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Study Description

This study aims 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 inform 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, and true functional differences of ostensibly identical cells (e.g., different olfactory receptors among 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. 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.

This first data release of SCAP-T (USC) includes the detailed phenotype information, experimental protocols, QC information, RNA-sequencing data and NGS results for 15 single cells from human brain. The SCAP-T data portal provides a customized interface for users to quickly identify and retrieve files by phenotypes, and data properties such as sequencing facility or coverage. For more information about the SCAP-T study and the data portal, please visit http://www.scap-t.org.

  • Study Weblink: SCAP-T
  • Study Type: Single Cell Analysis
  • Number of study subjects that have individual level data available through Authorized Access: 5

Authorized Access
Publicly Available Data (Public ftp)

Note: Access to publicly available data is available on the public ftp site for study phs000833.v1.p1.

Molecular Data
TypeSourcePlatformNumber of Oligos/SNPsSNP Batch IdComment
RNA Sequencing Illumina HiSeq 2000 N/A N/A
Selected publications
Diseases/Traits Related to Study (MESH terms)
Authorized Data Access Requests
Study Attribution