Abstract
The behavior of epigenetic mechanisms in the brain is obscured by tissue heterogeneity and diseaserelated histological changes. Not accounting for these confounders leads to biased results. We develop a statistical methodology that estimates and adjusts for celltype composition by decomposing neuronal and nonneuronal differential signal. This method provides a conceptual framework for deconvolving heterogeneous epigenetic data from postmortem brain studies. We apply it to find cellspecific differentially methylated regions between prefrontal cortex and hippocampus. We demonstrate the utility of the method on both Infinium 450k and CHARM data.
Keywords:
DNA methylation; epigenetics; differentially methylated region; brain region; celltype heterogeneity; deconvolution; NeuN; neuron; glia; postmortem brain; fluorescence activated cell sortingBackground
The brain is a particularly good example of highly specialized and diverse functions arising from the same genetic program. Epigenetic mechanisms copy information other than the sequence itself during cell division, such as DNA methylation and chromatin arrangements [1]. Therefore, epigenetics is an attractive substrate for understanding specialized brain function and its disruption in disease. An example of an epigenetic mechanism is DNA methylation, which at CpG dinucleotides is heritable during cell division, because that sequence is recognized by a DNA methyltransferase on newly replicated strands. In postmitotic cells such as neurons, DNA methylation has been shown to contribute to memory formation [2], other types of synaptic plasticity [3], drug addiction [4], and reversible behavior in the honeybee Apis mellifera [5]. Neurological diseases have also been linked to mutations in DNA methyltransferases [6] and methylCpGbinding proteins [7].
Despite its importance, the epigenetic profile of the brain has not yet been explored in depth due to, among other factors, brain region and celltype heterogeneity. The cerebral cortex has distinct functional regions, each organized into cell layers of neurons and glia that vary throughout the cortex [8]. While neurons are the main signaling unit, glia play an important role in scaffolding and maintaining synapses [9]. Epigenetic profiling of neurons and nonneurons using the Illumina GoldenGate assay has shown that neurons and glia have a unique DNA methylation signature that cannot be assessed using samples from bulk cortex [10]. This is important because shifts in glial cell populations such as oligodendrocytes contribute to defects in cortical myelination, and microglia activation has been linked to neurodegenerative disorders [11].
Traditional epidemiological studies using brain tissue done so far do not account for differences in celltype composition [1214]. Statistical methods for estimating celltype composition from genomic profiles have been developed for gene expression [1518], and DNA methylation in blood tissue [19] and in brain [20]. DNA methylation can then be used to calculate and potentially adjust for differing cell proportions, a crucial step when studying diseases where cell population shifts occur [21].
While DNA methylation data can now be used to calculate differing cell proportions, individual celltype profiling has not been done yet due to the extensive mixture combinations required for validation in blood (at least five different cell types) [19]. In contrast, cell profiling in the brain can be achieved by separating the cell types into two main compartments: neurons and glia. In a recent publication [20], a method is proposed for estimating neuron and glia proportions similar to the approach proposed for whole blood [19]. While this is a useful step toward correcting for cell distribution, this approach does not permit the unbiased estimation of glia and neuronspecific differences between two sets of samples [20]. Such calculated celltype specific analysis offers a crucial advantage in studies of the brain, where neurons and glia cannot generally be dissociated. For example, many brain bank specimens contain pulverized material or even paraffinfixed specimens, for which methods exist to isolate DNA for genomescale methylation analysis [22]. Flow sorting, as done here to develop this method, generally does not yield sufficient quantities of material for genomescale analysis, and is also extremely labor intensive and costly.
Here we have developed a novel statistical epigenetics approach that takes advantage of the stability and celltype specificity of DNA methylation, as well as the fact that the brain is made up of two major cell types, neurons and glia, in order to deconvolve the two main cell components in the brain. Thus, the method allows one to measure DNA methylation, for example, across brain regions, and from those data calculate to a first approximation the difference in DNA methylation that is neuron or gliaspecific. Moreover, once sorted data is available for a given brain region, investigators can use such data to calculate cell proportions on any unsorted sample measured on the same methylation platform without the need to sort themselves. This approach should have broad application to a range of problems in neurodevelopment and disease research.
Results and discussion
Estimation of mixture proportions
We measured DNA methylation profiles for dorsolateral prefrontal cortex (DLPFC), hippocampal formation (HF), and superior temporal gyrus (STG) samples dissected from frozen brains of normal individuals using the comprehensive highthroughput arrays for relative methylation (CHARM) technique [23]. We also labeled and separated neuronal nuclei in a subset of samples using a neuronspecific antibody (NeuN) and fluorescenceactivated cell sorting (FACS) [24,25]. Neuronal (NeuN+) and nonneuronal (NeuN) fractions from DLPFC, HF, and STG were collected for downstream processing and methylation analysis with CHARM (Additional File 1, Figure S1).
Additional file 1. Supplementary Information. A PDF file containing Figures S14 and Tables S14.
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To illustrate the downstream effects of the cell population confounding problem, and focusing on two brain regions for clarity, we examined a genomic region for which: (1) no difference was observed between DLPFC and HF in either neuronal or glial fractions; and (2) a difference was observed between neuronal and glial nuclei within each brain region (Figure 1a). Note that a strong methylation difference between brain regions is observed between the noncellsorted brain samples. This must be a falsepositive and, as we demonstrate below, must be due to differences in celltype composition between the brain regions.
Figure 1. The proportion of neuronal cells in a given brain region influences the identification of differentially methylated regions. (a) Wholetissue methylation signals show falsepositive brainregion differences. Panel shows a plot of smoothed methylation signals from sorted neuronal and glial cells (teal and purple lines) from DLPFC and HF (solid and dashed lines) as well as from wholetissue DLPFC (gold line) and HF (grey line). (b) Estimated neuronal fraction of cells for wholetissue samples differs between DLPFC and HF (mean DLPFC = 0.53 (n = 19), mean HF = 0.30 (n = 13), twosample ttest P value 6.3 × 10^{6}). (c) Estimated neuronal fraction of cells for wholetissue samples using universal DMRs vs. estimated neuronal fraction using brain regionspecific DMRs from DLPFC (gold), HF (grey), and STG (blue).
We modified a statistical method originally developed to estimate cell populations in blood [19] to calculate neuronal and glial proportions for each of our unsorted samples, adapting it to use a constrained linear optimization model (Figure 1b, see overview in Additional File 1, Figure S2a). We confirmed that our approach effectively estimated these cell proportions using a mixture experiment with an independent set of samples (Additional File 1, Figure S2b). To demonstrate that the falsepositive results of Figure 1 are due to difference in celltype distribution, we mathematically reconstructed the unsorted sample methylation profile using the pure neuronal and glial profiles and their estimated frequencies and predicted this result (Additional File 1, Figure S2c).
While the above results rely on having neuronal and glial methylation signals for each brain region, we performed additional analyses to determine whether accurate estimates of neuronal and glial proportions in unsorted samples from a brain region could be obtained using selected data from another brain region. Figure 1c shows the accuracy of estimates obtained from such 'universal' data, compared to estimates based on sorted data from each individual brain region. We also accurately reproduce the cell proportion estimates from our mixture experiment (Additional File 1, Figure S2d, see Materials and Methods for additional details of how this analysis was performed). Our results indicate that accurate estimates could be obtained for a new brain region without the need to sort samples from that region.
Generative model of methylation signal
Currently, obtaining celltype specific DMRs from unsorted samples is a mathematically intractable problem. However, because in human postmortem brain samples we are interested in just two cell fractions (neurons and glia), we were able to develop a novel statistical procedure to perform this deconvolution. The methylation signal for any sample i at a given genomic location, Y_{i}, can be modeled as a linear combination of the methylation levels of neuronal and glial fractions in the brain region where the sample i was obtained. Specifically, for any given CpG, the DNAm profile of a mixed sample can then be written as (see Materials and Methods):
Here, we define and to be the methylation level of neuronal and glial fractions, respectively, in DLPFC, with and defined similarly for HF. For each sample i, is 1 if sample was obtained from HF and 0 for DLPFC samples. We let to be the fraction of glia in sample , so that is the fraction of neurons. Finally, represents biological variability and measurement error. The statistical insight is that because the term can be estimated with high precision (Additional File 1, Figure S2c), it can be treated as fixed. With this assumption in place, the equation above is actually a linear model of the form
in which the parameters and represent the quantities we are interested in measuring, that is, the differences in neurons and glia, respectively, between brain regions. We refer to this model as M2. Fitting this linear model by least squares and obtaining estimates for millions of genomic locations is computationally feasible. (Fitting the model for 4 million probes took about 5 seconds on our laptop).
This statistical framework also exposes the problem with existing naïve approaches to assess DNA methylation signatures in mixed samples. To date, most published analyses ignore cell composition [2630] and look for associations in a way equivalent to fitting a simple linear regression model (where the ttest is derived from the ). We refer to this model as M1. In M1, the parameter represents a combination of the methylation differences in neurons and glia in which it is impossible to deconvolve celltypespecific contributions. Furthermore, we can mathematically demonstrate that the least squares estimate of will be biased by differences in celltype frequency under the null hypothesis of no difference in methylation between brain regions (Figure 2a, see Methods Section). Similarly, a naïve model suggested by Guintivano et al. [20] that incorporates celltype proportions (we refer to this as model M3) will lead to biased results as well, and to decreased power to detect methylation differences (Additional File 1, Figure S3). We also note that even the superior methods show a small amount of bias (boxplot not centered at 0), which can be explained by slightly inaccurate mixture estimates (see Materials and Methods).
Figure 2. Effects of direct modeling on falsepositives and accuracy. (a) Explicit modeling for differences in cell type reduces falsepositive rate. Boxplots of test statistics for the difference in means based on linear regression estimation from models M1 and M2. Eighty percent of regions from M1 show a statistically significant difference in overall mean (at level 0.05); 16% and 12% of regions from M2 show a statistically significant difference in neurons or glia, respectively (at level 0.05). (b) Explicit modeling of neuronal methylation differences improves estimation accuracy. Comparison of goldstandard mean difference in methylation in neuronspecific DMRs to the estimated mean difference from models M1 (left) and M2 (right), along with the linear regression fit to the data (95% CI for the slope of the regression line of M1 = (0.29, 0.44), for M2 = (0.68, 0.95).
To test the utility of our model, we confirmed our theoretical results with experimental data. First, we obtained estimates of significant neuronspecific methylation differences between DLPFC and HF using sorted brain samples (gold standard, FDR <0.05, Additional File 1, Table S1). We then used the unsorted brain data to calculate the parameters representing the differences in brainregion methylation using models M1 (total methylation difference, ) and M2(neuronspecific methylation difference, ). Figure 2b shows that we can estimate neuronspecific methylation differences more accurately with model M2. Therefore, we can assess neuronspecific methylation differences between DLPFC and HF using whole tissue after estimating cell proportions.
Using the sorted samples, we did not find statistically significant DMRs in the nonneuronal fraction, which highlights the importance of isolating a neuronal signal from total methylation values. The result is in agreement with recently published literature suggesting that glia cells, contained in the NeuN fraction, have less diverse transcription patterns across brain regions than neurons [31], the latter having a distinct DNAmethylation signature [10]. Interestingly, proteins involved in modifying chromatin were found among the brainregion neuronal DMRs, supporting the role of epigenetic mechanisms in neuronal function and synaptic plasticity [32]. For example, neuronspecific methylation of the histone methyltransferase SETD3, which methylates histone H3 at lysine 36, was lower in HF than in DLPFC, and histone deacetylase HDAC4 shows hypomethylation in DLPFC. Other genes involved in neural differentiation include JAG1, TTL1, NPAS4, CUX2, DOCK2, NGEF, OLFM1, SATB2, and GIT2.
Application to Illumina Infinium HumanMethyation450 Dataset
While the CHARM platform has many advantages for studying methylation patterns due to the high density and location of probes, the assay requires restrictionenzyme digestion and lacks singlebase resolution. The Illumina Infinium HumanMethylation450 (450K) array has emerged as an affordable alternative to obtain reliable quantitative measurements of methylation. To demonstrate the performance of our method on data from the 450K array, we used data accessible at NCBI GEO database (Guintivano et al. [20], accession GSE41826), consisting of 77 normal samples from prefrontal cortex, of which 29 were sorted into neuronal and glial fractions, nine were mixtures of neurons and glia of known proportions, and 10 were unsorted, wholetissue samples. We first applied our method to obtain accurate cellfraction estimates on the known mixture samples (Additional File 1, Figure S4a). Using these cellfraction estimates and the pure neuronal and glial profiles, we mathematically reconstructed the methylation profile for the mixture samples in a set of genomic regions and compared these results to the actual observed methylation for these samples (Additional File 1, Figure S4b). The cell proportion calculations agreed with Guintivano et al.'s estimates for prefrontal cortex. Our CHARM cell proportion estimates are on average higher than those obtained using 450K arrays, as the CHARM data were sampled using 2 mm dermal biopsy punches to minimize white matter contamination. The mathematical reconstruction of the methylation signal was also done for the unsorted samples (Additional File 1, Figure S4c).
Given that sorted data on the 450K array are only available for one brain region, we cannot demonstrate our improved ability to detect true brainregion differences in celltype specific methylation on this platform. However, to show our ability to reduce falsepositive signal, we constructed an artificial comparison by grouping the mixture samples with the highest and lowest neuronal fractions and applied models M1 and M2 to look for differences between these two groups. Any such differences are clearly due only to cellfraction variation, and model M2 reduces the number of falsepositive signals (Additional File 1, Figure S4d), as we saw for our CHARM data (Figure 2a). These results indicate that our methods apply well to data from the 450K array.
Conclusions
We describe an algorithm to address a gap in the analysis of methylation data from complex tissues with varying degrees of celltype heterogeneity such as the brain. To appropriately measure the methylation differences between two brain cortical regions, we separated a small number of samples of the brain nuclei into neuronal and nonneuronal fractions by cell sorting, and developed a statistical method to account for cell heterogeneity in a set of unsorted samples by decomposing the signal into its two components. Our proposed method takes advantage of the separation of the brain cells into two nuclei fractions. The neuronal fraction encompasses a diverse population of neuronal cells, and the nonneuronal nuclei contain astrocytes, oligodendrocytes, a minority of NeuNnegative neurons, and endothelial cells. To separate the methylation signal into more than two fractions is mathematically plausible, as one can simply define as the fraction of cells of the celltype of interest, fit model M2, and consider . However, investigating how robust our results are to the noise in cell fraction estimates when there are more than two cell types will require further study.
The experimental design presented here provides for efficient use of scarce tissue bank resources and limited funds for methylation profiling. Once purified methylation profiles are obtained from the brain regions of interest using a small number of samples, the goldstandard methylation data can be used for any further analysis, and by any laboratory, without the need to sort nuclei again. We have demonstrated our method on data from both CHARM and the Illumina 450K array. To apply our method to a new measurement platform or new brain regions, we recommend performing cell sorting on a subset of the samples to first obtain the celltype specific signals needed for the cellfraction estimation. If brainregion specific data are not available, we have also shown that for samples measured with CHARM, accurate estimates of cell proportions in samples from one brain region could be obtained using sorted data from another brain region. We provide a framework that can be applied, even retrospectively, to psychiatric casecontrol studies using frozen postmortem brain samples, and can be easily adapted to other microarray or sequencing platforms, and to other target tissues.
Materials and methods
Generative model of methylation signal
To illustrate our model, we consider the case of estimating differences in methylation between DLPFC (D) and HF (H). We assume these brain tissues are composed of two cell types, NeuN+ (+) and NeuN (). For a fixed genomic position, we let μj,k be the methylation level in region j, j ∈ {H, D} and cell type k, k ∈ {+, }. Scientifically, we are interested in identifying genomic locations where μH,k  μD,k ≠ 0, that is, where NeuN+ or NeuN have different methylation levels in the two brain regions.
Given a sample i and considering a fixed genomic position, we define X_{i }as the indicator that sample i is from the hippocampus, that is, X_{i }= 1 if sample i is from the hippocampus and 0 otherwise. We also define π_{i }to be the fraction of sample i that consists of NeuN cells (1  π_{i }is the fraction of NeuN+ cells). We can then derive the expected value of the methylation signal of sample i at that genomic position as
Rearranging terms gives:
Suppose we wanted to estimate whether there is a difference in methylation between the two brain regions being considered, H and D. If we fit a model with terms matching those above, that is,
then our estimated coefficients have interpretations equivalent to the generative model in Equation 1. Specifically, we can test the hypothesis of no difference in NeuN+ methylation between D and H (μ_{H,+ } μ_{D,+ }= 0) by testing the hypothesis that β_{2 }= 0, and the hypothesis of no difference in NeuN methylation between D and H (μ_{H, } μ_{D, }= 0) by testing the hypothesis that β_{3 }= 0.
From the equations above, we can see that estimating the fraction of cells of each type, π_{i}, allows us to explicitly find locations with brainregion differences specific to NeuN+ or NeuN cells.
Naïve models are biased
In general, π_{i }is unknown and therefore not included in the linear model, that is, the model
is fitted. However, this model does not account for all the sources of variation in Y_{i}, and the least squares estimate is a biased estimate of the difference in methylation between H and D under the null hypothesis. To see this, we can write , where X is the design matrix of the above model and is the vector (, ) and the hats represent least squares estimates. For simplicity, we assume equal numbers of samples from H and D. We then have
Where is the mean fraction of NeuN cells in region j. Under the null hypothesis of no difference between D and H in either + or , we have and also , which gives
This means that where + and  have different methylation levels (δ ≠ 0), a difference in the fractions of + and  cells in the different brain regions can lead to falsepositive signals of brain region differences in methylation.
Guintivano et al. [20] estimate and propose an ad hoc approach to adjust for this that is approximated by fitting the following model
However, this model does not account for all the sources of variation in Y_{i }either and the least squares estimate is a biased estimate of the difference in methylation between H and D. To see this, we can write , where X is the design matrix of the above model and is the vector and the hats represent least squares estimates. For simplicity, we assume equal numbers of samples from H and D. We then have
Where K is a function of the 's that does not depend on the sample size:
With and the average of the 's in all samples and H samples, respectively, and and the average of the 's in all samples and H samples, respectively. Note that the bias is directly proportional to the difference between NeuN+ and NeuN fractions, demonstrating that this approach is incapable of deconvolving these quantities of interest.
Estimation of mixture proportions
Although we have shown that fitting the misspecified model, which does not include the cellfraction terms, can lead to bias under the null hypothesis, the cell fractions for a given sample are unknown a priori. At any given methylation site, we are assuming that there is some underlying mean methylation value within each combination of cell type (+, ) and brain region (D, H). If we know these underlying means, we can derive an estimate of the unknown cell fraction at a particular site, given an observed methylation signal and assuming the generative model above. For example, suppose sample i is from D and we observe methylation signal Y_{i }at a given locus. From Equation 1, we have
If we assume and are known, is the only unknown in this equation, so it can be estimated. Note that we do need to constrain our estimate of to be between 0 and 1. Also, the means μ are not known, so we collected data to allow us to estimate these means, by measuring methylation in pure cell sorted + or  fractions from each brain region of interest. Given that these methylation measurements have uncertainty, we want to reduce the uncertainty in our estimate of by using many informative genomic regions. We first select a set of genomic regions where + and  methylation differs. We then find the optimal value of to explain the observed methylation for sample i in these locations, as a function of our estimated means and , subject to the constraint that is between 0 and 1. This procedure closely follows that presented by Houseman et al. [19].
Selection of the genomic locations can be based on a variety of factors, such as the range of observed methylation at these locations, the variance of the methylation estimates, and the length of the region of differential methylation. For our estimation, we chose the 500 genomic regions which were the strongest + vs.  DMR candidates in the brain region of interest in relation to the amount of methylation difference and the length of the region showing the methylation difference. We found that our results were quite robust to the number of regions selected, with 500 performing well.
To investigate whether it is absolutely necessary to have sorted data from a given brain region to estimate cell proportions in unsorted data from that region, we identified a set of 'universal' genomic regions. These universal regions had different NeuN+ and NeuN methylation signals within a brain region, but showed consistent NeuN+ and NeuN methylation levels across the three brain regions for which we had data (DLPFC, HF, and STG). Many of these + vs.  DMR candidates had consistent NeuN+ and NeuN levels across brain regions, with 14% to 17% of the probes in the + vs.  DMRs belonging to genomic regions of consistent signal. We estimated the means μ in these regions of consistent signal using sorted data from DLPFC alone, and then performed cell fraction estimation in the unsorted samples from DLPFC, HF, and STG using these mean values. Since we do not know the true cell fractions in these unsorted samples, we used the estimates we had obtained for each brain region using the regionspecific DMRs and mean values, as described above, as our gold standard.
All analysis was implemented in R (R Core Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing: Vienna, Austria, 2012; [33]). The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO series accession number GSE48610.
Effect of inaccurate mixture estimates
As previously described, failure to account for differences in cellmixtures in our samples can lead to biased estimates of brainregion differences under the null hypothesis of no brain region difference. However, inaccurate mixture estimates can also lead to bias. For example, consider the methylation signal in sample i
Now suppose we have an inaccurate estimate of , called , where . Using this inaccurate estimate gives us the following contribution to our regression formulation from sample i:
where η_{i }is between 0 and 1, and the third line follows from the fact that γ_{i }must be between and to ensure that is between 0 and 1. We can see that the coefficient of is no longer measuring just the quantity we are interested in (the difference between NeuN+ methylation in regions H and D), but it also has an additional factor related to the size of the estimation error, and similarly for the coefficient of .
CHARM DNA methylation analysis
Genomic DNA was isolated from brains using the Masterpure kit from Epicentre, according to the manufacturer's protocol. For genomewide DNA methylation assessment, 1 ug of genomic DNA from each sample was digested, fractionated, labeled, and hybridized to a CHARM array as described [34,35] using a custom Nimblegen 2.1 million feature array assaying 5,114,655 CpG sites. We used the Bioconductor package 'charm' for sample preprocessing along with the package 'bumphunter' for DMR identification and permutation computation.
Human postmortem brain samples
Fluorescenceactivated cell sorting was performed on frozen postmortem dorsolateral prefrontal cortex (n = 4), and hippocampal formation (n = 4) and superior temporal gyrus (n = 3) from individuals not affected with neurological or psychiatric disease. To validate the statistical model, we used nine additional healthy samples from the dorsolateral prefrontal cortex. These samples underwent nuclei extraction and sorting as described below. The model was applied to additional unsorted control samples (19 samples from DLPFC, 13 samples from HF, 31 samples from STG) to deconvolve NeuN+ and NeuN methylation signatures. All samples were obtained from the bank of the Center for Neurodegenerative Disease Research (CNDR) in the Department of Pathology and Laboratory Medicine at the University of Pennsylvania (directed by Dr John Q Trojanowski, see Additional File 1, Tables S24 for demographic information).
Nuclei extraction, NeuN labeling, and sorting
Total nuclei were extracted via sucrose gradient centrifugation as previously described [25]. A total of 250 mg of frozen tissue per sample was homogenized in 5 mL of lysis buffer (0.32M sucrose, 10 mM Tris pH 8.0, 5 mM CaCl_{2}, 3 mM Mg acetate, 1 mM DTT, 0.1 mM EDTA, 0.1% Triton X100) by douncing 50 times in a 40mL dounce homogenizer. Lysate was transferred to a 15 mL ultracentrifugation tube and 9 mL of sucrose solution (1.8 M sucrose, 10 mM Tris pH 8.0, 3 mM Mg acetate, 1 mM DTT) was pipetted to the bottom of the tube. The solution was then centrifuged at 27,000 rpm for 2.5 h at 4C (Beckman, L880 M; SW28.1 rotor). After centrifugation, the supernatant was removed by aspiration and the nuclei pellet was resuspended in 500 uL of PBS.
The nuclei were incubated in a staining mix (0.71% normal goat serum, 0.036% BSA, 1:1200 antiNeuN NeuN (Millipore, MAB377), 1:1400 Alexa647 goat antimouse secondary antibody (Invitrogen, 21236) for 45 min by rotating in the dark at 4°C. Unstained nuclei and nuclei stained with only secondary antibody served as negative controls. The fluorescent nuclei were run through a FACS machine with proper gate settings. A small portion of the NeuN^{+ }and NeuN^{ }nuclei were rerun on the FACS machine to validate the purity. Immunonegative (NeuN^{}) nuclei were collected in parallel. To pellet the sorted nuclei, 2 mL of sucrose solution, 50 uL of 1 M CaCl_{2}, and 30 uL of Mg acetate were added to 10 mL of nuclei in PBS, incubated on ice for 15 min, then centrifuged at 3,000 rpm for 20 min. The nuclei pellet was resuspended in 10 mM Tris (pH 7.5), 4 mM MgCl_{2}, and 1 mM CaCl_{2}. Fluorescent images were taken on a Zeiss Axio Observer. Z1 microscope with a PlanApochromat 100x/1.40 oilimmersion objective lens. Images were generated using an Axiocam MR3 microscope camera and Axiovision software (AxioVs40, version 4.8.2.0, Carl Zeiss, Inc). Images were processed using ImageJ.
Abbreviations
CHARM: comprehensive highthroughput arrays for relative methylation; DLPFC: dorsolateral prefrontal fortex; DMR: differentially methylated region; FACS: fluorescenceactivated cell sorting; FDR: false discovery rate; HF: hippocampal formation; NeuN+: NeuNpositive fraction; NeuN: NeuNnegative fraction; STG: superior temporal gyrus.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
CMM conceived of the study, designed and performed experiments, analyzed the data and developed the statistical method, and wrote the paper. MAT conceived of the study, analyzed the data and developed the statistical method, and wrote the paper. APF conceived of the study. RAI analyzed data and developed the statistical method. WEK designed experiments. KT selected and acquired samples. REG selected and acquired samples. All authors read and approved the final manuscript.
Acknowledgements
All samples were obtained from the bank of the Center for Neurodegenerative Disease Research (CNDR) in the Department of Pathology and Laboratory Medicine at the University of Pennsylvania (directed by Dr John Q Trojanowski). This work was funded by NIH Grant U01 MH085270 to APF and CMM, Department of Defense (CDMRP) AR080125 to APF and WEK, and NIH Grant R01 GM083084 to RAI and MAT. The research reported in this publication was also supported by NIAMS Award Number P30AR053503. We would like to thank Joe Chrest for his expertise in cell sorting, Rakel Tryggvadottir for her assistance with sample hybridizations, and Romeo Papazyan for his help with fluorescence microscopy imaging.
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Gibbs JR, van der Brug MP, Hernandez DG, Traynor BJ, Nalls MA, Lai SL, Arepalli S, Dillman A, Rafferty IP, Troncoso J, Johnson R, Zielke HR, Ferrucci L, Longo DL, Cookson MR, Singleton AB: Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain.

Davies MN, Volta M, Pidsley R, Lunnon K, Dixit A, Lovestone S, Coarfa C, Harris RA, Milosavljevic A, Troakes C, AlSarraj S, Dobson R, Schalkwyk LC, Mill J: Functional annotation of the human brain methylome identifies tissuespecific epigenetic variation across brain and blood.

Pardo LM, Rizzu P, Francescatto M, Vitezic M, Leday GG, Sanchez JS, Khamis A, Takahashi H, van de Berg WD, Medvedeva YA, van de Wiel MA, Daub CO, Carninci P, Heutink P: Regional differences in gene expression and promoter usage in aged human brains.

Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, Klotzle B, Bibikova M, Fan JB, Gao Y, Deconde R, Chen M, Rajapakse I, Friend S, Ideker T, Zhang K: Genomewide methylation profiles reveal quantitative views of human aging rates.

Ko Y, Ament SA, Eddy JA, Caballero J, Earls JC, Hood L, Price ND: Cell typespecific genes show striking and distinct patterns of spatial expression in the mouse brain.

Jaffe AE, Murakami P, Lee H, Leek JT, Fallin MD, Feinberg AP, Irizarry RA: Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies.

Irizarry RA, LaddAcosta C, Wen B, Wu Z, Montano C, Onyango P, Cui H, Gabo K, Rongione M, Webster M, Ji H, Potash JB, Sabunciyan S, Feinberg AP: The human colon cancer methylome shows similar hypo and hypermethylation at conserved tissuespecific CpG island shores.