Characterizing Cellular Differentiation Potency and Waddington Landscape via Energy Indicator

The precise characterization of cellular differentiation potency remains an open question, which is fundamentally important for deciphering the dynamics mechanism related to cell fate transition. We quantitatively evaluated the differentiation potency of different stem cells based on the Hopfield neural network (HNN). The results emphasized that cellular differentiation potency can be approximated by Hopfield energy values. We then profiled the Waddington energy landscape of embryogenesis and cell reprogramming processes. The energy landscape at single-cell resolution further confirmed that cell fate decision is progressively specified in a continuous process. Moreover, the transition of cells from one steady state to another in embryogenesis and cell reprogramming processes was dynamically simulated on the energy ladder. These two processes can be metaphorized as the motion of descending and ascending ladders, respectively. We further deciphered the dynamics of the gene regulatory network (GRN) for driving cell fate transition. Our study proposes a new energy indicator to quantitatively characterize cellular differentiation potency without prior knowledge, facilitating the further exploration of the potential mechanism of cellular plasticity.

Automated summary

This study quantitatively evaluated the differentiation potency of stem cells using the Hopfield neural network and found that it can be approximated by Hopfield energy values. The analysis of the energy landscape in embryogenesis and cell reprogramming processes revealed that cell fate decision is a continuous process. The study also deciphered the dynamics of the gene regulatory network involved in driving cell fate transition. These findings propose a new energy indicator for characterizing cellular differentiation potency and provide insights into the potential mechanism of cellular plasticity.