Computational Modeling of Heterogeneous Gene Expression in Single Cells

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 DESCRIPTION (provided by applicant): The recent development of single cell RNA-seq protocols enabled genomewide investigation of organismal systems at the cellular level, opening many new biological questions for study. Single cell resolution allows characterization of rare or unknown cell types, enables dissection of differentiation processes, and aids in decoding regulatory networks responsible for healthy and diseased states of cells. However, current single cell RNA-seq studies are limited by crucial gaps in existing computational methods. We have devised strategies to address three key limitations of current single cell RNA-seq analysis methods: (1) lack of models for isoform-specific expression, (2) inability to link gene expression differences with measurable changes in cell function, and (3) lack of methods for studying sequential progression of gene expression changes. To address the first shortcoming, we developed SingleSplice, an algorithm for identifying genes whose isoform ratios vary more than expected by chance across a set of single cells (Aim 1). We have also developed a novel microraft platform that allows culturing, functional characterization, isolation, and subsequent sequencing of single cells. Using data generated from this platform, we will perform supervised machine learning to identify genes linked to functional differences among cells (Aim 2). To address the third limitation, we will use locally linear embedding, a nonlinear dimensionality reduction technique, to identify "trajectories" of cells proceeding through sequential processes such as development and response to stimuli (Aim 3). We will apply our methods to our own data generated from microraft experiments, as well as publicly available single cell RNA-seq data from developing lung tissue and immune cells responding to immune stimulation. Using data from experiments in which spike-in transcripts are added at constant, known amounts to cells to mimic an alternative splicing change, we found that SingleSplice detects isoform switching with high sensitivity (73%) and specificity (93%). We used microrafts to sequence single cells from a pancreatic cancer cell line and found that this approach produced high-quality data comparable to that from the Fluidigm C1. The microraft technology also enabled us to sequence RNA from pancreatic cancer cells after gemcitabine treatment and measure the proliferation of the cells, identifying both "drug resistant" cells that divide and cels that do not proliferate, giving a dataset with matched functional and transcriptomic measurements. Preliminary investigation of a dataset in which dendritic cells were stimulated with bacterial lipopolysaccharide (LPS) shows that locally linear embedding (LLE) can order cells according to the length of time they have been exposed to LPS.