MOTIVATION: scATAC-seq enables high-resolution mapping of cis-regulatory elements. It has been widely applied to uncover cell-type-specific regulatory networks and complement scRNA-seq analysis in numerous studies. However, a large number of datasets generated by scATAC-seq remain underutilized due to limited exploration of super-enhancers/typical enhancers and gene markers. A comprehensive resource enabling cell-type-specific annotation of cis-regulatory elements and their dynamic enhancer-gene linkages remains an urgent unmet need for scATAC-seq. RESULTS: We present dbscATAC, a specialized single-cell database for annotating super-enhancers, gene markers, and enhancer-gene interactions derived from scATAC-seq data. Using improved machine learning algorithms, we identified 213 835 super-enhancers across 520 tissue/cell types from three species, as well as 347 484 gene markers, 13 470 526 enhancers, and 10 402 346 enhancer-gene interactions derived from 1 668 076 single cells spanning 1028 tissue/cell types in 13 species. An easy-to-use online platform with multiple analytic modules and hierarchical query options was developed for searching, browsing and visualizing single-cell super-enhancers, enhancers, and gene markers. dbscATAC provides a comprehensive resource to facilitate the exploration of enhancer landscapes, gene regulation, and cell-type-specific characteristics in single-cell epigenomics. AVAILABILITY AND IMPLEMENTATION: The database with all the super-enhancer/enhancer annotation data is available at http://singlecelldb.com/dbscATAC/index.php. And the source code of dbscATAC for prediction of SEs, enhancers, and gene markers are available at https://github.com/EvansGao/dbscATAC. The source code, tissue/cell type description, and data summary can be downloaded at DOI: 10.6084/m9.figshare.28706414.scATAC-seq, Database, Super-enhancers/enhancers, Gene markers.

The determination of the mechanical properties of cells plays an important role in biological studies and has gained acceptance recently as a possible label-free biomarker for cell status determination or diseases detection. Investigations on how external cellular properties affect cell mechanics are helpful in understanding cell disease processes and cell morphogenesis, which are of large significance in medical science. Although most researchers have focused on individual cell mechanics, or the effect of substrate stiffness on cells, cell mechanical response due to interactions among cells is yet to be examined. A reason for this is that the study of cell mechanical response to cell shape requires one to use a cell patterning process. However, existing cell patterning methods are very complex and time-consuming. In this paper, we describe a practical and rapid technique that can easily pattern cells into desired shapes, which allows investigations of the effect of external environment on cell stiffness. In the new technique, Poly-(ethylene) glycol diacrylate (PEGDA) hydrogel film with thickness 70–100 nm is controllably patterned on a hydrogenated amorphous silicon (a-Si:H) substrate by polymerizing PEGDA molecules in-situ using programmable visual light patterns. The idea is to enable the confinement of cells cultured on the hydrogels into special areas. The elastic modulus of the patterned cells is measured using an atomic force microscope. Experimental results have demonstrated the versatility of the technique as a tool for cell pattering and exploration of cell mechanics under external mechanical stimuli.