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Lookup NU author(s): Dr Helen DevineORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2021 The Authors. Brain Pathology published by John Wiley & Sons Ltd on behalf of International Society of Neuropathology. 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.
Author(s): Hagemann C, Tyzack GE, Taha DM, Devine H, Greensmith L, Newcombe J, Patani R, Serio A, Luisier R
Publication type: Article
Publication status: Published
Journal: Brain Pathology
Year: 2021
Volume: 31
Issue: 4
Print publication date: 01/07/2021
Online publication date: 11/02/2021
Acceptance date: 07/01/2021
Date deposited: 03/10/2024
ISSN (print): 1015-6305
ISSN (electronic): 1750-3639
Publisher: John Wiley and Sons Inc.
URL: https://doi.org/10.1111/bpa.12937
DOI: 10.1111/bpa.12937
Data Access Statement: We provide raw images and complete source code (which is not a software but rather a compilation of R and python) to readily reproduce figures, tables, and other results that involve computation in order to facilitate the development and evaluation of additional profiling methods. We also provide the measurements of each of the ~600 cells whose origins are annotated. The raw images, metadata and single-cell measurements provided as comma-delimited files have been deposited Zenodo under the accession number 3985099, together with the image processing pipelines. The scripts for automated detection of MNS subpopulation can be freely accessed on Github in the following repository: https://github.com/RLuisier/ALSdisMNs
PubMed id: 33576079
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