A Systems Biology Approach to Understanding ALS Pathogenesis in iPSC-Derived Motor Neurons

Identification: Thompson, Leslie


A Systems Biology Approach to Understanding ALS Pathogenesis in iPSC-Derived Motor Neurons
Leslie M. Thompson, the NeuroLINCS consortium and Answer ALS consortium
University of California, Irvine, Cedars Sinai Medical Center, Gladstone Institute, Johns Hopkins University, MIT
There is a critical need to define the state and predict the behavior of human brain cells in health and disease. The number of different cell types in the CNS remains undefined, and despite a demographically ordained wave of neurodegenerative diseases, no disease-modifying therapy exists. The foundation for intervening rationally in neurodegenerative disease would be dramatically advanced by generating integrated, quantitative molecular phenotypes or cell signatures. The NeuroLINCS Center is an NIH-funded multi-site collaborative effort between research groups with expertise in iPSC technology, disease modeling, imaging, OMICS methods, and computational biology. Using a novel network-based approach we can integrate these omics data sets and identify disease signatures which drive functional consequences in vivo and in vitro. We began by focusing on subjects with a hexanucleotide expansion mutation in the C9ORF72 gene, the most common form of familial Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia. iPSC cells were generated from unaffected individuals and patients with ALS carrying the C9ORF72 mutation. Using a protocol that produced both motor neurons and astrocytes, there were no overt differences in motor neuron differentiation and survival. In order to understand more about the evolving molecular changes that lead to motor neuron dysfunction underlying ALS, we carried out deep unbiased molecular phenotyping through whole genome sequencing, RNA seq, ATAC seq, advanced proteomics and longitudinal single cell analysis on parallel sets of motor neurons. Integrated data signatures were generated using computational methods to identify the most significant disease related interactions at both the genetic and epigenetic level. We discovered specific C9ORF72-related changes in general metabolism, nuclear pore complexes, RNA splicing, extracellular matrix and microtubules. Key pathways were then validated in a fly model of C9ORF72. This approach has been extended to apply these methods to Answer ALS, which seeks to provide a comprehensive assessment of the molecular foundation of 1000 patient-derived differentiated iPSCs and ultimately integrate with clinical and biological data. Specific “omics” profiles are associated with C9orf72 and sporadic ALS subjects and integrated signatures are being generated using bioinformatics, statistics, computational biology and novel software tools to establish patterns that may lead to a better understanding of the underlying mechanisms of ALS.
Funding: NIH and Answer ALS


Credits: None available.

You must be logged in and own this product in order to post comments.