Single cell sequencing has revolutionized the way in which we define cell types and understand tissues, and has tremendous potential for analyzing the heterogeneity of complex tumors or large perturbation screens. At present, however, this powerful technology has serious limitations, particularly in what cellular information can be detected and analyzed. Current high-throughput microfluidics single cell RNA sequencing (scRNA-seq) methods can conveniently process tens of thousands of cells but only capture the extreme 3? ends of the most abundant transcripts from each cell. The low sensitivity and partial transcript coverage hampers these methods? abilities to detect allele-specific or subtle perturbation effects, while also dramatically increasing the per-cell sequencing cost. Moreover, no existing single-cell sequencing method can read DNA genotype and RNA expression from the same cell, which is crucial to studying non-coding regulatory elements and somatic rearrangements -- where the most genetic variation associated with cancer and human disease resides. Here we propose to overcome these limitations by developing a flexible high throughput single-cell sequencing system that (1) Can target many different regulatory DNA elements and ?transcripts simultaneously in the same cell?, (2) Is sensitive enough to measure subtle, allele-specific effects, (3) measures the effects of different mutations across tens-of-thousands of cells in a single assay, and (4) is easily and rapidly adaptable for application to any biological system with a heterogeneous cell population. We iteratively develop this technology, ensuring that each step independently creates new capabilities that address current scRNA-Seq limitations and enables allele specific expression analysis and perturbation screens of non-coding elements. First?, we will modify the the inDrop bead manufacturing process to make it flexible and rapidly customizable so that one large batch of universal barcoded beads can be conveniently adapted to target many specific transcript pools, SNP-containing portions of transcripts, and even genomic DNA, in just 8 hours. Second?, we test the sensitivity and allele-specificity of our new method in a predictably heterogeneous system: random X inactivation in hybrid female (XX) cells. Using single-molecule RNA-FISH as a gold standard, we will measure the sensitivity, specificity, and efficiency of our targeted scRNA-Seq approach. Third?, we enable simultaneous DNA genotyping and transcript quantification by adapting our custom beads and reaction conditions for isothermal amplification of genomic DNA loci, simultaneous with RT of targeted transcripts in the same cells. Fourth?, we will combine our approaches above in a proof-of-principle application to characterize enhancer function using a CRISPR mutagenesis screen. CRISPR mutagenesis randomly creates different alleles in each cell. We then use targeted sequencing of neighboring genes and DNA genotyping to evaluate the effect of each allele on its target(s) in ?cis?.

Public Health Relevance

Single-cell sequencing offers great promise for the study of heterogeneous cell populations, but is currently hampered by its limitations, particularly for studying non-coding regulatory sequences where most disease-associated genetic variation resides. We propose a novel single-cell sequencing platform that incorporates innovative technologies to allow the unprecedented ability to simultaneously sequence both genotype ?and RNA output of a selected target gene set, in an allele-specific fashion, across a large cell population size. Moreover, our proposed platform is modular and nimble, allowing inexpensive and rapid customization to specialized target subsets in a matter of hours instead of weeks, to facilitate wide-ranging studies of heterogeneous biological systems far beyond those we carry out in this proposal.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21CA236594-01
Application #
9702249
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Li, Jerry
Project Start
2019-04-01
Project End
2022-03-31
Budget Start
2019-04-01
Budget End
2020-03-31
Support Year
1
Fiscal Year
2019
Total Cost
Indirect Cost
Name
University of Massachusetts Medical School Worcester
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
603847393
City
Worcester
State
MA
Country
United States
Zip Code
01655