Disteche, C. | bioRxiv Science

bioRxiv2020

Allele-specific gene regulation by KDM6A

KDM6A demethylates the repressive histone mark H3K27me3 and thus plays an important role in developmental gene regulation. KDM6A expression is female-biased due to escape from X inactivation, suggesting that this protein may play a role in sex differences. Here, we report that maternal and paternal alleles of a subset of mouse genes are differentially regulated by KDM6A. Knockouts of Kdm6a in male and female embryonic stem cells derived from F1 hybrid mice from reciprocal interspecific crosses resulted in preferential downregulation of maternal alleles of a number of genes implicated in development. Moreover, the majority of these genes exhibited a maternal allele expression bias, which was observed in both reciprocal crosses. Promoters of genes downregulated on maternal but not paternal alleles demonstrated a loss of chromatin accessibility, while the expected increase in H3K27me3 levels occurred only at promoters of genes downregulated on paternal but not maternal alleles. These results illustrate parent-of-origin mechanisms of gene regulation by KDM6A, consistent with histone demethylation-dependent and -independent activities.

molecular biology↗

bioRxiv2020

Unsupervised manifold alignment for single-cell multi-omics data

Integrating single-cell measurements that capture different properties of the genome is vital to extending our understanding of genome biology. This task is challenging due to the lack of a shared axis across datasets obtained from different types of single-cell experiments. For most such datasets, we lack corresponding information among the cells (samples) and the measurements (features). In this scenario, unsupervised algorithms that are capable of aligning single-cell experiments are critical to learning an in silico co-assay that can help draw correspondences among the cells. Maximum mean discrepancy-based manifold alignment (MMD-MA) is such an unsupervised algorithm. Without requiring correspondence information, it can align single-cell datasets from different modalities in a common shared latent space, showing promising results on simulations and a small-scale single-cell experiment with 61 cells. However, it is essential to explore the applicability of this method to larger single-cell experiments with thousands of cells so that it can be of practical interest to the community. In this paper, we apply MMD-MA to two recent datasets that measure transcriptome and chromatin accessibility in ~2000 single cells. To scale the runtime of MMD-MA to a more substantial number of cells, we extend the original implementation to run on GPUs. We also introduce a method to automatically select one of the user-defined parameters, thus reducing the hyperparameter search space. We demonstrate that the proposed extensions allow MMD-MA to accurately align state-of-the-art single-cell experiments.

bioinformatics↗