High-throughput, automated quantification of white matter neurons in mild malformation of cortical development in epilepsy

Abstract

Introduction: In epilepsy, the diagnosis of mild Malformation of Cortical Development type II (mMCD II) predominantly relies on the histopathological assessment of heterotopic neurons in the white matter. The exact diagnostic criteria for mMCD II are still ill-defined, mainly because findings from previous studies were contradictory due to small sample size, and the use of different stains and quantitative systems. Advance in technology leading to the development of whole slide imaging with high-throughput, automated quantitative analysis (WSA) may overcome these differences, and may provide objective, rapid, and reliable quantitation of white matter neurons in epilepsy. This study quantified the density of NeuN immunopositive neurons in the white matter of up to 142 epilepsy and control cases using WSA. Quantitative data from WSA was compared to two other systems, semi-automated quantitation, and the widely accepted method of stereology, to assess the reliability and quality of results from WSA.

Results: All quantitative systems showed a higher density of white matter neurons in epilepsy cases compared to controls (P = 0.002). We found that, in particular, WSA with user-defined region of interest (manual) was superior in terms of larger sampled size, ease of use, time consumption, and accuracy in region selection and cell recognition compared to other methods. Using results from WSA manual, we proposed a threshold value for the classification of mMCD II, where 78% of patients now classified with mMCD II were seizure-free at the second post-operatively follow up.

Conclusion: This study confirms the potential role of WSA in future quantitative diagnostic histology, especially for the histopathological diagnosis of mMCD.

Figure 1

Quantification of NeuN immunopositive neurons in the white matter. (A) A surgically-resected temporal lobe from a patient with epilepsy who had undergone standard temporal lobectomy. (B) A 7μm-thick section was cut, immunolabelled using the neuronal marker, NeuN, and scanned using the whole slide scanner (LEICA). Using WSA, the white matter was selected automatically or manually on digitised images of NeuN immunopositive sections. In WSA automated, the programme identified the white matter by following a series of algorithms such as tissue and background separation (C), and showed the training step, whereby the user selected ‘segments’ that were representative of the cortex (orange) or white matter (yellow; D), and the programme learned to identify other ‘patches’ of similar features (E). (F) In WSA manual, the user interactively outlined the white matter. After ROI selection, NeuN immunopositive cells in the white matter (G) of all cases were quantified by WSA (H). (I) In SA, images (yellow) were manually acquired by the user and then fed into a semi-quantitative analysis programme for quantification. (J) For stereology, a small area in the deep white matter was selected by the user and the programme moved through each point within the selection to allow the user to count NeuN immunopositive cells at 63× magnification. Scale bar = 1 cm (A), 2 mm (B-F, I-J), 200 μm (G-H).

Figure 2

Cell identification using WSA automated and manual. (A) In some cases, WSA automated quantified NeuN immunopositive cells close to the grey/white matter demarcation (white arrow); some of these cells might be neurons in the cortical layer VI. (B) In contrast, because the user drew the white matter with a set distance away (0.5 mm) from the grey/white matter demarcation in WSA manual, we could be confident that the counted cells were in the white matter.

Figure 3

Measure of reliability. Ten cases (and two cases for stereology) were re-analysed using all methods. (A) Table showing Pearson’s correlation coefficient (r), intraclass correlation coefficients (ICC) with 95% confidence interval (CI), ANOVA, and non-parametric P values for all methods. The 95% level of agreement’s upper and lower boundary values (UB, LB) in Bland and Altman plots were also presented. (B) Graphs showing the correlation (left) and Bland & Altman plots (right) of WMN density values between test and retest using all methods. (C) Table showing the time spent on each method. *see Eriksson et al. [29].

Figure 4

Density of WMN. (A) Boxplot showing quantitative results of all cases using WSA automated, WSA manual, SA and stereology. Horizontal line within box = median; top of the box = 75th percentile; bottom of box = 25th percentile; whiskers = 1.5x height of the box or maximum/minimum values. Tabulated results were expressed as cells/mm² or cells/mm³ after Abercrombie’s correction ± standard error of means (SE). (B) Boxplot showing significantly higher densities of WMN in epilepsy cases compared to controls using all methods. ^The methodology in stereology was a 3D cell counting method, so correction factors were not applied.

Figure 5

Proposed criteria for the classification of mMCD II in epilepsy. (A) This may be used as practical guidelines for neuropathologists to diagnose mMCD II in the temporal cortical resections from patients with mTLE and hippocampal sclerosis. Values are based on the present study which have used cortical resections (inclusive of the superior, middle, and inferior temporal gyrus that were 1–1.5 cm from pole), and immunohistochemistry using anti-NeuN (Millipore MAB377, 1:100, overnight). (B) In this study, WMN density values obtained using WSA manual, whereby the area of interest was outlined a set distance (0.5 mm) from the grey-white matter boundary, was regarded as the gold standard in the evaluation of neuronal counts. A graphical representation of our proposed numerical thresholds is presented here. Each point represents one case. WSA = whole slide imaging with automated analysis, control max = the highest WMN density value from controls, control mean = mean WMN density of controls, mMCD = mild Malformation of Cortical Development, SA = semi-automated analysis, sd = standard deviation, WMN = white matter neurons.