Summary As the use of single-cell technologies has grown, so has the need for tools to explore these large, complicated datasets. The UCSC Cell Browser is a tool that allows scientists to visualize gene expression and metadata annotation distribution throughout a single-cell dataset or multiple datasets. Availability and implementation We provide the UCSC Cell Browser as a free website where scientists can explore a growing collection of single-cell datasets and a freely available python package for scientists to create stable, self-contained visualizations for their own single-cell datasets. Learn more at https://cells.ucsc.edu. Supplementary information Supplementary data are available at Bioinformatics online.

Deep learning architectures such as variational autoencoders have revolutionized the analysis of transcriptomics data. However, the latent space of these variational autoencoders offers little to no interpretability. To provide further biological insights, we introduce a novel sparse Variational Autoencoder architecture, VEGA (VAE Enhanced by Gene Annotations), whose decoder wiring mirrors user-provided gene modules, providing direct interpretability to the latent variables. We demonstrate the performance of VEGA in diverse biological contexts using pathways, gene regulatory networks and cell type identities as the gene modules that define its latent space. VEGA successfully recapitulates the mechanism of cellular-specific response to treatments, the status of master regulators as well as jointly revealing the cell type and cellular state identity in developing cells. We envision the approach could serve as an explanatory biological model for development and drug treatment experiments.

Summary: As the use of single-cell technologies has grown, so has the need for tools to explore these large, complicated datasets. The UCSC Cell Browser is a tool that allows scientists to visualize gene expression and metadata annotation distribution throughout a single-cell dataset or multiple datasets. Availability and implementation: We provide the UCSC Cell Browser as a free website where users can explore a growing collection of single-cell datasets and a freely available python package for scientists to create stable, self-contained visualizations for their own single-cell datasets. Learn more at https://cells.ucsc.edu. Contact: cells@ucsc.edu

Our respiratory system is vital to protect us from the surrounding nonsterile environment; therefore, it is critical for a state of homeostasis to be maintained through a balance of inflammatory cues. Recent studies have shown that actively transcribed noncoding regions of the genome are emerging as key regulators of biological processes, including inflammation. lincRNA-Cox2 is one such example of an inflammatory inducible long intergenic noncoding RNA functioning to fine-tune immune gene expression. Using bulk and single-cell RNA sequencing, in addition to FACS, we find that lincRNA-Cox2 is most highly expressed in the lung and is most upregulated after LPS-induced lung injury (acute lung injury [ALI]) within alveolar macrophages, where it functions to regulate inflammation. We previously reported that lincRNA-Cox2 functions to regulate its neighboring protein Ptgs2 in cis, and in this study, we use genetic mouse models to confirm its role in regulating gene expression more broadly in trans during ALI. Il6, Ccl3, and Ccl5 are dysregulated in the lincRNA-Cox2–deficient mice and can be rescued to wild type levels by crossing the deficient mice with our newly generated lincRNA-Cox2 transgenic mice, confirming that this gene functions in trans. Many genes are specifically regulated by lincRNA-Cox2 within alveolar macrophages originating from the bone marrow because the phenotype can be reversed by transplantation of wild type bone marrow into the lincRNA-Cox2–deficient mice. In conclusion, we show that lincRNA-Cox2 is a trans-acting long noncoding RNA that functions to regulate immune responses and maintain homeostasis within the lung at baseline and on LPS-induced ALI.

Background Diffuse midline gliomas with histone H3 K27M (H3K27M) mutations occur in early childhood and are marked by an invasive phenotype and global decrease in H3K27me3, an epigenetic mark that regulates differentiation and development. H3K27M mutation timing and effect on early embryonic brain development are not fully characterized. Results We analyzed multiple publicly available RNA sequencing datasets to identify differentially expressed genes between H3K27M and non-K27M pediatric gliomas. We found that genes involved in the epithelial-mesenchymal transition (EMT) were significantly overrepresented among differentially expressed genes. Overall, the expression of pre-EMT genes was increased in the H3K27M tumors as compared to non-K27M tumors, while the expression of post-EMT genes was decreased. We hypothesized that H3K27M may contribute to gliomagenesis by stalling an EMT required for early brain development, and evaluated this hypothesis by using another publicly available dataset of single-cell and bulk RNA sequencing data from developing cerebral organoids. This analysis revealed similarities between H3K27M tumors and pre-EMT normal brain cells. Finally, a previously published single-cell RNA sequencing dataset of H3K27M and non-K27M gliomas revealed subgroups of cells at different stages of EMT. In particular, H3.1K27M tumors resemble a later EMT stage compared to H3.3K27M tumors. Conclusions Our data analyses indicate that this mutation may be associated with a differentiation stall evident from the failure to proceed through the EMT-like developmental processes, and that H3K27M cells preferentially exist in a pre-EMT cell phenotype. This study demonstrates how novel biological insights could be derived from combined analysis of several previously published datasets, highlighting the importance of making genomic data available to the community in a timely manner.