Frequent loss-of-heterozygosity in CRISPR-Cas9-edited early human embryos

Gregorio Alanis-Lobato, Human Embryo and Stem Cell Lab, The Francis Crick Institute

About this repository

CRISPR-Cas9 genome editing is a promising technique for clinical applications, such as the correction of disease-associated alleles in somatic cells. The use of this approach has also been discussed in the context of heritable editing of the human germline. However, studies assessing gene correction in early human embryos report low efficiency of mutation repair, high rates of mosaicism and the possibility of unintended editing outcomes that may have pathologic consequences. We developed computational pipelines to assess single cell genomics and transcriptomics datasets from OCT4 (POU5F1) CRISPR-Cas9-targeted, Cas9 and uninjected control human preimplantation embryos. This allowed us to evaluate on-target mutations that would be missed by more conventional genotyping techniques. This GitHub repository contains the scripts that we implemented to carry out these analyses.

Inputs and outputs

The following figure shows the different datasets that we analysed and the outputs that were produced with the pipelines listed below. Briefly, low-pass whole genome sequencing (WGS) data from OCT4-targeted, Cas9 and uninjected control single cells or trophectoderm (TE) biopsies from human embryo samples were used to generate copy-number profiles. The genomic DNA (gDNA) isolated from single cells or TE biopsies subjected to the G&T-seq protocol (short for single cell genome and transcriptome sequencing) or to whole genome amplification (WGA) was used for targeted deep sequencing of 15 PCR amplicons across the POU5F1 locus. We took advantage of the high coverage obtained at each of the sequenced amplicons to call single nucleotide polymorphisms (SNPs) and generate SNP profiles for each sample. Finally, the gene expression data from the G&T-seq samples were used to generate transcriptome-based digital karyotypes.

Pipelines

The scripts associated with each pipeline have been organised in different folders. Each script contains example execution guidelines.

Copy-number profiles from low-pass WGS data

Low-pass WGS samples were aligned to the human genome hg19 using BWA version 0.7.17 and the copy-number profiles were generated with the R package QDNAseq version 1.24.0 using bins of size 100kb.

The corresponding scripts are inside the lowpassWGS folder and must be executed in the following order to generate a copy-number profile for each sample:

• align_lowpassWGS.sh
• generate_CNP.R

SNP profiles from deep amplicon sequencing data

Deep amplicon sequencing paired-end reads were trimmed with DADA2 to remove low-quality regions. Then, we corrected substitution errors in the trimmed reads with RACER and mapped the corrected reads to the human genome hg38 with BWA version 0.7.17 in multi-threaded mode using the mem algorithm with default settings. Subsequently, SAM files were converted to the BAM format and post-processed (sorting, indexing and mate fixing) using Samtools version 1.3.1. SNP calling was performed with BCFtools version 1.8 using the mpileup (--max-depth 2000 -a 'AD,DP,ADF,ADR' -Ou) and call (-mv -V 'indels' -Ov) algorithms in multi-threaded mode. SNPs supported by less than 10 reads and with mapping quality below 50 were filtered out. To control for allele overamplification, we revisited the homozygous SNP calls in search for reads supporting the reference allele at those positions. We changed these homozygous SNPs to heterozygous if the fraction of reads supporting the reference allele was at least 6% of the total. This threshold corresponds to the median of the distribution of the fraction of reads supporting the reference allele across samples.

The corresponding scripts are inside the ampliconSeq folder and must be executed in the following order to generate a VCF file per sample containing its SNP profile:

• check_fastq_quality.R
• trim_and_filter.R
• correct_fastq.sh
• call_snps.sh
• correct_amp_bias.R

The resulting VCF files can be explored with, for example, the Integrative Genomics Viewer.

Digital karyotypes from gene expression data

For transcriptome-based digital karyotyping, we used the data deposited in the GEO repository under accession GSE100118. We applied two approaches to this dataset: z-score karyotyping [Groff et al. Genome Res. 29:1705-1718] and eSNP-Karyotyping [Weissbein et al. Nature Communications 7:12144].

We only provide scripts for z-score karyotyping in the digitalKaryo folder:

• z_scr_karyo.R

eSNP-karyotyping can be installed and applied to your data following the instructions in the following GitHub repository:

• https://github.com/BenvenLab/eSNPKaryotyping

Note that z-score karyotyping was applied to the processed gene expression matrix provided at the GEO, while eSNP-Karyotyping was applied directly to the FastQ files. We used version 0.6.5 of Trim Galore! on the raw FastQ files for quality and adapter trimming before the application of eSNP-Karyotyping.

Last compiled

This document was knitted on 22 September 2020.