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 i the human cortex in an unprecedented level of resolution.

Public Health Relevance

We will establish a three dimensional map of all transcriptional activities at the single-cell resolution in three areas of human cortex. Such a comprehensive characterization of transcriptional diversity and heterogeneity of the human cortex is crucial to understand its functions. It will enable the identification of accurate biomarkers for prognosis and diagnosis brain disorders.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project--Cooperative Agreements (U01)
Project #
4U01MH098977-05
Application #
9107515
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Yao, Yong
Project Start
2012-09-19
Project End
2017-05-31
Budget Start
2016-06-14
Budget End
2017-05-31
Support Year
5
Fiscal Year
2016
Total Cost
Indirect Cost
Name
University of California San Diego
Department
Engineering (All Types)
Type
Schools of Arts and Sciences
DUNS #
804355790
City
La Jolla
State
CA
Country
United States
Zip Code
92093
Lake, Blue B; Chen, Song; Sos, Brandon C et al. (2018) Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat Biotechnol 36:70-80
Preissl, Sebastian; Fang, Rongxin; Huang, Hui et al. (2018) Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation. Nat Neurosci 21:432-439
Wu, Yan; Tamayo, Pablo; Zhang, Kun (2018) Visualizing and Interpreting Single-Cell Gene Expression Datasets with Similarity Weighted Nonnegative Embedding. Cell Syst 7:656-666.e4
Lake, Blue B; Codeluppi, Simone; Yung, Yun C et al. (2017) A comparative strategy for single-nucleus and single-cell transcriptomes confirms accuracy in predicted cell-type expression from nuclear RNA. Sci Rep 7:6031
Ding, Bo; Zheng, Lina; Wang, Wei (2017) Assessment of Single Cell RNA-Seq Normalization Methods. G3 (Bethesda) 7:2039-2045
Lake, Blue B; Ai, Rizi; Kaeser, Gwendolyn E et al. (2016) Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science 352:1586-90
Dueck, Hannah R; Ai, Rizi; Camarena, Adrian et al. (2016) Assessing characteristics of RNA amplification methods for single cell RNA sequencing. BMC Genomics 17:966
Fan, Jean; Salathia, Neeraj; Liu, Rui et al. (2016) Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nat Methods 13:241-4
Lee, Je Hyuk; Daugharthy, Evan R; Scheiman, Jonathan et al. (2015) Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression profiling in intact cells and tissues. Nat Protoc 10:442-58
Ding, Bo; Zheng, Lina; Zhu, Yun et al. (2015) Normalization and noise reduction for single cell RNA-seq experiments. Bioinformatics 31:2225-7

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