Heat pre-treatment reduces multiplicity of plasmid transformations in yeast during electroporation, without diminishing the transformation efficiency

Abstract

High-throughput DNA transformation techniques are invaluable when creating high-diversity mutant libraries, and the success rate of any protein engineering endeavors is directly dependent on both the size and diversity of the mutant library that is to be screened. It is also widely accepted that in both bacteria and yeast there is an inverse correlation between the DNA transformation efficiency and the likelihood of transforming multiple DNA molecules into each cell. However, most successful high-throughput mutant screening efforts require high quality libraries, i.e., libraries comprised of cells with a clear phenotype-to-genotype relationship (one genotype/cell). Thus, DNA transformation methods with a high multiplicity of transformation are highly undesirable and detrimental to most mutant screening assays. Here we describe a simple, robust, and highly efficient yeast plasmid DNA transformation methodology, using a dual heat-shock and electroporation approach (HEEL) that generates more than 2 x 107 plasmid-transformed yeast cells per reaction, while simultaneously increasing the fraction of mono-transformed cells from 20% to more than 70% of the transformed population. By also using an automated yeast genotyping workflow coupled with a dual-barcoding approach, consisting of a SNP and high-diversity barcode (10N), we can consistently identify and enumerate unique plasmid genotypes within a heterogeneous population merely through Sanger sequencing. We demonstrate here that the size and quality of a transformed library no longer need to be inversely correlated when transforming large mutant DNA libraries in yeast using highly efficient DNA electroporation methods. SignificanceWith the recent expansion of artificial intelligence in the field of synthetic biology there has never been a greater need for high-quality data and reliable measurements of phenotype-to-genotype relationships. However, one major obstacle to creating accurate computer-based models is the current abundance of low-quality phenotypic measurements originating from numerous high-throughput, but low-resolution assays. Rather than increasing the quantity of measurements, new studies should aim to generate as accurate measurements as possible. The HEEL methodology presented here aims to address this issue by minimizing the problem of multi-plasmid uptake during high-throughput yeast DNA transformations, which leads to the creation of heterogeneous cellular genotypes. HEEL should enable highly accurate phenotype-to-genotype measurements going forward, which could be used to construct better computer-based models.