TY - JOUR
T1 - Computational modeling and analysis of the morphogenetic domain signaling networks regulating C. elegans embryogenesis
AU - Niu, Ben
AU - Bach, Thao Nguyen
AU - Chen, Xingyu
AU - Chandratre, Khyati Raghunath
AU - Murray, John Isaac
AU - Zhao, Zhongying
AU - Zhang, Michael
N1 - Funding Information:
The work was supported in part by NIH/MH109665 (MQZ) and NCI/CA245294 (MQZ). MQZ would also acknowledge the Cecil H. and Ida Green Distinguished Chair in Systems Biology Science. The authors thank for Professor Chao Tang and his student Dr. Guoye Guan for their helps in data processing. The authors also thank for Dr. Zhuo Du for his constructive suggestions in reading and revising the manuscript.
Publisher Copyright:
© 2022 The University of Texas at Dallas
PY - 2022/7/12
Y1 - 2022/7/12
N2 - Caenorhabditis elegans, often referred to as the ‘roundworm’, provides a powerful model for studying cell autonomous and cell–cell interactions through the direct observation of embryonic development in vivo. By leveraging the precisely mapped cell lineage at single cell resolution, we are able to study at a systems level how early embryonic cells communicate across morphogenetic domains for the coordinated processes of gene expressions and collective cellular behaviors that regulate tissue morphogenesis. In this study, we developed a computational framework for the exploration of the morphogenetic domain cell signaling networks that may regulate C. elegans gastrulation and embryonic organogenesis. We demonstrated its utility by producing the following results, i) established a virtual reference model of developing C. elegans embryos through the spatiotemporal alignment of individual embryo cell nuclear imaging samples; ii) integrated the single cell spatiotemporal gene expression profile with the established virtual embryo model by data pooling; iii) trained a Machine Learning model (Random Forest Regression), which predicts accurately the spatial positions of the cells given their gene expression profiles for a given developmental time (e.g. total cell number of the embryo); iv) enabled virtual 4-dimensional tomographic graphical modeling of single cell data; v) inferred the biology signaling pathways that act in each of morphogenetic domains by meta-data analysis. It is intriguing that the morphogenetic domain cell signaling network seems to involve some crosstalk of multiple biology signaling pathways during the formation of tissue boundary pattern. Lastly, we developed the Software tool ‘Embryo aligner version 1.0’ and provided it as an Open Source program to the research community for virtual embryo modeling, and phenotype perturbation analyses (https://github.com/csniuben/embryo_aligner/wiki and https://bioinfo89.github.io/C.elegansEmbryonicOrganogenesisweb/).
AB - Caenorhabditis elegans, often referred to as the ‘roundworm’, provides a powerful model for studying cell autonomous and cell–cell interactions through the direct observation of embryonic development in vivo. By leveraging the precisely mapped cell lineage at single cell resolution, we are able to study at a systems level how early embryonic cells communicate across morphogenetic domains for the coordinated processes of gene expressions and collective cellular behaviors that regulate tissue morphogenesis. In this study, we developed a computational framework for the exploration of the morphogenetic domain cell signaling networks that may regulate C. elegans gastrulation and embryonic organogenesis. We demonstrated its utility by producing the following results, i) established a virtual reference model of developing C. elegans embryos through the spatiotemporal alignment of individual embryo cell nuclear imaging samples; ii) integrated the single cell spatiotemporal gene expression profile with the established virtual embryo model by data pooling; iii) trained a Machine Learning model (Random Forest Regression), which predicts accurately the spatial positions of the cells given their gene expression profiles for a given developmental time (e.g. total cell number of the embryo); iv) enabled virtual 4-dimensional tomographic graphical modeling of single cell data; v) inferred the biology signaling pathways that act in each of morphogenetic domains by meta-data analysis. It is intriguing that the morphogenetic domain cell signaling network seems to involve some crosstalk of multiple biology signaling pathways during the formation of tissue boundary pattern. Lastly, we developed the Software tool ‘Embryo aligner version 1.0’ and provided it as an Open Source program to the research community for virtual embryo modeling, and phenotype perturbation analyses (https://github.com/csniuben/embryo_aligner/wiki and https://bioinfo89.github.io/C.elegansEmbryonicOrganogenesisweb/).
KW - Collective cell behavior
KW - Computational image analysis
KW - Machine learning
KW - Morphogenetic domain cell signaling network
KW - Single cell gene expression modeling
UR - http://www.scopus.com/inward/record.url?scp=85134349206&partnerID=8YFLogxK
U2 - 10.1016/j.csbj.2022.05.058
DO - 10.1016/j.csbj.2022.05.058
M3 - Article
AN - SCOPUS:85134349206
SN - 2001-0370
VL - 20
SP - 3653
EP - 3666
JO - Computational and Structural Biotechnology Journal
JF - Computational and Structural Biotechnology Journal
ER -