Jian Zhang

Double negative T cells (DNT cells) are a rare T cell population involved in immune regulation, inflammation, autoimmunity, transplantation, and tumor immunity. However, their low abundance and dispersed distribution across tissues have limited a systematic understanding of their cellular organization and transcriptional diversity. To address this problem, we developed an integrative single cell framework to reconstruct the mouse DNT cell landscape across tissues using multiple public single cell RNA sequencing datasets. Using transcriptomic criteria, we identified and integrated 7984 mouse RNA defined DNT cells. The integrated population was resolved into multiple transcriptionally distinct clusters. These clusters were organized into naive like, proinflammatory, cytotoxic, proliferative, and myeloid associated states, revealing that mouse RNA defined DNT cells constitute a highly heterogeneous yet structured transcriptional compartment. Integrated downstream analyses indicated transcriptional relationships among these subsets. Naive like populations showed inferred transcriptional relationships with inflammatory and cytotoxic programs, while virtual knockout and intercellular communication analyses identified distinct predicted regulatory and signaling features across states. To place these mouse DNT associated genes in a human disease context, selected genes were mapped to their corresponding human homologs and examined using TCGA pan cancer transcriptomic data. Together, these findings define a cross tissue transcriptional framework for mouse RNA defined DNT cells and provide a basis for further evaluating the conservation and relevance of these transcriptional programs in human DNT biology. More broadly, this study provides a generalizable integrative strategy for reconstructing and characterizing rare immune cell populations that are insufficiently represented in individual single cell datasets.

Single-cell RNA sequencing (scRNA-seq), which profiles gene expression at the cellular level, has effectively explored cell heterogeneity and reconstructed developmental trajectories. With the increasing research on diseases and biological processes, scRNA-seq datasets are accumulating rapidly, highlighting the urgent need for collecting and processing these data to support comprehensive and effective annotation and analysis. Here, we have developed a comprehensive S ingle- C ell transcriptome inte gration database fo r human and mouse (SCInter, https://bio.liclab.net/SCInter/index.php), which aims to provide a manually curated database that supports the provision of gene expression profiles across various cell types at the sample level. The current version of SCInter includes 115 integrated datasets and 1016 samples, covering nearly 150 tissues/cell lines. It contains 8016,646 cell markers in 457 identified cell types. SCInter enabled comprehensive analysis of cataloged single-cell data encompassing quality control (QC), clustering, cell markers, multi-method cell type automatic annotation, predicting cell differentiation trajectories and so on. At the same time, SCInter provided a user-friendly interface to query, browse, analyze and visualize each integrated dataset and single cell sample, along with comprehensive QC reports and processing results. It will facilitate the identification of cell type in different cell subpopulations and explore developmental trajectories, enhancing the study of cell heterogeneity in the fields of immunology and oncology.

Transcription factors (TFs) and their target genes have important functions in human diseases and biological processes. Gene expression profile analysis before and after knockdown or knockout is one of the most important strategies for obtaining target genes of TFs and exploring TF functions. Human gene expression profile datasets with TF knockdown and knockout are accumulating rapidly. Based on the urgent need to comprehensively and effectively collect and process these data, we developed KnockTF (http://www.licpathway.net/KnockTF/index.html), a comprehensive human gene expression profile database of TF knockdown and knockout. KnockTF provides a number of resources for human gene expression profile datasets associated with TF knockdown and knockout and annotates TFs and their target genes in a tissue/cell type-specific manner. The current version of KnockTF has 570 manually curated RNA-seq and microarray datasets associated with 308 TFs disrupted by different knockdown and knockout techniques and across multiple tissue/cell types. KnockTF collects upstream pathway information of TFs and functional annotation results of downstream target genes. It provides details about TFs binding to promoters, super-enhancers and typical enhancers of target genes. KnockTF constructs a TF-differentially expressed gene network and performs network analyses for genes of interest. KnockTF will help elucidate TF-related functions and potential biological effects.

Accurate predictions of classification biomarkers and disease status are indispensable for clinical cancer diagnosis and research. However, the robustness of conventional gene biomarkers is limited by issues with reproducibility across different measurement platforms and cohorts of patients. In this study, we collected 4775 samples from 12 different cancer datasets, which contained 4636 TCGA samples and 139 GEO samples. A new method was developed to detect miRNA‐mediated subpathway activities by using directed random walk (miDRW). To calculate the activity of each miRNA‐mediated subpathway, we constructed a global directed pathway network (GDPN) with genes as nodes. We then identified miRNAs with expression levels which were strongly inversely correlated with differentially expressed target genes in the GDPN. Finally, each miRNA‐mediated subpathway activity was integrated with the topological information, differential levels of miRNAs and genes, expression levels of genes, and target relationships between miRNAs and genes. The results showed that the proposed method yielded a more robust and accurate overall performance compared with other existing pathway‐based, miRNA‐based, and gene‐based classification methods. The high‐frequency miRNA‐mediated subpathways are more reliable in classifying samples and for selecting therapeutic strategies.

Competing endogenous RNAs (ceRNAs) represent a novel mechanism of gene regulation that may mediate key subpathway regions and contribute to the altered activities of pathways. However, the classical methods used to identify pathways fail to specifically consider ceRNAs within the pathways and key regions impacted by them. We proposed a powerful strategy named ce‐Subpathway for the identification of ceRNA‐mediated functional subpathways. It provided an effective level of pathway analysis via integrating ceRNAs, differentially expressed (DE) genes and their key regions within the given pathways. We respectively analysed one pulmonary arterial hypertension (PAH) and one myocardial infarction (MI) data sets and demonstrated that ce‐Subpathway could identify many subpathways whose corresponding entire pathways were ignored by those non‐ceRNA‐mediated pathway identification methods. And these pathways have been well reported to be associated with PAH/MI‐related cardiovascular diseases. Further evidence showed reliability of ceRNA interactions and robustness/reproducibility of the ce‐Subpathway strategy by several data sets of different cancers, including breast cancer, oesophageal cancer and colon cancer. Survival analysis was finally applied to illustrate the clinical application value of the ceRNA‐mediated functional subpathways using another data sets of pancreatic cancer. Comprehensive analyses have shown the power of a joint ceRNAs/DE genes and subpathway strategy based on their topologies.