Background Single-cell RNA sequencing (scRNA-seq) provides a powerful tool to capture transcriptomes at single-cell resolution. However, dropout events distort the gene expression levels and underlying biological signals, misleading the downstream analysis of scRNA-seq data. Results We develop a statistical model-based multidimensional imputation algorithm, scMTD, that identifies local cell neighbors and specific gene co-expression networks based on the pseudo-time of cells, leveraging information on cell-level, gene-level, and transcriptome dynamic to recover scRNA-seq data. Compared with the state-of-the-art imputation methods through several real-data-based analytical experiments, scMTD effectively recovers biological signals of transcriptomes and consistently outperforms the other algorithms in improving FISH validation, trajectory inference, differential expression analysis, clustering analysis, and identification of cell types. Conclusions scMTD maintains the gene expression characteristics, enhances the clustering of cell subpopulations, assists the study of gene expression dynamics, contributes to the discovery of rare cell types, and applies to both UMI-based and non-UMI-based data. Overall, scMTD’s reliability, applicability, and scalability make it a promising imputation approach for scRNA-seq data.

Background Liver fibrosis is the result of diffuse excessive deposition of extracellular matrix (ECM) in liver. Collagen is the main component of extracellular matrix. Absent in melanoma 2 (AIM2) is involved in the formation of inflammsome and plays an important role in inflammatory response. However, it is unclear whether AIM2 is involved in the pathogenesis of liver fibrosis. In the present study, we explored the role of AIM2 in the expression of collagen I. Methods In this study, AIM2 was used to co-culture with HepG2 cells. Cell counting kit-8 (CCK-8) was used to measure cell viability. Real time-quantitative PCR (RT-qPCR) and Western blotting were used to detect collagen I expression at mRNA or protein level, respectively. Then HepG2 cells were treated with caspase activation recruitment domain (ASC), pcDNA(+)-AIM2, small interfering RNA (siRNA) and Z-YVAD-fluoromethylketone (Z-YVAD-FMK) to explore their roles in collagen I expression, respectively. Results The viability of HepG2 cells could be not affected with the increased concentrations of AIM2 and Z-YVAD-FMK. The filamentous prisms and vacuoles of HepG2 cells became more obvious when the concentrations of AIM2 increased to 80ng/ml. The expression level of collagen I increased with the increased concentrations of AIM2. The expression level of collagen I could be also induced by pcDNA(+)-AIM2 vector. The expression level of collagen I could be inhibited by ASC siRNA and Z-YVAD-FMK, respectively. Conclusion Collagen I expression could be induced by AIM2 through ASC/caspase-1 signaling pathway. AIM2 might be involved in the pathogenesis of liver fibrosis through inducing collagen I expression.

Background: Liver fibrosis is a diffuse excessive deposition of extracellular matrix (especially collagen) in the liver. It is a repair response of the body to chronic liver injury. AIM2 regulates the activation of caspase-1 and thus promotes the maturation and secretion of the cytokine precursors of pro-IL-1β and pro-IL-18. It also regulates caspase-1-dependent pyroptosis. However, it is unclear whether AIM2 is involved in the pathogenesis of liver fibrosis. Methods: In the present study, CCK-8 was used to measure cell viability. Evaluation of functional AIM2-induced ECM and fibrosis using protein blotting and quantitative real-time polymerase chain reaction. Validation of the effect of the AIM2/ASC/caspase-1 signalling axis on liver fibrosis using ASC siRNA and caspase-1 inhibitors. Overexpression of AIM2 in cells using transient techniques to evaluate the extent of collagen and focal death. Cells with COL1A1 (pGL3-COL1A1) were constructed and characterised. Activation of its activity by AIM2 was measured in vitro. 2 Results: Absent in melanoma 2 (AIM2) is a pro-inflammatory cytokine that plays a key role in inflammation. However, the role of hepatocytes in AIM2 expression remains unclear. In the present study, we demonstrate that AIM2 increases COL1A1 expression via the COL1A1 promoter. AIM2 increases COL1A1 expression in a dose-dependent manner. Furthermore, we demonstrated that AIM2 failed to cause an increase in COL1A1 expression after inhibition of ASC and caspase-1 expression in HepG2 cells. These results suggest that the AIM2/ASC/caspase-1 signalling axis can induce liver fibrosis in HepG2 cells. Conclusion: Collagen I is expressed by hepatocytes through activation AIM2/ASC/caspase-1 pathway.

For most biological and medical applications of single-cell transcriptomics, an integrative study of multiple heterogeneous single-cell RNA sequencing (scRNA-seq) data sets is crucial. However, present approaches are unable to integrate diverse data sets from various biological conditions effectively because of the confounding effects of biological and technical differences. We introduce single-cell integration (scInt), an integration method based on accurate, robust cell–cell similarity construction and unified contrastive biological variation learning from multiple scRNA-seq data sets. scInt provides a flexible and effective approach to transfer knowledge from the already integrated reference to the query. We show that scInt outperforms 10 other cutting-edge approaches using both simulated and real data sets, particularly in the case of complex experimental designs. Application of scInt to mouse developing tracheal epithelial data shows its ability to integrate development trajectories from different developmental stages. Furthermore, scInt successfully identifies functionally distinct condition-specific cell subpopulations in single-cell heterogeneous samples from a variety of biological conditions.