lincRNA-Cox2 Functions to Regulate Inflammation in Alveolar Macrophages during Acute Lung Injury.

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.

UCSC Cell Browser: visualize your single-cell data.

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.

The manatee variational autoencoder model for predicting gene expression alterations caused by transcription factor perturbations.

We present the Manatee variational autoencoder model to predict transcription factor (TF) perturbation-induced transcriptomes. We demonstrate that the Manatee in silico perturbation analysis recapitulates target transcriptomic phenotypes in diverse cellular lineage transitions. We further propose the Manatee in silico screening analysis for prioritizing TF combinations targeting desired transcriptomic phenotypes.

A Spatiotemporal and Machine-Learning Platform Accelerates the Manufacturing of hPSC-derived Esophageal Mucosa.

Human pluripotent stem cell-derived tissue engineering offers great promise in designer cell-based personalized therapeutics. To harness such potential, a broader approach requires a deeper understanding of tissue-level interactions. We previously developed a manufacturing system for the ectoderm-derived skin epithelium for cell replacement therapy. However, it remains challenging to manufacture the endoderm-derived esophageal epithelium, despite both possessing similar stratified structure. Here we employ single cell and spatial technologies to generate a spatiotemporal multi-omics cell atlas for human esophageal development. We illuminate the cellular diversity, dynamics and signal communications for the developing esophageal epithelium and stroma. Using the machine-learning based Manatee, we prioritize the combinations of candidate human developmental signals for in vitro derivation of esophageal basal cells. Functional validation of the Manatee predictions leads to a clinically-compatible system for manufacturing human esophageal mucosa. Our approach creates a versatile platform to accelerate human tissue manufacturing for future cell replacement therapies to treat human genetic defects and wounds.

VEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics.

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.

Prioritizing transcriptional factors in gene regulatory networks with PageRank.

Biological states are controlled by orchestrated transcriptional factors (TFs) within gene regulatory networks. Here we show TFs responsible for the dynamic changes of biological states can be prioritized with temporal PageRank. We further show such TF prioritization can be extended by integrating gene regulatory networks reverse engineered from multi-omics profiles, e.g. gene expression, chromatin accessibility, and chromosome conformation assays, using multiplex PageRank.

Identification of a differentiation stall in epithelial mesenchymal transition in histone H3-mutant diffuse midline glioma.

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.