iPSCs are generated using the latest techniques to reprogram, expand and characterize human iPS cells from human skin or blood tissues of healthy subjects and diseased patients. Learn More...

iPS cells from patients with ALS and SMA and for unaffected controls are turned into motor neurons using standard operating procedures (SOPs), QC measures, and an improved protocol for the maintenance and differentiation of the cells. Learn More...

RNA-Seq is a deep sequencing approach to transcriptome profiling. Studies using mRNASeq, whole RNA-Seq and small RNA-Seq will precisely measure the extent and complexity of transcriptional perturbations in iPSC derived motor neurons. Learn More...

SWATH-MS, a data-independent acquisition (DIA) method is used to generate proteomic cell signatures.. Sample specific DIA libraries have been generated from aliquots of all iPSC and iMN samples respectively to generate an iPSC and iMN library. Learn More...

ATAC-Seq is a transposase-based, deep sequencing based epigenomic assay used to assess chromatin accessibility and identify functional regulatory sites involved in driving transcriptional changes associated with cell responses to perturbations. Learn More...

Automated robotic microscopy (RM) is used to identify and track live individual neurons in a high throughput and high content fashion over time. Automated image analysis is used to quantify intermediate changes in the physiology of a given cell and relate it to that cell’s fate to construct mulivariate predictive dynamic models of cell fate. Learn More...

The genetic disease background in iPSC lines from individuals with SMA (SMN1 mutation), C9ORF72 repeat expansion associated ALS (C9-ALS) represent genetic perturbagens. Chemical and genetic perturbagens will be selected based on cell signatures generated. Learn More...

Disease signatures are generated using several methods. Network optimization methods such as Omics Integrator and PIUMet identify molecular pathways that are altered in motor neuron diseases. Machine learning approaches identify signals that provide the most robust classification of samples. Learn More...