The objective of this proposal is to establish the feasibility of a broadly applicable technology 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 establish the feasibility of this method, our aims are 1) to robustly express tagRNAs that are stably attached to their targets, and 2) to test the single-cell specificity and efficiency of RNA proximity ligation to barcode transcriptomes.

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.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21GM126307-01
Application #
9433836
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Sammak, Paul J
Project Start
2018-02-01
Project End
2020-01-31
Budget Start
2018-02-01
Budget End
2019-01-31
Support Year
1
Fiscal Year
2018
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