[Paper Review] Quantitative study of crossregulation, noise and synchronization between microRNA targets in single cells

Carla Bosia, Francesco Sgrò+7 more|arXiv (Cornell University)|Mar 23, 2015

Gene Regulatory Network Analysis25 citations

TL;DR

This study uses quantitative single-cell flow cytometry and a stochastic titration model to demonstrate that microRNA (miRNA) targets exhibit crossregulation, noise modulation, and synchronization through competitive binding to a shared miRNA pool. Key findings show that crosstalk is maximized at physiological mRNA levels (~10–100 molecules/cell), with optimal stoichiometry inducing bimodal expression and strong correlations, validating a model of miRNA-mediated gene regulation in single cells.

ABSTRACT

Recent studies reported complex post-transcriptional interplay among targets of a common pool of microRNAs, a class of small non-coding downregulators of gene expression. Behaving as microRNA-sponges, distinct RNA species may compete for binding to microRNAs and coregulate each other in a dose-dependent manner. Although previous studies in cell populations showed competition in vitro, the detailed dynamical aspects of this process, most importantly in physiological conditions, remains unclear. We address this point by monitoring protein expression of two targets of a common miRNA with quantitative single-cell measurements. In agreement with a detailed stochastic model of molecular titration, we observed that: (i) crosstalk between targets is possible only in particular stoichiometric conditions, (ii) a trade-off on the number of microRNA regulatory elements may induce the coexistence of two distinct cell populations, (iii) strong inter-targets correlations can be observed. This phenomenology is compatible with a small amount of mRNA target molecules per cell of the order of 10-100.

Motivation & Objective

• To investigate the dynamical behavior of miRNA target crossregulation in single cells under physiological conditions.
• To determine how stoichiometric balance between miRNA and target mRNAs influences crosstalk, noise, and synchronization.
• To test whether a stochastic titration model accurately predicts experimental observations of protein expression and correlation in mammalian cells.
• To explore the emergence of bimodal expression states in cell populations due to competitive miRNA binding.

Proposed method

• Quantitative single-cell flow cytometry was used to measure protein expression of two fluorescently labeled miRNA targets in cotransfected mammalian cells.
• A stochastic chemical master equation model was formulated to describe miRNA-mediated titration of two target mRNAs, including translation, binding, and degradation dynamics.
• A Gaussian approximation of the master equation enabled analytical computation of mean protein levels and noise (coefficient of variation) across five molecular species.
• The moment-generating function approach was applied to derive a system of 20 equations for mean vector and covariance matrix, solvable numerically.
• Experimental validation used bidirectional plasmids with dual fluorescent reporters and varying miRNA regulatory element (MRE) copy numbers.
• Model predictions were compared to experimental data, including Pearson correlation coefficients and coefficient of variation (CV) of protein expression.

Experimental results

Research questions

• RQ1 Under what stoichiometric conditions does crossregulation between miRNA targets become significant in single cells?
• RQ2 How does the number of miRNA regulatory elements (MREs) on target mRNAs influence protein expression noise and correlation?
• RQ3 Can competitive titration of miRNAs by multiple targets lead to bimodal population distributions in protein expression?
• RQ4 To what extent do experimental observations of protein correlation and noise align with predictions from a stochastic titration model?
• RQ5 What is the physiological relevance of miRNA target crosstalk in terms of endogenous mRNA molecule counts?

Key findings

• Crosstalk between miRNA targets is maximized when the number of target mRNA molecules per cell is in the physiological range of 10 to 100.
• A trade-off in the number of miRNA regulatory elements (MREs) on targets leads to the emergence of two distinct cell populations with high and low expression states.
• Strong inter-target correlations (up to 12-fold higher than unregulated controls) are observed near a threshold of miRNA repression, consistent with model predictions.
• Protein expression noise (CV) is modulated by MRE copy number: mCherry CV increases with mCherry MREs but decreases with mCerulean MREs, indicating asymmetric regulation.
• Bimodal distributions in mCherry fluorescence emerge above a threshold of MREs, indicating a switch-like behavior driven by competitive miRNA titration.
• The stochastic titration model accurately predicts both mean protein levels and correlation dynamics, with Gaussian approximation providing reliable estimates of moments.