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Computational Modeling of Heterogeneous Gene Expression in Single Cells

单细胞异质基因表达的计算模型

基本信息

批准号：
9285609
负责人：
Joshua Welch
金额：
$ 1.18万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-06-01 至 2017-08-01
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9285609
关键词：
Address Algorithms Alpha Cell Alternative Splicing Area Biological Biological Process Cell Line Cell Proliferation Cell Separation Cell physiology Cells Computer Simulation Computing Methodologies Data Data Analyses Data Quality Data Set Dendritic Cells Detection Development Dimensions Disease Dissection Drug resistance Exhibits Gene Expression Genes Glean Health Heterogeneity Immune Immunization Individual Investigation Length Link Lipopolysaccharides Lung Machine Learning Malignant Neoplasms Malignant neoplasm of pancreas Measurable Measurement Measures Methodology Methods Modeling Molecular Profiling Mus Neurologic Noise Pancreas Pharmacotherapy Phenotype Population Process Property Protein Isoforms Protocols documentation Public Health Publishing RNA Sequences Research Research Personnel Resolution Sampling Specificity Stimulus Structure of parenchyma of lung Supervision System Techniques Technology Time Tissues Transcript Uncertainty Variant cell motility cell type computerized tools experimental study gemcitabine gene discovery genome-wide infancy innovation neoplastic cell novel pancreatic cancer cells prevent public health relevance response single cell analysis single cell sequencing stem cell therapy transcriptome transcriptome sequencing transcriptomics

项目摘要

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.