Dynamics of protein synthesis and degradation through the cell cycle

Protein expression levels depend on the balance between their synthesis and degradation rates. Even quiescent (G(0)) cells display a continuous turnover of proteins, despite protein levels remaining largely constant over time. In cycling cells, global protein levels need to be precisely doubled at each cell division in order to maintain cellular homeostasis, but we still lack a quantitative understanding of how this is achieved. Recent studies have shed light on cell cycle-dependent changes in protein synthesis and degradation rates. Here we discuss current population-based and single cell approaches used to assess protein synthesis and degradation, and review the insights they have provided into the dynamics of protein turnover in different cell cycle phases.

Single live cell monitoring of protein turnover reveals intercellular variability and cell cycle dependence of degradation rates

Andrea Brigitta Alber

Cells need to be able to precisely control their proteome composition in order to maintain homeostasis. This is of particular relevance in fast dividing cells, such as embryonic stem cells, since their global protein levels need to be doubled at each division to ensure reliable growth and pluripotency maintenance. However, how protein synthesis and degradation interplay to regulate global protein expression levels throughout the cell cycle remains unclear. In addition, while cell-to-cell variability at the level of transcription and protein expression is relatively well studied, it remains unknown to what extent protein degradation rates vary between single cells. Previous studies focusing on protein turnover have mainly relied on bulk biochemical approaches, thus only providing population averages and a low temporal resolution. In addition, many approaches to study the changes in protein turnover throughout the cell cycle have been dependent on cell cycle synchonizing drugs, which might interfere with protein synthesis and also reduce cell viability. Therefore, we further developed and applied two fluorescence time-lapse imaging strategies to study the cell cycle dependence and cell-to-cell variability of protein synthesis and degradation in unperturbed dividing mouse embryonic stem (ES) cells: 1) a Mammalian Cell-optimized Tandem Fluorescent Timer (MCFT), consisting of the fusion of the fast maturing green fluorescent protein superfolder GFP (sfGFP) and the slower maturing red fluorescent protein mOrange2, which in combination with computational modeling allows for disentangling changes in protein synthesis from changes in protein degradation in single living cells, and 2) fluorescent pulse labeling of protein tags (SNAP/Halo-tags) to monitor protein half-lives, as well as transient changes in protein degradation rates in single cells without the use of protein synthesis inhibitors. We discovered a substantial cell cycle-dependence in both protein synthesis and degradation. In particular, a large fraction (40%) of proteins was stabilized around cytokinesis, an effect that has not been described previously. Interestingly, protein degradation rates were highly variable between single cells and were positively correlated with synthesis rates in individual cells to buffer protein expression level variability. Furthermore, degradation rates of different proteins co-varied between single cells, both in ES cells and fibroblasts, suggesting variability in proteasome activity as a source of global extrinsic noise in gene expression. We identified the proteasome component ADRM1 as a potential limiting factor that contributes to generating such noise, as its expression levels were negatively correlated with protein half-lives in single cells. Our work demonstrates the importance of single cell live imaging approaches to study dynamic processes and cell-to-cell variability and sheds more light on the complex interplay of protein synthesis and degradation in determining protein expression levels in single mammalian cells.

EPFL2018

Quantitative analysis of gene expression and transcriptional memory in mouse embryonic stem cells

Aleksandra Mandic

Quantitative studies of gene expression in have shown widespread variability in transcript and protein product abundance at the single-cell level. Such fluctuations in gene expression have been shown to lead to heterogeneous cellular responses and phenotypes, acting in a range of systems, from immunity, cancer to development. To better understand heterogeneity in cellular populations, we developed a novel method for the precise quantification of gene expression in single living cells. Furthermore, we focused on whether and to which extent transcriptional expression profiles are transmitted during cell division. In the first section, we described a new approach for quantitative measurements of gene expression, based on internal tagging of endogenous proteins with a reporter allowing luminescence and fluorescence time-lapse imaging. Using the currently brightest luminescent reporter, fluctuations of protein expression levels could be monitored in single living cells with high sensitivity and temporal resolution over extended time periods. Moreover, our system allowed measurement of single-cell protein degradation rates in the absence of protein synthesis inhibitors, as well as calculation of absolute synthesis rates over the cell cycle. Finally, the internal tag could be excised by inducible expression of Cre recombinase, which enabled estimation of endogenous mRNA half-lives by monitoring the decay of luminescent signal. Second, to monitor transcriptional activity and quantify memory across multiple endogenous genes in mouse embryonic stem cells, we generated cell lines in which a short-lived luciferase reporter allowed sensitive monitoring of transcriptional kinetics by luminescence imaging at high time resolution over long periods of time. By coupling live single-cell monitoring of transcriptional activity of 9 different promoters with mathematical modelling, we found that different levels of expression fluctuations shape transcriptional memory. Long fluctuations (1-10 generations) and short fluctuations (1-3 h), as well as their amplitudes, vary widely between across different genes and together shape transcriptional memory. Surprisingly, we have shown that there was a large level of gene-specific cell-to-cell synchronicity in transcriptional profiles. Overall, we uncovered that noise and timescales of slow and fast fluctuations, as well as their synchronisation over the cell cycle, defined how transcriptional dynamics were correlated within a lineage of related cells. In conclusion, this thesis described an innovative method that allows measurement of absolute protein expression levels, protein turnover, as well as mRNA half-lives for endogenously expressed genes. Our approach opens new avenues in quantitative studies of gene expression in single living cells. Furthermore, we revealed different levels of transcriptional fluctuations that shape gene specific transcriptional memory and its timescales in mouse embryonic stem cells.

EPFL2018

Inference methods for the study of interacting biological oscillators in single-cells

Colas Noé Droin

Biological oscillators are pervasive in biology, covering all aspects of life from enzyme kinetics reactions to population dynamics. Although their behaviour has been intensively studied in the last decades, the recent advances of high-throughput experimental technologies in the fields of omics and microscopy has called for the development of new analysis methods. Among the many types of models and quantitative analyses, parametric approaches are promising as they enable for a mechanistic or physical explanation of the phenomena under study. In particular, dynamical systems theory seems particularly adapted as the vast majority of oscillators can be modelled through differential equations. Dynamical systems parameters can also be easily opti-mized via maximum likelihood approaches. The validity of the inferred model can then be assessed from the quality of its predictions. We here present three different scientific questions regarding noisy biological oscillators, which are answered using maximum-likelihood inference approaches applied to parametric models. We first take interest in the characterisation of the influence of the cell-cycle over the circadi-an clock in individual mammalian cells. To this end, we develop a method combining a Hidden Markov Model with an Expectation-Maximization algorithm to infer their coupling from single-cell microscopy traces. We show that this coupling predicts multiple phase-locked states exhib-iting different degrees of robustness against molecular fluctuations inherent to cellular scale bio-logical oscillators. We then try to understand how the mammalian transcriptome behaves in the liver. Thence, we use single-cell RNA sequencing (scRNA-seq) along with mixed-models to investigate the interplay between gene regulation in space and time. Categorising mRNA expression profiles using mixed-effect models and smFISH validations, we find that many genes in the liver are both zonated and rhythmic, most of them showing multiplicative space-time effects. Finally, we look more closely at the cell-cycle, as it is one of the main drivers of gene expres-sion cell-to-cell heterogeneity in otherwise homogeneous cell populations. Here, we would like to understand if and how cell-cycle velocity changes depending on the phase of the cycling cells. To that end, we formulate the problem in terms of an autonomous dynamical system and use this to infer consistent dynamics for the cell-cycle from scRNA-seq data. Phase inference being paramount in all of these three studies, a short technical review on the topic is also provided at the end of this thesis, along with Julia scripts for the main inference methods presented. Various computational tools assisting the understanding of the scientific questions at stake are also presented, including a Python Dash web-app, a D3 widget and many Matplotlib animations and widgets.

EPFL2020

Quantitative analysis of gene expression and transcriptional memory in mouse embryonic stem cells

Aleksandra Mandic

EPFL2018

Single live cell monitoring of protein turnover reveals intercellular variability and cell cycle dependence of degradation rates

Andrea Brigitta Alber

EPFL2018

Inference methods for the study of interacting biological oscillators in single-cells

Colas Noé Droin

EPFL2020