In this paper, we introduce Gene Knockout Inference (GenKI), a virtual knockout (KO) tool for gene function prediction using single-cell RNA sequencing (scRNA-seq) data in the absence of KO samples when only wild-type (WT) samples are available. Without using any information from real KO samples, GenKI is designed to capture shifting patterns in gene regulation caused by the KO perturbation in an unsupervised manner and provide a robust and scalable framework for gene function studies. To achieve this goal, GenKI adapts a variational graph autoencoder (VGAE) model to learn latent representations of genes and interactions between genes from the input WT scRNA-seq data and a derived single-cell gene regulatory network (scGRN). The virtual KO data is then generated by computationally removing all edges of the KO gene—the gene to be knocked out for functional study—from the scGRN. The differences between WT and virtual KO data are discerned by using their corresponding latent parameters derived from the trained VGAE model. Our simulations show that GenKI accurately approximates the perturbation profiles upon gene KO and outperforms the state-of-the-art under a series of evaluation conditions. Using publicly available scRNA-seq data sets, we demonstrate that GenKI recapitulates discoveries of real-animal KO experiments and accurately predicts cell type-specific functions of KO genes. Thus, GenKI provides an in-silico alternative to KO experiments that may partially replace the need for genetically modified animals or other genetically perturbed systems.

Cytokine storm and inflammatory cytokine release syndrome are often found to be associated with severe instances of the 2019 coronavirus disease (COVID-19). However, factors that contribute to the development of the COVID-19-associated cytokine storm and intensify the hyperinflammatory response are not well known. Here, we integratively analyzed scRNAseq data of 37,607 immune cells of eight different cell types from four studies involving COVID-19 patients in either moderate or severe conditions. Our analysis showed that pyroptosis—a lytic, inflammatory type of programmed cell death—may play a critical role in the SARS-CoV-2-induced cytokine storm. The expression of the key markers of pyroptosis, such as pro-inflammatory cytokine genes IL1B and IL18, is significantly higher in moderate and severe COVID-19 patients than in healthy controls. The pattern is more pronounced in macrophages and neutrophils than in adaptive immune cells such as T cells and B cells. Furthermore, the lack of interferon-gamma (IFN-γ) and overexpression of ninjurin 1 (NINJ1) in macrophages may exacerbate the systemic inflammation, as shown in severe COVID-19 patients. Finally, we developed a scoring metric to quantitatively assess single cell's pyroptotic state and demonstrated the use of this pyroptosis signature score to scRNAseq data. Taken together, our study underscores the importance of the pyroptosis pathway and highlights its clinical relevance, suggesting that pyroptosis is a cellular process that can be a potential target for the treatment of COVID-19 associated diseases.

Trajectory inference (TI) or pseudotime analysis has dramatically extended the analytical framework of single-cell RNA-seq data, allowing regulatory genes contributing to cell differentiation and those involved in various dynamic cellular processes to be identified. However, most TI analysis procedures deal with individual genes independently while overlooking the regulatory relations between genes. Integrating information from gene regulatory networks (GRNs) at different pseudotime points may lead to more interpretable TI results. To this end, we introduce scInTime—an unsupervised machine learning framework coupling inferred trajectory with single-cell GRNs (scGRNs) to identify master regulatory genes. We validated the performance of our method by analyzing multiple scRNA-seq data sets. In each of the cases, top-ranking genes predicted by scInTime supported their functional relevance with corresponding signaling pathways, in line with the results of available functional studies. Overall results demonstrated that scInTime is a powerful tool to exploit pseudotime-series scGRNs, allowing for a clear interpretation of TI results toward more significant biological insights.

Despite recent progress recognizing the importance of aryl hydrocarbon receptor (Ahr)-dependent signaling in suppressing colon tumorigenesis, its role in regulating colonic crypt homeostasis remains unclear. To assess the effects of Ahr on intestinal epithelial cell heterogeneity and functional phenotypes, we utilized single-cell transcriptomics and advanced analytic strategies to generate a high-quality atlas for colonic intestinal crypts from wild-type and intestinal-specific Ahr knockout mice. Here we observed the promotive effects of Ahr deletion on Foxm1-regulated genes in crypt-associated canonical epithelial cell types and subtypes of goblet cells and deep crypt–secretory cells. We also show that intestinal Ahr deletion elevated single-cell entropy (a measure of differentiation potency or cell stemness) and RNA velocity length (a measure of the rate of cell differentiation) in noncycling and cycling Lgr5+ stem cells. In general, intercellular signaling cross-talk via soluble and membrane-bound factors was perturbed in Ahr-null colonocytes. Taken together, our single-cell RNA sequencing analyses provide new evidence of the molecular function of Ahr in modulating putative stem cell driver genes, cell potency lineage decisions, and cell–cell communication in vivo. Prevention Relevance: Our mouse single-cell RNA sequencing analyses provide new evidence of the molecular function of Ahr in modulating colonic stemness and cell–cell communication in vivo. From a cancer prevention perspective, Ahr should be considered a therapeutic target to recalibrate remodeling of the intestinal stem cell niche.

The analysis of saponin contents in Panax notoginseng (Sanqi) is challenged by the lack of authentic reference standards. In this study, a gradient eluted HPLC method coupled with charged aerosol detector (CAD) has been established to solve this problem. The impact of structural features, including the type of aglycon, the optical rotations at C-20, the glycosyl substituent and the glycosyl linkage of dammarane-type tetracyclic triterpenoid saponins on their CAD response factors has been discovered. The rules of the impact have been utilized to predict CAD response factors of saponins in raw and processed notoginseng based on their structures elucidated by LC-QTOFMS. An intensive investigation of the saponin contents in raw (different cultivate places, sizes, and medicinal parts) and processed (steaming, baking, autoclaving, stewing and frying) Panax notoginseng were implemented. This method was successfully applied to distinguishing the quality of raw and processed Panax notoginseng, finding out biomarkers in processed notoginseng, and screening the best processing technique for this herb.