Automated Assembly of Programmable RNA-Based Sensors

Abstract

Engineered programmable RNA sensors have been applied in low-cost diagnostics, endogenous RNA detection, and multi-input genetic circuits. However, designing, producing, and screening high-performance RNA sensors remains time consuming and labor intensive. Here, we present an automated plasmid assembly pipeline using liquid handling robotics to enable high-throughput construction of plasmids with arbitrary sequences. We compare automated and manual assembly methods using the NGS Hamilton Microlab STAR across two plasmid backbones to evaluate efficiency and reliability. As a proof of concept, we use this modular platform to construct 144 total plasmids encoding riboregulators targeting diverse viral targets along with their cognate trigger sequences. We further demonstrate that the assembled plasmids are functional in both bacterial and cell-free expression systems.

Keywords: RNA sensors; automation; cell-free system; cloning; diagnostics; riboregulators.

Figure 1.. Characterization of plasmid construction using manual and automated workflows.

(A) Design, Build, Test cycle for construction of plasmids from linear ssDNA parts. (B) Representative agarose gel electrophoresis images for 8 of the 72 switch and trigger plasmids constructed manually (top) or by robot (bottom). (C) Violin plots showing quantification of gel band area for manual and automated PCR reactions for both switch and trigger constructs. (D) Representative LB-agar plate demonstrating colony forming units (CFU) for one plated transformation replicate of pColA plasmids constructed by Gibson assembly. (E) Violin plots showing CFU for manual and automated plated transformations after Gibson assembly. (F) dsDNA concentration in ng/μL after manual column-based plasmid purification and automated magnetic bead-based plasmid purification. Plots in (C, E, F) represent n=72 manual and n=72 automated reactions each for both trigger and switch constructs. Three different experiments on three different days of n=24 reactions comprise each set of 72 reactions. (G) A260/A230 purity values for n=144 manual and n=144 automated plasmid purifications. (H) A260/A280 purity values for n=144 manual and n=144 automated plasmid purifications. All dashed lines represent the median; black dotted lines represent quartiles. Statistical analysis was performed using a two-sided, paired t-test. ns = not significant. **** p < 0.0001

Figure 2.. Characterization of programmable RNA sensors constructed by manual and automated workflows.

(A) Four viral targets were chosen for design of toehold switches and were subsequently ordered as ssDNA templates. (B) Hands-on time for both manual and automated toehold switch assembly pipelines. (C) Quantification of time to assemble n=24 PCR, Gibson assembly, transformation, and miniprep reactions, excluding incubation times, for three experiments across three days. (D) Representative flow cytometry GFP fluorescence histograms for toehold switch 3 targeting influenza A. (E) ON/OFF GFP florescence fold changes obtained after 3 hours of induction for each viral target. Bars represent mean of n=6 reactions (n=3 manual, n=3 automated) plotted with standard deviation. (F) Violin plot showing fold change distribution for n=72 manual and n=72 automated cognate toehold switch-trigger pairs. Dashed lines represent the median; black dotted lines represent quartiles. Statistical analysis was performed using a two-sided, paired t-test. ns = not significant. (G) Quantification of fluorescence over time from toehold switches in cell-free transcription-translation reactions. Error bars represent mean and standard deviation of n=3 manually constructed plasmids and n=3 robotically constructed plasmids.