bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2022.03.13.484059

Comparison of CRISPR/Cas9-mediated megabase-scale genome deletion methods in mouse embryonic stem cells

Miyata, M.·Yoshida, J.·Takagishi, I.·Horie, K.
View original source ↗Export metadata ↓

Abstract

The genome contains large functional units ranging in size from hundreds of kilobases to megabases, such as gene clusters, promoter-enhancer loops, and topologically associating domains. To analyze these large functional units, the technique of deleting the entire functional unit is effective. However, deletion of such large regions is less efficient than conventional genome editing, especially in cultured cells, and a method that can ensure success is anticipated. Here, we compared methods to delete the 2.5-Mb Kruppel-associated box zinc finger protein (KRAB-ZFP) gene cluster on chromosome 4 in mouse embryonic stem cells using CRISPR/Cas9. Three methods were used: first, deletion by non-homologous end joining (NHEJ); second, homology-directed repair (HDR) using a single-stranded oligodeoxynucleotide (ssODN) with 70-bp homology arms; and third, HDR employing targeting vectors with a selectable marker and 1-kb homology arms. NHEJ-mediated deletion was achieved in 9% of the transfected cells. The deletion frequency of NHEJ and HDR was found to be comparable when the ssODN was transfected. Deletion frequency was highest when targeting vectors were introduced, with deletions occurring in 31-63% of the drug-resistant clones. Biallelic deletion was observed when targeting vectors were used. This study will serve as a benchmark for the introduction of large deletions into the genome.

Source connections

Explore related subjects

genomicsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Miyata, M., Yoshida, J., Takagishi, I., Horie, K.. 2022-03-13. Comparison of CRISPR/Cas9-mediated megabase-scale genome deletion methods in mouse embryonic stem cells. https://doi.org/10.1101/2022.03.13.484059

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

FiberBrowser: a unified framework for single-molecule genomics

Single-molecule footprinting methods are rapidly transforming chromatin biology. They resolve nucleosome positioning, regulatory-factor occupancy, and accessibility on individual DNA fibers, revealing combinatorial heterogeneity that bulk measurements average away. Emerging footprinting methods leverage diverse enzyme systems and sequencing platforms, necessitating tools that can accommodate these systems. We present FiberBrowser, a unified platform that converts these diverse datasets into a common per-molecule representation of nucleosomes, accessible DNA, and subnucleosomal footprints for interactive and scripted analyses. Its inference engine, FiberHMM, weighs each observation against the assay's informative sites and sequence-specific modification probabilities, separating missing information from protection in four assays. FiberBrowser clusters, sorts and compares these calls across thousands of molecules in seconds, linking chromatin architecture to haplotype, CpG methylation and non-reference structural variants on the same molecules. Because the representation is independent of chemistry, models also transfer between assays. A classifier trained on Fiber-seq footprints identifies 12 scDAF-seq cells as lymphoblastoid without retraining, whereas models trained on raw modification patterns fail to transfer. FiberBrowser thus provides a unified, expandable analytical framework for single-molecule chromatin footprinting.

genomics↗
bioRxiv2026

ChAI-Net learns a regulatory code for chromatin co-accessibility

The regulation of chromatin accessibility is critical for cell function and identity. Advances in single-molecule long-read footprinting have enabled near-nucleotide-resolution readout of chromatin fiber organization, capturing both coordination of transcription factor (TF) binding and variation in nucleosome positioning previously obscured by bulk methods. Here we combine this technique with deep learning to study how protein-DNA interactions are coordinated within different types of cis-regulatory elements in mouse embryonic stem cells. First, we quantify the frequency with which pairs of positions are simultaneously accessible on individual molecules. Then, we train ChAI-Net, a neural network that predicts these locus-specific chromatin co-accessibility maps from the underlying DNA sequence. Through model interpretation and in silico experiments, we show that ChAI-Net learns unique combinations of sequence motifs underlying co-accessibility within enhancers, promoters, and CTCF-bound sites. Many of these motifs are missed when modeling average accessibility, suggesting a specific role in coordinated binding. ChAI-Net's regulatory grammar also highlights the importance of TF motif orientation, spacing, positioning, and co-occurrence in driving co-accessibility. Thus, our framework for extracting regulatory rules from footprinting data expands understanding of TF cooperativity, while laying the groundwork to elucidate how these rules vary across cellular contexts as single-molecule genomics is deployed at scale.

genomics↗
bioRxiv2026

An invariant network architecture with broadly expressed regulators distinguishes cell-type from cell-state identity in situ

Core regulatory networks (CRNs) of tissue-specific master transcription factors (TFs) are essential for defining cell identity and are widely used to reprogram cells and to decipher pathogenic mechanisms. However, to date, CRNs have only been described at the cell-type level: their characteristics in the distinct states that a single cell type can adopt have not been examined. This leaves open what the cell-type CRNs actually represent. Here, using the zonated hepatocyte states of the mouse liver as a model system, we construct the first state-level CRNs in a native tissue. For this purpose, we developed a robust LCM-Spatial-ATAC-seq method that achieves bulk-level data quality from microdissected cell populations. By identifying what is shared among the state-level CRNs, we determined what is cell-type defining: an invariant sub-network of core-regulatory TFs (crTFs) that maintains identical positions and conserved interactions in the CRNs. Unexpectedly, the top positions of this invariant sub-network are occupied by broadly expressed TFs rather than the well-established master TFs, which reside downstream. Yet the state-level CRNs are clearly different, both in terms of composition and interactions. The former are dominated by zone-specific regulators, whereas the latter are spread throughout the network, encompassing about one third of all interactions between the states. Thus, our results reveal a novel dual character of a cell's CRN: an invariant sub-network that defines the cell-type identity, together with a variable peripheral network that underlies its state specialization.

genomics↗