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Automated and unbiased discrimination of ALS from control tissue at single cell resolution

Hagemann, C; Tyzack, GE; Taha, DM; Devine, H; Greensmith, L; Newcombe, J; Patani, R; ... Luisier, R; + view all (2021) Automated and unbiased discrimination of ALS from control tissue at single cell resolution. Brain Pathology , Article e12937. 10.1111/bpa.12937. (In press). Green open access

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Abstract

Histopathological analysis of tissue sections is invaluable in neurodegeneration research. However, cell‐to‐cell variation in both the presence and severity of a given phenotype is a key limitation of this approach, reducing the signal to noise ratio and leaving unresolved the potential of single‐cell scoring for a given disease attribute. Here, we tested different machine learning methods to analyse high‐content microscopy measurements of hundreds of motor neurons (MNs) from amyotrophic lateral sclerosis (ALS) post‐mortem tissue sections. Furthermore, we automated the identification of phenotypically distinct MN subpopulations in VCP‐ and SOD1‐mutant transgenic mice, revealing common morphological cellular phenotypes. Additionally we established scoring metrics to rank cells and tissue samples for both disease probability and severity. By adapting this paradigm to human post‐mortem tissue, we validated our core finding that morphological descriptors robustly discriminate ALS from control healthy tissue at single cell resolution. Determining disease presence, severity and unbiased phenotypes at single cell resolution might prove transformational in our understanding of ALS and neurodegeneration more broadly.

Type: Article
Title: Automated and unbiased discrimination of ALS from control tissue at single cell resolution
Location: Switzerland
Open access status: An open access version is available from UCL Discovery
DOI: 10.1111/bpa.12937
Publisher version: https://doi.org/10.1111/bpa.12937
Language: English
Additional information: Copyright © 2021 The Authors. Brain Pathology published by John Wiley & Sons Ltd on behalf of International Society of Neuropathology. This is an open access article under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords: ALS, histopathology, machine learning, protein mislocalization
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Department of Neuromuscular Diseases
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neuroinflammation
URI: https://discovery.ucl.ac.uk/id/eprint/10122398
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