ABSTRACT
Current rejuvenation strategies, which range from calorie restriction to in vivo partial reprogramming, only improve a few specific cellular processes. In addition, the molecular mechanisms underlying these approaches are largely unknown, which hinders the design of more holistic cellular rejuvenation strategies. To address this issue, we developed SINGULAR (Single-cell RNA-seq Investigation of Rejuvenation Agents and Longevity), a cell rejuvenation atlas that provides a unified system biology analysis of diverse rejuvenation strategies across multiple organs at single-cell resolution. In particular, we leverage network biology approaches to characterize and compare the effects of each strategy at the level of intracellular signaling, cell-cell communication, and transcriptional regulation. As a result, we identified master regulators orchestrating the rejuvenation response and propose that targeting a combination of them leads to a more holistic improvement of age-dysregulated cellular processes. Thus, the interactive database accompanying SINGULAR is expected to facilitate the future design of synthetic rejuvenation interventions.
Subject(s)
Rejuvenation , Rejuvenation/physiology , Animals , Humans , Gene Regulatory Networks , Single-Cell Analysis , Systems Biology , Gene Expression Regulation , Signal Transduction , Longevity/genetics , Longevity/physiology , Cell CommunicationABSTRACT
A major goal of regenerative medicine is to generate tissue-specific mature and functional cells. However, current cell engineering protocols are still unable to systematically produce fully mature functional cells. While existing computational approaches aim at predicting transcription factors (TFs) for cell differentiation/reprogramming, no method currently exists that specifically considers functional cell maturation processes. To address this challenge, here, we develop SinCMat, a single-cell RNA sequencing (RNA-seq)-based computational method for predicting cell maturation TFs. Based on a model of cell maturation, SinCMat identifies pairs of identity TFs and signal-dependent TFs that co-target genes driving functional maturation. A large-scale application of SinCMat to the Mouse Cell Atlas and Tabula Sapiens accurately recapitulates known maturation TFs and predicts novel candidates. We expect SinCMat to be an important resource, complementary to preexisting computational methods, for studies aiming at producing functionally mature cells.