AssemblyTron: flexible automation of DNA assembly with Opentrons OT-2 lab robots

Abstract

As one of the newest fields of engineering, synthetic biology relies upon a trial-and-error Design-Build-Test-Learn (DBTL) approach to simultaneously learn how a function is encoded in biology and attempt to engineer it. Many software and hardware platforms have been developed to automate, optimize and algorithmically perform each step of the DBTL cycle. However, there are many fewer options for automating the build step. Build typically involves deoxyribonucleic acid (DNA) assembly, which remains manual, low throughput and unreliable in most cases and limits our ability to advance the science and engineering of biology. Here, we present AssemblyTron, an open-source Python package to integrate j5 DNA assembly design software outputs with build implementation in Opentrons liquid handling robotics with minimal human intervention. We demonstrate the versatility of AssemblyTron through several scarless, multipart DNA assemblies, beginning from fragment amplification. We show that AssemblyTron can perform polymerase chain reactions across a range of fragment lengths and annealing temperatures by using an optimal annealing temperature gradient calculation algorithm. We then demonstrate that AssemblyTron can perform Golden Gate and homology-dependent in vivo assemblies (IVAs) with comparable fidelity to manual assemblies by simultaneously building four four-fragment assemblies of chromoprotein reporter expression plasmids. Finally, we used AssemblyTron to perform site-directed mutagenesis reactions via homology-dependent IVA also achieving comparable fidelity to manual assemblies as assessed by sequencing. AssemblyTron can reduce the time, training, costs and wastes associated with synthetic biology, which, along with open-source and affordable automation, will further foster the accessibility of synthetic biology and accelerate biological research and engineering.

Keywords: DNA assembly; design cycle; laboratory automation; molecular cloning; robotic liquid handling.

Graphical abstract

Figure 1.

AssemblyTron workflow: (A) Design: user must devise a DNA construct design and choose the appropriate assembly strategy (Golden Gate, Gibson, etc.). (B) After making the design in j5, the user receives a combinatorial design file as an output, which is used as an input for AssemblyTron. (C) The AssemblyTron Setup.py script runs an integrated R script to parse the j5 combinatorial design file and divides it into separate CSV files for different stages of assembly. This R script runs automatically in the Setup.py script. (D) Instruct: after initiating the Setup.py script and specifying the location of the parsed design files, the user receives a reagent_setup file as an output (Table S3). This file relays the deck setup to the user and specifies the arrangement of primers, templates, etc., on the OT-2 deck, which we term physical inputs. (E) Customize: the user is manually prompted to input template concentration, which parts of the protocol to run, etc. (F) Track: Setup.py calculates and tracks the location, concentration and volume of all primers, templates and final assemblies. This information is provided as CSV output files, which are provided for user reference and input for the OT-2. (G) Optimize: AssemblyTron optimizes the annealing temperature and extension time of PCRs with its optimal annealing algorithm. Instructions for arranging 100 µL PCR tubes in the OT-2 thermocycler are provided in reactions_setup.txt. (H) Facilitate: the user stages the OT-2 deck and adjusts it as necessary according to instructions text files and prompts from the run app. (I) The user is left with final assembly constructs as well as intermediate primer and template stocks, which we term physical outputs.

Figure 2.

PCR with AssemblyTron. (A) AssemblyTron PCR protocol was verified by successful amplification of DNA fragments with variable annealing temperatures and lengths. (B) A. graphical depiction of the optimal annealing temperature gradient calculated for the PCR. Each tube is assigned a temperature in the linear gradient, which corresponds to an annealing temperature of one or more fragments.

Figure 3.

Golden Gate assembly with AssemblyTron. (A) AssemblyTron protocol was verified by successful amplification of each chromoprotein assembly fragment. (B) A schematic diagram to specify how fragments are assembled to yield final constructs. (C) High transformation efficiency, high accuracy and correct chromoprotein expression validate the robustness of the Golden Gate protocol.

Figure 4.

Homology-dependent one-pot IVA with AssemblyTron. (A) AssemblyTron one-pot IVA was verified by amplification of appropriate bands via gel electrophoresis. Fragments 1 and 2 are indistinguishable due to size similarity; however there is a slight resolution between Fragments 1 and 3. (B) Consistent transformation efficiency and sequence verification validate the one-pot IVA protocol.

Figure 5.

Homology-dependent AQUA assembly with AssemblyTron. (A) AssemblyTron AQUA protocol was verified by successful amplification of each chromoprotein assembly fragment. (B) Schematic to specify how fragments are assembled to yield final constructs. (C) Consistent transformation efficiency, correct antibiotic resistance and correct chromoprotein expression further validate the AQUA protocol.