Dataset Information

Time-variant clustering model for understanding cell fate decisions.

ABSTRACT: Both spatial characteristics and temporal features are often the subjects of concern in physical, social, and biological studies. This work tackles the clustering problems for time course data in which the cluster number and clustering structure change with respect to time, dubbed time-variant clustering. We developed a hierarchical model that simultaneously clusters the objects at every time point and describes the relationships of the clusters between time points. The hidden layer of this model is a generalized form of branching processes. A reversible-jump Markov Chain Monte Carlo method was implemented for model inference, and a feature selection procedure was developed. We applied this method to explore an open question in preimplantation embryonic development. Our analyses using single-cell gene expression data suggested that the earliest cell fate decision could start at the 4-cell stage in mice, earlier than the commonly thought 8- to 16-cell stage. These results together with independent experimental data from single-cell RNA-seq provided support against a prevailing hypothesis in mammalian development.

SUBMITTER: Huang W

PROVIDER: S-EPMC4226122 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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Time-variant clustering model for understanding cell fate decisions.

Huang Wei W Cao Xiaoyi X Biase Fernando H FH Yu Pengfei P Zhong Sheng S

Proceedings of the National Academy of Sciences of the United States of America 20141022 44

Both spatial characteristics and temporal features are often the subjects of concern in physical, social, and biological studies. This work tackles the clustering problems for time course data in which the cluster number and clustering structure change with respect to time, dubbed time-variant clustering. We developed a hierarchical model that simultaneously clusters the objects at every time point and describes the relationships of the clusters between time points. The hidden layer of this mode ...[more]

PMID: 25339442

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Project description:Molecular mechanisms employed by individual multipotent cells at the point of lineage commitment remain largely uncharacterized. Current paradigms span from instructive to noise-driven mechanisms. Of considerable interest is also whether commitment involves a limited set of genes or the entire transcriptional program, and to what extent gene expression configures multiple trajectories into commitment. Importantly, the transient nature of the commitment transition confounds the experimental capture of committing cells. We develop a computational framework that simulates stochastic commitment events, and affords mechanistic exploration of the fate transition. We use a combined modeling approach guided by gene expression classifier methods that infers a time-series of stochastic commitment events from experimental growth characteristics and gene expression profiling of individual hematopoietic cells captured immediately before and after commitment. We define putative regulators of commitment and probabilistic rules of transition through machine learning methods, and employ clustering and correlation analyses to interrogate gene regulatory interactions in multipotent cells. Against this background, we develop a Monte Carlo time-series stochastic model of transcription where the parameters governing promoter status, mRNA production and mRNA decay in multipotent cells are fitted to experimental static gene expression distributions. Monte Carlo time is converted to physical time using cell culture kinetic data. Probability of commitment in time is a function of gene expression as defined by a logistic regression model obtained from experimental single-cell expression data. Our approach should be applicable to similar differentiating systems where single cell data is available. Within our system, we identify robust model solutions for the multipotent population within physiologically reasonable values and explore model predictions with regard to molecular scenarios of entry into commitment. The model suggests distinct dependencies of different commitment-associated genes on mRNA dynamics and promoter activity, which globally influence the probability of lineage commitment.

Dataset Information

Time-variant clustering model for understanding cell fate decisions.

Publications

Time-variant clustering model for understanding cell fate decisions.

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