Deep mutational scanning: A versatile tool in systematically mapping genotypes to phenotypes

Abstract

Unveiling how genetic variations lead to phenotypic variations is one of the key questions in evolutionary biology, genetics, and biomedical research. Deep mutational scanning (DMS) technology has allowed the mapping of tens of thousands of genetic variations to phenotypic variations efficiently and economically. Since its first systematic introduction about a decade ago, we have witnessed the use of deep mutational scanning in many research areas leading to scientific breakthroughs. Also, the methods in each step of deep mutational scanning have become much more versatile thanks to the oligo-synthesizing technology, high-throughput phenotyping methods and deep sequencing technology. However, each specific possible step of deep mutational scanning has its pros and cons, and some limitations still await further technological development. Here, we discuss recent scientific accomplishments achieved through the deep mutational scanning and describe widely used methods in each step of deep mutational scanning. We also compare these different methods and analyze their advantages and disadvantages, providing insight into how to design a deep mutational scanning study that best suits the aims of the readers' projects.

Keywords: biotechnology; deep mutational scanning; genotype-phenotype mapping; high-throughput analysis; massively parallel mutagenesis; systems biology.

FIGURE 1

An overview of the DMS procedure (A) A mutant DNA library is transformed into cell types of interest to generate a mutant cell library. Then, the mutant cell library goes through high-throughput phenotyping where cells carrying functional variants are enriched (cells filled with blue) while those with detrimental variants are depleted (cells filled with red or purple). Genetic variants are extracted and sequenced to calculate the relative enrichment changes before and after selection. Finally, the enrichment scores are analysed as the functional scores of mutations (B) Protein-Fragment Complementation Assay (PCA) (C) The underlying assumption is that the concentrations of functional DHFR are linearly related to cell survival (fitness) (D) BindingPCA captures mutational effects on both stability and protein-protein interactions without distinguishing them (E) AbundancePCA captures mutational effects on stability (F) ddPCA combines BindingPCA and AbundancePCA and enables the inference of the bbphysical effects of mutations by quantifying and comparing phenotypic effects.