Defining the features of cellular mixtures, where diverse cell types with distinct genomic characteristics are physically intermingled together, is a central problem in biology. During the past decade, single cell sequencing technologies have enabled a new era of high throughput and high resolution interrogation of cell type diversity, vastly expanding our understanding of the role that cell types play in development and disease. Yet, current studies in single cell genomics rely on short-read sequencing and thus suffer from limitations, including: (1) Most studies rely on short read counting which limits the study of alternative splicing. (2) Cell states are reflected by static snapshots, and while population dynamics can be deduced through trajectory and RNA velocity estimation, robust estimation of these parameters remains a major challenge. (3) Despite advances in single-cell DNA sequencing, there is yet no cost-effective way to simultaneously characterize both the genetic variants and transcriptome-level changes in a cell, which is crucial for diseases such as cancer. This proposal is motivated by technological breakthroughs in single-molecule sequencing (SMS) and the recent adaptation of SMS to the massively parallel sequencing of single cell transcriptomes in our lab. We propose to develop computational methods to harness the power of SMS in single cell transcriptomics. In particular, we have developed a new genomic approach which allows one to repeatedly interrogate complete transcripts from single cells using SMS long reads, rather than 3' or 5' counting with short reads. This technology allows experimental designs where specific transcript subsets and/or cellular subsets can be repeatedly targeted for deeper joint short and long read analysis over many iterations, which we will exploit to conduct analyses that were previously intractable.
During the past decade, single cell sequencing technologies have enabled a new era of high throughput and high resolution interrogation of cell type diversity, vastly expanding our understanding of the role that cell types play in development and disease. This proposal is motivated by technological breakthroughs in single-molecule sequencing (SMS) and the recent adaptation of SMS to the massively parallel sequencing of single cell transcriptomes. We propose to develop computational methods to harness the power of SMS in single cell transcriptomics, thus improving the analysis disease-related changes in individual genomes and cells.
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