Data Generation Component We propose three broad experimental aims based around the type of assay, perturbagens and technologies being applied that will overlap across the years. The first will use iPSCs from three disease states (non affected, SMA and ALS) in which we have shown specific phenotypes. We will use an iterative approach by first screening for a number of perturbagens of interest to the broad neuroscience community using cost effective assays including simple cell death models and a highly novel imaging analysis system. The second parallel effort will be to use the same iPSC lines but in this case test a set of known cell modifiers (Glutamate, ER stressor and SOD1 ASO) as perturbagens and perform massive parallel quantitative molecular phenotyping (QMP) to generate robust signatures and to define the responses of motor neuron cultures to these perturbagens. We will then perform QMP on neurons, astrocytes and oligodendrocytes from disease and control cells and in response to the same perturbations as above to elucidate signatures across broadly relevant neural cell types. This data will be compared to motor neuron cultures (where expected disease signature will be) with non motor neuron cultures (where no or a more restricted disease signature is expected) to resolve the question of cell type specificity. We will also generate new iPS lines from post mortem human patient tissues to allow clinical pathological signatures to be incorporated into the LINCS data, providing a unique resource to both the SMA and ALS scientific community and to researchers interested in larger questions relating to the CNS. The third is to bring in disease iPS lines from Huntington's and Parkinson's subjects (from the respective NIH consortia and in coordination with various foundations - see letters of support. Overall) and test the specificity of signatures seen in the motor neuron diseases with other neurodegenerative conditions (both disease and response to perturbagens). All of these studies will be done in close association with the data analysis component community section (being responsive to the needs of the community). Given the speed of discovery in iPSC and new molecule generation, we also aim to be flexible in our design to allow incorporation of "breakthrough" technologies or drugs should they arise.
This component of the NeuroLINCS proposal will generate data signatures from induced pluripotent stem cells and their neural derivatives with and without specific perturbagens. This information will be highly relevant for may scientists working with these cell types to model and treat neurological diseases that are a serious public health problem for the United States. It will allow the community to better understand how human neurons respond to specific compounds and therefore help develop better drugs in the future.