Bioinformatic Analysis of Single Nucleus Transcriptome Data of Huntington's Disease Item Type dissertation Authors Malaiya, Sonia Publication Date 2019 Abstract Huntington’s Disease (HD) is a dominantly inherited neurodegenerative disorder caused by a trinucleotide expansion in exon 1 of the Huntingtin (Htt) gene. The earliest changes in HD are observed in the striatum, prior to the onset of symptoms. Here w... Keywords Bioinformatics; Neurosciences; Genetics; eccentric spiny neurons; single cell RNA sequencing; single nucleus RNA sequencing; trajectory analysis; transcriptomics; Huntington Disease Download date 06/10/2021 08:56:53 Link to Item http://hdl.handle.net/10713/11617 CURRICULUM VITAE Name: Sonia Malaiya Contact Information: [email protected] Degree and Date to be Conferred: M.S., 2019 Collegiate Institutions Attended: 2017-2019 University of Maryland, Baltimore, M.S., December 2019 2012-2016 PES Institute of Technology, Bangalore, B.E., May 2016 Major: M.S. in Human Genetics and Genomic Medicine B.E. in Biotechnology Professional Positions Held: November 2019 Research Fellow, University of Maryland Baltimore, Institute of Genomic Sciences, 670, W. Lexington Street, Baltimore, MD-21201 March 2016-October 2016 Research Assistant, PES Institute of Technology, Outer Ring Road, Banashankari 3rd Stage, Banashankari, Bengaluru, Karnataka 560085, India ABSTRACT Title: Bioinformatic Analysis of Single Nucleus Transcriptome Data of Huntington’s Disease Sonia Malaiya, M.S., 2019 Thesis Directed By: Dr. Seth A. Ament, Assistant Professor, Department of Psychiatry Huntington’s Disease (HD) is a dominantly inherited neurodegenerative disorder caused by a trinucleotide expansion in exon 1 of the Huntingtin (Htt) gene. The earliest changes in HD are observed in the striatum, prior to the onset of symptoms. Here we use a knock in HttQ175/+ mouse model to perform single nucleus RNA sequencing (snRNAseq) of the striatums of four HttQ175/+ and three wild type 14 and 15 month old mice and obtain their expression profiles. Using available snRNAseq quality control and analysis methods, we identify eleven cell types within our samples, including the newly discovered “Eccentric MSNs”. We compute the differentially expressed genes between the two genotypes and find significant lowering of cell type specific markers in most cells with the HttQ175/+ mutation. Trajectory analyses reveal stages of HttQ175/+ MSNs that range from identical to extremely distinguished form the wild type MSNs, supporting the length dependent somatic expansion hypothesis. Bioinformatic Analysis of Single Nucleus Transcriptome Data of Huntington’s Disease by Sonia Malaiya Thesis submitted to the Faculty of the Graduate School of the University of Maryland, Baltimore in partial fulfillment of the requirements for the degree of Master of Science 2019 To my parents for the patience and support, To my siblings for the love and motivation, To my aunt and uncle for all the life guidance, And to the extended family and friends For never complaining when I forgot to call! iii Acknowledgements I would like to thank Dr. Seth Ament for being my teacher and mentor, for the endless patience as I tried to learn and understand new concepts every day. His guidance, expertise and contribution were invaluable at every step of this project. This project would not have been possible without the timely generation of the dataset, for which I am very grateful to Ms. Marcia Cortés-Gutiérrez, and the advice and recommendations of Dr. Brian Herb, who provided the first stepping stones that opened new paths when all hope was lost. I would also like to thank Dr. Carlo Colantuoni and Dr. Jeff Carroll, for their guidance in shaping this analysis and their inputs, their outlook has been invaluable, and to Dr. Michelle Giglio and Dr. Mervyn Monteiro for a fresh perspective and providing direction. In addition, I wish to acknowledge the input of the members of the Ament lab - ideas that were sparked during conversations have helped shaped a better, scientific way of thinking. iv Table of Contents INTRODUCTION ..................................................................................................... 1 METHODS ............................................................................................................. 14 RESULTS ............................................................................................................... 32 Specific Aim 1: Molecular identification of cell types in the mouse striatum using single nucleus transcriptome data ......................................................................... 32 Specific Aim 2. Identify cell type-specific molecular changes in a mouse model of the HD mutation .................................................................................................. 42 DISCUSSION ......................................................................................................... 64 REFERENCES........................................................................................................ 68 v List of tables Table 1. Cell counts after each step of QC……………………………………..……33 Table 2. Distribution of cell types across samples and genotypes………..…………39 vi List of Figures Figure 1. Violin plots of the nGene distribution per sample………………………......16 Figure 2. Original clustering of the 14 and 15 month old dataset with identifiable cell types but presence of mutually exclusive genes within a cluster. Penk is an MSN gene, Mbp is an oligodendrocyte gene and Gad1 has the highest expression in Pnoc and Pvalb interneurons…………………………………………………………………………..17 Figure 3. Contamination fractions of each sample. The Valley can be seen between 3.125 and 3.75 on the x-axis in all samples except BBY3 and BCR1 where it is shifted……………………………………………………………………………..….19 Figure 4. Possible types of droplets giving being sequenced to give rise to the libraries observed……………………………………………………………………………...20 Figure 5. tSNE of all eight of the 14 and 15 month old samples. Left: colored by the clusters identified by Seurat. Right: Colored by the age of the nuclei. A trail is formed where the younger samples group together and the older samples spread out………...23 Figure 6. Coloring tSNEs by sample for different variations of MNN. Since it was discovered that the results from MNN correction were dependent on the order of samples provided to the function, I tried different orders to cluster the nuclei and color by sample…………………………………………………………………………..…24 Figure 7. tSNE of 4524 high quality single nuclei, colored by cell type, sample and genotype…………………………………………………………………………...…35 Figure 8. 2873 neurons clustered and colored by A: cluster number; B: Sample of origin; C: Htt genotype; D: number of genes expressed by each nucleus; E: Expression of Drd1 and F: Expression of Adora2a………...……………………………………..37 Figure 9. Distribution of markers across cell types…………………………………...40 Figure 10. Distribution of markers across cell types……………………………….…41 Figure 11. Distribution of genes Snap25 and Bcan in astrocytes……………………..42 Figure 12. Distribution for genes Rsrp1 and Rgs9 in D1 MSNs………………………44 Figure 13. Distribution of genes Meg3 and Pde10a in D2 MSNs…………………….45 Figure 14. Distribution of genes Meg3 and Inf2 in Eccentric MSNs………………….46 vii Figure 15. Pairwise correlation between the log fold ratios of the 391 significantly differentially expressed genes between each combination of the 12 cell types, showing the correlation coefficient and the p-Values of correlation……………………………49 Figure 16. Correlation between D1 MSNs and D2 MSNs on the left and between D1 MSNs and Eccentric MSNs on the right………………………………………………50 Figure 17. Correlation between D2 MSNs and Oligodendrocytes on the left and D2 MSNs and Sst interneurons on the right………………………………………………51 Figure 18. Overlap among down regulated genes on the left and up regulated genes on the right……………………………………………………………………………….52 Figure 19. Rank Rank Hypergeometric overlap between A. D1 and D2 MSNs; B. D1 (x) and Eccentric MSNs (y) and C. D2 (x) and Eccentric MSNs (y)………………….53 Figure 20. Comparison of the interneuron populations, the Chat, Pnoc and Sst interneurons on the x axis and the canonical MSNs on the y axis……………………..54 Figure 21. Overlap between D2 MSNs on the x axis and Oligodendrocytes on the y axis…………………………………………………………………………………...55 Figure 22. Venn diagrams showing the number of common DEGs between different cell types. A: all neuronal and interneuron types; B: all non neuronal subtypes; C: all neuron subtypes; D: all interneuron subtypes………………………………………...56 Figure 23. ROC curves for the enrichment of the Langfelder modules in our data. The module has been denoted at the top of each plot along with information on whether it was up or down regulated in their dataset……………………………………………..59 Figure 24. tSNEs colored by the AUCell thresholds for the Langfelder modules. The pink or red cells are the ones that passed the threshold. The light blue cells were the ones closest to the threshold line to its left…………………………………………….60 Figure 25. Trajectories followed by each of the cell types generated using Monocle2……………………………………………………………………………..62 Figure 26. Violin plots of a component of the canonical MSN trajectory that shows a population of Q175 MSNs that are similar to wild type MSNs and intermediate stages that lead to a completely different stage in Q175 nuclei that cannot be seen in most wild type nuclei…………………………………………………………………………….63 viii INTRODUCTION A Brief History Indications of neurological disorders have been observed