scAED: a framework for mapping the enhancer state at single-cell resolution.

Cells within the same tissue exhibit heterogeneity, influenced by several factors including enhancers. Enhancers regulate precise spatial and temporal patterns of gene expression by switching between active or inactive states. Therefore, it is imperative to capture this dynamic dimension of enhancers that regulate genes by initiating transcription across large distances in each cell. Existing enhancer databases rely on data from pooled-cell sequencing or single-cell clustering. In both approaches, the dynamics of cell-specific enhancer states are concealed due to aggregation of data across multiple cells, which limits our understanding of cellular heterogeneity. To address this, we developed a novel computational framework to extract the chromatin activation state of each enhancer in each cell using sc-Multiome and matched snATACseq-snRNAseq datasets that resulted in the development of single-cell Active Enhancer Database (scAED). scAED is a perpetual project that currently contains active enhancers in one brain region (putamen) and two physiological states (control and healthy) of the pancreas comprising of 21 cell types. We have catalogued 2 291 987 unique active enhancer regions from a total of 34 124 988 active enhancer regions. Besides the characterization of active enhancers at single-cell resolution, scAED also introduces several formative advancements including the characterization of bidirectional enhancers, capture of trans-acting elements and their precise binding coordinates, incorporation of strand-specific information, and establishment of a unique enhancer ID system. These innovations collectively represent significant novel advancements in the field of enhancer biology. We will continue to grow scAED as new datasets become available on other organs, tissues, and cell types. We anticipate that the widespread adoption of this platform would accelerate generation of testable hypotheses on novel regulatory mechanisms to understand the molecular underpinnings of health and disease. Data accessibility: Data can be queried and downloaded from the scAED website at https://www.gudalab-rtools.net/scAED/. Additionally, the custom scripts for the scAED framework are available in the GitHub repository at https://github.com/GudaLab/scAED.