The objective was to investigate whether or not abnormalities of the white matter below frontal areas in autism observed with structural imaging in children persist in the brains of adults with autism. We used unbiased quantitative stereology to study myelinated axons at high resolution at the light microscope (LM) and their fine structure at the electron microscope (EM) below the ACC (A32), OFC (A11), and LPFC (A46) areas () in the brains of autistic (n=5, 1 female) and age-matched, typically developed controls (n=4, 2 females). We investigated the density of axons and thickness of axons and myelin sheaths. We examined only myelinated axons because they constitute the vast majority of axons in the frontal cortical white matter (~90%), the corpus callosum, anterior and hippocampal commissures in primates (LaMantia and Rakic, 1990b
). Further, myelinated axons can be labeled using immunohistochemical methods, which we used for an independent evaluation at the light microscope.
Figure 1 Map of prefrontal areas studied and segmentation of the white matter. A, Medial (top) and lateral (bottom) views of the human brain show the three prefrontal areas studied; ACC (A32, red; anterior A24, yellow); OFC (A11, green); LPFC (A46, blue). Dotted (more ...)
White matter segmentation
We investigated axons in the superficial and deep white matter separately for two reasons. First, structural imaging studies suggested possible differences in pathology in autism (Herbert et al., 2004
). And second, the deep white matter contains axons that communicate over long distances, whereas the superficial white matter contains axons that communicate mostly over short or medium distances (Schmahmann and Pandya, 2006
). We thus divided the white matter into superficial (outer or radiate) and deep (inner or sagittal) compartments, based on axon orientation and distance from the cortical grey matter (Meyer et al., 1999
). We determined axon alignment at the LM and at the EM in serial coronal ultrathin sections at gradually increasing distances from the grey-white matter border. The superficial compartment included axons that were mostly aligned radially and were immediately adjacent to layer VI of the overlying cortical areas (at a distance up to 2 mm from layer VI). The deep compartment included axons that run mainly sagittally and more or less parallel to the cerebral surface ().
prefrontal brain tissue was obtained from the Harvard Brain Tissue Resource Center through the Autism Tissue Program from five autistic adults (one female) and four typically developed, age-matched, controls (two females). The selection of matched cases used was based on tissue availability. The study was approved by the Institutional Review Board of Boston University. The diagnosis of autism was based on the Autism Diagnostic Interview–Revised (ADI-R) in all cases (Supplemental Table 1
). Clinical characteristics are summarized in and Supplemental Table 1
. Some autistic cases were diagnosed with seizure disorder (case 5173), depression (case 4871) and schizophrenia (case 4541). Results from the analysis of the features of axons in these and the three female cases did not differ from other cases within each group, in this and other studies that used tissue from the same cases (Buxhoeveden et al., 2006
; Schumann and Amaral, 2006
; Yip et al., 2007
We postfixed tissue blocks in 2% paraformaldehyde and 2.5% glutaraldehyde, in 0.1M phosphate buffer (PB, pH: 7.4) for 2 days at 4°C. To preserve the ultrastructure until processing, tissue blocks were immersed in anti-freeze solution (30% ethylene glycol, 30% glycerol, 40% 0.05M PB, pH: 7.4 with 0.05% azide) and stored at −20°C. The blocks were then rinsed in 0.1M PB and cut coronally at 50µm thick sections on a vibratome (Pelco, series 1000). In all cases, tissue blocks through the grey matter of the areas of the associated white matter sampled were frozen in −70°C isopentane, cut in a cryostat (CM 1500, Leica) in the coronal plane at 20 µm in 10 series and mounted on chrome-alum coated slides.
We conducted several immuno-assays to label specific axon features and white matter oligodendrocytes. At the light microscope we labeled oligodendrocytes with an antibody against Myelin and Oligodendrocyte Specific Protein (MOSP). We also labeled myelinated axons with an antibody against NeuroFilament Protein 200 KDa (NFP-200), and examined branching axons with an antibody for Growth Associated Protein 43 KDa (GAP-43). To sort out axons from the thalamus we used antibodies against calbindin (CB) and parvalbumin (PV), which label excitatory thalamic projections to the cortex, and examined labeling at the confocal microscope and EM.
Series of free floating coronal tissue sections (50 µm thick) or cryosections mounted on slides (20 µm thick) were used in all immunohistochemical procedures. Sections were rinsed in 0.01 M PBS, pH 7.4, followed by 10% normal goat serum, 5% bovine serum albumin, and 0.1% Triton X-100 in 0.01 M PBS blocking solution for 1 h and incubated for 1–2 days in primary antibody.
We labeled axons in the white matter with antibodies against calbindin (CB; mouse monoclonal; dilution 1:1,000; Swant and/or Sigma), parvalbumin (PV; rabbit polyclonal; dilution 1:1000; Swant and/or Sigma), NeuroFilament Protein 200 KDa (NFP-200; rabbit polyclonal; dilution 1:200; Chemicon), and Growth Associated Protein 43 KDa (GAP-43; mouse monoclonal; dilution 1:2,000; Chemicon). We labeled oligodendrocytes with a monoclonal antibody against Myelin and Oligodendrocyte Specific Protein (MOSP; mouse monoclonal; dilution 1:1,000; Chemicon). The sections were rinsed in PBS, incubated for 4 h with goat anti-mouse or anti-rabbit secondary antibodies conjugated with the fluorescent probes Alexa Fluor 488 (green) or 568 (red; 1:100; Invitrogen) and thoroughly rinsed with PBS. In some cases, a biotinylated secondary antibody and an avidin–biotin–peroxidase kit was used to label CB-positive or PV-positive axons with diaminobenzidine (DAB, Zymed laboratories, San Francisco, CA), which were further processed for EM (see below). To test for non-specific labeling we performed control experiments with sections adjacent to those used in the experiments. These included omission of the primary antibodies and incubation with secondary antisera. Control experiments showed no immunohistochemical labeling.
Tissue processing and pre-embedding immunohistochemical labeling for serial EM is an especially challenging technique for post-mortem
human brain tissue because of limited control over tissue extraction protocols, post-mortem
interval and post-fixation. In addition, processing and labeling of the tissue can degrade the ultrastructure and preclude quantitative analyses. To address these issues we have developed several novel protocols that maximize tissue quality and specificity of labeling (Zikopoulos and Barbas, 2006
; Zikopoulos and Barbas, 2007
). We preserve tissue blocks or sections at −20°C in anti-freeze buffer solution for long periods of time, fix and process tissue using a variable microwave, and label tissue before embedding, all of which enhance and accelerate penetration of reagents in brain sections during processing, reduce non-specific background staining, minimize the need for detergents that degrade fine structure, and decrease potential damage of a series. These protocols have markedly increased tissue quality and made it possible to conduct 3D quantitative reconstruction of identified structures.
Sections were rinsed briefly in 0.1M PB and postfixed in a variable wattage microwave oven (Biowave, Pelco) with 6% glutaraldehyde at 150W. Small blocks of sections containing the outer (superficial) or inner (deep) parts of the white matter below prefrontal cortices were cut under a dissecting microscope, postfixed in 1% osmium tetroxide with 1.5% potassium ferrocyanide in PB, washed in buffer (PB) and water and dehydrated in an ascending series of alcohols. While in 70% alcohol they were stained with 1% uranyl acetate for 30 min. Tissue sections were then cleared in propylene oxide and embedded in araldite at 60°C. Serial ultrathin sections (50 nm) were cut in the horizontal plane with a diamond knife (Diatome) using an ultramicrotome (Ultracut; Leica) and collected on single slot grids to view with a transmission electron microscope (100CX; Jeol), as described (Zikopoulos and Barbas, 2006
; Zikopoulos and Barbas, 2007
). Myelinated axons were easily identified at the EM by the darkly stained electron dense myelin sheath (Peters et al., 1991
One series of sections was stained for Nissl using thionin to view neurons and glia and examine the cytoarchitecture of each area, as described (Barbas and Pandya, 1989
; Dombrowski et al., 2001
). Sections were dried, defatted in a 1:1 solution of chloroform and 100% ethanol for 1 h, rehydrated through a series of graded alcohols and dH2
O, stained with 0.05% thionin (pH 4.5) for 15 min, differentiated through graded alcohols and xylenes and coverslipped with Entellan (Merck, Whitehouse, NJ).
To determine adequate sample size we performed a priori
power analysis, using repeated measures from a pilot study, and a posteriori
power analysis, using the actual data. The a posteriori
power analysis took into consideration all known and estimated variables, including age, sex, post-mortem
interval (PMI), and other diagnoses, and was used to test the validity of the pilot study and the a priori
power analysis, which always rely on fewer data points and make more assumptions. These analyses, which had an estimated large effect size in the population, 0.80, showed that the sampling ratios used exceeded the samples needed to detect differences with a greater than 90% probability. We used several additional computational and statistical methods to establish adequate sample size, including progressive means analysis with exhaustive sampling, and the formula of West et. al., (West et al., 1991
). We used higher sampling fractions in all analyses than the minimum of 3 cases and 3 sections required by the power analyses, and in most cases we expanded these numbers to 5 brains from autistic individuals, 4 brains from control cases, and more than 5 sections per case. In all analyses the sample size included number of cases, volume fraction of areas sampled, and number of individual axons examined, which were not only adequate but exceeded the estimated minimum requirements. Moreover, for each case we examined thousands of axons at very high resolution, totaling nearly 50,000 for the study. In one analysis (A32) tissue was available only for 3 control cases (1 female and 2 male).
Stereological analysis at the LM
We estimated the overall and laminar density of neurons in A32, A11, and A46 overlying the sites of white matter analysis, and the density of oligodendroglial cells in the superficial part of the white matter below OFC (A11) using the unbiased stereological method of the optical fractionator (Gundersen, 1986
; Howard and Reed, 1998
) with the aid of commercial software (StereoInvestigator; Microbrightfield, Williston, VT, USA), as described (Zikopoulos and Barbas, 2006
). For LM quantitative analyses we used a minimum of three sections from one series of coronal sections (20 µm thick) from each case. To ensure unbiased estimate of the number of neurons we first measured the thickness of each section, and used StereoInvestigator to set a guard zone at the bottom and top of each section to correct for objects plucked during sectioning; the disector thickness was thus smaller than the thickness of the section (Gundersen, 1986
; West et al., 1991
; Howard and Reed, 1998
). The sampling fraction was 1/50 of the total volume of the area examined, and was determined in pilot studies using exhaustive sampling and progressive means analysis so that final estimates had a standard error ≤ 10%. The use of uniform random sampling ensured that every part of the area examined had the same chance of being included in the sample. The numbers of neurons and volumes of the corresponding area and layers estimated with the Cavalieri method were divided to assess the density of neurons in each case. We normalized data by expressing the density of neurons as a percentage of the total density of all labeled neurons in each area in each case.
For the analysis of the number of myelinated axons with branches, and to estimate the number of axons that express GAP-43, we double labeled a minimum of three sections from one series per case with NFP-200 and GAP-43 and used systematic random sampling (sampling fraction 1:50) to capture stacks of confocal images at high magnification (×1,000). For analysis of axons with GAP-43 we examined another anterior cingulate area (A24), in addition to the neighboring A32, in order to increase the power of the analysis for the control cases in the anterior cingulate (3 control cases for A32 and 4 control cases for A24).
To reduce the fluorescent glare, we applied three-dimensional deconvolution algorithms to images prior to analysis with the aid of Autodeblur (Media Cybernetics). We used these image stacks to create three-dimensional projections in ImageJ, which we viewed and re-sliced in the X, Y or Z axis, in order to decipher axon branches from crosses. Profile counts of axons with branches, or axons that expressed GAP-43, were obtained using ImageJ and normalized by dividing with the total number of sampled axons in each case. We also assessed GAP-43 expression using an independent method by estimating the ratio of the surface area of GAP-43/NFP-200-positive axons to the total surface area of all axons labeled with NFP-200. Both high resolution quantitative methods allowed accurate quantification of axons expressing GAP-43, whilst excluding unrelated signal in glia or non-neural tissue that might have concealed potential differences.
We estimated the thickness of the cortical grey matter of ACC (A32) in a series of 20 µm thick coronal sections per case, including its divisions at the bottom of a sulcus, where the cortex is compressed, at the top of a gyrus, where the cortex is thick, and at relatively straight parts of the cortex, using ImageJ as described (Hilgetag and Barbas, 2006
Stereological analysis at the EM
To determine the density of axons and the thickness of axons and myelin in the white matter, we sampled a volume of approximately 1cm3 below each prefrontal cortical area, with a systematic random sampling fraction of 1:1,000 that yielded more than 2,000 axons, per case, per area. We divided the white matter (as described above) into a superficial part (closer to the grey matter) and a deep part.
High resolution images of areas of interest were captured with a digital camera attached to the electron microscope (ES1000W, Gatan), imported in ImageJ and calibrated. We estimated the overall density of axons at low magnification (×3,300) by dividing the number or the surface area of the axon profiles by the total surface area of the sampled region. We estimated the maximum inner and outer diameter as well as the thickness of the surrounding myelin sheath, at high magnification (×10,000). To minimize variability and test for errors due to sectioning we measured axons that were perpendicular to the cutting plane and appeared cylindrical. We then repeated the analysis to include all axons by measuring the diameter perpendicular to the center of the maximum diameter of the axon profile. The two analyses yielded similar results and were combined.
Three-Dimensional Reconstruction and Branching Analysis
We followed axons in the superficial part of the white matter of ACC in three autistic and three control cases in long, uninterrupted series of ~400 ultrathin sections (thickness: 150 nm each). The volume examined was ~600,000 µm3
. We viewed at least 200 axons per case at high magnification (×10,000) and photographed them throughout their extent in the series using exhaustive sampling. High resolution digital images were imported as a series in Reconstruct [http://www.bu.edu/neural
)] and aligned, as described (Zikopoulos and Barbas, 2006
). Axons were traced, reconstructed in three dimensions, and their average diameters calculated. Traces of small, medium, large and extra-large axons were color-coded for easy visualization. We estimated the number of all branching points and the number of axons (by size) with branches in each series. Branches were reliably identified and associated with parent axons based on the continuity of the axoplasm, and the thinning or disappearance of myelin at the branching points.
Data were gathered blind to condition and cortical region. Random codes for cases and images were broken after completion of each part of the study. In all cases data collection was performed by at least two investigators. Values obtained from the two independent measures were highly correlated (Pearson R = 0.97, p = 0.001). The samples were obtained from widely spaced sections (one every ten) and fields of view through systematic random sampling to minimize the likelihood of sampling axons from the same parent branch. This sampling scheme and the fact that most axons branch very close to or after they enter the grey matter minimized the likelihood of counting segments of the same axon more than once.
Data distributions for continuous variables were not significantly different from normal as determined by the Kolmogorov–Smirnov test, and thus allowed the use of parametric statistics. We initially used x2 and Kolmogorov–Smirnov tests to examine axon size distributions and multiple linear regression analysis to examine correlations. Data were evaluated with Statistica (StatSoft, Tulsa, OK), through scatter and frequency distribution plots and K-means cluster analysis with parameters set to maximize initial between-cluster distances. We used MANOVA to test for differences among axon and neuron populations and densities. We then used post hoc analyses using Bonferroni’s/Dunn’s (all means) to identify possible differences between groups. For the axon branching analysis we used a two-tailed t-test. For all analyses p values <0.05 were taken as statistically significant.
We also employed three different multivariate analyses to assess global similarities and dissimilarities of the white matter below prefrontal cortices based on all the ultrastructural features examined. We performed discriminant analysis to identify experimental measures that minimize the overlap and clearly separate the distributions of individual data points belonging to different cortical areas for each case. We performed hierarchical cluster analysis (HCA) to group areas based on (dis)similarities in their parameter profiles. In this test, the relative similarity of areas is expressed as the distance between two branching points in a cluster tree diagram. Finally, we used nonmetric multidimensional scaling (NMDS), to arrange prefrontal areas in control and autistic cases in a low-dimensional (2D) space based on the pairwise correlation (dis)similarities between areas. The relative proximity among items in an NMDS diagram represents their relative similarity. We performed NMDS using both the mean values for each area for the autistic and control cases to maximize their separation, as well as using the entire range of values for each case to take into account sample variability. HCA and NMDS analyses employed squared area (dis)similarity matrices derived from the normalized areal profiles by Pearson's correlation.
We also examined potential effects of sex, PMI, age at death, and other diagnoses (i.e. seizures) on all estimates for axon density, size, branching, expression of GAP-43, as well as neuronal and glial cell densities, using correlation analysis (Supplemental Figure 1A–F
). In addition we compared all estimated variables between and within control and autistic cases using MANCOVA with sex, PMI, age at death, and other diagnoses as the covariate and compared the results from this analysis with the MANOVA outcome.
The PMI for the control cases averaged 17.5 hours (±1.5 SEM) and for the autistic cases 39.8±16.9 hours (). This number was significantly higher for the autistic cases because of case 4871, which had a PMI of 99 hours. Without this case the PMI for the autistic cases was comparable to the control cases (25±4.8 hours). Examination of the structural integrity of the tissue and the quality of labeling revealed no differences between case 4871 and the other autistic cases at the light or confocal microscope. At the EM the density of axons in case 4871 was not affected but the membranes of some glial cells and small parts of the myelin surrounding some axons were compromised. As a result we did not use this case for glial cell density estimates at the EM and we sampled a much larger area to estimate axon and myelin diameter. The results obtained from case 4871 correlated well with measurements from the other autistic cases and were thus included in the analyses.
We performed additional statistical analyses to assess the generalizability of the results and to estimate whether the data could be used to accurately predict relationships between the estimated variables in independent samples. To this effect we used cross-validation techniques specifically designed to test the validity of the results and groupings derived from ANOVA, cluster and discriminant analyses, and NMDS. This method involved partitioning the sample of cases into N=30 complementary subsets, performing the analyses on N-1=29 subsets, validating it on the other subset, and then repeating the process N times. The N=30 sample subsets was determined so as to include all possible combinations of control and autistic cases in groups of 3, which was a minimum requirement to perform all analyses (e.g., autistic group 1: cases 4541, 4871, 5173; control group 1: cases 4786, 4981, 5353; autistic group 2: cases 6232, 6677, 5173; control group 2: cases 6004, 5353, 4981; … etc). This method yields fits of predicted and actual data, which are acceptable if the root mean squared errors remain low. Finally, we used two complete datasets collected independently by two investigators and repeated all analyses using a repeated measures design.