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.

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.

BackgroundCancer 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.MethodsIn 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.ResultsOur 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.ConclusionsThis 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.

Revealing fine-scale cellular heterogeneity among spatial context and the functional and structural foundations of tissue architecture is fundamental within biological research and pharmacology. Unlike traditional approaches involving single molecules or bulk omics, cutting-edge, spatially resolved transcriptomics techniques offer near-single-cell or even subcellular resolution within tissues. Massive information across higher dimensions along with position-coordinating labels can better map the whole 3D transcriptional landscape of tissues. In this review, we focus on developments and strategies in spatially resolved transcriptomics, compare the cell and gene throughput and spatial resolution in detail for existing methods, and highlight the enormous potential in biomedical research.

For multicellular organisms, cell-cell communication is essential to numerous biological processes. Drawing upon the latest development of single-cell RNA-sequencing (scRNA-seq), high-resolution transcriptomic data have deepened our understanding of cellular phenotype heterogeneity and composition of complex tissues, which enables systematic cell-cell communication studies at a single-cell level. We first summarize a common workflow of cell-cell communication study using scRNA-seq data, which often includes data preparation, construction of communication networks, and result validation. Two common strategies taken to uncover cell-cell communications are reviewed, e.g., physically vicinal structure-based and ligand-receptor interaction-based one. To conclude, challenges and current applications of cell-cell communication studies at a single-cell resolution are discussed in details and future perspectives are proposed.