The objective of this proposal is to develop a broadly applicable technology, called SCALoP (Single Cell Analysis of Localized RNA on whole Populations), to measure gene expression in single cells, without the need to perform technically challenging manipulations on individual cells. Currently, large-scale genetic screens based on new technologies are leading to important advances in our understanding of mammalian genomes and genetic variation linked to human disease. However, the power of these screens is limited by the lack of methods to measure gene expression in these large screens. Our proposed technology would fill this major unmet need, and thus have a broad impact on mammalian genetics. Our goal is to develop a method to attach single-cell-specific sequence barcodes to transcripts, using RNA proximity ligation in pooled samples. We propose to do this by designing barcoded ?tagRNAs? which are expressed in cells and targeted to specific RNA binding proteins. These tagRNAs are attached to transcripts derived from the same single cell by proximity ligation. To develop this method, our aims are 1) optimize and quantify the efficiency of methods to link tagRNAs and cellular RNA, 2) optimize in vivo specificity and single cell resolution of SCALoP, and 3) develop RNA aptamers to natural proteins and alternate location targets.

Public Health Relevance

Large-scale genetic studies of mammalian cells are critical for advancing our understanding of the genetic basis of development and human disease. However, we lack methods to collect critical data on gene regulation in these large-scale studies. We propose to develop a method to measure gene regulation and expression in large-scale genetic studies, called SCALoP (Single Cell Analysis of Localized RNA on whole Populations), that achieves single-cell resolution without physically isolating single cells.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
1R01GM140013-01
Application #
10096934
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Sammak, Paul J
Project Start
2020-09-20
Project End
2023-07-31
Budget Start
2020-09-20
Budget End
2021-07-31
Support Year
1
Fiscal Year
2020
Total Cost
Indirect Cost
Name
Washington University
Department
Genetics
Type
Schools of Medicine
DUNS #
068552207
City
Saint Louis
State
MO
Country
United States
Zip Code
63130