High-throughput, automated quantification of white matter neurons in mild malformation of cortical development in epilepsy
© Liu et al.; licensee BioMed Central Ltd. 2014
Received: 11 April 2014
Accepted: 9 June 2014
Published: 13 June 2014
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.
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.
This study confirms the potential role of WSA in future quantitative diagnostic histology, especially for the histopathological diagnosis of mMCD.
KeywordsmMCD Immunohistochemistry Quantitation Stereology Heterotophic Neurons
According to the Palmini classification, the observation of ‘excess’ heterotrophic neurons in layer I and the white matter are classified as mild forms of Malformation of Cortical Development (mMCD) type I and II respectively . There are presently no other criteria to aid the assessment of mMCD II . Despite many previous studies in this field, the oldest dating back to 1984 , it is still uncertain whether the density of WMN is increased in patients with epilepsy, what the threshold for the diagnostic confirmation of mMCD II should be , whether the severity of this pathological feature correlates with pre-operative MRI, and represents a clinically relevant, prognostic biomarker, in particular for outcome following epilepsy surgery [4–8]. Findings from previous quantitative studies have been contradictory [3, 9–14], due to the small sample size used (<52 cases), and differing counting methods, equipment, regions of analysis, and cell types studied (Additional file 1: Table S1). Excess density of WMN has also been reported in patients with schizophrenia and autism [15–19], suggesting the possibility that the WMN density may be an indicator of neurodevelopmental disorders beyond epilepsy. Consequently, there is a clinical need for the accurate, rapid evaluation of the numerical threshold of ‘excess’ WMN in epilepsy, and to determine any possible relationships between WMN density and epileptogenesis, pharmacoresistance and surgical outcome.
Recent technological advances in quantitative histology, with systems such as whole slide imaging with high-throughput, automated analysis (WSA), means that it is now possible to more accurately and efficiently quantify histological stains and immunolabelling, which may aid the clinical diagnosis of a wide range of diseases [20–24] and be instrumental in the design of personalised treatment plans which may improve patients’ chances of recovery [25, 26]. In fact, there is an increasing official acceptance of WSA: the United States Food and Drug Administration has approved the WSA of several major companies for the diagnostic evaluation/screening of HER2, PR, ER, Ki67 and/or p53 immunolabelling (Additional file 2: Table S2), underlining the importance and relevance that quantitative histopathology has in personalised medicine. However, before such advanced system is used in routine clinical laboratory, it is important to carefully compare the cost and applicability, and validate the quality of results obtained against traditionally used methods such as semi-automated analysis, and established, gold standard approaches for quantifying the number of neurons in volume of tissue, such as stereology .
As such, this study aims to quantify the density of NeuN immunopositive neurons in the white matter of up to 130 samples from patients with epilepsy who have undergone a standardised surgical procedure (anterior temporal lobectomy) for the same underlying pathology (hippocampal sclerosis) and 12 controls using three quantitative methods: (i) WSA, (ii) semi-automated analysis that processes pre-acquired images of the white matter (SA), and (iii) stereology (n = 50 cases). This highly homogenous group of epilepsy patients has comprehensive clinical data (Additional file 3: Table S3) to allow us to investigate the clinical relevance of WMN density in epilepsy. Quantification of WMN using the three systems would also allow us to compare the quantitative results, ease of use, time consumption, reliability, and accuracy in the selection of regions and identification of cells.
Material and methods
This study was approved by the Joint Research Ethics Committee of the National Hospital for Neurology and Neurosurgery and the UCL Institute of Neurology. Patients had given consent to the use of their brain tissue in research, and the tissue was used in accordance with the current UK Human Tissue Authority guidelines. Anterior temporal lobectomy specimens from 130 epilepsy cases and 12 controls (five normal, post mortem controls with no brain pathology, and seven non-epilepsy, surgical controls) were studied. In the epilepsy cases, the resected sample was taken 1 to 1.5 cm from the temporal pole and the sample included the superior, middle, and inferior temporal gyrus with underlying white matter extending to the ventricle. The average age of cases with epilepsy and non-epilepsy surgical controls was 37 years and 39 years respectively. Clinical details of surgical epilepsy and control cases are summarised in Additional file 3: Table S3. Seven to 20 μm-thick, formalin-fixed, paraffin-embedded brain tissue sections from each case were processed and immunolabelled using the marker for mature neurons, Neuronal Nuclei (NeuN, 1:100, Millipore MAB377, UK), and counterstain with haematoxylin (VWR International, UK). The exact section thickness of each case (or the depth of field ‘z’ reading) was later measured using the Zeiss Axio Imager Z2 Motorised microscope with 63× objective lens (average actual thickness ± standard deviation, 17 μm ± 5). NeuN immunopositive cells in the white matter of each sections were counted using (i) WSA: whole slide imaging with high-throughput, automated analytic (Tissue Studio and Developer XD, Germany) with automated and manual ROI selections, and (ii) SA: semi-automated image analytic (ImageProPlus version 6.2, Media Cybernetics, USA), and (iii) three dimensional stereology (Histometrix, Kinetic Imaging, UK).
The images of 142 epilepsy and control cases were categorised into groups or ‘classes’ with similar intensity of haematoxylin staining and NeuN immunopositive labelling so the same algorithm could be applied to all sections of the same class; this would assist and speed up the user-training process. During the user-training process in WSA with automated ROI selection, the researcher ‘trained’ the program to recognise the cerebral cortex, grey/white matter border, white matter and empty spaces by selecting a few segments of well-defined areas within each region from two scanned images within each class. To optimise the area of white matter, filtering algorithms were applied to the grey/white matter boundary where the interface between compartments was blurred. Then the white matter was classified using the ‘nearest neighbour’ option, and relative area of less than 0.1 unit. These parameters/algorithms were then applied to all images in the same class.
For WSA with manual ROI selection, the researcher outlined the white matter of each image using a stylus pen on a touch-sensitive computer screen. This boundary was drawn at a set distance of 0.5 mm from the cortical margin, to exclude layer VI neurons. After either manual or automated ROI selection, WSA identified and counted NeuN immunopositive cells if labelled cells had an intensity of 3.1 units. All counts were expressed as cells per mm2 or mm3 adjusted by Abercrombie’s correction factor to correct for section thickness and mean cell size . Actual section thickness was measured on the Zeiss Axio Imager Z2 microscope at 63× magnification, and the cell size of NeuN immunopositive labelled cells was measured using WSA. WSA also gave the percentage of NeuN immunopositive cells that were small (area of <126.36 μm2), medium (126.36 – 370 μm2) or large (>370 μm2).
For SA, 15 images of the white matter were pre-acquired from each scanned image at 10× magnification. NeuN immunopositive cells in each image were then automatically selected by thresholding based on the intensities in red, green, and blue channels.
49 epilepsy cases and one control were quantified using stereological cell counting methods employed in our previous published studies [11, 29, 30]. The region of interest in the deep white matter of each section was first outlined at 2.5× objective on a the Zeiss Axioskop microscope linked to a stereological software (Histometrix, Kinetic Imaging, Nottingham, UK). All NeuN immunopositive cells were counted with a 63× objective using the optical dissector with uniform random field sampling, and a sampling fraction of between 25–100% to minimise coefficient of error to 0.1 unit or less where possible.
Nonparametric Kruskal-Wallis and post-hoc comparisons, Mann–Whitney, Spearman correlation, and intraclass correlation coefficient tests were performed using SPSS 20 (IBM, USA). Correlations were conducted between density of WMN and age, gender, length of seizure history, history and frequency of simple partial seizures, generalised seizures, secondary generalised seizures, and various post-operative follow-ups (1st to 18th). To test the reliability of each method, ten randomly-selected cases (two for stereology) were re-analysed. Bland and Altman plots were presented, and Pearson’s correlation and intra-class correlation coefficients using absolute agreement definition in a two-way mixed model, and single measure were calculated.
The quantitative techniques and results from WSA were compared with two other quantitative systems, SA, and stereology.
Area of white matter quantified
The area of white matter sampled in 142 cases was significantly different between WSA automated and manual, SA and stereology (P < 0.000), but not between SA and stereology (P = 0.06). The range and average area sampled were the highest using WSA automated (246.1-6.4 mm2, 74.3 mm2), followed by WSA manual (178.8-3.4 mm2, 46.7 mm2), SA (7.1-0.4 mm2, 5.0 mm2) and stereology (11.9-1.2, 4.0 mm2) respectively; this is expected since the WSA allows the entire white matter to be sampled (Figure 1).
ROI selection and identification of neurons
NeuN immunopositive cells in all our cases were darkly labelled, and they were distinct from the background, so it was easy to train WSA and SA to accurately recognise and count these cells, and for the user to identify immunopositive cells in stereology using a 63× objective.
Reliability of methods
Time consumption and ease of use
It took an average of six hours per case to quantify WMN using stereology  compared to 45 minutes per case using SA, 15 and ten minutes per case using WSA automated and manual respectively (Figure 3C). We found that WSA and SA were easier to use than stereology, with fewer steps involved.
Density of WMN
Comparison of WMN density between cases with epilepsy and controls
Quantitation revealed a significantly higher corrected density of WMN in up to 130 epilepsy cases compared to up to 12 controls using WSA automated (P = 0.022; Figure 4B), WSA manual (P = 0.004) and SA (P = 0.036). Only one control case was analysed using stereology and statistical analysis was hence not performed. The maximum WMN density for epilepsy cases in each method were two or three-fold higher than the maximum WMN density for controls. The corrected density of WMN in control and epilepsy cases did overlap but no control case had over 2298 cells/mm3 using WSA automated. Our controls included five post mortem, normal cases, and seven surgical, non-epilepsy cases. Surgical tissue from neurologically normal patients is by definition not available. Therefore, we used as controls histologically normal, surgically resected tissue from non-epileptic patients with traumatic brain injury or tumours. No significant difference in WMN density was observed between these groups of controls using WSA automated and manual, and SA. The corrected WMN density was significantly different between epilepsy cases and post mortem controls (P < 0.05), but not against surgical non-epilepsy controls using WSA automated and manual selections, and SA. We cannot exclude that subtle oedema or mild gliosis in our surgical non-epilepsy controls may have influenced the density and location of WMN neurons in these surgical controls.
The area of white matter sampled, thickness, and average cell diameter were not significantly different between epilepsy cases and controls using any of the methods (P > 0.05). The percentages of small, medium, and large NeuN immunopositive cells and their average intensity measured by WSA automated were not significantly different between epilepsy cases and post mortem and surgical controls (Additional file 4: Table S4).
No significant correlation between the corrected density of WMN in epilepsy cases and clinical parameters was observed after Bonferroni correction for multiple comparison was applied at P < 0.002.
This is the first study that has investigated and compared the reliability, accuracy, the ease of use and time consumption of three quantitative systems, WSA, SA, and stereology. We found that WSA sampled larger regions, performed faster analysis, and provided the most reproducible results compared to other systems. Using WSA, we found that a higher density of WMN in the 130 epilepsy cases compared to controls, but the density of WMN in epilepsy cases did not correlate with clinical parameters relating to seizure frequency or surgical outcome.
With the introduction of many different systems for quantitative histology, it is important that systems are rigorously tested for both reliability and cost-effectiveness, especially if results are to be used to assist in clinical diagnosis. Consequently, we compared the quantitative findings from three systems, employing three different counting approaches; stereology, SA and WSA. Three-dimension stereology or design-based stereology has been long considered as the most accurate method to quantify neurons in an ‘optically-defined volume of tissue’ . Stereology avoids the need to correct for variation in section thickness and cell size of each case as established in the Abercrombie principle [28, 32], with confident, unbiased live counting of neurons at high magnification using 63 or 100× objectives. However, stereology is time-consuming, taking approximately six hours per case , compared to ten to 15 minutes per case with a maximally automated system such as WSA. Stereology is, therefore, impractical in a diagnostic laboratory. Findings from this study suggest that automated systems such as WSA may be a good alternative to stereology, considering that no significant difference in WMN density values was observed between stereology and WSA manual, and the results obtained from WSA manual were reproducible and reliable as evident in the intra-rater reliability coefficient tests. Furthermore, the WSA system is superior over stereology: WSA has the power to examine a large area and it avoids user-selected bias of only small areas of interest (for SA and stereology) which may not be representative of the whole sample; it can also quantify sections rapidly, thus allowing for more cases and additional morphometric data to be examined without adding extra time to the count data (whereas in stereology, the use of the optical dissector method to measure the size of each cell drastically increases the analysis time). Together, these advantages support the use of WSA in quantitative histology.
The cause of the increased WMN density in epilepsy is unknown. The failure of neuronal migration, and/or the elimination or de novo generation of WMN, as either a cause or effect of seizures, have all been postulated but remain to be proven. In regards to their influence on epileptogenesis, previous studies on the functional activity of WMN (normal or pathological) are limited and conflicting [5, 18, 36–40]. Three studies have reported that WMN density may have a predictive effect on seizure outcome following surgical resection which would support a role of increased WMN density in seizure networks in the temporal lobe [9, 11, 14], but this finding was not observed in other similar studies .
This study showed that WSA, a modern high-throughput, automated analysis is more efficient in selecting larger regions and cells of interest, and in processing cell counts and morphometric data compared to the other systems. These obvious advantages of whole slide scanning have to be balanced with the practicalities and expense in that not every neuropathology laboratory currently has access to slide scanners and a whole slide image analysis software solution. Furthermore, users should be reminded that both WSA and SA rely on user-defined size and/or thresholding parameters to identify cells of interest, so the accuracy of quantification will ultimately be dependent on the users’ settings and the quality of the acquired images/scans. Therefore, quality assurance by trained personnel after defining the parameters and repeated counts will be important. Nevertheless, the high-throughput data generated with the WSA systems can be used as a ‘gold standard’ to provide bench-mark criteria (Figure 5) to translate into diagnostic practice using a conventional microscope for the future assessment of mild MCDs.
Whole slide imaging with automated analysis
Intraclass correlation coefficient
mild Malformation of Cortical Development
Magnetic resonance imaging
Region of interest
White matter neurons.
This study was supported by the Medical Health Research Council, UK (Grant number MR/J01270X/1). This work was undertaken at University College London and University College London Hospital, who receive part of their funding from the Department of Health NIHR Biomedical Centre scheme. It was also supported by the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n°602102 (EPITARGET) (SMS). We are grateful to Georgia Lockwood-Estrin for her help with stereology analyses, Lillian Martinian for her technical help in immunohistochemistry, and Lila Krishna for useful discussion on quantitative data. The WSA system, Definiens Tissue Studio, is supported by the research facility UCL IQPath.
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