Molecular Tools for Transcriptomic Measurements

Abstract

Despite tremendous advances in next generation sequencing, we are still far from understanding our own genome. Much of the sequencing we do today measures genotype, providing valuable insights to the makeup of our DNA. However, to really understand our genome we must get closer to phenotype. We need methods that capture what biological functions are being carried out in a tissue. Transcriptome sequencing provides a snapshot of gene expression, providing insights on what proteins and subsequent function might be present. This thesis discusses the development of new technologies that build on the current tools for transcriptome level measurements. First I will discuss the development of single-cell RNA-seq method we call SPLiT-seq, which labels the cellular origin of RNA through combinatorial barcoding. SPLiT-seq is compatible with fixed cells or nuclei, allows efficient sample multiplexing and requires no customized equipment. To demonstrate the power of SPLiT-seq, we analyzed 156,049 single-nucleus transcriptomes from postnatal day 2 and 11 mouse brains and spinal cords. Over 100 cell types were identified, with gene expression patterns corresponding to cellular function, regional specificity, and stage of differentiation. Pseudotime analysis revealed transcriptional programs driving four developmental lineages, providing a snapshot of early postnatal development in the murine central nervous system. Next, I will describe a targeted enrichment method we call CleavR to increase detection of rare molecules while bringing sequencing costs down. CleavR selectively captures molecules comprising a user-defined sequence by leveraging the highly specific and active nature of Rnases. Finally, I will discuss our development of a massively parallel reporter splicing assay to measure how DNA mutations impact alternative splicing. It is my hope that this thesis will serve a resource for the entire genomics community - whether scientific experts adopt one of these technologies to gain further understanding of their work, or other technology enthusiasts use it to build better tools for the future.