ScInTime: A Computational Method Leveraging Single-Cell Trajectory and Gene Regulatory Networks to Identify Master Regulators of Cellular Differentiation

Guanxun Li,Yan Zhong,Yu-Te Lin,Yongjian Yang,Daniel Osorio,Xiuren Zhang,James J. Cai,Qian Xu

doi:10.3390/genes13020371

Abstract

Trajectory inference (TI) or pseudotime analysis has dramatically extended the analytical framework of single-cell RNA-seq data, allowing regulatory genes contributing to cell differentiation and those involved in various dynamic cellular processes to be identified. However, most TI analysis procedures deal with individual genes independently while overlooking the regulatory relations between genes. Integrating information from gene regulatory networks (GRNs) at different pseudotime points may lead to more interpretable TI results. To this end, we introduce scInTime—an unsupervised machine learning framework coupling inferred trajectory with single-cell GRNs (scGRNs) to identify master regulatory genes. We validated the performance of our method by analyzing multiple scRNA-seq data sets. In each of the cases, top-ranking genes predicted by scInTime supported their functional relevance with corresponding signaling pathways, in line with the results of available functional studies. Overall results demonstrated that scInTime is a powerful tool to exploit pseudotime-series scGRNs, allowing for a clear interpretation of TI results toward more significant biological insights.

Highlights

Single-cell RNA sequencing has revolutionized modern biology by allowing researchers to explore cellular dynamic processes at unprecedented resolution
To identify the cell stage according to their pseudotime and biological transition states, cells were ordered according to their pseudotime or their differentiation potency and divided into multiple (e.g., 10) pseudotime-series subgroups corresponding to cells’ biological transition states (Figure 1A, up)
Thereby, cells were assumed to be sorted according to their biological transition states, which could come from cellular processes, such as cell differentiation or cell reprogramming in response to a stimulus

Summary

Introduction

Single-cell RNA sequencing (scRNA-seq) has revolutionized modern biology by allowing researchers to explore cellular dynamic processes at unprecedented resolution. Each cell is at a distinct stage of the dynamic differentiation process These dynamic processes can be studied computationally using trajectory inference (TI) approaches, known as pseudotime analyses. Cells are ordered along a computationally-defined trajectory based on their expression profile similarity [1]. When a sample contains cells spanning a broad spectrum of differentiation stages, such as from a progenitor to many differentiated cellular states, computation-based TI can help gain transcriptomic insights into these complex processes. Thanks to these breakthroughs, researchers may investigate intricate differentiation patterns and infer dynamic processes without collecting time-resolved data

Methods

Results

Conclusion