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. Author manuscript; available in PMC: 2013 May 13.
Published in final edited form as: J Magn Reson Imaging. 2011 Nov 1;35(3):528–536. doi: 10.1002/jmri.22837

In Vivo Assessment of Neurodegeneration in Niemann-Pick Type C Mice by Quantitative T2 Mapping and Diffusion Tensor Imaging

John W Totenhagen 1, Silvia Lope-Piedrafita 2,3, Ivan A Borbon 1, Eriko S Yoshimaru 1, Robert P Erickson 4,5, Theodore P Trouard 1,*
PMCID: PMC3652272  NIHMSID: NIHMS325440  PMID: 22045516

Abstract

Purpose

To quantitatively and non-invasively assess neurological disease progression in a mouse model of Niemann-Pick type C (NPC) disease by measuring white matter status with MRI techniques of T2 mapping and Diffusion Tensor Imaging (DTI).

Materials and Methods

Quantitative T2 and DTI experiments were performed in-vivo in NPC disease model and control mice at three time points to quantify differences and changes in white matter with measurements of T2 relaxation and DTI parameters. Histological staining for myelin content was also performed at two time points to compare with the MRI findings.

Results

The results of the T2 and DTI measurements show significant differences in white matter areas of the brain in the NPC disease model compared to control mice at several time points, and were seen to change over time in both groups.

Conclusions

The findings of this study suggest that quantitative MRI measurements may be suitable in-vivo biomarkers of disease status for future studies of NPC disease models. The changes in white matter measurements between time points in both control and NPC disease groups suggest that white matter structures continue to change and develop over time in the NPC model and can be tracked with MRI techniques.

Keywords: Niemann-Pick type C disease, NPC, DTI, T2, neurodegeneration, myelin

Introduction

Niemann-Pick Type C (NPC) disease is a rare genetic neurodegenerative disease with no cure and few effective options for treatment. Disease prevalence estimates vary due to lack of clinical awareness and difficulties in biochemical diagnostic testing, but have been reported as approximately 1/150,000 live births (1). The underlying defect in the most common form of the disease is mutation of the NPC1 gene (2), which has been located on chromosome 18q11 in humans (3), and chromosome 18 in mice (4). The NPC1 gene codes for the transmembrane protein NPC1. This protein is the subject of much study and has been described in detail in several recent publications (5,6). Briefly, NPC1 is located in the membrane of endosomes/lysosomes in cells throughout the body and is involved in intracellular cholesterol transport. The lack of functional NPC1 protein causes impaired cholesterol trafficking and a buildup of cholesterol and glycolipids in cells, resulting in the pathologies of NPC disease.

The clinical presentation of NPC disease varies with the precise nature of the patient's genetic mutation in the NPC1 gene, and can include visceral involvement of the liver, spleen, and lung. Commonly reported symptoms of NPC disease include hepatosplenomegaly, vertical supranuclear gaze palsy, progressive ataxia, dystonia, dysphagia, and dementia. The age at which the disease presents varies but ultimately all patients develop a progressive and fatal neurological disease. Diagnosis is most often made during childhood with symptoms progressively worsening and leading to death prior to adulthood (1,7,8). Magnetic Resonance Imaging (MRI) of clinical NPC disease has been reported in case studies and has shown brain atrophy, ventricular enlargement, and T2-weighted hyperintensities in white matter regions (9-12). Diffusion Tensor Imaging (DTI) was performed in two reported clinical studies, showing reduced Fractional Anisotropy (FA) in NPC patients compared to control subjects (13,14). A recent MRI study of adult NPC patients reported widespread reductions in gray matter volumes across the brain as well as reduced myelination and axonal structure as measured by DTI (15). In this study, we have investigated the use of quantitative T2-mapping and DTI techniques to follow the progression of NPC disease in mice as measured by changes in white matter integrity.

The current study made use of the most common mouse model of NPC disease, NPCNIH Balb/C (Npc1−/−), which was originally identified from a spontaneous mutation and has been studied for decades (16). This mouse model has a mutation in the Npc1 gene, resulting in loss of Npc1 protein, and mimics a severe neurological form of NPC disease (3,17). The Npc1−/− mouse presents with neurological symptoms comparable to human disease and lives to approximately 10 weeks of age, compared to wild-type control (WT) mice which commonly live up to 100 weeks. The Npc1−/− mouse model has been used in many studies of proposed treatments for NPC disease, including cyclodextrins which have been the subject of several past and recent studies (18-20).

MRI techniques have been used to characterize the neurological disease state with DTI in two reported studies of Npc1−/− mice (21,22) at single time points, and to measure brain atrophy in another (23). The DTI studies found that quantification of white matter integrity with diffusion MRI techniques could provide a biomarker of disease progression and possibly response to treatment.

The study described here is the first reported use of T2 & DTI measurements at multiple time points as biomarkers of neurological NPC disease progression in the Npc1−/− mouse model. The parameters obtained from these measurements are compared to histological measures of myelination.

Materials and Methods

Animals

Wild-type control (WT) and Npc1−/− mice were studied in-vivo at 3, 6, and 9 weeks of age with T2 mapping and DTI techniques. All animal experiments were carried out under protocols approved by the local Institutional Animal Care and Use Committee. For all imaging experiments, mice were anesthetized with isoflurane gas at a concentration of 1.5%, breathing rate monitored, and body temperature monitored and maintained at 37° C with a circulating heated water system.

MRI: T2 mapping

All T2-weighted imaging was carried out on a 7T horizontal bore Bruker Biospec instrument using a 72 mm ID birdcage coil for excitation and a four element phased array surface coil for reception (Bruker BioSpin, Billerica, MA). Animals were imaged in an animal bed restraint system with ear bars and bite bar for head fixation.

T2-weighted datasets were acquired with a 2D radial fast spin-echo sequence using imaging parameters: TR=5000 ms, ETL=8, echo spacing=10ms, 1024 radial lines acquired with 170 data points collected per line, 21 coronal 0.5 mm slices, and a scan time of 10:40 (min:sec).

T2-weighted image reconstruction and T2 map calculations were performed using programs written with MATLAB software (Mathworks, Natick, MA). A reconstruction method taking advantage of the oversampling of the center of k-space in radial sampling (24) was used to obtain 9 sets of images for each slice, one for each of the 8 effective echo times (TEeff) as well as a set constructed from all echoes with an average TE (TEave) of 45 ms, all with in-plane resolutions of 100 × 100 μm2. T2 maps were calculated from the 8 sets of echo images. Region of interest (ROI) analyses were carried out with the aid of MRIcro software (http://www.cabiatl.com/mricro/). Average values of T2 were measured within four brain structures known to contain large amounts of white matter, the Corpus Callosum (CC), External Capsule (EC), Internal Capsule (IC), and Fimbria (FI). Regions were manually outlined on several slice images for each brain structure with the aid of a mouse brain atlas (25) and T2 values averaged for each region. T2 values were also measured in regions of cortical gray matter.

MRI: DTI

DTI experiments were carried out on a 4.7T horizontal bore Bruker Biospec instrument using a 20 mm Litz Transmit/Receive coil (Doty Scientific Inc, Columbia, SC). Animals were positioned within a mouse holder custom-built to fit inside the coil.

A 2D diffusion-weighted radial spin-echo pulse sequence (26) was used with the following parameters: Repetition Time (TR) = 2000 ms, number of averages = 6, Echo Time (TE) = 54 ms, Field of View (FOV) = 1.92 × 1.92 cm2, and acquisition matrix = 128 × 128 (data points along a radial line × number of radial lines) for an in-plane resolution of 150 × 150 μm2. Twelve contiguous 0.5 mm coronal slices were acquired. Seven image sets were collected for each slice, one without diffusion weighting (b=0) and six with a diffusion weighting of b = 1010 s/mm2, Δ = 25 ms, and δ = 9 ms, along six non-colinear directions (27). The total scan time for each animal was approximately 3 hours.

Diffusion weighted images were reconstructed with a magnitude-only filtered back projection reconstruction and FA, apparent diffusion coefficient (ADC), axial diffusivity (AD), and radial diffusivity (RD) maps were calculated using standard algorithms (28). FA provides a measure of the anisotropy of water diffusion in tissue, with values ranging from 0 (isotropic diffusion) to 1 (diffusion restricted to a single direction). Axial and radial diffusivity measurements provide more details about the observed anisotropy and have been reported to provide useful insight into white matter diseases. Decreases in axial diffusivity have been associated with axonal injury and dysfunction, and increases in radial diffusivity with myelin injury (29). A ROI analysis was used to obtain average FA, ADC, axial diffusivity, and radial diffusivity values from the white matter areas of the CC, EC, IC, and FI. Measurements were also obtained in regions of cortical gray matter. Average diffusion weighted images were created by averaging the intensities from the 6 diffusion weighted image sets for each slice. DTI image reconstructions and analyses were carried out using programs written with MATLAB and Interactive Data Language (IDL) software (Research System, Boulder, CO).

Histology & Electron Microscopy

WT and Npc1−/− mice were studied ex-vivo to obtain histological measures of myelination at two time points and to obtain electron microscopy images of white matter myelination. For histology, mice were anesthetized, perfused, and their brains removed and placed in formalin for fixation. After fixation, brains were paraffin-embedded, sectioned with a microtome into coronal 5 μm slices at locations separated by 200 μm, and stained using Luxol Fast Blue (LFB) with Hematoxylin as a counter stain to aid in identification of brain structures. Histology sections were photographed and regions of interest manually drawn to outline the white matter structures of the CC, EC, IC, and FI with the aid of a mouse brain atlas (25). A hue transformation was performed to isolate color information from the LFB histology images. A range of hue values from 170-220 (green-blue) was found to correspond to LFB-stained myelin in areas of control wild-type mice known to contain white matter. The number of pixels in each ROI with hue values in the green to blue range was divided by the total number of pixels in the ROI to obtain a percentage of pixels classified as containing myelin by LFB staining. Analysis was performed with programs written in MATLAB software.

Electron microscopy was performed to examine myelination in the CC region at the cellular level. Several 1 × 1 mm2 pieces of CC tissue were fixed overnight in Karnovsky's fixative (2% paraformaldehyde, 2.5% glutaraldehyde in 0.1M cacodylate and 0.05% CaCl2, pH7.3) at 4C. They were then washed 3 times for 10-15 minutes in storage buffer (0.1M cacodylate, 0.1M sucrose, 0.05% CaCl2, pH7.3). Thin sections were stained with osmium tetroxide and uranylic acid and imaged with a Zeiss 910 electron microscope.

Results

Representative T2-weighted images of WT and Npc1−/− mice at 3, 6, and 9 weeks of age are shown in Figure 1. Differences in the contrast between white matter and gray matter in WT and Npc1−/− mice are visible at all ages studied. These differences are most easily seen in the CC and EC regions and are indicated by arrows in Figure 1. The white matter of the CC and EC appears darker than the surrounding gray matter in the WT images (a,c,e), but appears equally intense or even brighter than the gray matter in the Npc1−/− mice (b,d,e). Brain atrophyin Npc1−/− mice can be seen in the apparent size differences of the images shown in panels e and f. The Npc1−/− brain in panel f appears smaller than that of the WT brain in panel e, although slight variations in animal positioning and slice location can complicate precise comparisons between slices in 2D MRI images with anisotropic voxel resolutions.

Figure 1.

Figure 1

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Representative T2-weighted in-vivo mouse brain MRI scans are shown. Wild type (a,c,e) and Npc1−/− scans are shown (b,d,f). Mice are shown aged 3 weeks (a,b), 6 weeks (c,d), and 9 weeks (e,f). Differences in contrast between white and gray matter structures in the two mouse types are evident in the areas of the Corpus Callosum and External Capsule, as indicated by arrows (e,f).

T2 mapping was carried out to quantify the differences seen on the T2-weighted images. Examples of T2-weighted images reconstructed at different TEeff are shown in Figure 2. The radial pulse sequence and tiered reconstruction resulted in 8 sets of T2-weighted images with different TEeff, shown in panels a-h. An example TEave image is shown in panel i. The T2 map created from the 8 TEeff images is shown in panel j. The decreasing sensitivity of the surface coils towards the ventral (bottom of the coronal slices) side of the mice results in decreased signal to noise ratio in the T2-weighted images and significant noise in the T2 maps.

Figure 2.

Figure 2

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Example T2-weighted images of a 9 week old WT mouse, images are shown reconstructed at 8 TEeff values for a single slice (a-h), as well as the corresponding TEave=45ms image (i) used for drawing of regions of interest, and T2 parameter map (j).

Representative DTI images and parametric maps of a single slice from WT and Npc1−/− mice at 9 weeks of age are shown in Figure 3. T2-weighted images without diffusion weighting (b = 0 s/mm2) are shown in panels a and b. Average diffusion images at b = 1010 s/mm2 are shown in panels c and d. Maps of ADC, FA, axial diffusivity (AD), and radial diffusivity (RD) are shown in panels e-f, g-h, i-j, and k-l respectively. To allow visual comparison between mouse types, maps are displayed with identical grayscale. Notable features in these maps include increases in ventricular size in the Npc1−/− mouse brain as well as general reduction of FA values and focal increases in the values of radial diffusivity.

Figure 3.

Figure 3

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Example DTI images are shown at 9 weeks of age in WT (a,c,e,g,i,k) and Npc1−/− (b,d,f,h,j,l) mice. T2-weighted images with zero diffusion weighting (b=0 s/mm2) are shown for a representative slice (a,b), as well as Average diffusion images (b=1010 s/mm2) (c,d), and maps of ADC (e,f), FA (g,h), axial diffusivity (i,j), and radial diffusivity (k,l).

Figure 4 shows representative ROIs drawn on a single WT mouse at age 9 weeks for quantitative T2 analysis. The EC region was able to be identified in the greatest number of slices for each set of images, followed by the CC, IC, and FI. Similar regions were drawn directly on the FA maps for DTI analyses. These ROIs were then applied to the ADC, AD and RD maps.

Figure 4.

Figure 4

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Representative images of regions of interest (ROI) drawn for analyses of white matter regions of the Corpus Callosum (CC), External Capsule (EC), Internal Capsule (IC), and Fimbria (FI). Regions are shown overlaid upon T2-weighted images of a WT mouse at 9 weeks of age.

Differences in myelination in white matter regions of the WT and Npc1−/− mice brains were confirmed with LFB histology and electron microscopy, examples of which are shown in Figure 5 for 9-week old mice. In WT mice, the EC and CC can be seen as thick sections of tissue stained a darker blue color compared to surrounding tissue. In Npc1−/− mice, these areas are much thinner and stain lighter in color. The FI region of the WT brain section stains blue with LFB, while the same region in the Npc1−/− mice shows very little LFB staining. Interestingly, the IC appears to contain similar amounts of LFB staining in WT and Npc1−/− sections. Electron microscopy images from the CC region of WT and Npc1−/− mice are shown in panels e and f of figure 5, respectively. The cross sections of myelinated axons are visible in both images and the myelin sheaths can be seen as dark bands along the circumference of the axons. Axons in Npc1−/−mice appear to be both smaller and less myelinated compared to those in WT mice.

Figure 5.

Figure 5

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Example Luxol-Fast Blue stained histology (a-d), and electron microscopy (e,f) images of WT (a,c,e) and Npc1−/− (b,d,f) mice at 9 weeks of age. Regions of white matter in the Corpus Callosum (CC) and External Capsule (EC) are indicated with labels in panels a and b. Regions of the Internal Capsule (IC) and Fimbria (FI) labeled in panels c and d. Dark bands of myelin are visible on the perimeter of the myelinated neuronal cells in the EM images of the CC region (e,f).

Quantitative measurements of T2, FA, ADC, axial diffusivity, radial diffusivity, and histological staining in brain regions of WT and Npc1−/− mice at 3 ages are plotted in Figure 6. T2 relaxation times shown in panels a-e of figure 6 show a significant (p<.001) difference between the WT and Npc1−/− mouse groups in the regions of the CC and EC at all time points, but not in the IC, FI, or gray matter at any time points. The T2 values measured in the WT and Npc1−/− mice were seen to decrease over from the 3 week to 9 week time points in all regions studied. There is no significant difference in the values of ADC between groups or over time in the CC, EC and IC (f-g). However, in the fimbria ROI, significant differences between WT and Npc1−/− mice are observed at all ages with the ADC of the WT mice less than that of the Npc1−/− mice (i). Gray matter ADC is significantly different only at 9 weeks of age (j). Significant differences (p < 0.05) were found in FA measurements between the WT and Npc1−/− mice in all four regions of white matter at all time points, as seen in panels k-n of figure 6. The FA results obtained at 3 weeks of age were described in previously published work (22). Interestingly, the FA is seen to increase from the 3 week to 9 week time points in the CC and IC regions of the WT and Npc1−/− groups, but not in the EC and FI regions. The AD results are shown in panels p-t and show a significantly decreased diffusivity in Npc1−/− mice compared to WT in the internal capsule at all time points. RD measurements are shown in panels u-y and demonstrate a significant increase in diffusivity in the Npc1−/− mice compared to WT in the corpus callosum, internal capsule, and fimbria at each time point. The radial diffusivity results shown at 3 weeks of age were described in previously published work (22).

Figure 6.

Figure 6

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Summary of results of region of interest analyses in WT (black lines, solid circles) and Npc1−/− (gray lines, open triangles) mice. T2 measurements are shown at 3 time points (a,b,c,d,e) with standard deviations (N=5,6,4 for WT mice at 3,6,9 weeks of age, and N=6,4,6 for Npc1−/− mice at 3,6,9 weeks of age respectively). Measurements of ADC (f,g,h,i,j), FA (k,l,m,n,o), axial diffusivity (p,q,r,s,t), and radial diffusivity (u,v,w,x,y) are shown at 3,6,9 weeks of age with standard deviations (N=4 for WT and Npc1−/− mice at all time points). The FA and radial diffusivity results shown at week 3 were described in previous work (22). Significant differences between WT and Npc1−/− mice are indicated (* P < 0.05, ** P < 0.01, *** P < 0.001). Results of Luxol Fast Blue histology quantification are shown (z,aa,bb,cc,dd) at two time points (N=1).

Quantitative LFB staining is shown in panels z-dd of Figure 6 at the youngest and oldest ages studied. The percentage of pixels in the regions stained by LFB is higher in all regions of the WT mouse compared to the Npc1−/− mouse, and interestingly, increased over time in both WT and Npc1−/− mice for all regions studied. The IC shows a relatively large amount of myelination compared to the other regions (panel bb), even in the Npc1−/− mouse, as can be seen in the example histology images (figure 5).

Discussion

The major findings of this work are the measured differences in T2 and diffusion anisotropy between WT and Npc1−/− mice at different stages of life. The first time point studied at 3 weeks of age is shortly after weaning and the Npc1−/− mice at this point are pre-symptomatic. At six weeks of age, the Npc1−/− mice are symptomatic with weight loss and ataxia compared to WT mice (19), and at 9 weeks of age are near death due to disease progression.

T2 measurements have not been previously reported in the Npc1−/− mouse model, but increased T2 values have been reported in the shiverer mouse, a model of dismyelination (30). The myelin present in the axons of neurons normally causes the rotational tumbling rate of local water molecules to be reduced compared to other tissues. The decreased tumbling rate results in a greater signal dephasing and increased spin-spin relaxation (lower T2 times). This leads to the characteristic contrast seen in T2-weighted brain imaging, in which white matter appears dark relative to gray matter. This contrast is absent, and even reversed, in white matter regions of the Npc1−/− mouse, illustrated in figure 1. Quantitative T2 measurements in this study of four white matter regions show differences between WT and Npc1−/− mice. In the CC and EC, Npc1−/− mice have significantly higher T2 relaxation times than do WT mice at all ages studied. In the IC and FI, this trend is observed, although differences do not reach significance. This may be due to the difficulty in drawing reliable ROIs in these areas of the brain, particularly in the Npc1−/− mice, which lack the normal gray/white matter contrast used for identifying brain regions. The use of an automated atlas-based registration method of identifying brain regions may be a way of eliminating the errors associated with manual ROIs in future studies. The T2 values measured in all regions of the brain are seen to decrease over time, this is consistent with reports of decreasing T2 values in human brain structures throughout development and maturation (31,32).

Significant differences in FA and RD have previously been reported in Npc1−/− mice at 3 weeks of age with a ROI based analysis (22) and suggested at 9 weeks of age with a whole-brain analysis (21). Decreased FA and increased RD have also been reported in the shiverer mouse model of dismyelination (33). Myelin in white matter normally causes increased anisotropy relative to gray matter due to the myelin sheath's restriction of the translational movement of water perpendicular to the long axis of the axons. Decreased myelination would be expected to result in decreased FA and increased RD, as the translational movement of water perpendicular to the long axis of the axon would tend to increase with fewer hydrophobic barriers. Although FA and RD are sensitive to the translational mobility of water and T2 relaxation is sensitive to the rotational mobility of water, it is likely that biological changes that affect one, will affect the other. If tissue water becomes more like free water, increases in both rotational and translational mobility are expected, causing increased T2, decreased FA, and increased RD. The current study expands upon previous DTI studies in the Npc1−/− mice by including FA and RD measurements of specific brain regions at 6 and 9 weeks of age. The FA values in Npc1−/− mice are significantly lower than those in the same regions of WT mice at all ages in the current study. The RD values in Npc1−/− mice are significantly higher than those in the same regions of WT mice at most ages studied. In two of the regions studied, the CC and IC, increases in FA and decreases in RD are seen in both WT and Npc1−/− mice as the their age increases. This could indicate some process of white matter maturation, even in the Npc1−/− mice. The significant differences in FA and RD seen in all of the brain regions studied, and the changes seen at different ages, suggest that DTI measurements may be suitable biomarkers for in-vivo therapeutic studies of the Npc1−/− model. In addition to FA and RD, the ADC and axial diffusivity were also determined. The ADC results did not show significant differences between the WT and Npc1−/− mice, except in the fimbria, which may be susceptible to partial volume contamination from the adjacent lateral ventricles which are expanded in the Npc1−/− mice. The axial diffusivity of the Npc1−/− mice was decreased significantly in the IC region at all ages studied. Other regions showed little difference between WT and Npc1−/− mice. Because of this, it appears that the changes observed in the FA are being driven for the most part by changes in RD and to a lesser extent by AD. This would be expected with a deficient myelination in the Npc1−/− mice. The five quantitative MRI parameters presented in this study all show significant changes between Npc1−/− and WT mice, with T2 showing the greatest significant separation in the CC and IC regions at all time points (p<.001), FA in the IC at all time points (p<.001), and RD in the FI (p<.01 at 3 weeks, p<.001 at 6 and 9 weeks). The ROI based results in the current study vary from region to region, indicating a differential response of the various MRI parameters to the disease in different brain regions. This suggests possible variability in disease progression in different brain regions and that there is not one parameter that is “best” for evaluating the disease for all ROIs. It should be noted that the ability to draw consistent ROIs of a specific brain region can be affected by the presence of the disease, and that this may play a role in obtaining ROI-dependent results. A major drawback of the DTI method used in this work is the long time required (∼3 hours). Advancements in DTI pulse sequences, scanner hardware, animal fixation, and improved MRI coils may allow faster and more practical DTI studies of the Npc1−/− mouse in the future.

The differences measured in both T2 and diffusion anisotropy (FA and RD) are likely a result of the lack of myelination in the Npc1−/− mouse seen in both LFB histology and electron microscopy images of the Npc1−/− mouse, shown in figure 5. The LFB histology results in this work, which resemble earlier reported histological studies of myelination in the Npc1−/− mouse (34,22,35), show differences in myelination on a regional scale. The Npc1−/−mouse has decreased staining for myelination in all regions studied compared to WT, and staining increased over time along with the WT mouse. This corresponds with the results of the T2 and DTI studies and suggests an increased maturation of white matter over time, even in the dismyelinated Npc1−/− mouse. The Npc1−/− mouse shows a dramatic decrease in the quantity of myelinated axons in the CC region at 9 weeks of age compared to the WT mouse, similar to past studies of the Npc1−/− mouse model at 3 and 7 weeks of age (36,37).

The MRI changes found in this study corroborate and extend previous neuropathological findings in the Npc1−/− mouse model. Neurodegeneration and a lack of myelinated axons are commonly reported features of the Npc1−/− mouse, and are associated with activation of glial cells and increased apoE and apoD synthesis (38) but likely not due to apoptosis (39,40). The quantitative MRI and histology results presented here suggest an increase in myelination over time in both WT and Npc1−/− mice. Histological studies in larger numbers of mice would be needed to conclusively demonstrate an increase in myelination over time in regions of the Npc1−/− mouse brain, and may be a worthwhile avenue of future study. An interesting finding in the current work is the relative preservation of myelination in the IC region of the Npc1−/− mouse at 9 weeks shown in figure 5, and quantified in figure 6. This is similar to a reported clinical case study (13), which found nearly normal FA values in the IC even at an advanced disease state.

In conclusion, the results of this study show that MRI measurements of T2 and diffusion anisotropy (FA and RD) can be quantifiably obtained at multiple time points in the Npc1−/− mouse model and show promise as useful biomarkers for future studies of disease progression and proposed treatments in NPC disease models.

Acknowledgments

The authors thank Christine Howison for animal assistance, and Andrea Grantham for assistance with histology.

Grant Support: National Institutes of Health grant R01-EB000343

References

  • 1.Patterson MC, Vanier MT, Suzuki K, et al. Niemann-Pick Disease Type C: A Lipid Trafficking Disorder. In: Scriver CR BA, Sly WS, Valle D, Childs B, Kinzler KW, Vogelstein B, editors. The metabolic and molecular bases of inherited disease. 8th. New-York: McGraw Hill; 2001. pp. 3611–3633. [Google Scholar]
  • 2.Carstea ED, Morris JA, Coleman KG, et al. Niemann-Pick C1 disease gene: homology to mediators of cholesterol homeostasis. Science. 1997;277:228–231. doi: 10.1126/science.277.5323.228. [DOI] [PubMed] [Google Scholar]
  • 3.Loftus SK, Morris JA, Carstea ED, et al. Murine model of Niemann-Pick C disease: mutation in a cholesterol homeostasis gene. Science. 1997;277:232–235. doi: 10.1126/science.277.5323.232. [DOI] [PubMed] [Google Scholar]
  • 4.Erickson RP, Aviles RA, Zhang J, Kozloski MA, Garver WS, Heidenreich RA. High-resolution mapping of the spm (Niemann-Pick Type C) locus on mouse chromosome 18. Mamm Genome. 1997;8:355–356. doi: 10.1007/s003359900440. [DOI] [PubMed] [Google Scholar]
  • 5.Kwon HJ, Abi-Mosleh L, Wang ML, et al. Structure of N-terminal domain of NPC1 reveals distinct subdomains for binding and transfer of cholesterol. Cell. 2009;137:1213–1224. doi: 10.1016/j.cell.2009.03.049. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Lloyd-Evans E, Platt FM. Lipids on trial: the search for the offending metabolite in Niemann-Pick type C disease. Traffic. 2010;11:419–428. doi: 10.1111/j.1600-0854.2010.01032.x. [DOI] [PubMed] [Google Scholar]
  • 7.Patterson MC. A riddle wrapped in a mystery: understanding Niemann-Pick disease, type C. Neurologist. 2003;9:301–310. doi: 10.1097/01.nrl.0000094627.78754.5b. [DOI] [PubMed] [Google Scholar]
  • 8.Vanier MT. Niemann-Pick disease type C. Orphanet J Rare Dis. 2010;5:16. doi: 10.1186/1750-1172-5-16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Palmeri S, Battisti C, Federico A, Guazzi GC. Hypoplasia of the corpus callosum in Niemann-Pick type C disease. Neuroradiology. 1994;36:20–22. doi: 10.1007/BF00599187. [DOI] [PubMed] [Google Scholar]
  • 10.Suzuki K, Parker CC, Pentchev PG, et al. Neurofibrillary tangles in Niemann-Pick disease type C. Acta Neuropathol. 1995;89:227–238. doi: 10.1007/BF00309338. [DOI] [PubMed] [Google Scholar]
  • 11.Hsu YS, Hwu WL, Huang SF, et al. Niemann-Pick disease type C (a cellular cholesterol lipidosis) treated by bone marrow transplantation. Bone Marrow Transplant. 1999;24:103–107. doi: 10.1038/sj.bmt.1701826. [DOI] [PubMed] [Google Scholar]
  • 12.Iturriaga C, Pineda M, Fernandez-Valero EM, Vanier MT, Coll MJ. Niemann-Pick C disease in Spain: clinical spectrum and development of a disability scale. J Neurol Sci. 2006;249:1–6. doi: 10.1016/j.jns.2006.05.054. [DOI] [PubMed] [Google Scholar]
  • 13.Trouard TP, Heidenreich RA, Seeger JF, Erickson RP. Diffusion tensor imaging in Niemann-Pick Type C disease. Pediatr Neurol. 2005;33:325–330. doi: 10.1016/j.pediatrneurol.2005.05.004. [DOI] [PubMed] [Google Scholar]
  • 14.Scheel M, Abegg M, Lanyon LJ, Mattman A, Barton JJ. Eye movement and diffusion tensor imaging analysis of treatment effects in a Niemann-Pick Type C patient. Mol Genet Metab. 2010;99:291–295. doi: 10.1016/j.ymgme.2009.10.180. [DOI] [PubMed] [Google Scholar]
  • 15.Walterfang M, Fahey M, Desmond P, et al. White and gray matter alterations in adults with Niemann-Pick disease type C: a cross-sectional study. Neurology. 2010;75:49–56. doi: 10.1212/WNL.0b013e3181e6210e. [DOI] [PubMed] [Google Scholar]
  • 16.Pentchev PG, Gal AE, Booth AD, et al. A lysosomal storage disorder in mice characterized by a dual deficiency of sphingomyelinase and glucocerebrosidase. Biochim Biophys Acta. 1980;619:669–679. doi: 10.1016/0005-2760(80)90116-2. [DOI] [PubMed] [Google Scholar]
  • 17.Vanier MT, Millat G. Niemann-Pick disease type C. Clin Genet. 2003;64:269–281. doi: 10.1034/j.1399-0004.2003.00147.x. [DOI] [PubMed] [Google Scholar]
  • 18.Camargo F, Erickson RP, Garver WS, et al. Cyclodextrins in the treatment of a mouse model of Niemann-Pick C disease. Life Sci. 2001;70:131–142. doi: 10.1016/s0024-3205(01)01384-4. [DOI] [PubMed] [Google Scholar]
  • 19.Davidson CD, Ali NF, Micsenyi MC, et al. Chronic cyclodextrin treatment of murine Niemann-Pick C disease ameliorates neuronal cholesterol and glycosphingolipid storage and disease progression. PLoS One. 2009;4:e6951. doi: 10.1371/journal.pone.0006951. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Liu B, Turley SD, Burns DK, Miller AM, Repa JJ, Dietschy JM. Reversal of defective lysosomal transport in NPC disease ameliorates liver dysfunction and neurodegeneration in the npc1-/- mouse. Proc Natl Acad Sci U S A. 2009;106:2377–2382. doi: 10.1073/pnas.0810895106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Ahmad I, Lope-Piedrafita S, Bi X, et al. Allopregnanolone treatment, both as a single injection or repetitively, delays demyelination and enhances survival of Niemann-Pick C mice. J Neurosci Res. 2005;82:811–821. doi: 10.1002/jnr.20685. [DOI] [PubMed] [Google Scholar]
  • 22.Lope-Piedrafita S, Totenhagen JW, Hicks CM, Erickson RP, Trouard TP. MRI detects therapeutic effects in weanling Niemann-Pick type C mice. J Neurosci Res. 2008 doi: 10.1002/jnr.21707. [DOI] [PubMed] [Google Scholar]
  • 23.Bae JS, Carter JE, Jin HK. Adipose tissue-derived stem cells rescue Purkinje neurons and alleviate inflammatory responses in Niemann-Pick disease type C mice. Cell Tissue Res. 2010;340:357–369. doi: 10.1007/s00441-010-0942-3. [DOI] [PubMed] [Google Scholar]
  • 24.Altbach MI, Bilgin A, Li Z, Clarkson EW, Trouard TP, Gmitro AF. Processing of radial fast spin-echo data for obtaining T2 estimates from a single k-space data set. Magn Reson Med. 2005;54:549–559. doi: 10.1002/mrm.20611. [DOI] [PubMed] [Google Scholar]
  • 25.Franklin K, Paxinos G. The Mouse Brain in Stereotaxic Coordinates. 3rd. New York, NY: Elsevier Academic Press; 2008. [Google Scholar]
  • 26.Trouard TP, Theilmann RJ, Altbach MI, Gmitro AF. High-resolution diffusion imaging with DIFRAD-FSE (diffusion-weighted radial acquisition of data with fast spin-echo) MRI. Magn Reson Med. 1999;42:11–18. doi: 10.1002/(sici)1522-2594(199907)42:1<11::aid-mrm3>3.0.co;2-j. [DOI] [PubMed] [Google Scholar]
  • 27.Hasan KM, Basser PJ, Parker DL, Alexander AL. Analytical computation of the eigenvalues and eigenvectors in DT-MRI. J Magn Reson. 2001;152:41–47. doi: 10.1006/jmre.2001.2400. [DOI] [PubMed] [Google Scholar]
  • 28.Pierpaoli C, Basser PJ. Toward a quantitative assessment of diffusion anisotropy. Magn Reson Med. 1996;36:893–906. doi: 10.1002/mrm.1910360612. [DOI] [PubMed] [Google Scholar]
  • 29.Budde MD, Kim JH, Liang HF, et al. Toward accurate diagnosis of white matter pathology using diffusion tensor imaging. Magn Reson Med. 2007;57:688–695. doi: 10.1002/mrm.21200. [DOI] [PubMed] [Google Scholar]
  • 30.Dyakin VV, Chen Y, Branch CA, et al. The contributions of myelin and axonal caliber to transverse relaxation time in shiverer and neurofilament-deficient mouse models. NeuroImage. 2010;51:1098–1105. doi: 10.1016/j.neuroimage.2010.03.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Hasan KM, Walimuni IS, Kramer LA, Frye RE. Human brain atlas-based volumetry and relaxometry: application to healthy development and natural aging. Magn Reson Med. 2010;64:1382–1389. doi: 10.1002/mrm.22515. [DOI] [PubMed] [Google Scholar]
  • 32.Kumar R, Delshad S, Macey PM, Woo MA, Harper RM. Development of T2-relaxation values in regional brain sites during adolescence. Magn Reson Imaging. 2010;29:185–193. doi: 10.1016/j.mri.2010.08.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Nair G, Tanahashi Y, Low HP, Billings-Gagliardi S, Schwartz WJ, Duong TQ. Myelination and long diffusion times alter diffusion-tensor-imaging contrast in myelin-deficient shiverer mice. NeuroImage. 2005;28:165–174. doi: 10.1016/j.neuroimage.2005.05.049. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Weintraub H, Abramovici A, Sandbank U, Booth AD, Pentchev PG, Sela B. Dysmyelination in NCTR-Balb/C mouse mutant with a lysosomal storage disorder. Morphological survey Acta Neuropathol. 1987;74:374–381. doi: 10.1007/BF00687215. [DOI] [PubMed] [Google Scholar]
  • 35.Zhang M, Strnatka D, Donohue C, Hallows JL, Vincent I, Erickson RP. Astrocyte-only Npc1 reduces neuronal cholesterol and triples life span of Npc1-/- mice. J Neurosci Res. 2008;86:2848–2856. doi: 10.1002/jnr.21730. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.German DC, Liang CL, Song T, Yazdani U, Xie C, Dietschy JM. Neurodegeneration in the Niemann-Pick C mouse: glial involvement. Neuroscience. 2002;109:437–450. doi: 10.1016/s0306-4522(01)00517-6. [DOI] [PubMed] [Google Scholar]
  • 37.Takikita S, Fukuda T, Mohri I, Yagi T, Suzuki K. Perturbed myelination process of premyelinating oligodendrocyte in Niemann-Pick type C mouse. J Neuropathol Exp Neurol. 2004;63:660–673. doi: 10.1093/jnen/63.6.660. [DOI] [PubMed] [Google Scholar]
  • 38.Li H, Repa JJ, Valasek MA, et al. Molecular, anatomical, and biochemical events associated with neurodegeneration in mice with Niemann-Pick type C disease. J Neuropathol Exp Neurol. 2005;64:323–333. doi: 10.1093/jnen/64.4.323. [DOI] [PubMed] [Google Scholar]
  • 39.Erickson RP, Bernard O. Studies on neuronal death in the mouse model of Niemann-Pick C disease. J Neurosci Res. 2002;68:738–744. doi: 10.1002/jnr.10257. [DOI] [PubMed] [Google Scholar]
  • 40.Ko DC, Milenkovic L, Beier SM, Manuel H, Buchanan J, Scott MP. Cell-autonomous death of cerebellar purkinje neurons with autophagy in Niemann-Pick type C disease. PLoS Genet. 2005;1:81–95. doi: 10.1371/journal.pgen.0010007. [DOI] [PMC free article] [PubMed] [Google Scholar]

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