Efficient and error-free fluorescent gene tagging in human organoids without double-strand DNA cleavage.

CRISPR-associated nucleases are powerful tools for precise genome editing of model systems, including human organoids. Current methods describing fluorescent gene tagging in organoids rely on the generation of DNA double-strand breaks (DSBs) to stimulate homology-directed repair (HDR) or non-homologous end joining (NHEJ)-mediated integration of the desired knock-in. A major downside associated with DSB-mediated genome editing is the required clonal selection and expansion of candidate organoids to verify the genomic integrity of the targeted locus and to confirm the absence of off-target indels. By contrast, concurrent nicking of the genomic locus and targeting vector, known as in-trans paired nicking (ITPN), stimulates efficient HDR-mediated genome editing to generate large knock-ins without introducing DSBs. Here, we show that ITPN allows for fast, highly efficient, and indel-free fluorescent gene tagging in human normal and cancer organoids. Highlighting the ease and efficiency of ITPN, we generate triple fluorescent knock-in organoids where 3 genomic loci were simultaneously modified in a single round of targeting. In addition, we generated model systems with allele-specific readouts by differentially modifying maternal and paternal alleles in one step. ITPN using our palette of targeting vectors, publicly available from Addgene, is ideally suited for generating error-free heterozygous knock-ins in human organoids.

Stem cell mutations, associated cancer risk, and consequences for regenerative medicine.

Mutation accumulation in stem cells has been associated with cancer risk. However, the presence of numerous mutant clones in healthy tissues has raised the question of what limits cancer initiation. Here, we review recent developments in characterizing mutation accumulation in healthy tissues and compare mutation rates in stem cells during development and adult life with corresponding cancer risk. A certain level of mutagenesis within the stem cell pool might be beneficial to limit the size of malignant clones through competition. This knowledge impacts our understanding of carcinogenesis with potential consequences for the use of stem cells in regenerative medicine.

One-step generation of tumor models by base editor multiplexing in adult stem cell-derived organoids.

Optimization of CRISPR/Cas9-mediated genome engineering has resulted in base editors that hold promise for mutation repair and disease modeling. Here, we demonstrate the application of base editors for the generation of complex tumor models in human ASC-derived organoids. First we show efficacy of cytosine and adenine base editors in modeling CTNNB1 hot-spot mutations in hepatocyte organoids. Next, we use C > T base editors to insert nonsense mutations in PTEN in endometrial organoids and demonstrate tumorigenicity even in the heterozygous state. Moreover, drug sensitivity assays on organoids harboring either PTEN or PTEN and PIK3CA mutations reveal the mechanism underlying the initial stages of endometrial tumorigenesis. To further increase the scope of base editing we combine SpCas9 and SaCas9 for simultaneous C > T and A > G editing at individual target sites. Finally, we show that base editor multiplexing allow modeling of colorectal tumorigenesis in a single step by simultaneously transfecting sgRNAs targeting five cancer genes.

Comprehensive single-cell genome analysis at nucleotide resolution using the PTA Analysis Toolbox.

Detection of somatic mutations in single cells has been severely hampered by technical limitations of whole-genome amplification. Novel technologies including primary template-directed amplification (PTA) significantly improved the accuracy of single-cell whole-genome sequencing (WGS) but still generate hundreds of artifacts per amplification reaction. We developed a comprehensive bioinformatic workflow, called the PTA Analysis Toolbox (PTATO), to accurately detect single base substitutions, insertions-deletions (indels), and structural variants in PTA-based WGS data. PTATO includes a machine learning approach and filtering based on recurrence to distinguish PTA artifacts from true mutations with high sensitivity (up to 90%), outperforming existing bioinformatic approaches. Using PTATO, we demonstrate that hematopoietic stem cells of patients with Fanconi anemia, which cannot be analyzed using regular WGS, have normal somatic single base substitution burdens but increased numbers of deletions. Our results show that PTATO enables studying somatic mutagenesis in the genomes of single cells with unprecedented sensitivity and accuracy.