Xiao-Tai Huang1,2, Yuan Zhu3,4, Lai Hang Leanne Chan2, Zhongying Zhao5, Hong Yan2. 1. School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, China. 2. Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China. 3. School of Automation, China University of Geosciences, Wuhan, China. 4. Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems. 5. Department of Biology, Hong Kong Baptist University, Hong Kong, China.
Abstract
MOTIVATION: Cell fate specification plays a key role to generate distinct cell types during metazoan development. However, most of the underlying signaling networks at cellular level are not well understood. Availability of time lapse single-cell gene expression data collected throughout Caenorhabditis elegans embryogenesis provides an excellent opportunity for investigating signaling networks underlying cell fate specification at systems, cellular and molecular levels. RESULTS: We propose a framework to infer signaling networks at cellular level by exploring the single-cell gene expression data. Through analyzing the expression data of nhr-25 , a hypodermis-specific transcription factor, in every cells of both wild-type and mutant C.elegans embryos through RNAi against 55 genes, we have inferred a total of 23 genes that regulate (activate or inhibit) nhr-25 expression in cell-specific fashion. We also infer the signaling pathways consisting of each of these genes and nhr-25 based on a probabilistic graphical model for the selected five founder cells, 'ABarp', 'ABpla', 'ABpra', 'Caa' and 'Cpa', which express nhr-25 and mostly develop into hypodermis. By integrating the inferred pathways, we reconstruct five signaling networks with one each for the five founder cells. Using RNAi gene knockdown as a validation method, the inferred networks are able to predict the effects of the knockdown genes. These signaling networks in the five founder cells are likely to ensure faithful hypodermis cell fate specification in C.elegans at cellular level. AVAILABILITY AND IMPLEMENTATION: All source codes and data are available at the github repository https://github.com/xthuang226/Worm_Single_Cell_Data_and_Codes.git . CONTACT: zhuyuan@cug.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Cell fate specification plays a key role to generate distinct cell types during metazoan development. However, most of the underlying signaling networks at cellular level are not well understood. Availability of time lapse single-cell gene expression data collected throughout Caenorhabditis elegans embryogenesis provides an excellent opportunity for investigating signaling networks underlying cell fate specification at systems, cellular and molecular levels. RESULTS: We propose a framework to infer signaling networks at cellular level by exploring the single-cell gene expression data. Through analyzing the expression data of nhr-25 , a hypodermis-specific transcription factor, in every cells of both wild-type and mutant C.elegans embryos through RNAi against 55 genes, we have inferred a total of 23 genes that regulate (activate or inhibit) nhr-25 expression in cell-specific fashion. We also infer the signaling pathways consisting of each of these genes and nhr-25 based on a probabilistic graphical model for the selected five founder cells, 'ABarp', 'ABpla', 'ABpra', 'Caa' and 'Cpa', which express nhr-25 and mostly develop into hypodermis. By integrating the inferred pathways, we reconstruct five signaling networks with one each for the five founder cells. Using RNAi gene knockdown as a validation method, the inferred networks are able to predict the effects of the knockdown genes. These signaling networks in the five founder cells are likely to ensure faithful hypodermis cell fate specification in C.elegans at cellular level. AVAILABILITY AND IMPLEMENTATION: All source codes and data are available at the github repository https://github.com/xthuang226/Worm_Single_Cell_Data_and_Codes.git . CONTACT: zhuyuan@cug.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.