Our initial endeavors in characterizing our isogenic

Our initial endeavors in characterizing our isogenic iPSCs lines included a microarray-based large-scale gene dopamine receptor antagonist analysis comparing the HD iPSCs with the corrected line C116 iPSCs (An et al., 2012). These studies were restricted to a comparison of only the iPSCs lines but yielded several useful insights regarding the biology of these established cell models. Specifically, we found that global gene expression remained essentially unchanged at the iPSC state upon analysis of isogenic pairs (HD iPSCs versus corrected iPSCs). We identify an order of magnitude fewer significantly differentially expressed (DE) genes when compared to a separate experiment, evaluating a non-isogenic pair of HD iPSCs versus normal iPSCs derived from an unrelated healthy individual, likely due to differences in genetic background as found in other studies (Fogel et al., 2014). The low degree of DE genes between corrected and uncorrected isogenic iPSCs supports several important points regarding the biological characterization of this HD model: (1) isogenic gene modification does not dramatically alter the expression profile of these cells, (2) HTT gene correction does not markedly alter gene expression at the iPSCs state—consistent with disease biology, (3) gene expression analysis in controlled isogenic cell line studies may represent a cleaner approach to discovery of disease-relevant pathway effects, and (4) further analysis in disease-affected cell types may allow the ability to resolve disease-specific coexpression traits unique to those cell types. Here, we present transcriptomic and bioinformatic analysis of disease-relevant and non-relevant cell types in tandem with an isogenic human stem cell model of HD. DE analysis and weighted coexpression analyses confirmed the cell-specific nature of gene expression changes due to mHTT. We used weighted gene coexpression network analysis (WGCNA) to analyze the non-disease and disease states of our isogenic HD stem cell model and determine clusters of co-regulated genes, known as modules, that define each state (Langfelder and Horvath, 2008). The modules were analyzed by Genemania, functionally annotated by DAVID, analyzed by Enrichr, and the top, most connected transcripts (hubs) were studied in detail. Inclusion of HD iPSCs, which do not show a phenotype, enabled the isolation of coexpression traits specific to the HD NSCs and the identification of pathways involved in HD pathogenesis. Further modulation of key members of these signaling pathways rescued some HD disease phenotypes.
Results
Discussion In our previous work, we found cellular and molecular phenotypes associated with HD in NSCs derived from HD patient-specific iPSCs when compared to isogenic controls. The phenotypes we reported included susceptibility to cellular death as measured by caspase activity and TUNEL staining, mitochondrial deficits, lower levels of BDNF, altered cadherin and TGF signaling (An et al., 2012). The cellular and molecular phenotypes were only associated with the differentiated HD NSC state and were not detected in HD iPSCs. This finding suggests that the first disease phenotypes in HD manifest during early development at the NSC stage. This is interesting because we may be detecting HD developmental changes that occur early in disease progression, in our human HD NSC models. To examine the molecular basis of the phenotypes found in HD NSCs, we used RNA-seq to measure expression changes in these cellular states. Inclusion of HD iPSCs, which do not show a distinct disease-associated phenotype, enabled specific isolation of coexpression traits specific to the HD NSCs and the identification of pathways involved in HD pathogenesis. WGCNA analysis revealed the effects of HTT CAG expansion on NSCs and identified two modules driving HD phenotypes—the black and red modules. By studying gene expression changes in prenatal human striatum during development, using WGCNA analysis, another group identified a module they named M25 whose members are highly expressed in the developing human striatum (Onorati et al., 2014). We found that the black and red modules were enriched for genes found in the M25 module, which is associated with human striatal tissue. In addition to the black and red module, we also evaluated other key striatal-specific and well known genes in human striatal development including CTIP2 (BCL11B), DARPP-32 (PPP1R1B), ISL1, TBR1, FOXP1, and PAX6, which were all downregulated in HD NSCs compared to control NSCs. This is consistent with a number of studies showing mHTT alters cortical and striatal neurogenesis in mouse models of HD (Molero et al., 2009), and the normal function of HTT is implicated in neurogenesis. Indeed, knockin HD mice with Q111 repeats exhibited delayed striatal cytoarchitecture with altered expression of markers of MSNs (Molero et al., 2009). The role of development in neurodegeneration has been addressed in a related polyglutamine expansion disease, SCA1 (Serra et al., 2006). When ATXN1[82Q] expression in SCA1 mice was delayed during postnatal development, the disease was less severe. This correlation of genes found in our NSC analysis and in previous mouse studies in the field support the concept that NSCs can be a valuable tool for modeling a disease in a dish.