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.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZRG1-CB-D (52))
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Yao, Yong
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University of California San Diego
Engineering (All Types)
Schools of Arts and Sciences
La Jolla
United States
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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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