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Single-cell analysis of thymocyte differentiation: identification of transcription factor interactions and a major stochastic component in αβ-lineage commitment.

ABSTRACT: T cell commitment and αβ/γδ lineage specification in the thymus involves interactions between many different genes. Characterization of these interactions thus requires a multiparameter analysis of individual thymocytes. We developed two efficient single-cell methods: (i) the quantitative evaluation of the co-expression levels of nine different genes, with a plating efficiency of 99-100% and a detection limit of 2 mRNA molecules/cell; and (ii) single-cell differentiation cultures, in the presence of OP9 cells transfected with the thymus Notch1 ligand DeltaL4. We show that during T cell commitment, Gata3 has a fundamental, dose-dependent role in maintaining Notch1 expression, with thymocytes becoming T-cell-committed when they co-express Notch1, Gata3 and Bc11b. Of the transcription factor expression patterns studied here, only that of Bcl11b was suggestive of a role in Pu1 down-regulation. Individual thymocytes became αβ/γδ lineage-committed at very different stages (from the TN2a stage onwards). However, 20% of TN3 cells are not αβ/γδ-lineage committed and TN4 cells comprise two main subpopulations with different degrees of maturity. The existence of a correlation between differentiation potential and expression of the pre-TCR showed that 83% of αβ-committed cells do not express the pre-TCR and revealed a major stochastic component in αβ-lineage specification.

SUBMITTER: Boudil A

PROVIDER: S-EPMC3787938 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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.

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Single-cell analysis of thymocyte differentiation: identification of transcription factor interactions and a major stochastic component in αβ-lineage commitment.

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