Single-cell Physiology

Below, you can find the updated list of Bachelor and Master internships available at the moment.

For more information on available internships in the Single-cell Physiology Team, please contact directly the teamleaders:

Evelina Tutucci –

Johan van Heerden –

Project titleType of researchSupervisor(s)
A single cell perspective on the diauxic shift in Saccharomyces cerevisiaeExperimentalPhilipp Savakis (p.e.savakis[at]])
The favourite carbon source of the budding yeast Saccharomyces cerevisiae is glucose, which at high levels is metabolised to ethanol, a compound of industrial importance. Preferential metabolism of glucose is ensured by a regulatory program (glucose repression) that implements sensory input mainly on the transcriptional level.
After glucose has been consumed, yeast utilises the previously excreted ethanol as carbon source. This requires a complete turnaround of metabolism, which is mediated by several transcription factors. The switch to ethanol metabolism can be decomposed into different commited steps; for instance the highly regulated phosphoenolpyruvate carboxykinase, which allows gluconeogenesis by tapping the TCA cycle.
While the transcriptional regulation and proteome changes have been mapped out in fair detail on the level of the entire population, a zoomed-in perspective of the regulation in single cells is still missing, and the influence of noise on this aspect of cellular decision making is still poorly understood, in part due to restrictions in experimental design.
In this project, we will investigate the coordination of cellular sensing and the commitment to different metabolic states in vivo in dynamic environments and in living cells. This project provides an opportunity to learn molecular cloning, fluorescence microscopy, microfluidics, and flow cytometry.
FILLED until July 2021
Studying gene expression using single mRNA imaging technologies in S. cerevisiaeExperimental (Master)Evelina Tutucci (evelina.tutucci[at]
The dynamic control of gene expression lies at the basis of cellular adaptation. mRNAs are transcribed from genes, transported in the cytoplasm and translated into proteins. The number of mRNAs available for translation is determined by the balance between their synthesis and degradation rates. Cells tune both processes to rapidly change the mRNA levels.
While previous studies extensively characterized the regulation of transcription, mRNA decay remains poorly understood. Interestingly, mRNAs coding for proteins with similar functions (e.g. cell-cycle regulators, histones, ribosomes) have similar mRNA and protein half-lives, suggesting that evolution led to correlated mRNA and protein turnover rates to control the temporal order of gene expression. So, it is likely that mRNA degradation plays an important role in the temporal control of gene expression.
The aim of this project is to investigate whether and how cells dynamically regulate mRNA degradation in response to changing environmental conditions. At what stages of the mRNA life cycle is the decay modulated? How is mRNA decay coordinated with the other steps of mRNA metabolism, namely transcription, localization and translation?
To address these questions, we will characterize the degradation of mRNAs encoding proteins controlling metabolic genes. Using the yeast Saccharomyces cerevisiae as model organism, we will investigate the importance of rapid mRNA degradation upon environmental changes.
In this project you will use single-molecule FISH (smFISH) to study and quantify the expression of mRNAs of genes involved in yeast cell cycle-regulation. You will apply molecular biology and yeast genetics techniques, in addition to more advanced microscopy and image-processing procedures.
By generating different mutants we are going to identify the mRNA characteristics controlling the timing and specificity of mRNA decay.
Your CV
Practical experience working in a laboratory is a required. In addition, as this project will involve a lot of computation and data processing, ideally you have some affinity with programming (R, Python, Java or Matlab).
Number of positions available
Duration of project
6 months
Starting date
As soon as possible

FILLED until July 2022
Pseudohyphal Growth and Biofilm formation in S. cerevisiae resolved by single cell imaging Theoretical (Bachelor or Master)Evelina Tutucci (
Many fungi such as Saccharomyces cerevisiae or Candida albicans are able to switch between a unicellular (yeast) form to a multicellular filamentous form in response to changes in the environment (e.g. nutrients availability, stress). This morphological transition allows fungi to adopt different survival strategies and in some instances become pathogenic.
During filamentous growth cells acquire an elongated shape and unipolar budding pattern, allowing for greater exploration of the environment. A more advanced strategy is the formation of biofilms, multicellular structures that consist of different cell types (both yeast form and filamentous form) as well as an extracellular matrix, offering both increased structural integrity and resistance to antifungal drugs. While many of the genes required for this differentiation process have been identified through bulk analysis (e.g. RNA seq), their expression in single cells and during differentiation has remained largely unstudied.
In this project, we investigate at the single cell level, the gene expression changes occurring during fungal differentiation. By using a fluorescence-based RNA imaging technique called smFISH (see pictures here: we visualize and quantify individual mRNA molecules in single cells to investigate the spatiotemporal control of gene expression during filamentation. Furthermore, we investigate how the spatial organization of cells in biofilms influences gene expression.
Planned activities (and methods)
This computational project consists of analysing the acquired ‘3D’ smFISH images of pseudohyphal yeast and early biofilm forming colonies. With this analysis, you will investigate the heterogeneity in the gene expression of cells in colonies and you will couple spatial information such as cell volume and cell length to the RNA spot count in filamentous cells. Since the microscopy data is very rich of information it will be possible to further expand the analysis, depending on your computational skills and your curiosity.
With this internship, you will learn how to perform imaging analysis and to perform basic coding using Python, Image J and R . You will participate and present in our Single-cell group meeting and Journal club. Previous experience with coding is a plus.
6 Months
Starting date
As soon as possible
FILLED until July 2022