Computational Modeling of Heterogeneous Gene Expression in Single Cells

 描述(由申请人提供)：单细胞RNA-SEQ协议的最新发展使在细胞水平上对生物系统的全基因组研究成为可能，为研究打开了许多新的生物学问题。单细胞分辨率允许表征稀有或未知的细胞类型，使分化过程得以解剖，并有助于解码负责细胞健康和疾病状态的调节网络。然而，目前的单细胞RNA-SEQ研究受到现有计算方法中的关键空白的限制。我们已经设计了策略来解决当前单细胞RNA-SEQ分析方法的三个关键局限性：(1)缺乏针对异构体特异性表达的模型，(2)无法将基因表达差异与细胞功能的可测量变化联系起来，以及(3)缺乏研究基因表达变化的顺序进展的方法。为了解决第一个缺点，我们开发了SingleSplice，这是一种算法，用于识别其异构体比率在一组单个细胞中的变化超出预期的基因(目标1)。我们还开发了一种新型的微创平台，它允许培养、功能鉴定、分离、 并随后对单个细胞进行测序。使用这个平台产生的数据，我们将进行有监督的机器学习，以识别与细胞之间功能差异有关的基因(目标2)。为了解决第三个限制，我们将使用局部线性嵌入，一种非线性降维技术，来识别细胞在发育和对刺激的反应等连续过程中的“轨迹”(目标3)。我们将把我们的方法应用于我们自己从微观实验中产生的数据，以及来自发育中的肺组织和免疫细胞对免疫刺激做出反应的公开可用的单细胞RNA-SEQ数据。使用来自实验的数据，在这些实验中，以恒定的已知数量向细胞中添加尖峰转录，以模拟另一种剪接变化，我们发现SingleSplice检测异构体转换具有高敏感性(73%)和特异性(93%)。我们使用显微测序技术对胰腺癌细胞株中的单个细胞进行测序，发现这种方法产生的高质量数据与Fluidigm C1的测序结果相当。该技术还使我们能够对吉西他滨治疗后的胰腺癌细胞的RNA进行测序，并测量细胞的增殖，识别分裂的“耐药”细胞和不增殖的细胞，提供具有匹配的功能和转录测量的数据集。对细菌脂多糖(LPS)刺激树突状细胞的数据集的初步研究表明，局部线性包埋(LLE)可以根据细胞暴露在LPS中的时间长度对细胞进行排序。

 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.