Deep mutational scanning of proteins in mammalian cells

Abstract

Protein mutagenesis is essential for unveiling the molecular mechanisms underlying protein function in health, disease, and evolution. In the past decade, deep mutational scanning methods have evolved to support the functional analysis of nearly all possible single-amino acid changes in a protein of interest. While historically these methods were developed in lower organisms such as E. coli and yeast, recent technological advancements have resulted in the increased use of mammalian cells, particularly for studying proteins involved in human disease. These advancements will aid significantly in the classification and interpretation of variants of unknown significance, which are being discovered at large scale due to the current surge in the use of whole-genome sequencing in clinical contexts. Here, we explore the experimental aspects of deep mutational scanning studies in mammalian cells and report the different methods used in each step of the workflow, ultimately providing a useful guide toward the design of such studies.

Keywords: CP: Biotechnology; CP: Genetics; deep mutational scanning; genome editing; mammalian; saturation mutagenesis; structure/function analysis; variants of unknown significance.

Figure 1

The deep mutational scanning workflow A schematic overview of the workflow for deep mutational scanning of target proteins in mammalian cells. The different steps follow the sections within the text. (1) Library generation through pooled in vitro mutagenesis. (2) Library introduction as a single variant per cell. (3) Library generation through large-scale genome editing of the endogenous locus. CRISPR-Cas9-based systems generate one variant in each cell. (4) Library screening through functional selection. Functional assays quantitatively couple the function of a variant to a selectable phenotype so that selective pressure changes the frequency of the variant in the library in accordance with its function. Selectable phenotypes are typically a fitness effect for growth selection or a fluorescent signal for fluorescence-activated cell sorting (FACS). (5) Quantification of variant function by sequencing during selection. Next-generation sequencing (NGS) allows the quantification of the selection-induced frequency change of each variant in the library. (6) Data analysis. The quantitative information from NGS is used to calculate a functional score for each variant, typically visualized on a heatmap.

Figure 2

Overview of approaches to generate a library of variants in mammalian cells with one variant per cell (A–C) Exogenous approaches stably introducing plasmid variant libraries. (A) Transfection of EBV-derived episomal plasmids, diluted with a large excess of carrier DNA. (B) Retroviral transduction at a low multiplicity of infection (MOI). LTR, long terminal repeat. (C) Site-specific recombination in a cell line carrying a single copy of a landing pad containing the recombination site and other components (e.g., selection marker). (D) CRISPR-based approaches performing large-scale CRISPR-Cas9-based genome editing through saturation genome/prime editing in (partially) haploid cells or base editing with low-MOI lentiviral transduction of the guide RNA (gRNA) library. PAM, protospacer adjacent motif; HDR, homology-directed repair; pegRNA, prime editing gRNA; UGI, uracyl glycosylase inhibitor.

Figure 3

Sequencing long variable regions incompatible with Illumina read length (A) Parallel amplicon sequencing (post-selection). Subdivision of the variable region into shorter fragments that are sequenced in parallel. When each variant contains one mutation, the read obtained from the fragment covering the mutation is sufficient for variant identification and quantification. Wild-type reads are disregarded, as they could originate from variants with mutations outside of the read. (B) Subassembly of barcoded variant libraries (pre-selection). The variable region is divided into fragments of differing lengths that maintain their link to the barcode. Paired-end sequencing of these fragments generates one read for the barcode and one read for a part of the variable region. All partial variable region reads associated with one barcode are subsequently combined and assembled to reconstitute the full-length sequence of the variant associated with that barcode, generating a barcode-variant lookup table. Sequencing post-selection is reduced to the barcodes only.