Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.

Panax ginseng (Meyer) and Panax notoginseng (Burkill), belonging to the family Araliaceae, are used worldwide as medicinal and functional herbs. Numerous publications over the past decades have revealed that both P. notoginseng and P. ginseng contain important bioactive ingredients such as ginsenosides and exert multiple pharmacological effects on nervous system and immune diseases. However, based on traditional Chinese medicine (TCM) theory, their applications clearly differ as ginseng reinforces vital energy and notoginseng promotes blood circulation. In this article, we review the similarities and differences between ginseng and notoginseng in terms of their chemical composition and pharmacological effects. Their chemical comparisons indicate that ginseng contains more polysaccharides and amino acids, while notoginseng has more saponins, volatile oil, and polyacetylenes. Regarding pharmacological effects, ginseng exhibits better protective effects on cardiovascular disease, nerve disease, cancer, and diabetes mellitus, whereas notoginseng displays a superior protective effect on cerebrovascular disease. The evidence presented in this review facilitates further research and clinical applications of these two herbs, and exploration of the relationship between the chemical components and disease efficacy may be the critical next step.

Cell–cell communications in multicellular organisms generally involve secreted ligand–receptor (LR) interactions, which is vital for various biological phenomena. Recent advancements in single-cell RNA sequencing (scRNA-seq) have effectively resolved cellular phenotypic heterogeneity and the cell-type composition of complex tissues, facilitating the systematic investigation of cell–cell communications at single-cell resolution. However, assessment of chemical-signal-dependent cell–cell communication through scRNA-seq relies heavily on prior knowledge of LR interaction pairs. We constructed CellTalkDB (http://tcm.zju.edu.cn/celltalkdb), a manually curated comprehensive database of LR interaction pairs in humans and mice comprising 3398 human LR pairs and 2033 mouse LR pairs, through text mining and manual verification of known protein–protein interactions using the STRING database, with literature-supported evidence for each pair. Compared with SingleCellSignalR, the largest LR-pair resource, CellTalkDB includes not only 2033 mouse LR pairs but also 377 additional human LR pairs. In conclusion, the data on human and mouse LR pairs contained in CellTalkDB could help to further the inference and understanding of the LR-interaction-based cell–cell communications, which might provide new insights into the mechanism underlying biological processes.

Background: Cancer is a heterogeneous disease with many genetic variations. Lines of evidence have shown copy number variations (CNVs) of certain genes are involved in development and progression of many cancers through the alterations of their gene expression levels on individual or several cancer types. However, it is not quite clear whether the correlation will be a general phenomenon across multiple cancer types. Methods: In this study we applied a bioinformatics approach integrating CNV and differential gene expression mathematically across 1025 cell lines and 9159 patient samples to detect their potential relationship. Results: Our results showed there is a close correlation between CNV and differential gene expression and the copy number displayed a positive linear influence on gene expression for the majority of genes, indicating that genetic variation generated a direct effect on gene transcriptional level. Another independent dataset is utilized to revalidate the relationship between copy number and expression level. Further analysis show genes with general positive linear influence on gene expression are clustered in certain disease-related pathways, which suggests the involvement of CNV in pathophysiology of diseases. Conclusions: This study shows the close correlation between CNV and differential gene expression revealing the qualitative relationship between genetic variation and its downstream effect, especially for oncogenes and tumor suppressor genes. It is of a critical importance to elucidate the relationship between copy number variation and gene expression for prevention, diagnosis and treatment of cancer.

Background: Deciphering intra‐ and inter‐tumoural heterogeneity is essential for understanding the biology of gastric cancer (GC) and its metastasis and identifying effective therapeutic targets. However, the characteristics of different organ‐tropism metastases of GC are largely unknown.Methods: Ten fresh human tissue samples from six patients, including primary tumour and adjacent non‐tumoural samples and six metastases from different organs or tissues (liver, peritoneum, ovary, lymph node) were evaluated using single‐cell RNA sequencing. Validation experiments were performed using histological assays and bulk transcriptomic datasets.Results: Malignant epithelial subclusters associated with invasion features, intraperitoneal metastasis propensity, epithelial–mesenchymal transition‐induced tumour stem cell phenotypes, or dormancy‐like characteristics were discovered. High expression of the first three subcluster‐associated genes displayed worse overall survival than those with low expression in a GC cohort containing 407 samples. Immune and stromal cells exhibited cellular heterogeneity and created a pro‐tumoural and immunosuppressive microenvironment. Furthermore, a 20‐gene signature of lymph node‐derived exhausted CD8⁺ T cells was acquired to forecast lymph node metastasis and validated in GC cohorts. Additionally, although anti‐NKG2A (KLRC1) antibody have not been used to treat GC patients even in clinical trials, we uncovered not only malignant tumour cells but one endothelial subcluster, mucosal‐associated invariant T cells, T cell‐like B cells, plasmacytoid dendritic cells, macrophages, monocytes, and neutrophils may contribute to HLA‐E‐KLRC1/KLRC2 interaction with cytotoxic/exhausted CD8⁺ T cells and/or natural killer (NK) cells, suggesting novel clinical therapeutic opportunities in GC. Additionally, our findings suggested that PD‐1 expression in CD8⁺ T cells might predict clinical responses to PD‐1 blockade therapy in GC.Conclusions: This study provided insights into heterogeneous microenvironment of GC primary tumours and organ‐specific metastases and provide support for precise diagnosis and treatment.

Recent advancements in single-cell RNA sequencing (scRNA-seq) have facilitated the classification of thousands of cells through transcriptome profiling, wherein accurate cell type identification is critical for mechanistic studies. In most current analysis protocols, cell type-based cluster annotation is manually performed and heavily relies on prior knowledge, resulting in poor replicability of cell type annotation. This study aimed to introduce a single-cell Cluster-based Automatic Annotation Toolkit for Cellular Heterogeneity (scCATCH, https://github.com/ZJUFanLab/scCATCH). Using three benchmark datasets, the feasibility of evidence-based scoring and tissue-specific cellular annotation strategies were demonstrated by high concordance among cell types, and scCATCH outperformed Seurat, a popular method for marker genes identification, and cell-based annotation methods. Furthermore, scCATCH accurately annotated 67%-100% (average, 83%) clusters in six published scRNA-seq datasets originating from various tissues. The present results show that scCATCH accurately revealed cell identities with high reproducibility, thus potentially providing insights into mechanisms underlying disease pathogenesis and progression.