Quantitative neuropathology: an update on automated methodologies and implicationsfor large scale cohorts

  1. Lookup NU author(s)
  2. Dr Lauren Walker
  3. Dr Kirsty McAleese
  4. Mary Johnson
  5. Dr Ahmad Khundakar
  6. Daniel Erskine
  7. Professor Alan Thomas
  8. Professor Ian McKeith
  9. Professor Johannes Attems
Author(s)Walker L, McAleese KE, Johnson M, Khundakar AA, Erskine D, Thomas AJ, McKeith IG, Attems J
Publication type Article
JournalJournal of Neural Transmission
IssueePub ahead of Print
ISSN (electronic)1435-1463
Full text is available for this publication:
A tissue microarray (TMA) has previously been developed for use in assessment ofneurodegenerative diseases. We investigated the variation of pathology loads in semiquantitativescore categories and how pathology load related to disease progression.Post-mortem tissue from 146 cases were used; Alzheimer's disease (AD) (n=36), Lewybody disease (LBD) (n=56), Mixed AD/dementia with Lewy bodies (n = 14) andcontrols (n=40). TMA blocks (one per case) were constructed using tissue cores from15 brain regions including cortical and subcortical regions. TMA tissue sections werestained for hyperphosphorylated tau (HP-T), β amyloid and α-synuclein (αsyn), andquantified using an automated image analysis system.Cases classified as Braak stage VI displayed a wide variation in HP-T pathology in theentorhinal cortex (interquartile range, 4.13%-44.03%). The interquartile range for βamyloid in frontal cortex in cases classified as Thal phase 5 was 6.75%-17.03% andfor αsyn in the cingulate in cases classified as McKeith neocortical LBD was 0.2%-0.58%. In AD and control cases, HP-T load predicted the Braak stage (p<0.001), βamyloid load predicted Thal phase (p<0.001) and αsyn load in LBD cases predictedMcKeith type of LBD (p<0.001). Quantitative data from TMA assessment highlight the range in pathological load across cases classified with 'severe' pathology and is beneficial to further elucidate theheterogeneity of neurodegenerative diseases. Quantifying pathology in multiple brainregions may allow identification of novel clinico-pathological phenotypes for theimprovement of intra vitam stratification of clinical cohorts according to underlyingpathologies.
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