et al., 2019), as occurs in MS.

Prior studies demonstrated that certain signal changes in MRI, such as magnetization transfer ratio (MTR), correlate with demyelination and remyelination in MS lesions (Chen et al., 2008; Chen et al., 2013; Filippi et al., 2012; Absinta et al., 2016; Laule and Moore, 2018). This has also been investigated in animal models, albeit mainly in rodent cortex (Yano et al., 2018; Schmierer et al., 2010) rather than white matter. It has also been demonstrated that partial remyelination can occur and can be localized either to specific parts of the lesion (most commonly the lesion edge) or to the whole lesion (Filippi et al., 2019; Patrikios et al., 2006).

Here, we studied focal WML in marmoset EAE. We utilized serial in vivo MRI, mainly involving proton density-weighted (PDw), T1-weighted (T1w) before and after intravenous injection of a gadolinium chelate, and MTR sequences, to age and characterize lesions and predict remyelination. We further analyzed WML histopathology, focusing on myelin lipids and proteins as well as mature oligodendrocytes and their precursors, to compare and study the reliability of using various in vivo MRI sequences to predict remyelination in the context of recovery from inflammatory demyelination.

In this study, we determined whether high-resolution, serial, in vivo conventional PDw MRI can effectively predict remyelination status in focal WML of marmoset EAE, a relatively faithful MS model. We found that remyelination is relatively common in this model: four of the six marmosets studied had remyelinated lesions on either MRI or histopathology, and nearly half (18) of the 40 lesions identified on MRI were predicted to be remyelinated. In people, WML repair is an age-dependent process that is variable across patients and clinical disease classification (Chari, 2007; Lassmann et al., 1997; Patrikios et al., 2006). Interestingly, the two animals that did not show remyelination by in vivo MRI, M#5 and M#6, had the shortest experimental duration (16–18 weeks, compared to 40–61 weeks for the other four animals), suggesting more aggressive lesions and/or insufficient time for remyelination.

Our characterization of remyelinated lesions showed low residual levels of inflammation (microglia/macrophages) and an extensive but incomplete (using NAWM as reference) repopulation of mature oligodendrocytes, specifically a higher number of ASPA+/Olig2+ cells in the lesion core compared to chronic, at least partially demyelinated lesions. Lower percentage of unstained area on LFB and PLP slides (<6%), consistent with data from MS (Chari, 2007; Lucchinetti et al., 2000; Lucchinetti et al., 1999; Patani et al., 2007), was also observed in remyelinated lesions. Remyelinated lesions were nearly isointense to healthy white matter at the terminal in vivo PDw MRI timepoint and showed no enhancement on post-gadolinium T1w images.

Interestingly, one lesion appeared remyelinated on PLP with less than 6% of unstained area but demyelinated on LFB (82% of unstained area) and hyperintense on PDw MRI. This result suggests potentially distinct timelines for reconstitution of myelin lipids vs. protein in the context of remyelination (Chari, 2007). As previously described, MRI seems to be more sensitive to content of myelin lipid than of myelin protein (Leuze et al., 2017).

As expected, assessment of early active and chronic, at least partially demyelinated lesions showed fewer mature oligodendrocytes in the lesion core and edge as well as a percentage of demyelinated area >20% on LFB and PLP staining. Both early active and chronic, at least partially demyelinated were hyperintense on terminal in vivo PDw MRI. Taken together, these data suggest that in marmoset EAE, lesions that initially present as hyperintense foci on PDw MRI and subsequently return toward isointensity over time are undergoing remyelination. Our data further suggest that signal intensity time courses, as well as lesion size, can be used to infer lesion status in the setting of early lesion development and recovery, despite the fact that PDw MRI is also known to be sensitive to tissue-level factors other than myelin.

The time course of marmoset EAE lesion development and repair is relatively stereotyped, as confirmed here. Previous work from our group has shown that once lesions form, the period of demyelination lasts around 6 weeks (Lee et al., 2019), consistent with data from this study (4–7 weeks; see Figure 4). The time course of marmoset remyelination is somewhat slower than toxin-induced demyelinating models in rodents, further raising potential relevance of the marmoset EAE model for preclinical testing of putative remyelination therapies in MS (McMurran et al., 2019). Our data also suggest that, during the lifespan of a lesion, if its size exceeds ~0.5 µL, the lesion will most likely not start the repair process and remain chronically demyelinated.

Of the 12 early active lesions, 7 showed an increased number of ASPA-/Olig2+ cells in the lesion vicinity (Figure 5). This pattern, never observed in remyelinated lesions or NAWM, suggests a possible proliferative OPC response, which may indicate attempted repair or potentially an inflammatory role of OPC in this setting (Harrington et al., 2020).

In this study, we compared PDw and MTR MRI, since MTR is widely used to assay remyelination in vivo in MS (Chen et al., 2008; Chen et al., 2013; Mallik et al., 2014), and found that in the context of marmoset EAE, PDw, in close connection with histopathology, appears to have higher sensitivity and specificity for remyelination status. MTR may be low in remyelinated lesions compared to healthy white matter because of the presence of incomplete or morphologically altered myelin sheaths (Brown et al., 2014; Mallik et al., 2014). Unlike MTR, which suffers from signal-to-noise reduction due to its calculation as a voxel-wise division of signal intensity measures, PDw signal intensity is directly measured by the MRI system, and thus PDw may prove more reliable for simply discriminating the presence or absence of remyelination and for characterizing its time course.

To determine whether corticosteroid treatment alters the course of lesion repair, half of the marmosets (for each twin pair, the one that showed lesions first) received 5-day treatment courses. We found no differences by corticosteroid treatment status in prevalence or time course of remyelination on either MRI or histological quantification. However, it remains possible that optimal timing of corticosteroid treatment might influence remyelination. For example, it is possible that early initiation of corticosteroid treatment (as soon as the first lesion was detected) in this study, resulting in treatment completion before lesions were even 1 week old. This might have been too early to influence lesion outcome, as the initial demyelination period typically lasts 4–7 weeks before remyelination ensues. It is also possible that reduction of inflammation via corticosteroid treatment could have hampered more rapid remyelination by slowing clearance of myelin debris, which is a prerequisite for OPC recruitment (Cunha et al., 2020) and differentiation (Gruchot et al., 2019; Rawji et al., 2016).

The main limitation of the study is its small sample size. This limitation is partially compensated by our focus on individual lesions, rather than the number of animals. Another limitation is that different EAE immunization protocols were applied across marmosets, though we did not observe any difference in lesion evolution or outcome either radiologically or histologically. Notably, previous studies from our group with a similar variety of EAE induction methods have also not shown consistent differences in disease course or lesion pathobiology (Lee et al., 2019; Lee et al., 2018). Finally, we did not obtain ultrathin sections to quantify myelin thickness, as we were interested in performing a battery of stains on our tissue, and as lesions were found at a variety of orientations relative to axons, which would complicate such quantification.

In conclusion, in vivo longitudinal PDw MRI can effectively predict remyelination in the context of marmoset EAE, with high sensitivity and specificity relative to histology. Given strong similarities between marmoset EAE and MS with respect to lesion development and repair, these results suggest a paradigm for preclinical – and possibly early-phase clinical – studies to investigate putative remyelinating therapies.

All of the 6 marmosets' serial in vivo MRI images, including all the sequences used for analysis and figure generation, were uploaded in an easily accessible format (NIFTI). The file names are titled with the corresponding animal # used in the manuscript, as well as the date of MRI acquisition. All the Iba1 and PLP immunohistochemistry stains have been uploaded as well.

1. 1. Absinta M
   2. Nair G
   3. Filippi M
   4. Ray-Chaudhury A
   5. Reyes-Mantilla MI
   6. Pardo CA
   7. Reich DS

   (2014) Postmortem magnetic resonance imaging to guide the pathologic cut: Individualized, 3-dimensionally printed cutting boxes for fixed brains

   Journal of Neuropathology and Experimental Neurology 73:780–788.

   https://doi.org/10.1097/NEN.0000000000000096

   • PubMed
   • Google Scholar
2. 1. Absinta M
   2. Sati P
   3. Reich DS

   (2016) Advanced MRI and staging of multiple sclerosis lesions

   Nature Reviews. Neurology 12:358–368.

   https://doi.org/10.1038/nrneurol.2016.59

   • PubMed
   • Google Scholar
3. 1. Brown RA
   2. Narayanan S
   3. Arnold DL

   (2014) Imaging of repeated episodes of demyelination and remyelination in multiple sclerosis

   NeuroImage 6:20–25.

   https://doi.org/10.1016/j.nicl.2014.06.009

   • Google Scholar
4. 1. Chari DM

   (2007) Remyelination in multiple sclerosis

   International Review of Neurobiology 79:589–620.

   https://doi.org/10.1016/S0074-7742(07)79026-8

   • PubMed
   • Google Scholar
5. 1. Chen JT
   2. Collins DL
   3. Atkins HL
   4. Freedman MS
   5. Arnold DL
   6. Group CMBS

   (2008) Magnetization transfer ratio evolution with Demyelination and Remyelination in multiple sclerosis lesions

   Annals of Neurology 63:254–262.

   https://doi.org/10.1002/ana.21302

   • PubMed
   • Google Scholar
6. 1. Chen JT-H
   2. Easley K
   3. Schneider C
   4. Nakamura K
   5. Kidd GJ
   6. Chang A
   7. Staugaitis SM
   8. Fox RJ
   9. Fisher E
   10. Arnold DL
   11. Trapp BD

   (2013) Clinically feasible MTR is sensitive to cortical demyelination in MS

   Neurology 80:246–252.

   https://doi.org/10.1212/WNL.0b013e31827deb99

   • PubMed
   • Google Scholar
7. 1. Cunha MI
   2. Su M
   3. Cantuti-Castelvetri L
   4. Müller SA
   5. Schifferer M
   6. Djannatian M
   7. Alexopoulos I
   8. van der Meer F
   9. Winkler A
   10. van Ham TJ
   11. Schmid B
   12. Lichtenthaler SF
   13. Stadelmann C
   14. Simons M

   (2020) Pro-Inflammatory activation following demyelination is required for myelin clearance and oligodendrogenesis

   The Journal of Experimental Medicine 217:e20191390.

   https://doi.org/10.1084/jem.20191390

   • PubMed
   • Google Scholar
8. 1. Filippi M
   2. Rocca MA
   3. Barkhof F
   4. Brück W
   5. Chen JT
   6. Comi G
   7. DeLuca G
   8. De Stefano N
   9. Erickson BJ
   10. Evangelou N
   11. Fazekas F
   12. Geurts JJG
   13. Lucchinetti C
   14. Miller DH
   15. Pelletier D
   16. Popescu BFG
   17. Lassmann H
   18. Attendees of the Correlation between Pathological MRI findings in MS workshop

   (2012) Association between pathological and MRI findings in multiple sclerosis

   The Lancet. Neurology 11:349–360.

   https://doi.org/10.1016/S1474-4422(12)70003-0

   • PubMed
   • Google Scholar
9. 1. Filippi M
   2. Brück W
   3. Chard D
   4. Fazekas F
   5. Geurts JJG
   6. Enzinger C
   7. Hametner S
   8. Kuhlmann T
   9. Preziosa P
   10. Rovira À
   11. Schmierer K
   12. Stadelmann C
   13. Rocca MA

   (2019) Association between pathological and MRI findings in multiple sclerosis

   The Lancet Neurology 18:198–210.

   https://doi.org/10.1016/S1474-4422(18)30451-4

   • Google Scholar
10. 1. Goodin DS

    (2014) Glucocorticoid treatment of multiple sclerosis

    Clinical Neurology 122:455–464.

    https://doi.org/10.1016/B978-0-444-52001-2.00020-0

    • PubMed
    • Google Scholar
11. 1. Gruchot J
    2. Weyers V
    3. Göttle P
    4. Förster M
    5. Hartung H-P
    6. Küry P
    7. Kremer D

    (2019) The molecular basis for remyelination failure in multiple sclerosis

    Cells 8:825.

    https://doi.org/10.3390/cells8080825

    • PubMed
    • Google Scholar
12. 1. Harrington EP
    2. Bergles DE
    3. Calabresi PA

    (2020) Immune cell modulation of oligodendrocyte lineage cells

    Neuroscience Letters 715:134601.

    https://doi.org/10.1016/j.neulet.2019.134601

    • PubMed
    • Google Scholar
13. 1. Jagessar SA
    2. Dijkman K
    3. Dunham J
    4. ’t Hart BA
    5. Kap YS

    (2016) Experimental autoimmune Encephalomyelitis in Marmosets

    Methods in Molecular Biology 1304:171–186.

    https://doi.org/10.1007/7651_2014_113

    • PubMed
    • Google Scholar
14. 1. Kap YS
    2. Laman JD
    3. ‘t Hart BA

    (2010) Experimental autoimmune encephalomyelitis in the common marmoset, a bridge between rodent EAE and multiple sclerosis for immunotherapy development

    Journal of Neuroimmune Pharmacology 5:220–230.

    https://doi.org/10.1007/s11481-009-9178-y

    • Google Scholar
15. 1. Kap YS
    2. Jagessar SA
    3. Dunham J
    4. ‘t Hart BA

    (2016) The common marmoset as an indispensable animal model for immunotherapy development in multiple sclerosis

    Drug Discovery Today 21:1200–1205.

    https://doi.org/10.1016/j.drudis.2016.03.014

    • Google Scholar
16. 1. Kipp M
    2. van der Star B
    3. Vogel DYS
    4. Puentes F
    5. van der Valk P
    6. Baker D
    7. Amor S

    (2012) Experimental in vivo and in vitro models of multiple sclerosis: EAE and beyond

    Multiple Sclerosis and Related Disorders 1:15–28.

    https://doi.org/10.1016/j.msard.2011.09.002

    • PubMed
    • Google Scholar
17. 1. Kipp M
    2. Nyamoya S
    3. Hochstrasser T
    4. Amor S

    (2017) Multiple sclerosis animal models: a clinical and histopathological perspective

    Brain Pathology 27:123–137.

    https://doi.org/10.1111/bpa.12454

    • PubMed
    • Google Scholar
18. 1. Kremer D
    2. Göttle P
    3. Flores-Rivera J
    4. Hartung HP
    5. Küry P

    (2019) Remyelination in multiple sclerosis: from concept to clinical trials

    Current Opinion in Neurology 32:378–384.

    https://doi.org/10.1097/WCO.0000000000000692

    • Google Scholar
19. 1. Lassmann H
    2. Brück W
    3. Lucchinetti C
    4. Rodriguez M

    (1997) Remyelination in multiple sclerosis

    Multiple Sclerosis 3:133–136.

    https://doi.org/10.1177/135245859700300213

    • PubMed
    • Google Scholar
20. 1. Laule C
    2. Moore GRW

    (2018) Myelin water imaging to detect demyelination and remyelination and its validation in pathology

    Brain Pathology 28:750–764.

    https://doi.org/10.1111/bpa.12645

    • PubMed
    • Google Scholar
21. 1. Lee NJ
    2. Ha S-K
    3. Sati P
    4. Absinta M
    5. Luciano NJ
    6. Lefeuvre JA
    7. Schindler MK
    8. Leibovitch EC
    9. Ryu JK
    10. Petersen MA
    11. Silva AC
    12. Jacobson S
    13. Akassoglou K
    14. Reich DS

    (2018) Spatiotemporal distribution of fibrinogen in Marmoset and human inflammatory Demyelination

    Brain 141:1637–1649.

    https://doi.org/10.1093/brain/awy082

    • PubMed
    • Google Scholar
22. 1. Lee NJ
    2. Ha SK
    3. Sati P
    4. Absinta M
    5. Nair G
    6. Luciano NJ
    7. Leibovitch EC
    8. Yen CC
    9. Rouault TA
    10. Silva AC
    11. Jacobson S
    12. Reich DS

    (2019) Potential role of iron in repair of inflammatory demyelinating lesions

    The Journal of Clinical Investigation 129:4365–4376.

    https://doi.org/10.1172/JCI126809

    • PubMed
    • Google Scholar
23. 1. Leuze C
    2. Aswendt M
    3. Ferenczi E
    4. Liu CW
    5. Hsueh B
    6. Goubran M
    7. Tian Q
    8. Steinberg G
    9. Zeineh MM
    10. Deisseroth K
    11. McNab JA

    (2017) The separate effects of lipids and proteins on brain MRI contrast revealed through tissue clearing

    NeuroImage 156:412–422.

    https://doi.org/10.1016/j.neuroimage.2017.04.021

    • PubMed
    • Google Scholar
24. 1. Lubetzki C
    2. Zalc B
    3. Williams A
    4. Stadelmann C
    5. Stankoff B

    (2020) Remyelination in multiple sclerosis: from basic science to clinical translation

    The Lancet Neurology 19:678–688.

    https://doi.org/10.1016/S1474-4422(20)30140-X

    • Google Scholar
25. 1. Lucchinetti C
    2. Brück W
    3. Parisi J
    4. Scheithauer B
    5. Rodriguez M
    6. Lassmann H

    (1999) A quantitative analysis of oligodendrocytes in multiple sclerosis lesions: A study of 113 cases

    Brain 122 (Pt 12):2279–2295.

    https://doi.org/10.1093/brain/122.12.2279

    • PubMed
    • Google Scholar
26. 1. Lucchinetti C
    2. Brück W
    3. Parisi J
    4. Scheithauer B
    5. Rodriguez M
    6. Lassmann H

    (2000) Heterogeneity of multiple sclerosis lesions: Implications for the pathogenesis of Demyelination

    Annals of Neurology 47:707–717.

    https://doi.org/10.1002/1531-8249(200006)47:6<707::aid-ana3>3.0.co;2-q

    • PubMed
    • Google Scholar
27. 1. Luciano NJ
    2. Sati P
    3. Nair G
    4. Guy JR
    5. Ha SK
    6. Absinta M
    7. Chiang WY
    8. Leibovitch EC
    9. Jacobson S
    10. Silva AC
    11. Reich DS

    (2016) Utilizing 3D printing technology to merge MRI with histology: A protocol for brain Sectioning

    Journal of Visualized Experiments 5:54780.

    https://doi.org/10.3791/54780

    • Google Scholar
28. 1. Maggi P
    2. Macri SMC
    3. Gaitán MI
    4. Leibovitch E
    5. Wholer JE
    6. Knight HL
    7. Ellis M
    8. Wu T
    9. Silva AC
    10. Massacesi L
    11. Jacobson S
    12. Westmoreland S
    13. Reich DS

    (2014) The formation of inflammatory Demyelinated lesions in cerebral white matter

    Annals of Neurology 76:594–608.

    https://doi.org/10.1002/ana.24242

    • PubMed
    • Google Scholar
29. 1. Maggi P
    2. Sati P
    3. Massacesi L

    (2017) Magnetic resonance imaging of experimental autoimmune Encephalomyelitis in the common Marmoset

    Journal of Neuroimmunology 304:86–92.

    https://doi.org/10.1016/j.jneuroim.2016.09.016

    • PubMed
    • Google Scholar
30. 1. Mallik S
    2. Samson RS
    3. Wheeler-Kingshott CAM
    4. Miller DH

    (2014) Imaging outcomes for trials of remyelination in multiple sclerosis

    Journal of Neurology, Neurosurgery, and Psychiatry 85:1396–1404.

    https://doi.org/10.1136/jnnp-2014-307650

    • PubMed
    • Google Scholar
31. 1. McMurran CE
    2. Zhao C
    3. Franklin RJM

    (2019) Toxin-Based models to investigate demyelination and remyelination

    Methods in Molecular Biology 1936:377–396.

    https://doi.org/10.1007/978-1-4939-9072-6_21

    • PubMed
    • Google Scholar
32. 1. Patani R
    2. Balaratnam M
    3. Vora A
    4. Reynolds R

    (2007) Remyelination can be extensive in multiple sclerosis despite a long disease course

    Neuropathology and Applied Neurobiology 33:277–287.

    https://doi.org/10.1111/j.1365-2990.2007.00805.x

    • PubMed
    • Google Scholar
33. 1. Patrikios P
    2. Stadelmann C
    3. Kutzelnigg A
    4. Rauschka H
    5. Schmidbauer M
    6. Laursen H
    7. Sorensen PS
    8. Brück W
    9. Lucchinetti C
    10. Lassmann H

    (2006) Remyelination is extensive in a subset of multiple sclerosis patients

    Brain 129:3165–3172.

    https://doi.org/10.1093/brain/awl217

    • PubMed
    • Google Scholar
34. 1. Rawji KS
    2. Mishra MK
    3. Yong VW

    (2016) Regenerative capacity of macrophages for remyelination

    Frontiers in Cell and Developmental Biology 4:47.

    https://doi.org/10.3389/fcell.2016.00047

    • PubMed
    • Google Scholar
35. 1. Reich DS

    (2017) Imag (in) ING multiple sclerosis: time to take better pictures

    Journal of Neuroimmunology 304:72–80.

    https://doi.org/10.1016/j.jneuroim.2016.09.015

    • PubMed
    • Google Scholar
36. 1. Reich DS
    2. Lucchinetti CF
    3. Calabresi PA

    (2018) Multiple sclerosis

    The New England Journal of Medicine 378:169–180.

    https://doi.org/10.1056/NEJMra1401483

    • PubMed
    • Google Scholar
37. Conference

    1. Roy S
    2. Butman JA
    3. Chan L
    4. Pham DL

    (2018a) TBI Contusion Segmentation from MRI using Convolutional neural networks

    IEEE 15th International Symposium on Biomedical Imaging.

    https://doi.org/10.1109/ISBI.2018.8363545

    • Google Scholar
38. Preprint

    1. Roy S
    2. Butman JA
    3. Reich DS
    4. Calabresi PA
    5. Pham DL

    (2018b) Multiple sclerosis lesion Segmentation from brain MRI via fully Convolutional neural networks

    arXiv.

    https://arxiv.org/abs/1803.09172

    • Google Scholar
39. 1. Sati P
    2. Silva AC
    3. van Gelderen P
    4. Gaitan MI
    5. Wohler JE
    6. Jacobson S
    7. Duyn JH
    8. Reich DS

    (2012) In vivo quantification of T₂ anisotropy in white matter fibers in marmoset monkeys

    NeuroImage 59:979–985.

    https://doi.org/10.1016/j.neuroimage.2011.08.064

    • PubMed
    • Google Scholar
40. 1. Schindelin J
    2. Arganda-Carreras I
    3. Frise E
    4. Kaynig V
    5. Longair M
    6. Pietzsch T
    7. Preibisch S
    8. Rueden C
    9. Saalfeld S
    10. Schmid B
    11. Tinevez J-Y
    12. White DJ
    13. Hartenstein V
    14. Eliceiri K
    15. Tomancak P
    16. Cardona A

    (2012) Fiji: An open-source platform for biological-image analysis

    Nature Methods 9:676–682.

    https://doi.org/10.1038/nmeth.2019

    • PubMed
    • Google Scholar
41. 1. Schmierer K
    2. Parkes HG
    3. So PW
    4. An SF
    5. Brandner S
    6. Ordidge RJ
    7. Yousry TA
    8. Miller DH

    (2010) High field (9.4 Tesla) magnetic resonance imaging of cortical grey matter lesions in multiple sclerosis

    Brain 133:858–867.

    https://doi.org/10.1093/brain/awp335

    • PubMed
    • Google Scholar
42. 1. Sellebjerg F
    2. Barnes D
    3. Filippini G
    4. Midgard R
    5. Montalban X
    6. Rieckmann P
    7. Selmaj K
    8. Visser LH
    9. Sørensen PS
    10. EFNS Task Force on Treatment of Multiple Sclerosis Relapses

    (2005) EFNS guideline on treatment of multiple sclerosis relapses: report of an EFNS Task force on treatment of multiple sclerosis relapses

    European Journal of Neurology 12:939–946.

    https://doi.org/10.1111/j.1468-1331.2005.01352.x

    • PubMed
    • Google Scholar
43. 1. ’t Hart BA
    2. Massacesi L

    (2009) Clinical, pathological, and immunologic aspects of the multiple sclerosis model in common marmosets (Callithrix jacchus)

    Journal of Neuropathology and Experimental Neurology 68:341–355.

    https://doi.org/10.1097/NEN.0b013e31819f1d24

    • PubMed
    • Google Scholar
44. 1. Yano R
    2. Hata J
    3. Abe Y
    4. Seki F
    5. Yoshida K
    6. Komaki Y
    7. Okano H
    8. Tanaka KF

    (2018) Quantitative temporal changes in DTI values coupled with histological properties in cuprizone-induced demyelination and remyelination

    Neurochemistry International 119:151–158.

    https://doi.org/10.1016/j.neuint.2017.10.004

    • PubMed
    • Google Scholar

Decision letter after peer review:

Thank you for submitting your article "in vivo MRI of Endogenous Remyelination in a Nonhuman Primate Model of Multiple Sclerosis" for consideration by eLife. We appreciate your patience during the handling of this submission. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Jeannie Chin as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this response to help you prepare a revised submission. As you will see below, there was an agreement that your work is an important step towards the MRI-based diagnosis of CNS remyelination in multiple sclerosis. However, it was also felt that the present manuscript has shortcomings in the presentation of histological data and some missing information about data acquisition. Please look for details below how to improve a revised version of your manuscript that better supports the claims made.

Reviewer #1 (Recommendations for the authors):

Similar to the serial MRI, the manuscript would be enhanced by supplementing Iba1- and PLP-stained sections of each lesion.

Reviewer #2 (Recommendations for the authors):

Before publication I think that this would be improved by consideration of the following

How do the authors know that PDw is imaging new myelin formation rather than loss of inflammation? The signal is maintained in acute and chronic inflammation, and lost in remyelination -which is also associated with reduced inflammation. It's not clear to me how the two events can be distinguished by this imaging protocol. Is the time course of inflammatory loss different to that for every myelination? The gadolinium experiments mentioned in the methods but not included in the results might address this?

How is the distinction between acute and chronic inflammatory lesions made. Is it just the time of onset after immunisation?

In figure 5 it is not clear how the transition from demyelination to remyelination was established, and so the evaluation of the time course for remyelination is not clear

Reviewer #3 (Recommendations for the authors):

If possible, it would be useful to have images of lesions (histology) at somewhat lower magnification as well, to appreciate differences in density etc. This would be very helpful particularly in the case of Luxol Fast Blue staining of remyelinated lesions so that pale staining compared to non-affected tissue can be appreciated, and for ASPA/Olig2 and Bielschowsky staining, to illustrate differences in density.

Regarding Iba1 staining, clear cellular labeling pattern is observed only in Figure 4, while Figure 2 shows high background and very few if any clearly labeled cells, which is a bit better in Figure 3.

Points to clarify:

1. Figure 5 shows the estimated timing of demyelination/remyelination. Three out of four graphs are from methylprednisolone (MP)-treated animals. It was indicated in Methods that MP administration was performed after demyelination was first detected, but the first graph shows 1 double bar, at 12 weeks (prior to the first peak), and a triple bar at 18 weeks approx. Was this animal treated twice?

2. In the same figure, 2 lesions from M#5 are shown for which, as the legend indicates, remyelination was confirmed histologically. Yet, on page 16, it is indicated that remyelination was detected in animals M#2-4 by MRI and confirmed by histology, and that M#5 had a single remyelinated lesion not seen on MRI, which seems to contradict Figure 5. Could this be clarified?

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "in vivo MRI of Endogenous Remyelination in a Nonhuman Primate Model of Multiple Sclerosis" for further consideration by eLife. Your revised article has been evaluated by Jeannie Chin (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are remaining issues that need to be addressed, as described in the individual reviewer comments at the end of this correspondence. A brief summary of those essential issues follows:

Essential revisions:

1) Related to the finding that none of the lesions returning to isointensity on PDw MRI demonstrated gadolinium enhancement on T1w scans, while the gadolinium data is very helpful, no quantification is provided. This should be added. Ideally, additional histological evidence of remyelination should be provided to strengthen the power of PDw to classify the lesions. Also, please clarify whether gadolinium enhancement is also considered as a marker of acute versus chronic lesions in this paper.

2) The authors assert that prior data shows that the changes seen occur at the same time as remyelination and don't correlate with any changes in inflammation, but this was not sufficiently addressed. Please include a more detailed analysis of remyelination and inflammation in the lesions on which the MRI was performed, rather than a comparison with older data. Of particular interest would be histological evidence of remyelination such as reduced myelin density, shorter and thinner myelin internodes and myelin protein staining of remyelinating oligodendrocyte cytoplasm.

3) A concern raised in the previous review was that it was not possible to see loss of oligodendroglia based on the image provided to illustrate acute lesions (now in Figure 1), in which there is a small oligo-free area surrounding what may be blood vessels, but the remaining PLP-free area contains many oligodendroglial cells. The question of oligodendroglial loss could be addressed by quantifying the density in perilesional area and comparing it to the intralesional density. However, the authors simply refer to a previous manuscript (JCI, 2019). Were the same animals/lesions analyzed in this study? This point needs to be clearly addressed throughout the manuscript wherever references to previous data are made. Please add quantitative data to demonstrate oligo depletion followed by repopulation in remyelinated lesion. If the quantification does not support the loss of oligodendroglia, then these claims should be removed.

4) The histological criteria for lesion classification is slightly confusing, which was brought up in the original review as well. The authors assert that experienced histopathologists blindly classified these lesions in Kuhlmann 2017, but it is not really clear that exactly the same immunolabelings were performed. Please provide a clear statement on which histological parameters (immunolabelings) were considered by the histopathologists to classify the lesions in this study.

Note: Because the results of this manuscript have important implications for pre-clinical testing of remyelinating drugs, solid data on the lesions analyzed in this manuscript should be provided to reinforce the conclusions.

Reviewer #1 (Recommendations for the authors):

There is no histological evidence of remyelination such as reduced myelin density, shorter and thinner myelin internodes and myelin protein staining of remyelinating oligodendrocyte cytoplasm. Most lesional areas are small. It is most likely that many of the MRI ROIs are due to alterations in myelinated white matter. It may be that all lesion are remyelinated to a point that they cannot be distinguished from normal myelinated white matter. It would seem unlikely that a lesion in the process of remyelination is not present in all the lesions investigated. This reduces the utility of the model to investigate remyelination. What can be measured? Unlikely that remyelination can be enhanced. I do not consider this as a reliable model for remyelination. This a relevant question based upon the species utilized in this study.

Reviewer #2 (Recommendations for the authors):

My key point of concern was

"How do the authors know that PDw is imaging new myelin formation rather than loss of inflammation? The signal is maintained in acute and chronic inflammation, and lost in remyelination -which is also associated with reduced inflammation. It's not clear to me how the two events can be distinguished by this imaging protocol. Is the time course of inflammatory loss different to that for every myelination? The gadolinium experiments mentioned in the methods but not included in the results might address this?"

This is rebutted as follows

"- This is an essential point, and we thank the reviewer for the inquiry. We agree (and have now clarified in the paper) that PDw imaging is sensitive to inflammation and other changes; the lack of specificity to myelin is indeed well known in the field. What is critical for our purposes, however, is the ability to infer changes in myelin from the combination of in-vivo MRI data and extensive knowledge of the time course of lesion development and repair, which in our hands is derived from the analysis of ~100 animals over more than a decade, as reflected in our prior publications and the current manuscript. Hence, our paper is essentially about sensitivity of PDw MRI to myelin changes in this context, not about specificity of the technique. It is worth noting, in this context, that chronic EAE lesions are typically either gliotic (chronic inactive) or remyelinated, with few inflammatory cells; thus, the difference in MRI signal between remyelinated and chronically demyelinated lesions cannot be explained purely by inflammation. Furthermore, none of the lesions returning to isointensity on PDw MRI demonstrated gadolinium enhancement on T1w scans. In the revision, we have added material under the description on remyelinated lesions in the Results section."

The gadolinium data is very helpful, although no quantification is provided. This should be added.

I take the point that the prior data argues that the changes seen occur at the same time as remyelination and don't correlate with any changes in inflammation, but feel my concern is not really addressed in the discussion despite it being recognised as "essential". At the very least I'd like to see an addition to the discussion here, although Id much prefer a more detailed analysis of remyelination and inflammation in the lesions on which the MRI was performed rather than a comparison with older data

Reviewer #3 (Recommendations for the authors):

My general feeling is that, while several issues were successfully addressed by this revision, referring to previous manuscripts has not provided answers to some fundamental questions raised in the previous review.

One of my points was that I could not see loss of oligodendroglia based on the image provided to illustrate acute lesions (now in Figure 1), in which there is a small oligo-free area surrounding what may be blood vessels, but the remaining PLP-free area contains many oligodendroglial cells. The question of oligodendroglial loss could be addressed by quantifying the density in perilesional area and comparing it to the intralesional density. However, the authors simply refer to a previous manuscript (JCI, 2019). Were the same animals/lesions analyzed in this study? This is really not clear from the manuscript. One sentence in the Discussion refers to previous analyses of oligo repopulation and remyelination, but is it the same lesions/animals? This should be clarified.

Because some acutely demyelinating lesions show oligo preservation, I do not think that showing a decrease in oligos is necessary to claim that the lesion is demyelinated, but I do think that if this claim is made, it should be supported by data, especially when the image provided does not illustrate the claim. Otherwise, the claim should be omitted. Thus, I recommend adding some quantitative data to demonstrate oligo depletion followed by repopulation in remyelinated lesions. If no depletion is evidenced in acute lesions, then it can be discussed that demyelination with oligodendroglial preservation has also been observed in early MS lesions. If these data on the same lesions are provided in previous publications, it should be clearly indicated that these are the same lesions.

A crucial issue raised in the previous round was that of PDw sequences detecting changes other than demyelination/remyelination. The authors did mention that gadolinium enhancement was present in acute but not chronic lesions, both of which were demyelinated and show similar Pdw, which, in my opinion, does suggest that changes in PDw signal are not due to inflammatory infiltrates, even though does not exclude that other events (axonal changes, astrocytes, diffuse microgliosis) might alter the signal. Including gadolinium data and potentially, quantification, would be a good argument to exclude inflammatory infiltrates as the cause of changes in PDw. This point should be reinforced in the Discussion. Ideally, additional histological evidence of remyelination should be provided to strengthen the power of PDw to classify the lesions.

I still find the histological criteria for lesion classification slightly confusing. The answer to my suggestion to clearly define lesion classification criteria applied in this paper was that experienced histopathologists blindly classified these lesions based on Tanja Kuhlmann paper from 2017, but from the data presented, it is not really clear that exactly the same immunolabelings were performed (as compared to Kuhlmann paper). From the authors' response, it can be concluded that all these analyses were performed in their previous work on marmoset lesions (again, were these the same lesions?), and thus not repeated in this study. I think that the authors need to provide a clear statement on which histological parameters (immunolabellings) were considered by the histopathologists to classify the lesions in this study.

The authors state that lesions were classified as acute if younger than 5 weeks and remyelinated or chronic if older than 5 weeks. I suppose this refers to MRI classification of the lesions, and not the histological one. As the authors also describe acute lesions as gadolinium enhancing, and chronic lesions (and remyelinated ones) gadolinium enhancement-free, was gadolinium enhancement also considered as a marker of acute versus chronic lesions in this paper?

Because the results of this manuscript have important implications for pre-clinical testing of remyelinating drugs, solid data on the lesions analyzed in this manuscript should be provided to reinforce the conclusions.

Reviewer #1 (Recommendations for the authors):

Similar to the serial MRI, the manuscript would be enhanced by supplementing Iba1- and PLP-stained sections of each lesion.

We agree that supplementing all the lesions with Iba1 and PLP stains would be a useful addition and have now added them to the supplementary data section of the manuscript, under Source data 9.

Reviewer #2 (Recommendations for the authors):

Before publication I think that this would be improved by consideration of the following

How do the authors know that PDw is imaging new myelin formation rather than loss of inflammation? The signal is maintained in acute and chronic inflammation, and lost in remyelination -which is also associated with reduced inflammation. It's not clear to me how the two events can be distinguished by this imaging protocol. Is the time course of inflammatory loss different to that for every myelination? The gadolinium experiments mentioned in the methods but not included in the results might address this?

This is an essential point, and we thank the reviewer for the inquiry. We agree (and have now clarified in the paper) that PDw imaging is sensitive to inflammation and other changes; the lack of specificity to myelin is indeed well known in the field. What is critical for our purposes, however, is the ability to infer changes in myelin from the combination of in-vivo MRI data and extensive knowledge of the time course of lesion development and repair, which in our hands is derived from the analysis of ~100 animals over more than a decade, as reflected in our prior publications and the current manuscript. Hence, our paper is essentially about sensitivity of PDw MRI to myelin changes in this context, not about specificity of the technique.

It is worth noting, in this context, that chronic EAE lesions are typically either gliotic (chronic inactive) or remyelinated, with few inflammatory cells; thus, the difference in MRI signal between remyelinated and chronically demyelinated lesions cannot be explained purely by inflammation. Furthermore, none of the lesions returning to isointensity on PDw MRI demonstrated gadolinium enhancement on T1w scans. In the revision, we have added material under the description on remyelinated lesions in the Results section.

How is the distinction between acute and chronic inflammatory lesions made. Is it just the time of onset after immunisation?

We thank the reviewer for the opportunity to clarify this. The differentiation between acute and chronic is solely based on the timeline of the lesion progression, detected by the serial in vivo MRI, and not on the time after immunization.

In figure 5 it is not clear how the transition from demyelination to remyelination was established, and so the evaluation of the time course for remyelination is not clear

We thank the reviewer for pointing out the unclear time course as highlighted on Figure 5 (now Figure 4). Our evaluation was based on the MRI signal intensity change. We have clarified this point in our manuscript in the figure legend.

Reviewer #3 (Recommendations for the authors):

If possible, it would be useful to have images of lesions (histology) at somewhat lower magnification as well, to appreciate differences in density etc. This would be very helpful particularly in the case of Luxol Fast Blue staining of remyelinated lesions so that pale staining compared to non-affected tissue can be appreciated, and for ASPA/Olig2 and Bielschowsky staining, to illustrate differences in density.

As mentioned above, we agree that lower magnification images will be helpful to identify areas of thinly remyelinated regions and have included these.

Regarding Iba1 staining, clear cellular labeling pattern is observed only in Figure 4, while Figure 2 shows high background and very few if any clearly labeled cells, which is a bit better in Figure 3.

We included both a higher and lower magnification of the Iba1 staining on Figure 2 (now Figure 1) for better visualization. We hope this clarifies the issue.

Points to clarify:

1. Figure 5 shows the estimated timing of demyelination/remyelination. Three out of four graphs are from methylprednisolone (MP)-treated animals. It was indicated in Methods that MP administration was performed after demyelination was first detected, but the first graph shows 1 double bar, at 12 weeks (prior to the first peak), and a triple bar at 18 weeks approx. Was this animal treated twice?

We apologize for the lack of clarity in our description. As you mentioned, the animal was treated twice after the first steroid treatment did not result in any clinical or radiological improvement. We have clarified this in our figure (now Figure 4) legend.

2. In the same figure, 2 lesions from M#5 are shown for which, as the legend indicates, remyelination was confirmed histologically. Yet, on page 16, it is indicated that remyelination was detected in animals M#2-4 by MRI and confirmed by histology, and that M#5 had a single remyelinated lesion not seen on MRI, which seems to contradict Figure 5. Could this be clarified?

We thank the reviewer for catching this error; we have now clarified and corrected the error in our revised manuscript.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Essential revisions:

1) Related to the finding that none of the lesions returning to isointensity on PDw MRI demonstrated gadolinium enhancement on T1w scans, while the gadolinium data is very helpful, no quantification is provided. This should be added. Ideally, additional histological evidence of remyelination should be provided to strengthen the power of PDw to classify the lesions. Also, please clarify whether gadolinium enhancement is also considered as a marker of acute versus chronic lesions in this paper.

We appreciate the comment and have made substantial changes to the paper in response.

First, we would like to clarify that most of the lesions newly detected on PDw MRI showed gadolinium enhancement on T1w images. Only lesions identified as “early active” on pathology were found to be enhancing on the terminal MRI. We added the following paragraph in the Results section:

“T1w gadolinium enhancement as a marker of acute inflammation

Across the 6 animals scanned longitudinally, 82% of the lesions newly detected on PDw MRI presented T1w gadolinium enhancement. Enhancement was occasionally seen at the following timepoint (10–15 days after first detection). No chronic, at least partially demyelinated or remyelinated WML presented gadolinium enhancement on the terminal scan. M#5–6 presented at least one early active WML enhancing lesion at their terminal scan.”

The use of gadolinium enhancement to categorize lesions as acute or chronic has now been clarified in the Results section, in the section entitled “MRI WML categorization.”

Second, as suggested, we have added more histological evidence of lesion remyelination in the revision. To do so, we assessed, over 31 lesions, the percentage of unstained area on PLP and LFB stains for myelin protein and lipid, respectively. We also added 10 NAWM areas from the same animals as control. As expected, we observed a larger unstained area for demyelinated lesions vs remyelinated lesions on both PLP and LFB. We have added details to the method section as well as a detailed paragraph in results:

“Histological quantification recapitulates MRI rater analysis of lesion myelin status

Assessment of PLP staining in 31 lesions and 10 normal appearing white matter (NAWM) areas (1500 µm² centered over the lesion core) demonstrated larger unstained areas in early active (58±25%) and chronic, at least partially demyelinated (38±25%) lesions compared to remyelinated lesions (4.5±1.1%) or NAWM (2.6±0.3%) (Figure 5A). LFB assessment showed similar results: 63±26% unstained area in early active lesions, 43±26% in chronic, at least partially demyelinated lesions, 15±25% in remyelinated lesions, and 3.0±0.4% in NAWM (Figure 5B). We observed a significantly smaller unstained PLP area in NAWM compared to remyelinated lesions (2-sample t-test, p<0.001). There were no apparent differences between PLP and LFB staining in the different lesion categories and in NAWM (2-sample t-test). Interestingly, 1 lesion appeared remyelinated on PLP, with less than 6% of unstained, area but demyelinated on LFB (82% of unstained area).”

Oligodendrocyte density was addressed with ASPA and Olig2 staining. ASPA+/Olig2+ cells, reflecting mature oligodendrocytes, and ASPA-/Olig2+ cells, potentially reflecting OPC, were counted for every lesion in comparison to 10 NAWM areas. Our analysis showed a higher ASPA+/Olig2+ cell count in remyelinated lesions compared to acute or chronic lesions as well as negative correlation between lesion volume at terminal PDw MRI and ASPA+/Olig2+ cell number. A detailed paragraph was added in the Results section:

“Oligodendrocyte and OPC counts are consistent with degree of demyelination in lesions Quantitative assessment of ASPA+/Olig2+ (mature oligodendrocytes) and ASPA-/Olig2+ (OPC) across the 31 lesions and 10 NAWM areas showed, as expected, more mature oligodendrocytes in remyelinated lesions and NAWM than early active or chronic, at least partially demyelinated lesions (Figure 5C). Consistent with PLP observations, we observed significantly more oligodendrocytes in NAWM compared to remyelinated lesions (2-sample t-test, p<0.001). Interestingly, more OPC were found in early active demyelinating lesions than in remyelinated lesions or NAWM (Figure 5D). Younger lesions (<10 weeks old by MRI) had more OPC than older lesions, highlighted by a negative correlation between lesion age and OPC count (r = -0.46; p = 0.009).”

The revision includes a newly added Figure 5, which summarizes the quantitative histological analysis. Adequate description in the method section was also added (“Histological WML categorization”). We have also added additional comments in the Discussion section.

2) The authors assert that prior data shows that the changes seen occur at the same time as remyelination and don't correlate with any changes in inflammation, but this was not sufficiently addressed. Please include a more detailed analysis of remyelination and inflammation in the lesions on which the MRI was performed, rather than a comparison with older data. Of particular interest would be histological evidence of remyelination such as reduced myelin density, shorter and thinner myelin internodes and myelin protein staining of remyelinating oligodendrocyte cytoplasm.

As requested and described in the response to essential revision #1 (above), we have added additional histological analysis to back up and quantify our findings regarding remyelination.

In addition to myelin staining, we did additional characterization of CD3+ T cells and CD20+ B cells to assess inflammation in each type of lesion. More details were added in the paragraph “Histological WML categorization” in the method section for each lesion category.

We also included enhanced discussion on those points in the appropriate section of the paper.

We added acknowledgement in the discussion that additional immunohistochemistry would be useful to further characterize myelination status, but unfortunately we were not able to achieve this in our tissue:

“Finally, we did not obtain ultrathin sections to quantify myelin thickness, as we were interested in performing a battery of stains on our tissue, and as lesions were found at a variety of orientations relative to axons, which would complicate such quantification.”

3) A concern raised in the previous review was that it was not possible to see loss of oligodendroglia based on the image provided to illustrate acute lesions (now in Figure 1), in which there is a small oligo-free area surrounding what may be blood vessels, but the remaining PLP-free area contains many oligodendroglial cells. The question of oligodendroglial loss could be addressed by quantifying the density in perilesional area and comparing it to the intralesional density. However, the authors simply refer to a previous manuscript (JCI, 2019). Were the same animals/lesions analyzed in this study? This point needs to be clearly addressed throughout the manuscript wherever references to previous data are made. Please add quantitative data to demonstrate oligo depletion followed by repopulation in remyelinated lesion. If the quantification does not support the loss of oligodendroglia, then these claims should be removed.

We appreciate the request for clarification. With respect to the second point, the lesions and animals studied in this project were different those reported in our previous publication (JCI, 2019).

Please see the response to essential revision #1 (above) for the revisions we made to quantify OPC and oligodendrocytes. The new analysis is summarized in the newly added Figure 5, and the methods are described under “Histological WML categorization.”

4) The histological criteria for lesion classification is slightly confusing, which was brought up in the original review as well. The authors assert that experienced histopathologists blindly classified these lesions in Kuhlmann 2017, but it is not really clear that exactly the same immunolabelings were performed. Please provide a clear statement on which histological parameters (immunolabelings) were considered by the histopathologists to classify the lesions in this study.

In response to this critique, we reshaped the method section to give more detail on our lesion categorization for both MRI and histopathology. The following paragraphs were added or changed:

“MRI WML categorization

MRI WML categorization and prediction were set based on our prior experience and performed independently by 2 experienced raters, blinded to histology. WML were classified according to the terminal MRI as follows: 1 — predicted early active, described as hyperintense on PDw and enhancing on T1w scans after injection of gadolinium; 2 — predicted chronic, at least partially demyelinated, described as hyperintense on PDw and not enhancing on T1w scans after injection of gadolinium; and 3 — predicted remyelinated, described as initially hyperintense on PDw, iso-intense on PDw at the terminal scan, and not enhancing on T1w scans after injection of gadolinium.”

“Histological WML categorization

Histological analysis and characterization were performed by 1 experienced rater blinded to the MRI. The categorization was performed according to our experience with marmoset EAE lesions as detailed in previous publications^14,24,44. The lesions were categorized as follows: 1 — early active: LFB-PAS shows prominent demyelination with LFB+, PAS+, and/or PLP+ phagocytes, indicating ingestion of myelin breakdown products (Figure 1C). PLP immunohistochemistry also demonstrates demyelination with myelin debris. ASPA/Olig2 double immunohistochemistry demonstrates loss of oligodendrocytes. Qualitative assessment highlights prominent Iba1+ cell infiltration, CD3+, and CD20+ cells in the perivascular cuff and lesion core (not shown), and loss of axons on Bielschowsky silver staining. HE staining shows edema marked by irregular clear spaces around cells. 2 — Chronic, likely at least partially demyelinated: LFB-PAS staining and PLP immunohistochemistry show areas of complete demyelination (Figure 2C). Lesions contain few Iba1+ cells, and CD3+ and CD20+ cells (not shown) are scarce and only found around vessels. There is loss of both oligodendrocytes and OPC on ASPA/Olig2 staining. There is less edema compared to early active lesions (HE staining), and there is substantial loss of axons (Bielschowsky silver stain). 3 — Remyelinated; LFB-PAS staining and PLP immunohistochemistry show nearly normal myelin structure (Figure 3C). Both oligodendrocytes and OPC are present, as demonstrated by staining with ASPA/Olig2. Inflammatory cells are less prominent, with few Iba1+ microglia/macrophages or CD3+ and CD20+ lymphocytes (not shown). Bielschowsky staining shows some preservation of normal axon structures.

Quantitative measurement of demyelination and remyelination was performed by a single experienced rater (MD). To obtain a quantitative measurement of demyelination and remyelination, the percentage of demyelinated area for each lesion was extracted on LFB and PLP staining. For consistency, a region of interest (ROI) of 1500 µm² centered on each lesion core was placed; this ROI was large enough to include even the biggest lesion in our sample. The demyelinated area for each stain was calculated using the thresholding tool on FIJI45 as follows: number of pixels with a null value*100/total number of pixels. ASPA+/Olig2+ cell count (oligodendrocytes) and ASPA-/Olig2+ cell count (OPC) were assessed using thresholding tools in FIJI45 within the same ROI for each lesion.”

Author details

1. Maxime Donadieu

   Translational Neuroradiology Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States

   Contribution

   Data curation, Formal analysis, Validation, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Nathanael J Lee

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0009-0003-7349-1648

2. Nathanael J Lee

   1. Translational Neuroradiology Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States
   2. Department of Neurology and Neurological Sciences, Stanford University, Palo Alto, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Maxime Donadieu

   Competing interests

   No competing interests declared

3. María I Gaitán

   Translational Neuroradiology Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

4. Seung-Kwon Ha

   1. Translational Neuroradiology Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States
   2. Department of Neurobiology, University of Pittsburgh, Pittsburgh, United States

   Contribution

   Data curation, Formal analysis, Writing – original draft, Investigation, Methodology

   Competing interests

   No competing interests declared

5. Nicholas J Luciano

   Translational Neuroradiology Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States

   Contribution

   Data curation, Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

6. Snehashis Roy

   Section on Neural Function, National Institute of Mental Health, National Institutes of Health, Bethesda, United States

   Contribution

   Data curation, Formal analysis, Validation, Methodology

   Competing interests

   No competing interests declared

7. Benjamin Ineichen

   1. Translational Neuroradiology Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States
   2. Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland, Switzerland

   Contribution

   Data curation, Formal analysis, Validation, Investigation, Methodology

   Competing interests

   No competing interests declared

8. Emily C Leibovitch

   Viral Immunology Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States

   Contribution

   Data curation

   Competing interests

   No competing interests declared

9. Cecil C Yen

   Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States

   Contribution

   Data curation, Formal analysis

   Competing interests

   No competing interests declared

10. Dzung L Pham

    Department of Radiology and Radiological Sciences, Uniformed Services University of the Health Sciences, Bethesda, United States

    Contribution

    Data curation, Formal analysis

    Competing interests

    No competing interests declared

11. Afonso C Silva

    1. Department of Neurobiology, University of Pittsburgh, Pittsburgh, United States
    2. Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States

    Contribution

    Supervision, Validation, Methodology

    Competing interests

    No competing interests declared

12. Mac Johnson

    Vertex Pharmaceuticals Incorporated, Boston, United States

    Contribution

    Supervision, Funding acquisition, Writing – review and editing

    Competing interests

    is a shareholder and employee of Vertex Pharmaceuticals, Inc

13. Steve Jacobson

    Viral Immunology Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States

    Contribution

    Supervision, Project administration

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-3127-1287

14. Pascal Sati

    1. Translational Neuroradiology Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States
    2. Neuroimaging Program, Department of Neurology, Cedars Sinai, Los Angeles, United States

    Contribution

    Conceptualization, Data curation, Formal analysis, Supervision, Validation, Investigation, Methodology, Writing – original draft, Writing – review and editing

    For correspondence

    pascal.Sati@cshs.org

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-6763-0125

15. Daniel S Reich

    Translational Neuroradiology Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States

    Contribution

    Conceptualization, Resources, Funding acquisition, Validation, Investigation, Methodology, Writing – review and editing, Supervision

    For correspondence

    daniel.reich@nih.gov

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-2628-4334

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