Automated and unbiased discrimination of ALS from control tissue at single cell resolution
| Autor: | Cathleen Hagemann, Raphaëlle Luisier, Giulia E. Tyzack, Linda Greensmith, Andrea Serio, Doaa M Taha, Rickie Patani, Jia Newcombe, Helen Devine |
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| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
medicine.medical_specialty protein mislocalization Cell Mice Transgenic Computational biology Biology Pathology and Forensic Medicine Mice 03 medical and health sciences 0302 clinical medicine medicine Animals Amyotrophic lateral sclerosis Research Articles Motor Neurons Superoxide Dismutase General Neuroscience Amyotrophic Lateral Sclerosis Neurodegeneration Resolution (electron density) Morphological descriptors medicine.disease Phenotype Mitochondria machine learning 030104 developmental biology medicine.anatomical_structure Tissue sections Spinal Cord histopathology Histopathology Neurology (clinical) ALS 030217 neurology & neurosurgery Research Article |
| Zdroj: | Brain Pathology |
| ISSN: | 1750-3639 1015-6305 |
| DOI: | 10.1111/bpa.12937 |
| Popis: | 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. With their novel pipeline for automated segmentation, profiling and identification of phenotypically distinct motor neuron subpopulations in ALS pathological tissue sections, Hageman et al. report that morphological descriptors strongly discriminate ALS from control healthy tissue at the single‐cell level. |
| Databáze: | OpenAIRE |
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