Abstract

Senile plaques are a prominent pathological feature of Alzheimer's disease (AD), but little is understood about the association of glial cells with plaques or about the dynamics of glial responses through the disease course. We investigated the progression of reactive glial cells and their relationship with AD pathological hallmarks to test whether glial cells are linked only to amyloid deposits or also to tangle deposition, thus integrating both lesions as a marker of disease severity. We conducted a quantitative stereology-based post-mortem study on the temporal neocortex of 15 control subjects without dementia and 91 patients with AD, including measures of amyloid load, neurofibrillary tangles, reactive astrocytes, and activated microglia. We also addressed the progression of glial responses in the vicinity (≤50 μm) of dense-core plaques and tangles. Although the amyloid load reached a plateau early after symptom onset, astrocytosis and microgliosis increased linearly throughout the disease course. Moreover, glial responses correlated positively with tangle burden, whereas astrocytosis correlated negatively with cortical thickness. However, neither correlated with amyloid load. Glial responses increased linearly around existing plaques and in the vicinity of tangles. These results indicate that the progression of astrocytosis and microgliosis diverges from that of amyloid deposition, arguing against a straightforward relationship between glial cells and plaques. They also suggest that reactive glia might contribute to the ongoing neurodegeneration. Senile plaques are a prominent pathological feature of Alzheimer's disease (AD), but little is understood about the association of glial cells with plaques or about the dynamics of glial responses through the disease course. We investigated the progression of reactive glial cells and their relationship with AD pathological hallmarks to test whether glial cells are linked only to amyloid deposits or also to tangle deposition, thus integrating both lesions as a marker of disease severity. We conducted a quantitative stereology-based post-mortem study on the temporal neocortex of 15 control subjects without dementia and 91 patients with AD, including measures of amyloid load, neurofibrillary tangles, reactive astrocytes, and activated microglia. We also addressed the progression of glial responses in the vicinity (≤50 μm) of dense-core plaques and tangles. Although the amyloid load reached a plateau early after symptom onset, astrocytosis and microgliosis increased linearly throughout the disease course. Moreover, glial responses correlated positively with tangle burden, whereas astrocytosis correlated negatively with cortical thickness. However, neither correlated with amyloid load. Glial responses increased linearly around existing plaques and in the vicinity of tangles. These results indicate that the progression of astrocytosis and microgliosis diverges from that of amyloid deposition, arguing against a straightforward relationship between glial cells and plaques. They also suggest that reactive glia might contribute to the ongoing neurodegeneration. Activated glia is a prominent feature of Alzheimer's disease (AD) neuropathological features, with both reactive astrocytes and activated microglia clustering around and within dense-core amyloid plaques (ie, thioflavin-S–positive plaques).1Itagaki S. McGeer P.L. Akiyama H. Zhu S. Selkoe D. Relationship of microglia and astrocytes to amyloid deposits of Alzheimer disease.J Neuroimmunol. 1989; 24: 173-182Abstract Full Text PDF PubMed Scopus (765) Google Scholar A better understanding of how these reactive glial cells accrue during the disease course and how they relate to the classic AD pathological hallmarks [ie, amyloid plaques and neurofibrillary tangles (NFTs)] is crucial for the following reasons: i) a body of preclinical evidence implicates these glial cells in AD pathophysiological features; ii) new positron emission tomographic (PET) radiotracers for amyloid plaques, NFTs, and, particularly, activated glial cells are being developed as diagnostic and progression biomarkers; and iii) clinical trials with anti-inflammatory therapies, ranging from nonsteroidal anti-inflammatory drugs (NSAIDs) to i.v. Ig, are under development. In a previous quantitative neuropathological study,2Ingelsson M. Fukumoto H. Newell K.L. Growdon J.H. Hedley-Whyte E.T. Frosch M.P. Albert M.S. Hyman B.T. Irizarry M.C. Early Aβ accumulation and progressive synaptic loss, gliosis, and tangle formation in AD brain.Neurology. 2004; 62: 925-931Crossref PubMed Scopus (523) Google Scholar we observed a positive linear correlation between astrocytosis in the temporal neocortex, as measured with a glial fibrillary acidic protein (GFAP) enzyme-linked immunosorbent assay, and the duration of the disease from the onset of cognitive symptoms, despite the plaque burden remaining stable throughout the course of the disease. We hypothesized that a certain threshold of amyloid burden might be needed to trigger glial responses within a particular region of the cortex and that, once triggered, glial responses would reflect a pathogenic cascade increasingly independent of plaques. In the present study, we sought to extend that observation and test the hypothesis that, although initially linked to plaques, glial responses increasingly reflect the widespread ongoing neurodegenerative process. We quantified the number of reactive astrocytes and activated microglial cells in the temporal neocortex of a large cohort of controls without dementia and subjects with AD at different stages of the disease and investigated both their apparent progression throughout the disease course and their relation to the local burden of amyloid plaques and NFTs. Although glial association with amyloid plaques has long been assumed, we found a dissociation between these pathological features, with a linear increase of reactive glia despite a relatively stable plaque burden. The magnitude of these glial changes correlated with the burden of NFTs. A closer analysis in a subset of subjects with AD revealed that reactive glial cells increased both in the proximity of dense-core plaques and near NFTs, thus supporting a previously not described association between glial responses and neurofibrillary degeneration. Formalin-fixed, paraffin-embedded tissue specimens from the temporal association isocortex (Brodmann area 38) of 91 patients with AD and 15 controls without dementia were obtained from the Massachusetts Alzheimer Disease Research Center Brain Bank. They were consecutively selected by tissue availability. All of the study subjects or their next of kin gave written informed consent for the brain donation, and the Massachusetts General Hospital Institutional Review Board approved the study protocol. The demographic characteristics of both groups are depicted in Table 1. All of the patients with AD fulfilled the National Institute of Neurological and Communicative Disorders and Stroke–Alzheimer's Disease and Related Disorders Associations criteria for probable AD3McKhann G. Drachman D. Folstein M. Katzman R. Price D. Stadlan E.M. Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of the Department of Health and Human Services Task Force on Alzheimer's Disease.Neurology. 1984; 34: 939-944Crossref PubMed Google Scholar and the National Institute on Aging–Reagan criteria for high likelihood of AD.4The National Institute on Aging, and Reagan Institute Working Group on Diagnostic Criteria for the Neuropathological Assessment of Alzheimer's Disease Consensus recommendations for the postmortem diagnosis of Alzheimer's disease.Neurobiol Aging. 1997; 18: S1-S2PubMed Google Scholar Cases with cerebrovascular disease considered severe enough to contribute to the dementia syndrome were excluded because cerebrovascular disease is a major cause of focal gliosis. Cases with Lewy body pathological features were also excluded. Sections (8-μm thick) were deparaffinized for immunohistochemistry by standard methods. Primary and secondary antibodies, pretreatments for antigen retrieval, and visualization strategies are summarized in Table 2. For stereological quantitative studies, immunostained sections were developed with 3,3′-diaminobenzidine (Vector Laboratories, Burlingame, CA), lightly counterstained with Mayer's hematoxylin, dehydrated with increasing concentrations of ethanol, cleared with xylene, and cover-slipped with Permount mounting media (Fisher Scientific, Fair Lawn, NJ). Nearly adjacent temporal sections from a subset of 40 subjects with AD and six controls without dementia were immunostained using fluorescently labeled secondary antibodies, counterstained with 0.05% thioflavin-S (Sigma, St Louis, MO) in 50% ethanol for 8 minutes, and cover-slipped with Vectashield mounting media with 4',6-diamidino-2-phenylindole (DAPI) (Vector Laboratories).Table 1Demographic Characteristics of the Cohorts without Dementia and with AD and Their Corresponding Subsets Included in the Quantitative Neuropathological StudyCharacteristicsControl cohort (n = 15)AD cohort (n = 91)P valueControl subset (n = 6)AD subset (n = 40)P valueAge at death (years)⁎Data are given as mean ± SD. P values were obtained using the two-tailed Mann-Whitney U-test.79.9 ± 13.379.0 ± 7.8NS83.7 ± 14.077.6 ± 8.60.0429Female sex†Data are given as number (percentage) of each group. P values were obtained using the two-tailed χ2 test with Fisher's exact test.10 (66.7)58 (63.7)NS4 (66.7)26 (65.0)NSDisease duration (years)‡Data are given as median (interquartile range).NA9.8 (6.8–13.7)NANA9.9 (5.4–15.5)NAAPOE genotype†Data are given as number (percentage) of each group. P values were obtained using the two-tailed χ2 test with Fisher's exact test. APOEε4 carriers4 (26.7)59 (64.8)0.00902 (33.3)21 (52.5)NS APOEε4 alleles§To obtain percentages, the denominators for this row were doubled.4 (13.3)70 (38.5)0.00702 (16.7)25 (31.2)NSPost-mortem interval (hours)⁎Data are given as mean ± SD. P values were obtained using the two-tailed Mann-Whitney U-test.22.3 ± 12.813.9 ± 9.10.008521.0 ± 11.114.1 ± 6.2NSInformation about cause of death was available in only 44 of the 91 subjects with AD because nursing homes are the main source of our brain donation program. Patients with protracted death (mostly aspirative pneumonia and cancer, n = 31) did not differ from patients with sudden death (mostly pulmonary emboli and myocardial infarction, n = 13) regarding their age at death (P = 0.2261), disease duration (P = 0.9898), or amount of astrocytosis (P = 0.1870) and microgliosis (P = 0.9180). Statistically significant P values are boldfaced.NA, not applicable; NS, not significant. Data are given as mean ± SD. P values were obtained using the two-tailed Mann-Whitney U-test.† Data are given as number (percentage) of each group. P values were obtained using the two-tailed χ2 test with Fisher's exact test.‡ Data are given as median (interquartile range).§ To obtain percentages, the denominators for this row were doubled. Open table in a new tab Table 2Antibodies, Antigen Retrieval Protocols, and Visualization Strategies Used in the IHC StudiesPrimary antibodyHostDilutionAntigen retrieval⁎Citrate buffer + MW indicates 0.01 mol/L citrate buffer (pH 6.0) with 0.05% Tween 20 in a microwave oven at 95°C for 20 minutes.Secondary antibody†All secondary antibodies were obtained from Jackson ImmunoResearch Labs (West Grove, PA).Visualization strategy10D5 (Elan Pharmaceuticals, Inc.)Ms1:50Citrate buffer + MW and 90% formic acid for 5 minutesHRP anti-Ms (1:200)DAB (Vector Laboratories)PHF1 (gift from Dr. Peter Davies)Ms1:200Citrate buffer + MWBiotin anti-Ms (1:200)ABC kit + DAB (Vector Laboratories for both)GFAP (catalogue no. G9269; Sigma)Rb1:1000Citrate buffer + MWi) Biotin anti-Rb (1:200) and ii) Cy3 anti-Rb (1:200)i) ABC kit + DAB (Vector Laboratories for both) and ii) noneCD68 (catalogue no. M0814; Dako, Glostrup, Denmark)Ms1:100Citrate buffer + MWBiotin anti-Ms (1:200)ABC kit + DAB (Vector Laboratories for both)Iba1 (catalogue no. 019-19741; Wako)Rb1:250Citrate buffer + MWCy3 anti-Rb (1:200)NoneNAB61 (gift from Dr. Virginia Lee)Ms1:500NoneBiotin anti-Ms (1:200)ABC kit (Vector Laboratories) + streptavidin-Cy3 (1:200) (Invitrogen)ABC, avidin-biotin complex; DAB, 3,3′-diaminobenzidine; HRP, horseradish peroxidase; Ms, mouse; Rb, rabbit. Citrate buffer + MW indicates 0.01 mol/L citrate buffer (pH 6.0) with 0.05% Tween 20 in a microwave oven at 95°C for 20 minutes.† All secondary antibodies were obtained from Jackson ImmunoResearch Labs (West Grove, PA). Open table in a new tab Information about cause of death was available in only 44 of the 91 subjects with AD because nursing homes are the main source of our brain donation program. Patients with protracted death (mostly aspirative pneumonia and cancer, n = 31) did not differ from patients with sudden death (mostly pulmonary emboli and myocardial infarction, n = 13) regarding their age at death (P = 0.2261), disease duration (P = 0.9898), or amount of astrocytosis (P = 0.1870) and microgliosis (P = 0.9180). Statistically significant P values are boldfaced. NA, not applicable; NS, not significant. ABC, avidin-biotin complex; DAB, 3,3′-diaminobenzidine; HRP, horseradish peroxidase; Ms, mouse; Rb, rabbit. We took advantage of stereology tools to perform unbiased quantitative neuropathological studies in these brain specimens. All analyses were conducted blinded to disease status. Cortical thickness was measured in sections stained with Luxol fast blue H&E, as previously described.5Freeman S.H. Kandel R. Cruz L. Rozkalne A. Newell K. Frosch M.P. Hedley-Whyte E.T. Locascio J.J. Lipsitz L.A. Hyman B.T. Preservation of neuronal number despite age-related cortical brain atrophy in elderly subjects without Alzheimer disease.J Neuropathol Exp Neurol. 2008; 67: 1205-1212Crossref PubMed Scopus (144) Google Scholar Briefly, the image analysis software CAST (Olympus, Copenhagen, Denmark), mounted on an upright BX51 Olympus microscope (Olympus) and coupled with a motorized stage and a charge-coupled device camera, was used to randomly sample the cortex of the entire section and measure the thickness of the full cortex. The measurements of full cortical thickness in 20 random sites were averaged. Amyloid load and stereology-based studies on 3,3′-diaminobenzidine sections were conducted in an upright Leica DMRB microscope (Leica, Wetzlar, Germany) equipped with a motorized stage and a charge-coupled device camera (model DC330; DAGE-MTI, Inc., Michigan City, IN) and coupled with the software BIOQUANT NOVA PRIME, version 6.90.10 (MBSR, Nashville, TN). Amyloid load was measured as the percentage of total surface stained by the N-terminal–specific anti-amyloid β (Aβ) antibody 10D5 (Elan Pharmaceuticals, Inc., Dublin, Ireland) in a full-thickness strip of cortex (approximately 1-cm long) using the optical threshold application of the software. The total number of amyloid plaques in a 1-cm-long strip of cortex was calculated by dividing the total number of particles higher than the threshold by the area analyzed (both parameters provided by the software) and then correcting the resultant density by the cortical thickness. Paired helical filament 1–positive NFTs, GFAP-positive astrocytes, and CD68-positive microglial cells were counted with the optic dissector technique,6Hyman B.T. Gómez-Isla T. Irizarry M.C. Stereology: a practical primer for neuropathology.J Neuropathol Exp Neurol. 1998; 57: 305-310Crossref PubMed Scopus (77) Google Scholar using either 100 cells or 1000 optical dissectors as the end point. The objective/dissector size used in each case was 40/150 × 150 μm for paired helical filament 1–positive neurons, 40/50 × 50 μm for GFAP-positive astrocytes, and 100/20 × 20 μm for CD68-positive microglial cells. Intraneuronal and extracellular ghost tangles were not distinguished. Because different pathological features tend to accumulate in specific layers of the cortex (ie, reactive astrocytes in layer I and NFTs in layers II and V), care was taken to cover all of the six cortical layers in the systematic random sampling to avoid selection bias. As with the amyloid plaques, the densities of NFTs, astrocytes, and microglial cells were calculated by dividing the number of cells counted in single sections by the total area of the dissectors analyzed. To avoid any overestimation of densities because of disease-related cortical atrophy, these densities were then corrected by the cortical thickness to estimate the total number of cells within a full-thickness 1-cm-long strip of cortex. We performed additional quantitative studies in a subset of 40 AD cases selected from the original AD cohort on the basis of a wide range of disease duration and in a subset of six controls without dementia (ie, those with enough dense-core plaques). These subsets were representative of their corresponding cohorts in demographic characteristics, and the AD subset was also comparable to the entire AD cohort in neuropathological quantitative measures (Table 1; see also Supplemental Table S1 and Figure S1 at http://ajp.amjpathol.org). To study the progression of compact and oligomeric species of Aβ, we quantified the number of dense-core plaques and oligomeric Aβ-positive plaques in sections doubly stained with thioflavin-S and NAB61 antibody. The NAB61 antibody was provided by Dr. Virginia Lee (University of Pennsylvania, Philadelphia) and has been previously characterized. It is a conformation-specific anti-Aβ mouse monoclonal antibody that binds to Aβ dimers, small oligomers, and higher-order Aβ assemblies and stains a subset of mature dense-core plaques.7Lee E.B. Leng L.Z. Zhang B. Kwong L. Trojanowski J.Q. Abel T. Lee V.M. Targeting amyloid-β peptide (Aβ) oligomers by passive immunization with a conformation-selective monoclonal antibody improves learning and memory in Aβ precursor protein (APP) transgenic mice.J Biol Chem. 2006; 281: 4292-4299Crossref PubMed Scopus (257) Google Scholar Virtually no thioflavin-S–negative plaque was immunoreactive for NAB61. In this study, 100 dense-core plaques per case were randomly sampled, as previously described, and their positivity for NAB61 was qualitatively assessed. The densities of dense-core plaques and NAB61-positive plaques obtained were corrected by the cortical thickness to calculate total numbers of plaques within a 1-cm-long full-thickness strip of cortex. Single sections from the subset of 40 AD cases were also doubly stained with thioflavin-S and GFAP or Iba1 to investigate the spatial relationship between glial responses and dense-core plaques and NFTs along the course of the disease. Optimal fluorescent immunolabeling of activated microglia was achieved with antibody Iba1 (Wako, Osaka, Japan), another marker widely used for activated microglia. Sections were placed on the motorized stage of an upright BX51 Olympus microscope equipped with CAST stereology software. One hundred GFAP-positive astrocytes or Iba1-positive microglial cells per section were randomly selected under the ×20 or the ×40 objective, respectively, and their distance with respect to the closest dense-core plaque or NFT was measured with the appropriate tool of the software. For consistency, only cells with a visible nucleus in the DAPI staining were considered. Astrocytes and microglial cells were classified into three categories: i) close to plaques, if located ≤50 μm from the edge of a plaque (regardless of the presence of an NFT within this boundary); ii) close to NFTs, if located ≤50 μm from an NFT but far (>50 μm) from dense-core plaques; and iii) far from plaques and NFTs, if the closest plaque and NFT to the glial cell were located >50 μm. Densities of glial cells in each of these categories were obtained as previously described. The APOE genotype was determined in all of the study subjects by restriction fragment length polymorphism analysis, as previously described.8Ingelsson M. Shin Y. Irizarry M.C. Hyman B.T. Genotyping of apolipoprotein E: comparative evaluation of different protocols.Curr Protoc Hum Gen. 2003; 38: 1-13Google Scholar Statistics were performed, and graphs were obtained with GraphPad Prism software for Mac, version 5.0. The normality of data sets was tested with the D'Agostino-Pearson omnibus test. For correlations of cortical thickness, amyloid load, and total number of astrocytes/microglia with disease duration, two different fit models were examined using the least-squares fitting method: linear regression versus one-phase exponential association (or decay in the case of cortical thickness). The first model assumes a linear increase of the pathological features over time, whereas the second model consists of an initial increase followed by a plateau. Next, these two fit models were compared using the Akaike's Informative Criteria method with no constraints, and the model most likely to have generated the data was selected based on the magnitude of the difference between both fit models, the probabilities of the models being correct (as calculated by the statistical software), and their goodness of fit (R2). When the straight-line model was preferred, a P value indicating whether the slope of the straight line is significantly different from 0 and both the correlation coefficient (r) and the P value of Spearman's rank correlation test were also reported. Because none of the data sets was normally distributed, cross correlations between these pathological quantitative measures were investigated with the Spearman's rank correlation test. The significance level was set at a two-sided P 99.99 Goodness (R2)0.03910.05750.21010.29830.29520.4258 Slope ≠ 0? (P)0.0602NA99.9999.99<0.01 Goodness (R2)0.09600.04310.1728−0.14890.2109−0.1755 Slope ≠ 0? (P)0.0028NA<0.0001NA<0.0001NA Spearman's r0.3545NA0.4326NA0.4728NA Spearman's P0.0006NA<0.0001NA<0.0001NAThe probability of being correct and the goodness of fit (R2) of both the linear regression and the one-phase exponential association models (or decay, in the case of cortical thickness) are shown for the main neuropathological measures in the AD cohort alone, the AD cohort plus the controls without dementia and with plaques, and the AD cohort plus the entire control cohort. The best-fit model is boldfaced. In the linear regression model, P indicates whether the slope is significantly different from 0. When the linear regression model was the preferred-fit model, the correlation coefficient and the P value from the Spearman's rank correlation test are also shown. For the amyloid burden and the total number of amyloid plaques, the nonlinear model remains the best fit, despite the linear fit yielding a straight line with a slope significantly different from 0 (because of the anchoring effect of controls close to 0). Also, the R2 of the one-phase exponential association model is negative for some neuropathological measures, indicating that the best-fit curve fits the data even worse than a horizontal line. Statistics in Materials and Methods provides further details.ΔAICc, magnitude of the difference between both fit models; CTRL, control without dementia; NA, not applicable. Open table in a new tab The probability of being correct and the goodness of fit (R2) of both the linear regression and the one-phase exponential association models (or decay, in the case of cortical thickness) are shown for the main neuropathological measures in the AD cohort alone, the AD cohort plus the controls without dementia and with plaques, and the AD cohort plus the entire control cohort. The best-fit model is boldfaced. In the linear regression model, P indicates whether the slope is significantly different from 0. When the linear regression model was the preferred-fit model, the correlation coefficient and the P value from the Spearman's rank correlation test are also shown. For the amyloid burden and the total number of amyloid plaques, the nonlinear model remains the best fit, despite the linear fit yielding a straight line with a slope significantly different from 0 (because of the anchoring effect of controls close to 0). Also, the R2 of the one-phase exponential association model is negative for some neuropathological measures, indicating that the best-fit curve fits the data even worse than a horizontal line. Statistics in Materials and Methods provides further details. ΔAICc, magnitude of the difference between both fit models; CTRL, control without dementia; NA, not applicable. Next, we traced the progression of amyloid deposition and patterns of glial immunostaining throughout the clinical disease course. Amyloid burden, determined as the percentage of cortical surface immunoreactive for the anti-Aβ antibody 10D5, reached a plateau early after symptomatic onset and remained relatively stable thereafter (Figure 1B and Table 3). An analysis of total number of plaques yielded similar results (Figure 1C and Table 3). Like 10D5-immunoreactive plaques in the original AD cohort, the number of dense-core plaques determined in a subset of 40 AD cases remained relatively stable throughout the disease clinical course after an initial increase (Figure 2, A and B, and Table 4). Last, the amount of NAB61-positive oligomeric Aβ-enriched plaques also remained constant throughout the disease clinical course (Figure 2, C and D, and Table 4).Table 4Summary of the Results from the AD and Control Subsets Concerning Fibrillar and Oligomeric Aβ BurdenVariableAD subset (n = 40)AD + CTRL with dense-core plaques (n = 46)LinearOne-phase exponentialLinearOne-phase exponentialTotal dense-core plaques ΔAICc0.25251.833 Probability (%)46.8553.1528.5771.43 Goodness (R2)0.02480.03100.12970.1637 Slope ≠ 0? (P)0.3313NA0.0140NATotal NAB61 + plaques ΔAICcNANA Probability (%)100Not converged100Not converged Goodness (R2)0.00020.0361 Slope ≠ 0? (P)0.93130.2060 Spearman's r−0.0037NA0.2267NA Spearman's P0.9816NA0.1298NAThe ΔAICc represents the magnitude of the difference between the two fit models compared. The best-fit model is boldfaced. In the linear model, P indicates whether the slope is significantly different from 0. When the linear regression model was the preferred-fit model, the correlation coefficient and the P value from the Spearman's rank correlation test are also shown. The nonlinear model is the best fit for total dense-core plaques in the AD + CTRL analysis, despite the linear fit yielding a straight line with a slope significantly different from 0 (likely because of the anchoring effect caused by the controls). Statistics in Materials and Methods provides further details.ΔAICc, magnitude of the difference between both fit models; CTRL, control without dementia; NA, not applicable. Open table in a new tab The ΔAICc represents the magnitude of the difference between the two fit models compared. The best-fit model is boldfaced. In the linear model, P indicates