University of Pennsylvania : ROLE OF SINGLE CELL MRNA VARIATION IN SYSTEMS ASSOCIATED ELECTRICALLY EXCITABLE CELLS
The goal of this U01 is to characterize and understand the variability in the expressed transcriptome of human excitable cells. There are two predominant types of excitable cells in the human body, neurons and muscle cells, including cardiac cells. Many human CNS diseases result from modulation of the electrical responsiveness of neurons while cardiac arrhythmias account for most of heart associated deaths. However, at the level of individual cells there is considerable heterogeneity in function, response, and dysfunction. We hypothesize that there is a many-to-one relationship between transcriptome states and a cell's phenotype. In this relationship the functional molecular ratios of the RNA are determined by the cell systems' stoichiometric constraints, which underdetermine the transcriptome state. Because a broad set of multi-genic combinations support a particular phenotype, changes in the transcriptome state do not necessarily lead to changes in the phenotype potentially explaining cellular heterogeneity in phenotype response to variant conditions such as the application of therapeutic molecules. To test this we propose to investigate the extent of single cell variation for the whole transcriptome for excitable cells that are in their natural environment using a novel mRNA capture methodology (TIVA-tag), and on a subset of the transcriptome, the mRNAs encoding the therapeutically important and manipulable G protein-coupled receptor (GPRC) pathways.
University of California at San Diego: SINGLE-CELL SEQUENCING AND IN SITU MAPPING OF RNA TRANSCRIPTS IN HUMAN BRAINS
A comprehensive characterization of transcriptional diversity and heterogeneity of the human cortex is crucial to understand its functions in healthy and disease conditions. The diversity and cellular states of the densely packed cellular network in the cortex can be accurately captured by the transcriptional activities of individual cells. An overarching goal is to establish a high-resolution three-dimensional map of all transcriptional activities in the human cortex. In this project we will generate 10,000 sets of full transcriptome data on single cells and nuclei from three areas (visual, temporal, prefrontal) of the human cortex, using a new RNA sequencing method developed by Illumina that can capture all mRNA, miRNA, piRNA and other non-coding RNA species in single cells. In addition, we will develop a novel RNA in situ sequencing method, and apply it to cortex sections to map and quantify at least 500 transcripts directly within the tissue at a spatial resolution of single cells. Using the spatial information of these ~500 transcripts as fingerprints, we will computationally map the additional transcripts in the 10,000 full transcriptome data sets to the cortex sections at the single-cell resolution, which will yield a highly comprehensive map of transcriptional activities in the human cortex at an unprecedented level of resolution.
University of Southern California: EVALUATION OF CELLULAR HETEROGENEITY USING PATCHCLAMP AND RNA-SEQ OF SINGLE CELLS
Our overall aim is to assess the technical and biological noise in measured RNA levels in single cells in a number of human tissue types, and to develop analytical tools to address the complexity observed at the single-cell level. Understanding the sources and relative sizes of technical and biological noise has become essential, as the lower detection limit of RNA-Seq is now in the range of 10 picograms of total RNA -- i.e. the amount of RNA in single cells. Technical noise can come from several different sources that we will attempt to evaluate separately. These include: 1) sample procurement and RNA retrieval, 2) sequencing library preparation, 3) sequencing methodology, 4) batch effects in sequencing experiments, 5) bioinformatics approaches for data analysis, 6) gene-gene variability. Assessing the relative magnitude of technical noise from different sources will infer how to reduce that noise in future experiments, and thereby reduce interference with studies of meaningful biological variations or noise. Biological noise, or inter-cell differences arise from differences in cellular history or fate, stages of cell cycle, connections to neighboring cells, a true functional differences of ostensibly identical cells (e.g., different olfactory receptors on olfactory neurons). We propose to study three different cellular systems that we expect to have different levels of inter-cell variation (biological noise): first, syncytiotrophoblast cells from placenta, which are expected to have relatively low inter-cell variation; second, olfactory neurons from nasal neuroepithelium, each of which is expected to express a different olfactory receptor, providing a positive control for differences in the RNA-Seq data; and third, individual Purkinje neurons from the cerebellum, which may have larger inter-cell variation. The method to extract cytoplasm from individual cells -- patch-clamp pipette extraction -- does not require fully disrupting the tissue or dispersing the cells. We have already used patch clamp to determine the transcriptomes of multiple individual neurons in the mouse brain, using the cytoplasm extracted from single cells on which we had already performed patch-clamp electrophysiology recordings, followed by RNA-Seq. For each of the cell types chosen - syncytiotrophoblasts, olfactory neurons, Purkinje neurons, cortical neurons we will generate single-cell transcriptome datasets to evaluate heterogeneity among ostensibly similar cells, using patch clamp to extract cell contents and RNA-Seq; investigate sources of technical noise and apply a systematic approach to reduce technical noise. We will test whether neuronal plasticity is reflected as a change in the transcriptome.
© 2015 University of Pennsylvania