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Developmental alterations in Huntington's disease neural cells and pharmacological rescue in cells and mice

Nature Neuroscience volume 20, pages 648–660 (2017)Cite this article

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Abstract

Neural cultures derived from Huntington's disease (HD) patient-derived induced pluripotent stem cells were used for 'omics' analyses to identify mechanisms underlying neurodegeneration. RNA-seq analysis identified genes in glutamate and GABA signaling, axonal guidance and calcium influx whose expression was decreased in HD cultures. One-third of gene changes were in pathways regulating neuronal development and maturation. When mapped to stages of mouse striatal development, the profiles aligned with earlier embryonic stages of neuronal differentiation. We observed a strong correlation between HD-related histone marks, gene expression and unique peak profiles associated with dysregulated genes, suggesting a coordinated epigenetic program. Treatment with isoxazole-9, which targets key dysregulated pathways, led to amelioration of expanded polyglutamine repeat-associated phenotypes in neural cells and of cognitive impairment and synaptic pathology in HD model R6/2 mice. These data suggest that mutant huntingtin impairs neurodevelopmental pathways that could disrupt synaptic homeostasis and increase vulnerability to the pathologic consequence of expanded polyglutamine repeats over time.

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Figure 1: Patient iPSC lines and statistical analysis of RNA-seq.
Figure 2: RNA-seq identifies altered neurodevelopmental genes and functions.
Figure 3: RNA-seq reveals altered pathways involved in neuronal function.
Figure 4: Gene expression changes reflect altered striatal development.
Figure 5: ChIP-seq in iPSC-derived neurons shows that epigenetic profiles in non-disease cells are correlated with transcriptional changes in HD.
Figure 6: Isx-9 induces expression of NEUROD1.
Figure 7: Isx-9 treatment improves CAG repeat-associated phenotypes.
Figure 8: Treatment with Isx-9 improves cognition and rescues loss of cortico-striatal synapses in R6/2 mice.

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Acknowledgements

We thank the patients and their families for their essential contributions to this research. We also thank E. Cattaneo and J. Arjomond for discussions of the data, D. Merry for critique of the manuscript and data, S. Svendsen for editorial assistance, J. Dunn for technical culture assistance, G. Vatine (Cedars-Sinai Medical Center, Los Angeles) for the iPSC-derived oligodendrocyte precursors, M. Godoy and G. Gowing (Cedars-Sinai Medical Center, Los Angeles) for the rat muscle and R. Barrett (Cedars-Sinai Medical Center, Los Angeles) for the definitive endoderm positive control. We also thank F. Bennet and Ionis Pharmaceuticals for providing the HTT ASO. Primary support for this work was from NIH NS078370 (L.M.T., C.N.S., J.F.G., M.E.M., C.A.R. and S.F.) and from the CHDI Foundation (J.M.C, P.J.K. and N.D.A.). Additional support was provided by NIH: U54 NS091046 NeuroLINCS center (L.M.T., C.N.S., E.F.); NIH NS089076 (L.M.T., D.E.H., E.F.); P50NS16367 (HD Center Without Walls); NIH R01GM089903 (E.F.); NIH NS101996-01 (S.F.); NIH R01NS084298 (B.S.); American Heart Association, CIRM and NRSA fellowships (R.G.L.); the Hereditary Disease Foundation (V.B.M.); the Taube-Koret Center and the Hellman Family Foundation (S.F.); the UCI Institute for Clinical and Translational Science (L.M.T.); Huntington's Disease Society of America (L.L.S); HD CARE (L.M.T.) and the NIH Biotechnology Training Program Fellowship (T32GM008334, A.J.K.). Additional support was provided by grants from the Ministerio de Economia y Competitividad (SAF 2014-57160-R to JA; SAF2015-66505-R to J.M.C.), from the ISCIII-Subdirección General de Evaluación and European Regional Development Fund (ERDF) (RETICS to JMC (RD12/0019/0002; Red de Terapia Celular); ADVANCE(CAT) with the support of ACCIÓ (Catalonia Trade & Investment; Generalitat de Catalunya) and the European Community under the Catalonian ERDF operational program 2014-2020), Spain, from the European Union FP7 (P.J.K. and N.D.A.) and from the Ser Cymru Life Sciences & Health Network in Drug Discovery Programme (M.W.S.). This work was made possible, in part, through access to the Genomic High Throughput Facility Shared Resource of the Cancer Center Support Grant (CA-62203) at the University of California, Irvine. Support also included computing resources from National Science Foundation grant DB1-0821391 and sequencing support from National Institutes of Health grant P30-ES002109.

Author information

Author notes

  1. Ryan G Lim, Lisa L Salazar, Daniel K Wilton, Alvin R King, Jennifer T Stocksdale, Delaram Sharifabad, Alice L Lau, Beth Stevens, Jack C Reidling, Sara T Winokur, Malcolm S Casale, Leslie M Thompson, Mónica Pardo, A Gerardo García Díaz-Barriga, Marco Straccia, Phil Sanders, Jordi Alberch, Josep M Canals, Julia A Kaye, Mariah Dunlap, Lisa Jo, Hanna May, Elliot Mount, Cliff Anderson-Bergman, Kelly Haston, Steven Finkbeiner, Amanda J Kedaigle, Theresa A Gipson, Ferah Yildirim, Christopher W Ng, Pamela Milani, David E Housman, Ernest Fraenkel, Nicholas D Allen, Paul J Kemp, Ranjit Singh Atwal, Marta Biagioli, James F Gusella, Marcy E MacDonald, Sergey S Akimov, Nicolas Arbez, Jacqueline Stewart, Christopher A Ross, Virginia B Mattis, Colton M Tom, Loren Ornelas, Anais Sahabian, Lindsay Lenaeus, Berhan Mandefro, Dhruv Sareen and Clive N Svendsen: Authors listed by individual consortium group appear in the Supplementary Note.

Authors and Affiliations

  1. Department of Biological Chemistry, University of California, Irvine, Irvine, California, USA.,

    Ryan G Lim & Leslie M Thompson

  2. Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, California, USA.,

    Lisa L Salazar & Leslie M Thompson

  3. F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA

    Daniel K Wilton & Beth Stevens

  4. Department of Neurobiology and Behavior, University of California, Irvine, Irvine, California, USA.,

    Alvin R King, Delaram Sharifabad, Sara T Winokur & Leslie M Thompson

  5. UCI MIND, University of California, Irvine, Irvine, California, USA.,

    Jennifer T Stocksdale, Alice L Lau, Jack C Reidling, Malcolm S Casale & Leslie M Thompson

  6. Sue and Bill Gross Stem Cell Center, University of California, Irvine, Irvine, California, USA.,

    Leslie M Thompson

  7. Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona Spain; August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain; and Networked Biomedical Research Centre for NeuroDegenerative Disorders (CIBERNED), Barcelona, Spain.,

    Mónica Pardo, A Gerardo García Díaz-Barriga, Marco Straccia, Phil Sanders, Jordi Alberch & Josep M Canals

  8. August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain,

    Mónica Pardo, A Gerardo García Díaz-Barriga, Marco Straccia, Phil Sanders, Jordi Alberch & Josep M Canals

  9. Gladstone Institutes and the Taube/Koret Center of Neurodegenerative Disease Research, Roddenberry Stem Cell Research Program, San Francisco, California, USA

    Julia A Kaye, Mariah Dunlap, Lisa Jo, Hanna May, Elliot Mount, Kelly Haston & Steven Finkbeiner

  10. Sandia National Laboratories, Livermore, California, USA

    Cliff Anderson-Bergman

  11. Departments of Neurology and Physiology, University of California, San Francisco, San Francisco, California, USA

    Steven Finkbeiner

  12. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Amanda J Kedaigle, Ferah Yildirim, Pamela Milani & Ernest Fraenkel

  13. Center for Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Theresa A Gipson, Christopher W Ng & David E Housman

  14. Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Theresa A Gipson, Christopher W Ng & David E Housman

  15. School of Biomedical Sciences, Cardiff University, Cardiff, UK

    Nicholas D Allen & Paul J Kemp

  16. Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, USA

    Ranjit Singh Atwal, Marta Biagioli, James F Gusella & Marcy E MacDonald

  17. Division of Neurobiology, Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA

    Sergey S Akimov, Nicolas Arbez, Jacqueline Stewart & Christopher A Ross

  18. Departments of Neurology, Neuroscience and Pharmacology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA

    Christopher A Ross

  19. The Board of Governors Regenerative Medicine Institute and Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA

    Virginia B Mattis, Colton M Tom, Loren Ornelas, Anais Sahabian, Lindsay Lenaeus, Berhan Mandefro, Dhruv Sareen & Clive N Svendsen

Consortia

The HD iPSC Consortium

  • Ryan G Lim
  • , Lisa L Salazar
  • , Daniel K Wilton
  • , Alvin R King
  • , Jennifer T Stocksdale
  • , Delaram Sharifabad
  • , Alice L Lau
  • , Beth Stevens
  • , Jack C Reidling
  • , Sara T Winokur
  • , Malcolm S Casale
  • , Leslie M Thompson
  • , Mónica Pardo
  • , A Gerardo García Díaz-Barriga
  • , Marco Straccia
  • , Phil Sanders
  • , Jordi Alberch
  • , Josep M Canals
  • , Julia A Kaye
  • , Mariah Dunlap
  • , Lisa Jo
  • , Hanna May
  • , Elliot Mount
  • , Cliff Anderson-Bergman
  • , Kelly Haston
  • , Steven Finkbeiner
  • , Amanda J Kedaigle
  • , Theresa A Gipson
  • , Ferah Yildirim
  • , Christopher W Ng
  • , Pamela Milani
  • , David E Housman
  • , Ernest Fraenkel
  • , Nicholas D Allen
  • , Paul J Kemp
  • , Ranjit Singh Atwal
  • , Marta Biagioli
  • , James F Gusella
  • , Marcy E MacDonald
  • , Sergey S Akimov
  • , Nicolas Arbez
  • , Jacqueline Stewart
  • , Christopher A Ross
  • , Virginia B Mattis
  • , Colton M Tom
  • , Loren Ornelas
  • , Anais Sahabian
  • , Lindsay Lenaeus
  • , Berhan Mandefro
  • , Dhruv Sareen
  •  & Clive N Svendsen

Contributions

Designed the experiments: R.G.L., L.L.S., D.K.W., J.C.R., S.T.W., L.M.T., J.A., J.M.C., N.D.A., P.J.K., J.A.K., S.F., F.Y., D.E.H., E.F., J.F.G., M.E.M., S.S.A., N.A., C.A.R., V.B.M. and C.N.S. Generated iPSC lines in study: L.O., A.S., L.L., B.M. and D. Sareen. iPSC culture and neuronal differentiation: V.B.M., L.L.S., A.R.K., J.T.S., C.M.T., S.S.A., J.A.K., H.M. and M.D. Carried out experiments: R.G.L. and T.A.G., RNA-seq; L.L.S., Isx-9 qPCR and NEUROD1 overexpression; A.L.L., M.P., A.G.G.D.-B., M.S. and P.S., mouse neurodevelopment studies; A.G.G.D.-B., comparison between mouse and human data; V.B.M. and C.M.T., cell counts; V.B.M., immunocytochemistry; F.Y., R.S.A. and M.B., ChIP; S.S.A., Cell Titer-Glo cell survival assay; S.S.A., N.A. and L.L.S., NEUROD1 knockdown; N.A. and J.S., cell culture and transfection of mouse primary neurons and nuclear condensation assay; S.S.A., Western analysis; E.M., J.A.K., M.D. and H.M., Isx-9 neuron assays; D.K.W., Isx-9 synaptic assays; J.C.R. and D. Sharifabad, mouse Isx-9 studies. Analyzed the data: R.G.L., L.L.S., D.K.W., B.S., J.C.R., M.S.C., S.T.W., L.M.T., J.A.K., M.D., H.M., L.J., D.K.W., C.A.-B., S.F., A.J.K., T.A.G., F.Y., C.W.N., P.M., D.E.H., E.F., J.F.G., M.E.M., S.S.A., N.A., C.A.R., V.B.M. and C.N.S. Wrote the manuscript: R.G.L., L.L.S., D.K.W., J.C.R., S.T.W., L.M.T., J.M.C., N.D.A., P.J.K., J.A.K., K.H., S.F., A.J.K., T.A.G., P.M., D.E.H., E.F., M.E.M., J.F.G., S.S.A., C.A.R., V.B.M. and C.N.S. A list of authors by individual consortium group appears in the Supplementary Note.

Corresponding author

Correspondence to Leslie M Thompson.

Ethics declarations

Competing interests

The author declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Staining and quantification of neuronal and oligodendrocyte differentiation markers.

(a) Representative images of immunocytochemistry at day 56 of differentiation demonstrate that both HD (HD109 pictured here) and non-disease (CTR21 pictured here) iPSC lines can generate glial (GFAP) and neuronal (TUJ1, MAP2ab, DARPP32) cells. Scale bar represents 100 mm. (b) Non-biased stereological counts of TUJ1 at day 56 of differentiation indicate that there is no difference in the percent of cells TUJ1-positive between HD and non-disease (CTR). The HD109Q line did have significantly lower TUJ1-positive cells than the CTR21Q and the HD60Q lines (one-way ANOVA, * p<0.05, ** p<0.01); however, this reflects line-to-line variability versus a CAG repeat effect. (c) Non-biased stereological counts of MAP2ab at day 56 of differentiation indicate that there is no difference in the percent of cells MAP2ab-positive between HD and non-diseased. (d) Non-biased stereological counts of DARPP32 at day 56 of differentiation indicate that while there is no difference in the percent of DARPP32-positive cells between HD and non-disease. (e) Non-biased stereological counts of GFAP indicate there is no difference in the percent of cells GFAP-positive between HD and non-diseased. (f) Glial markers PDGFRα and O4 were found to be absent in the iPSC-neural cultures at day 60 of differentiation, whereas at day 112 of oligodendrocyte differentiation from iPSCs, these glial markers were found. Approximately 1000 cells were counted per slide for three independent differentiations.

Supplementary Figure 2 Staining of endoderm, mesoderm and microglial differentiation markers and legend for IPA results.

(a) The endodermal markers SOX17 and FOXA2 were absent in the iPSC-neural cultures (CTR21) at day 60 of differentiation, but they were present in differentiated cultures of iPSC-endoderm (CTR33). (b) The microglial marker Iba1 was absent in day 60 iPSC-neural cultures, but present in wild-type mouse brain sections. Likewise, the mesodermal marker myosin was absent in day 60 iPSC-neural cultures, but present in sections of rat gastrocnemius muscle. (c) IPA gene symbols and relationships. IPA legend for gene networks and relationships/edges.

Supplementary Figure 3 Quantitative PCR supports altered gene expression identified by RNA-seq and hierarchical clustering of genes differentially expressed in several top canonical pathways.

(a-f) New differentiations of representative non-disease (21Q) and HD (109Q) lines were performed and select genes evaluated by RT-qPCR. All exhibited altered expression consistent with that identified by RNA-seq. Two differentiated wells of each line were pooled for RNA extraction and three technical replicates of qPCR performed. A statistical difference in gene expression was determined using an unpaired two-tailed t-test in GraphPad Prism. NEUROD1: P=0.0003, t=11.38, df=4; CALB1: P=0.0005, t=10.23, df=4; CAMK4: P=0.0003, t=11.72, df=4; POU4F2: P=0.0005, t=10.25, df=4; GAD1: P<0.0001, t=21.15; df=4; KCNQ3: P<0.0001, t=24.22, df=4). (g) Heat map showing hierarchical clustering of log2 transformed gene expression values (RPKMs) of the significantly differentially expressed genes (1869) found in several of the top affected canonical pathways (colors displayed by row min and max values, yellow = higher and blue = lower expression). Genes/rows are sorted by involvement in specific pathways that have been marked by color and shows pathway convergence on key regulator genes founds in both axonal guidance and either CREB or WNT signaling. Fold change is shown to the right of the gene symbol and represents the average change of HD/non-disease. A clear pattern of differential gene expression in each pathway provides an indication of activation or inhibition of the pathway. (h) Table showing normalized log2 gene expression values of the top up and down regulated genes in Fig. S3g. Each gene is categorized by pathway involved and HD vs non-disease fold change is displayed.

Supplementary Figure 4 GO enrichment of common striatal genes.

GO enrichment graphs of lowest p-value categories for each cluster depicted in Figure. 4a, values are in –Log10. For GO enrichment, hypergeometric test was used with p-value < 0.05 adjusted with Benjamini-Hochberg correction.

Supplementary Figure 5 RNA-seq functional subcategories, interaction map of common striatal genes, and table of genes that regulate human striatal maturation and HD differential gene expression.

(a) RNA-Seq functional subcategories under the super-category nervous system development and function. Select categories listed are predicted to be decreased by calculation of activation z-score. P-value calculated by Fisher’s exact test of expected/observed genes found within specific subcategories. Predicted state of subcategories shows a decrease in neuronal development and function, including: synaptic transmission, long-term potentiation, guidance of axons, and organismal effects to learning and memory. (b) Interaction map: Largest connected Network by Direct Interaction sets in Metacore ® platform, derived from Cluster I in Figure S4. Green lines represent activation red lines inhibition. (c) 1869 DEGs from RNAseq analysis between HD and non-diseased differentiated iPSCs were compared to genes involved in human striatal maturation. Genes relevant to human striatal maturation were also genes predicted to be upstream regulators of the DEGs found in the HD versus non-diseased iPSC-derived samples.

Supplementary Figure 6 RNA-seq and ChIP-seq correlation plots, differential peak height, motif analysis and GO analysis.

(a-c) The number of ChIP-Seq read counts mapped to the area around gene transcription start sites (−2kb up to +3 kb) was correlated with the expression level of that gene according to RNA-Seq. Data are for the 21Qn1 cell line, for all genes in HD-associated epigenetic classes. Pearson r and two-tailed p values are shown. Read counts were calculated after normalizing data with FIXSEQ (82). (d-f) The number of ChIP-Seq read counts mapped to the area around gene transcription start sites was correlated with the expression level of that gene, according to RNA-Seq for genes in all epigenetic classes. (g) The number of H3K27Ac ChIP-Seq reads and RNA reads mapped to the area of the human genome around ELAVL3, a neuron-specific ribosome binding protein that is down regulated in HD. Reads from the 21Qn1 non-disease cell line (top) and 60Qn8 HD line (bottom) are shown. Using k-means clustering of the peak profile of ELAVL3 was assigned to class 1, with its broad peak extending into the gene body from the TSS. Reads were visualized using Integrated Genome Browser. (h) The top 10 motifs found under H3K27ac peaks enriched in HD versus non-disease samples are shown. Motifs were clustered according to similar binding site sequences. The left column shows all members of a cluster, the middle column shows an example binding site for that cluster, and the third column shows the p-value of enrichment of the best member of that motif cluster over the peaks enriched in the opposite treatment group. (i) Gene Ontology categories assigned to enriched transcription factor motifs in HD or non-disease samples and false discovery rate adjusted p-value.

Supplementary Figure 7 NEUROD1 overexpression and Isx-9 increase expression of downregulated transcripts.

(a) NEUROD1 over-expression enhances neuronal gene expression in high CAG repeat lines. Several genes in addition to those depicted in Figure 6 were evaluated in the high CAG repeat HD line 109Q after either human NEUROD1 overexpression lentivirus or pFUGW-eGFP. qPCR demonstrates increased expression of GAD1 (P=0.0002, t=13.20, df=4), ATP2B2 (P=0.0087, t=4.790, df=4) and KCNQ3 (P=0.0121, t=4.352, df=4). (b) Isx-9 enhances expression of NEUROD1 (33Q: P=0.4290, t=0.879, df=4; 60Q: P=0.0173, t=3.92, df=4; 109Q: P=0.0374, t=3.066, df=4) and other neuronal genes POU4F2 (33Q: P<0.0001, t=19.95, df=4; 60Q: P<0.0001, t=16.49, df=4; 109Q: P<0.0001, t=51.96; df=4), CAMK4 (33Q: P=0.0002, t=13.45, df=4; 60Q: P<0.0001, t=24.01, df=4; 109Q: P=0.0004, t=10.98, df=4) and CALB1 (33Q: P<0.0001, t=19.00, df=4; 60Q: P=0.0001, t=15.36, df=4; 109Q: P<0.0001, t=17.18, df=4) in non-disease (33Q) and HD cultures (60Q, 109Q). (c) Additional genes were tested in 21Q and 109Q lines with increased expression of GAD1 (21Q: P<0.0001, t=21.86, df=4; 109Q: P<0.0001, t=27.90, df=4), CALB1 (21Q: P=0.0004, t=10.72, df=4; 109Q P=0.0004, t=11.19; df=4), CAMK4 (21Q: P=0.0007, t=9.628, df=4; 109Q: P=0.0011, t=8.390, df=4), POU4F2 (21Q: P<0.0001, t=16.32, df=4; 109Q: P<0.0001, t=25.26, df=4), KCNQ3 (21Q: P<0.0001, t=21.84, df=4; 109Q: P<0.0001, t=168.1, df=4) and CACNA1A (21Q: P<0.0001, t=50.08, df=4; 109Q: P=0.0005, t=10.19, df=4) in all cultures, as well as increased expression of ATP2B2 in the HD 109Q line (21Q: P=0.0548; t=2.688, df=4; 109Q: P=0.001, t=8.618, df=4). For qPCR analysis, a statistical difference in gene expression between GFP and NEUROD1 overexpression or vehicle and Isx-9 treatment was determined using an unpaired two-tailed t-test in GraphPad Prism. (d) Isx-9 western analysis. 21Q and 109Q cell lines demonstrate increased protein expression of CALB1 and CAMKIV after Isx-9 treatment. WB results (densitometry) were normalized to ACTIN. Statistical significance between Isx-9 and untreated cells (NIM) was determined by one-way ANOVA. CALB1: 21Q plus ISX9 (P=0.0001, n = 5 independent differentiations, df=1, F=123.59). 109Q plus ISX9 (P=0.0001, n=5 independent differentiations, df=1, F=54.11). 109Q plus BDNF (P=0.000457, n=5 independent differentiations, df=1, F=32.43). CAMKIV: 21Q plus ISX9 (P=0.001359, n=3 independent differentiations, df=1, F=63.12). 109Q plus ISX9 (P=0.000337, n=3 independent differentiations, df=1, F=130.03). Full blots in S8.

Supplementary Figure 8 Full western blots with original markings

Full scanned gels for western blots shown in figures 6d (a-b) and S7d (b-f). Some gel images were cropped to merge mw markers with protein bands while removing samples unrelated to study which were ran on the same gel.

Supplementary Figure 9 HD and control neural cells show no difference in cell composition after Isx-9 treatment; representative pictures used for nuclear condensation assay; and cumulative risk of death for adult-onset-range differentiated iPSCs.

(a) Immunocytochemistry at day 56 of differentiation demonstrate that both HD and non-disease iPSC lines can generate neural progenitors (NESTIN, DOUBLECORTIN). Scale bar represents 100 um.(b) Differentiated iPSC cultures were treated with ISX-9 or BDNF for 48 h. Nuclei were stained with Hoechst 3343. The images were obtained on an Axiovert 200 inverted microscope and quantified using Volocity. Representative images are show for each condition. Inserts illustrate healthy nuclei (left panel) and condensed nuclei (right panel). (c) The cumulative risk of death of both the 46Qn1 (HR= 2.1, p= 4.16e-06) and 46Qn10 (HR= 2.9, p= 0.000243) are greater than the control 18Qn2 line. Addition of 20 μM ISX-9 decreases the cumulative risk of death of 46Qn1 (HR= 0.79, p= 0.004961) and in the 46Qn10 (HR=0.65, p= 5.42e-06). (d) Although we saw a trend in the survival curve of the 46Qn10, we did not see significant differences in the CRD between the 18Qn6 risk of death to the 46Qn1 (HR= 0.87, p= 0.4) and the 46Qn10 (HR= 1.2106, p= 0 0.367148) likely due to a low n as when we combine clones the difference becomes significant as in Figure 7. Control 18Qn2 +DMSO, n=69 cells, Control 18Qn +ISX-9, n= 78 cells, Control 18Qn6+DMSO, n=55 cells, Control 18Qn6+ISX-9, n= 66 cells, (n=2 experiments). 46Qn1 + DMSO, n= 414 cells, 46Qn1+ISX-9, n= 474 cells, (n=5 experiments); 46Qn10+DMSO, n= 289 cells, 46Qn10+ISX-9 n= 310 cells, (n=5 experiments). (e) We evaluated the cumulative risk of death of the 53Qn3 lines compared to the controls. We found evidence of non-proportionality for a subset of the lines so we used log rank tests to assess the differences of survival between the HD and controls lines and the effects of ISX-9 on the HD and control lines. All p values are reported from the log rank test; however, we report the hazard ratios as an estimate of the hazard from the Cox proportional hazards model. (f) The cumulative risk of death of the 18Qn2 is lower risk of death than the 53Qn3 (HR= 1.4, p= 0.06 approaching significance) and 53Qn5 (HR=1.3 p= 0.00572). Addition of 20 μM ISX-9 increases survival of 53Qn3 (HR= 0.74, p= 2.4e-06) and 53Qn5 (HR=0.83, p= 0.0198) i-neurons. ISX-9 does not significantly change cumulative risk of death for the control Q18n2 (p= 0.931). The cumulative risk of death of 18Qn6 is lower than the 53Qn3 (HR= 1.2, p= 0.0535 approaching significance) and 53Qn5 (HR=1.3, p= 0.00622). ISX-9 does not significantly change cumulative risk of death for the control Q18n6 (p= 0.197). Control 18Qn2 +DMSO, n=345 cells, 18Qn +ISX-9, n= 324 cells, (n=4 experiments) Control 18Qn6+DMSO, n=145cells, 18Qn6 + DMSO, n= 128 cells, (n=3 experiments); 53Qn3+DMSO, 695 cells, 53Qn3+ISX-9, n= 726 cells, (n=6 experiments); 53Qn5+DMSO, 352 cells, 53Qn5+ISX-9, 381cells (n=3 experiments).

Supplementary Figure 10 Representative images of immunocytochemistry staining of differentiated cells that were used in robotic microscopy for survival and neurite-like process length analysis, and representative images of neurons that were subjected to neurite-like length analysis.

(a-h) Cells stain for ~5-15% DARPP-32 positive cells and ~1-15 % Ki67 cells. To determine percent of cells staining for DARPP-32 or Ki67, we subjected the DAPI nuclear stained images to our custom plugin in ImageJ to create an ROI for each cell. The images were processed, thresholded, and masked. These masks subjected to particle analysis that constraints size and shape to reduce the measuring of artifacts. The masked ROIs were overlayed on to the green channel images and pixels were measured for fluorescent intensity and size. The average intensity values were normalized to the size of the nuclei, and only the nuclei with a value or 0.2 or greater were counted as significant. The number of positive staining nuclei (cyan, blue channel) was divided by total number of nuclei to determine the percentage of KI-67 and DARPP-32 positive cells. MAP-2 (red, red channel) and DARPP-32 (yellow, green channel) staining (a,c, e, g) and staining for MAP-2 (red) and Ki67 (b, d, f,h), of the (a, b) control 28Q, (c, d) 46Q (c, g) 53Q and (e, f) 109Q. Scale bar is 100 μm. (i-p) Example images of cells analyzed for process length, (i) 18Qn2, (j) 28Qn6 (k) 46Qn1 (l) 46Qn10 (m) 53Qn3 (n) 53Qn5 (o) 109n4 (p) 109Qn5. Scale bar is 100 um.

Supplementary Figure 11 Motor behaviors are not rescued by Isx-9 treatment in R6/2 mice.

Mice were initially tested for specific HD related behavior deficits using tasks that examine motor impairment. Genotype effects were observed by 8 weeks; however, Isx9 did not provide statistically significant differences in treated versus control mice in rotarod (a) pole test (b) or grip strength (c) assays. (a) Rotarod indicates no motor improvements in Isx-9 treated mice. Non-transgenic (NT) and R6/2 mice treated with vehicle (veh) or Isx-9 were subjected to the rotarod task to evaluate motor ability. Impairment was observed in R6/2 mice. Points represent individual mouse scores of time on rotarod measured in seconds. Center lines represent mean of each groups data set and error bar whiskers are indicative of SEM (n=10/group at 8 weeks age). Statistical tests used one-way ANOVA followed by Tukey HSD Test with Scheffé, Bonferroni and Holm multiple comparison calculation also performed post hoc. One-way ANOVA F value 92.8 with 3 degrees of freedom, ***p=0.001. (b) Pole test indicates no motor improvements in Isx-9 treated R6/2 mice. Non-transgenic (NT) and R6/2 mice treated with vehicle (veh) or Isx-9 were subjected to the pole test task to evaluate motor ability. Impairment was observed in R6/2 mice. Points represent individual mouse scores of time to descend measured in seconds. Center lines represent mean of each groups data set and error bar whiskers are indicative of SEM (n=10/group at 9 weeks age). Statistical tests used one-way ANOVA followed by Tukey HSD Test with Scheffé, Bonferroni and Holm multiple comparison calculation also performed post hoc. One-way ANOVA F value 9.7 with 3 degrees of freedom, ***p=0.001. (c) Grip strength test indicates no improvements in Isx-9 treated mice. Non-transgenic (NT) and R6/2 mice treated with vehicle (veh) or Isx-9 were subjected to the grip strength task to evaluate forelimb grip. Impairment was observed in R6/2 mice. Points represent individual mouse scores of grip measured in grams of strength. Center lines represent mean of each groups data set and error bar whiskers are indicative of SEM (n=10/group at 10 weeks age). Statistical tests used one-way ANOVA followed by Tukey HSD Test with Scheffé, Bonferroni and Holm multiple comparison calculation also performed post hoc. One-way ANOVA F value 13.6 with 3 degrees of freedom, ***p=0.001.

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The HD iPSC Consortium. Developmental alterations in Huntington's disease neural cells and pharmacological rescue in cells and mice. Nat Neurosci 20, 648–660 (2017). https://doi.org/10.1038/nn.4532

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