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. 2021 Dec 15;131(24):e154482. doi: 10.1172/JCI154482

Figure 1. Reverse engineering of single-cell RNA sequencing data profiles regulatory protein activity in human islet cells.

Figure 1

The activity of regulatory proteins in each human islet cell changes, depending on factors such as cell identity (α vs. β cells) and disease status (nondiabetic [non-DM] vs. T2D). Son, Ding, et al. (7) used reverse engineering to extrapolate the activity of each regulatory protein from the expression level of target genes. Gene expression profiles were obtained by scRNA-Seq analysis, and regulatory protein activity was predicted using two algorithms, ARACNe and metaVIPER. Metabolically inflexible (MI) β cells showed transcription factor (TF) and co-TF activity changes, as exemplified in this model by increases in PPARγ and FOXO1 activities in T2D islets. Notably, BACH2 was implicated as one driver of T2D regulatory protein activities.