Motivation Controlling the outcomes of CRISPR editing is crucial for the success of gene therapy. Since donor template-based editing is often inefficient, alternative strategies have emerged that leverage mutagenic end-joining repair instead. Existing machine learning models can accurately predict end-joining repair outcomes; however, generalisability beyond the specific cell line used for training remains a challenge, and interpretability is typically limited by suboptimal feature representation and model architecture. Results We propose X-CRISP, a flexible and interpretable neural network for predicting repair outcome frequencies based on a minimal set of outcome and sequence features, including microhomologies (MH). Outperforming prior models on detailed and aggregate outcome predictions, X-CRISP prioritised MH location over MH sequence properties such as GC content for deletion outcomes. Through transfer learning, we adapted X-CRISP pre-trained on wild-type mESC data to target human cell lines K562, HAP1, U2OS, and mESC lines with altered DNA repair function. Adapted X-CRISP models improved over direct training on target data from as few as 50 samples, suggesting that this strategy could be leveraged to build models for new domains using a fraction of the data required to train models from scratch.

Correction to: Nature Cell Biologyhttps://doi.org/10.1038/s41556-023-01194-w. Published online 19 July 2023. In the version of this article originally published, the name of Tianyang Xu was misspelled as Tiangyang Xu. The name has been corrected in the HTML and PDF versions of the article.

The Human BioMolecular Atlas Program (HuBMAP) aims to create a multi-scale spatial atlas of the healthy human body at single-cell resolution by applying advanced technologies and disseminating resources to the community. As the HuBMAP moves past its first phase, creating ontologies, protocols and pipelines, this Perspective introduces the production phase: the generation of reference spatial maps of functional tissue units across many organs from diverse populations and the creation of mapping tools and infrastructure to advance biomedical research.

Understanding the impact of guide RNA (gRNA) and genomic locus on CRISPR-Cas9 activity is crucial to design effective gene editing assays. However, it is challenging to profile Cas9 activity in the endogenous cellular environment. Here we leverage our TRIP technology to integrate ~ 1k barcoded reporter genes in the genomes of mouse embryonic stem cells. We target the integrated reporters (IRs) using RNA-guided Cas9 and characterize induced mutations by sequencing. We report that gRNA-sequence and IR locus explain most variation in mutation efficiency. Predominant insertions of a gRNA-specific nucleotide are consistent with template-dependent repair of staggered DNA ends with 1-bp 5′ overhangs. We confirm that such staggered ends are induced by Cas9 in mouse pre-B cells. To explain observed insertions, we propose a model generating primarily blunt and occasionally staggered DNA ends. Mutation patterns indicate that gRNA-sequence controls the fraction of staggered ends, which could be used to optimize Cas9-based insertion efficiency.

Identifying patterns in temporal data supports complex analyses in several domains, including stock markets (finance) and social interactions (social science). Clinical and biological applications, such as monitoring patient response to treatment or characterizing activity at the molecular level, are also of interest. In particular, researchers seek to gain insight into the dynamics of biological processes, and potential perturbations of these leading to disease, through the discovery of patterns in time series gene expression data. For many years, clustering has remained the standard technique to group genes exhibiting similar response profiles. However, clustering defines similarity across all time points, focusing on global patterns which tend to characterize rather broad and unspecific responses. It is widely believed that local patterns offer additional insight into the underlying intricate events leading to the overall observed behavior. Efficient biclustering algorithms have been devised for the discovery of temporally aligned local patterns in gene expression time series, but the extraction of time-lagged patterns remains a challenge due to the combinatorial explosion of pattern occurrence combinations when delays are considered. We present heuristic approaches enabling polynomial rather than exponential time solutions for the problem.