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Scientists Develop New Approach to Infer Differentiation Time by Integrating Single Cell and Bulk Transcriptomes

Single-cell RNA sequencing technology is a powerful method for analyzing intercellular heterogeneity during development and reprogramming. A key aim of examining such heterogeneity is to discover unknown cellular states or developmental lineage trajectories. Many existing methods are quite subject to confounding factors biologically or non-biologically, such as the cell cycle, which is usually removed using computational method. However, in some cases the cell cycle plays a regulatory role. For example, the length of G1 and M phases has been shown to directly affect neural lineage determination. Recent cellular and molecular studies have uncovered many molecules and signaling pathways participating in neural commitment. However, how these regulators and other unidentified components act together to regulate early neural commitment is still poorly understood, particularly at the single cell level.

A team of scientists, led by Prof. Jing-Dong Jackie HAN from CAS-MPG Partner Institute for Computational Biology, Prof. JING Naihe from Institute of Biochemistry and Cell Biology and Prof. SHEN Qin from Tsinghua University, proposed an approach to unbiasedly assess the contribution of cell cycle to a development trajectory by including cell population RNA-seq (cpRNA-seq) data in parallel to the single cell RNA-seq (scRNA-seq) data as a reference, and then order the single cell trajectories not based on their inter-cell expression distance, but instead on the external reference time (actual time) derived from the cpRNA-seq data.

The researchers applied their method to the in vitro neural differentiation process of mouse embryonic stem cells (mESCs), and show that it can more effectively align the single-cell differentiation trajectories than routine single-cell distance based on pseudotime reconstruction methods. Based on the model-derived time of single cells they identified the genes that show correlated expression with a single cells’ differentiation time (“timer” genes). Surprisingly, they found cell-cycle regulators are involved in timing the differentiation progress of a cell. Moreover, the researchers inferred the regulatory network from cpRNA-seq and scRNA-seq, and key regulatory genes, such as Smad1, Fyn and Trp53, as hub genes that coordinate cell-cycle progression and neural commitment at the single cell level. Finally, by generating a CRISPR/Cas9 knockout of Fyn or perturbing mESCs with a small molecule inhibitor that promotes M phase, they experimentally validated the role of Fyn and M phase in controlling differentiation timing.

The study entitled “Inference of Differentiation Time for Single Cell Transcriptomes Using Cell Population Reference Data” was published online in Nature Communications on Nov.30, 2017.

This work was supported by the National Natural Science Foundation of China, Chinese Academy of Sciences, and China Ministry of Science and Technology.


Prof. Jing-Dong Jackie HAN

CAS-MPG Partner Institute for Computational Biology,

Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences,

320 Yueyang Road, Shanghai200031, China

Email: jdhan@picb.ac.cn


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