Integrating single-cell transcriptomics into model systems to decode neurodegeneration in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterised by motor neuron degeneration. Several genetic risk factors, including mutations in C9orf72 , SOD1 , TARDBP and FUS have been linked to disease. However, the precise cellular pathogenic mechanisms of ALS remain poorly understood, impeding the development of effective therapeutics. ALS model systems, ranging from Drosophila , mice to human induced pluripotent stem cells (hiPSCs), are powerful research platforms for decoding these mechanisms, with their high genetic tractability allowing for the tracking of disease across the life course. Recently, the application of single cell resolution approaches have allowed the dissection of the heterogenous nature of cellular response to disease in these models, revealing novel ALS pathogenic mechanisms and therapeutic targets. In this Review, we explore the unique benefits and challenges of using single-cell RNA-sequencing (scRNA-seq)/single-nucleus RNA-sequencing (snRNA-seq) analysis of in vivo and in vitro ALS model systems to identify disease pathways and drive our mechanistic understanding of ALS forward. By using single cell approaches in models of ALS, researchers can identify novel, druggable, cellular pathways acting across all disease stages that may be obscured in late-stage post-mortem tissue.

Authors

Institutions

Publication Details

Journal
Frontiers in Cellular Neuroscience
Published
2026-10-26
DOI
https://doi.org/10.3389/fncel.2026.1911064
Primary Topic
Amyotrophic Lateral Sclerosis Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Integrating single-cell transcriptomics into model systems to decode neurodegeneration in amyotrophic lateral sclerosis

Teresa Niccoli, David Orsulik
Frontiers in Cellular Neuroscience
Amyotrophic Lateral Sclerosis Research
article

Integrating single-cell transcriptomics into model systems to decode neurodegeneration in amyotrophic lateral sclerosis

Teresa Niccoli, David Orsulik
article en

Abstract

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterised by motor neuron degeneration. Several genetic risk factors, including mutations in C9orf72 , SOD1 , TARDBP and FUS have been linked to disease. However, the precise cellular pathogenic mechanisms of ALS remain poorly understood, impeding the development of effective therapeutics. ALS model systems, ranging from Drosophila , mice to human induced pluripotent stem cells (hiPSCs), are powerful research platforms for decoding these mechanisms, with their high genetic tractability allowing for the tracking of disease across the life course. Recently, the application of single cell resolution approaches have allowed the dissection of the heterogenous nature of cellular response to disease in these models, revealing novel ALS pathogenic mechanisms and therapeutic targets. In this Review, we explore the unique benefits and challenges of using single-cell RNA-sequencing (scRNA-seq)/single-nucleus RNA-sequencing (snRNA-seq) analysis of in vivo and in vitro ALS model systems to identify disease pathways and drive our mechanistic understanding of ALS forward. By using single cell approaches in models of ALS, researchers can identify novel, druggable, cellular pathways acting across all disease stages that may be obscured in late-stage post-mortem tissue.

Frontiers in Cellular NeuroscienceVol. 20
UK Dementia Research Institute (GB), University College London (GB)
Openalex Percentile: Top 13%
Amyotrophic Lateral Sclerosis Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon