Mitochondrial Phenotypes Distinguish Pathogenic MFN2 Mutations by Pooled Functional Genomics Screen

Abstract

Most human genetic variation is classified as VUS - variants of uncertain significance. While advances in genome editing have allowed innovation in pooled screening platforms, many screens deal with relatively simple readouts (viability, fluorescence) and cannot identify the complex cellular phenotypes that underlie most human diseases. In this paper, we present a generalizable functional genomics platform that combines high-content imaging, machine learning, and microraft isolation in a new method termed “Raft-Seq”. We highlight the efficacy of our platform by showing its ability to distinguish pathogenic point mutations of the mitochondrial regulator MFN2, even when the cellular phenotype is subtle. We also show that our platform achieves its efficacy using multiple cellular features, which can be configured on-the-fly. Raft-Seq enables a new way to perform pooled screening on sets of mutations in biologically relevant cells, with the ability to physically capture any cell with a perturbed phenotype and expand it clonally, directly from the primary screen.

Massively Parallel CRISPR‐Based Genetic Perturbation Screening at Single‐Cell Resolution

“…Raft-seq and CRaft-ID both permits the collection of fixed or live cells, but those methods need cDNA preamplification for the least amount of material and one-by-one sequencing. [49] Furthermore, Raft-seq does not utilize any existing sgRNA detecting strategy but sequences the target gene indels as perturbation markers. Given that the authors merely investigate a single gene in their research, we recommend using the sgRNA identifier (barcode or sgRNA itself) in parallel with genes investigation.…”

“…With spatially resolved phenotyping and genotyping, scCRISPR has found precise and subtle correlations between phenotypes and genotypes. Imaging-based screens, including those developed by Wang et al, Feldman et al, Wheeler et al, and Yenkin et al, were utilized to screen for genes regulating long noncoding RNA location, [64] p65 nuclear translocation, [44b] stress granule abundance, [48] and mitochondrial anomaly [49] concerning neurodegenerative diseases (Figure 4C). All of these methods adopt machine learning models to estimate the cell state or fluorescence location, which indicates the importance of image recognition in massive phenotyping.…”