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 infor 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, an true functional differences of ostensibly identical cells (e.g., different olfactory receptors amon 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. All analytical tools and the transcriptome database developed here will be shared openly on our website and all project data will be deposited into dbGAP and the Short Read Archive (or its replacement) 6 months after data QC.

Public Health Relevance

Now that today's tools have become powerful enough to allow us to look into the molecules that code for cell function and identity, we will address a fundamental question: How similar or different are ostensibly identical cells? And, how much do cells change due to influences of other cells or due to aging and disease.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01MH098937-03
Application #
8701402
Study Section
Special Emphasis Panel (ZRG1-CB-D (52))
Program Officer
Yao, Yong
Project Start
2012-09-21
Project End
2017-05-31
Budget Start
2014-06-01
Budget End
2015-05-31
Support Year
3
Fiscal Year
2014
Total Cost
$1,720,109
Indirect Cost
$665,366
Name
University of Southern California
Department
Psychiatry
Type
Schools of Medicine
DUNS #
072933393
City
Los Angeles
State
CA
Country
United States
Zip Code
90089