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Temporal dynamics and transcriptional control using single-cell gene expression analysis

Tsukasa Kouno12, Michiel de Hoon12, Jessica C Mar3, Yasuhiro Tomaru2, Mitsuoki Kawano2, Piero Carninci12, Harukazu Suzuki12, Yoshihide Hayashizaki24 and Jay W Shin12*

Author Affiliations

1 RIKEN Center for Life Science Technologies, Division of Genomic Technologies, 1-7-22 Suehiro-cho, Tsurumi-Ku, Yokohama 230-0045, Japan

2 RIKEN Omics Science Center, Yokohama 230-0045, Japan

3 Department of Systems & Computational Biology, Albert Einstein College of Medicine, Bronx, NY 10461, USA

4 RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Saitama 351-0198, Japan

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Genome Biology 2013, 14:R118  doi:10.1186/gb-2013-14-10-r118

Published: 24 October 2013

Abstract

Background

Changes in environmental conditions lead to expression variation that manifest at the level of gene regulatory networks. Despite a strong understanding of the role noise plays in synthetic biological systems, it remains unclear how propagation of expression heterogeneity in an endogenous regulatory network is distributed and utilized by cells transitioning through a key developmental event.

Results

Here we investigate the temporal dynamics of a single-cell transcriptional network of 45 transcription factors in THP-1 human myeloid monocytic leukemia cells undergoing differentiation to macrophages. We systematically measure temporal regulation of expression and variation by profiling 120 single cells at eight distinct time points, and infer highly controlled regulatory modules through which signaling operates with stochastic effects. This reveals dynamic and specific rewiring as a cellular strategy for differentiation. The integration of both positive and negative co-expression networks further identifies the proto-oncogene MYB as a network hinge to modulate both the pro- and anti-differentiation pathways.

Conclusions

Compared to averaged cell populations, temporal single-cell expression profiling provides a much more powerful technique to probe for mechanistic insights underlying cellular differentiation. We believe that our approach will form the basis of novel strategies to study the regulation of transcription at a single-cell level.