Background Large-scale single-cell transcriptomic datasets generated using different technologies contain batch-specific systematic variations that present a challenge to batch-effect removal and data integration. With continued growth expected in scRNA-seq data, achieving effective batch integration with available computational resources is crucial. Here, we perform an in-depth benchmark study on available batch correction methods to determine the most suitable method for batch-effect removal. Results We compare 14 methods in terms of computational runtime, the ability to handle large datasets, and batch-effect correction efficacy while preserving cell type purity. Five scenarios are designed for the study: identical cell types with different technologies, non-identical cell types, multiple batches, big data, and simulated data. Performance is evaluated using four benchmarking metrics including kBET, LISI, ASW, and ARI. We also investigate the use of batch-corrected data to study differential gene expression. Conclusion Based on our results, Harmony, LIGER, and Seurat 3 are the recommended methods for batch integration. Due to its significantly shorter runtime, Harmony is recommended as the first method to try, with the other methods as viable alternatives.

The ability to study cellular heterogeneity at single cell resolution is making single-cell sequencing increasingly popular. However, there is no publicly available resource that offers an integrated cell atlas with harmonized metadata that users can integrate new data with. Here, we present DISCO (https://www.immunesinglecell.org/), a database of Deeply Integrated Single-Cell Omics data. The current release of DISCO integrates more than 18 million cells from 4593 samples, covering 107 tissues/cell lines/organoids, 158 diseases, and 20 platforms. We standardized the associated metadata with a controlled vocabulary and ontology system. To allow large scale integration of single-cell data, we developed FastIntegration, a fast and high-capacity version of Seurat Integration. We also developed CELLiD, an atlas guided automatic cell type identification tool. Employing these two tools on the assembled data, we constructed one global atlas and 27 sub-atlases for different tissues, diseases, and cell types. DISCO provides three online tools, namely Online FastIntegration, Online CELLiD, and CellMapper, for users to integrate, annotate, and project uploaded single-cell RNA-seq data onto a selected atlas. Collectively, DISCO is a versatile platform for users to explore published single-cell data and efficiently perform integrated analysis with their own data.

ObjectiveTissue stem cells are central regulators of organ homoeostasis. We looked for a protein that is exclusively expressed and functionally involved in stem cell activity in rapidly proliferating isthmus stem cells in the stomach corpus.DesignWe uncovered the specific expression of Iqgap3 in proliferating isthmus stem cells through immunofluorescence and in situ hybridisation. We performed lineage tracing and transcriptomic analysis of Iqgap3 +isthmus stem cells with the Iqgap3-2A-tdTomato mouse model. Depletion of Iqgap3 revealed its functional importance in maintenance and proliferation of stem cells. We further studied Iqgap3 expression and the associated gene expression changes during tissue repair after tamoxifen-induced damage. Immunohistochemistry revealed elevated expression of Iqgap3 in proliferating regions of gastric tumours from patient samples.ResultsIqgap3 is a highly specific marker of proliferating isthmus stem cells during homoeostasis. Iqgap3+isthmus stem cells give rise to major cell types of the corpus unit. Iqgap3 expression is essential for the maintenance of stem potential. The Ras pathway is a critical partner of Iqgap3 in promoting strong proliferation in isthmus stem cells. The robust induction of Iqgap3 expression following tissue damage indicates an active role for Iqgap3 in tissue regeneration.ConclusionIQGAP3 is a major regulator of stomach epithelial tissue homoeostasis and repair. The upregulation of IQGAP3 in gastric cancer suggests that IQGAP3 plays an important role in cancer cell proliferation.