Abstract
Induced pluripotent stem cells (iPSCs) provide a great model to study the process of stem cell reprogramming and differentiation. Single-cell RNA sequencing (scRNA-seq) enables us to investigate the reprogramming process at single-cell level. Here, we introduce single-cell entropy (scEntropy) as a macroscopic variable to quantify the cellular transcriptome from scRNA-seq data during reprogramming and differentiation of iPSCs. scEntropy measures the relative order parameter of genomic transcriptions at single cell level during the process of cell fate changes, which show increase tendency during differentiation, and decrease upon reprogramming. Hence, scEntropy provides an intrinsic measurement of the cell state, and can be served as a pseudo-time of the stem cell differentiation process. Moreover, based on the evolutionary dynamics of scEntropy, we construct a phenomenological Fokker-Planck equation model and the corresponding stochastic differential equation for the process of cell state transitions during pluripotent stem cell differentiation. These equations provide further insights to infer the processes of cell fates changes and stem cell differentiation. This study is the first to introduce the novel concept of scEntropy to quantify the biological process of iPSC, and suggests that the scEntropy can provide a suitable macroscopic variable for single cells to describe cell fate transition during differentiation and reprogramming of stem cells.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
This paper was aimed at proposing a method of quantifying iPSC differentiation/reprogramming through single-cell RNA seq data. However, in revising the paper, we realize that the original example based on the data set (GSE123380) is not very proper, since this data set come from a bulk data. To overcome this issue, we applied the method to another data set (GSE118184) of single-cell RNA-seq obtained from separate time point during kidney organoid from human pluripotent stem cells. In this revision, we have replace the results of Figure 1 and all discussions in Sections 2.2 and 2.3 by the results from the data set GSE118184. The main results and biological relevance remain unchanged when we apply the method to different data sets. Moreover, the technique of scEntropy is also valid for bulk data set, and hence we keep the original figure 1(which now figure 3), and so that the main result is consistent even when we apply the method of scEntropy to bulk data, i.e., the scEntropy increase with the differentiation process. Through this major revision, we believe that the manuscript now becomes more consistent, and complete.