Developing an ultra-high throughput droplet microfluidic workflow for genetic circuit characterization

NIH RePORTER · NIH · F31 · $48,974 · view on reporter.nih.gov ↗

Abstract

PROJECT SUMMARY Existing single cell sequencing technologies provide an unprecedented understanding of the unique genomic and transcriptomic differences that underlie heterogeneous biological samples. These differences are key to understand disease pathologies in clinical samples and fundamental mechanisms in basic biological applications. Recent attention has been directed towards the development of multiomic technologies that better capture the differences in paired datasets such as genome and transcriptome or transcriptome and epigenome. Currently however, there is no single cell technology that can profile sequence information with the corresponding phenotypic behavior of the cell. The purpose of this project is to develop a novel sequencing platform to address this technology gap. To demonstrate the utility of this platform, I will then apply it to rapidly characterize a tunable genetic oscillator. To accomplish this goal, I propose the following 2 specific aims. In Aim 1, I will develop FAB-seq (Fluorescence Annotated Barcoding and Sequencing). I will first demonstrate a novel dual barcoding approach that co-delivers optical and DNA barcodes to create single cell maps between microscopy image data and sequence data. I will also show that FAB-seq can be used to perform targeted sequencing according to an arbitrary phenotype. Then, I will leverage additional injected DNA barcodes to enhance the total barcode space of FAB-seq. In Aim 2, I will then demonstrate that FAB-seq can be used to rapidly characterize a tunable genetic oscillator. First, I will demonstrate that FAB-seq can detect oscillatory phenotypes from a circuit library containing oscillatory and non-oscillatory genetic circuits. Then I will show that when the oscillatory behavior of the circuit is perturbed, FAB-seq can map individual oscillation dynamics to the corresponding single cell transcriptome. The long-term goal of this project is to develop a platform technology that can map single cell microscopy data to single cell sequencing data at ultra-high throughput. I envision FAB-seq to be a transformative tool in addressing questions at the frontier of the genomics field.

Key facts

NIH application ID
10837003
Project number
5F31HG013052-02
Recipient
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
Principal Investigator
Rohan Thakur
Activity code
F31
Funding institute
NIH
Fiscal year
2024
Award amount
$48,974
Award type
5
Project period
2023-06-01 → 2025-05-31