Winer, B. | bioRxiv Science

bioRxiv2022

Pantr2, a trans-acting lncRNA, modulates the differentiation potential of neural progenitors in vivo

Ablation of the long non-coding RNA (lncRNA) Pantr2 results in microcephaly in a knockout murine model of corticogenesis, however, the precise mechanisms used are unknown. We present evidence that Pantr2 is a trans-acting lncRNA that regulates gene expression and chromatin accessibility both in vivo and in vitro. We demonstrate that ectopic expression of Pantr2 in a neuroblastoma cell line alters gene expression under differentiating conditions, and that both loss and gain of function of Pantr2 results in changes to cell-cycle dynamics. We show that expression of both the transcription factor Nfix and the cell cycle regulator Rgcc are negatively regulated by Pantr2. Using RNA binding protein motif analysis and existing CLIP-seq data, we annotate potential HuR and QKI binding sites on Pantr2, and demonstrate that HuR does not directly bind Pantr2 using RNA immunoprecipitation assay. Finally, using Gene Ontology enrichment analysis, we identify disruption of both Notch and Wnt signaling following loss of Pantr2 expression, indicating potential Pantr2-dependent regulation of these pathways.

neuroscience↗

bioRxiv2021

Universal prediction of cell cycle position using transfer learning

BackgroundThe cell cycle is a highly conserved, continuous process which controls faithful replication and division of cells. Single-cell technologies have enabled increasingly precise measurements of the cell cycle both as a biological process of interest and as a possible confounding factor. Despite its importance and conservation, there is no universally applicable approach to infer position in the cell cycle with high-resolution from single-cell RNA-seq data. ResultsHere, we present tricycle, an R/Bioconductor package, to address this challenge by leveraging key features of the biology of the cell cycle, the mathematical properties of principal component analysis of periodic functions, and the use of transfer learning. We estimate a cell cycle embedding using a fixed reference dataset and project new data into this reference embedding; an approach that overcomes key limitations of learning a dataset dependent embedding. Tricycle then predicts a cell-specific position in the cell cycle based on the data projection. The accuracy of tricycle compares favorably to gold-standard experimental assays, which generally require specialized measurements in specifically constructed in vitro systems. Using internal controls which are available for any dataset, we show that tricycle predictions generalize to datasets with multiple cell types, across tissues, species and even sequencing assays. ConclusionsTricycle generalizes across datasets, is highly scalable and applicable to atlas-level single-cell RNA-seq data.

genomics↗