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

Lookup NU author(s): Dr Helen DevineORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 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.


Publication metadata

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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Funding

Funder referenceFunder name
Cancer Research UK (FC010110)
Francis Crick Institute
Idiap Research Institute
MRC Senior Clinical Fellowship [MR/S006591/1]
Wellcome Trust (FC010110)
UK Medical Research Council (FC010110)
Wellcome Trust [213949/Z/18/Z]

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