No abstract

Objective: To analyse the benefit of cochlear implantation in young deaf children with Waardenburg syndrome (WS) compared to a reference group of young deaf children without additional disabilities. Method: A retrospective study was conducted on children with WS who underwent cochlear implantation at the age of 2 years or younger. The post-operative results for speech perception (phonetically balanced standard Dutch consonant-vocal-consonant word lists) and language comprehension (the Reynell Developmental Language Scales, RDLS), expressed as a language quotient (LQ), were compared between the WS group and the reference group by using multiple linear regression analysis. Results: A total of 14 children were diagnosed with WS, and 6 of them had additional disabilities. The WS children were implanted at a mean age of 1.6 years and the 48 children of the reference group at a mean age of 1.3 years. The WS children had a mean phoneme score of 80% and a mean LQ of 0.74 at 3 years post-implantation, and these results were comparable to those of the reference group. Only the factor additional disabilities had a significant negative influence on auditory perception and language comprehension. Conclusions: Children with WS performed similarly to the reference group in the present study, and these outcomes are in line with the previous literature. Although good counselling about additional disabilities concomitant to the syndrome is relevant, cochlear implantation is a good rehabilitation method for children with WS.

Microfold (M) cells reside in the intestinal epithelium of Peyer's patches (PP). Their unique ability to take up and transport antigens from the intestinal lumen to the underlying lymphoid tissue is key in the regulation of the gut-associated immune response. Here, we applied a multi-omics approach to investigate the molecular mechanisms that drive M cell differentiation in mouse small intestinal organoids. We generated a comprehensive profile of chromatin accessibility changes and transcription factor dynamics during in vitro M cell differentiation, allowing us to uncover numerous cell type-specific regulatory elements and associated transcription factors. By using single-cell RNA sequencing, we identified an enterocyte and M cell precursor population. We used our newly developed computational tool SCEPIA to link precursor cell-specific gene expression to transcription factor motif activity in cis-regulatory elements, uncovering high expression of and motif activity for the transcription factor ONECUT2. Subsequent in vitro and in vivo perturbation experiments revealed that ONECUT2 acts downstream of the RANK/RANKL signalling axis to support enterocyte differentiation, thereby restricting M cell lineage specification. This study sheds new light on the mechanism regulating cell fate balance in the PP, and it provides a powerful blueprint for investigation of cell fate switches in the intestinal epithelium.

The recent development of single-cell techniques is essential to unravel complex biological systems. By measuring the transcriptome and the accessible genome on a single-cell level, cellular heterogeneity in a biological environment can be deciphered. Transcription factors act as key regulators activating and repressing downstream target genes, and together they constitute gene regulatory networks that govern cell morphology and identity. Dissecting these gene regulatory networks is crucial for understanding molecular mechanisms and disease, especially within highly complex biological systems. The gene regulatory network analysis software ANANSE and the motif enrichment software GimmeMotifs were both developed to analyse bulk datasets. We developed scANANSE, a software pipeline for gene regulatory network analysis and motif enrichment using single-cell RNA and ATAC datasets. The scANANSE pipeline can be run from either R or Python. First, it exports data from standard single-cell objects. Next, it automatically runs multiple comparisons of cell cluster data. Finally, it imports the results back to the single-cell object, where the result can be further visualised, integrated, and interpreted. Here, we demonstrate our scANANSE pipeline on a publicly available PBMC multi-omics dataset. It identifies well-known cell type-specific hematopoietic factors. Importantly, we also demonstrated that scANANSE combined with GimmeMotifs is able to predict transcription factors with both activating and repressing roles in gene regulation.