How does the small intestine maintain itself? What mechanism does it use to achieve
homeostasis, and how optimal is this mechanism? We approached these questions
mainly using cell tracking in organoids, for which we developed new cell trackers.
We found that the intestinal crypt uses a surprisingly simple and effective strategy.
Finally, we showed that cell segmentation and tracking is possible in organoids without fluorescent markers.

Throughout the lifetime of living systems, tissue homeostasis and renewal constantly take place to confront challenging conditions, both internally, such as cell aging, and externally, such as infections, so that health can be maintained. Such processes require a tight balance between cell proliferation and differentiation. When homeostasis is disturbed, diseases like cancer can develop. Therefore, understanding the regulation of tissue homeostasis is a key question in biology. However, directly monitoring the dynamics of proliferation and differentiation in live animals remains extremely challenging. Common methods, such as immunostaining and single-cell RNA sequencing, require killing the animal and fixing the cells. Therefore, they can merely provide information in a single time frame. As a result, lineage tracing techniques are introduced, where cells are labeled with a heritable marker that can be detected in progeny after a certain period by fluorescence microscopy or sequencing. Nevertheless, they only produce lineage dynamics indirectly.

The interaction between proteins is central not only to this thesis, but to most processes in the cell. After millions of years of evolution, the accomplished variety, complexity and beauty of the proteomic network is astonishing. When one realizes that these interplays rely on the delicate process of protein folding, a very special sort of protein interaction comes into play: that between molecular chaperones and their clients. Chaperones are specialized proteins crucial to protein folding. They are thought to guide polypeptides through their conformational search from synthesis, preventing alternative hazardous pathways, and to rescue proteins from misfolded and aggregated states....

Throughout their lifetime, animals face a wide variety of biological challenges. Starting out as single cells, their _rst challenge is to undergo development and become fully grown and functional adults. Remarkably, this incredibly complex process occurs in a highly reproducible manner despite the huge variability in environmental, genetic and molecular uctuations they encounter during their journey through development. However, the challenges do not end here; once animals become adults, their tissues are constantly subject to damage, either by external sources such as disease, or due to intrinsic causes such as cellular aging. Thus, adult tissues are constantly renewing themselves, and must do so in a tightly controlled manner in order to maintain homeostasis. In this thesis, we explore how animals cope with a few of such challenges with the help of two model systems, C. elegans worms and stem cell-derived organoids.

Life of single cells is not deterministic, but virtually all processes in a cell are subject to stochastic fluctuations. As consequence, genetically identical cells, which are living in the same environment, can behave differently. They can, for example, produce different amounts of proteins, grow at different rates and can even specialize into completely different phenotypes. The discovery of cell-to-cell variability opened up many questions, ranging from what are the origins of the fluctuations, to whether its consequences are detrimental or beneficial for cells.
The aim of this thesis is to better understand stochasticity in gene expression and growth rate of bacterial cells. To this end, we investigate whether, next to gene expression, also growth rate of single cells fluctuates dynamically, which we will show to be the case. We then focus on the following questions about gene expression and growth: What are the origins of fluctuations? Can fluctuations propagate from one to the other? To address these questions, we work with a model organism, the bacteriumE. coli . This organism is simple enough to allow for quantitative experiments, but already so complex that many processes inside these cells have not been understood yet.
Studying stochastic fluctuations in gene expression and growth rate in single cells requires precise and rather high throughput tools to measure these quantities. In chapter 2 and 3 we describe these methods. By using automated time-lapse microscopy, we acquire movies of growing E. coli cells which are expressing fluorescent proteins. We then use automated analysis software to segment, track and analyze cells. To accurately measure gene expression, a fluorescent protein reporter of high quality is needed. Therefore, we present a comparison of different fluorescent proteins which assesses their suitability for time-lapse experiments.
In chapter 4 we investigate the general interdependence of fluctuations in gene expression and growth rate. We find that the noise intensities of both fluctuations are very strongly correlated and that they scale linearly. In apparent contrast to that, the fluctuating time traces are shown to be only modestly correlated. We develop a linear noise model to explain these observations and analyze what constraints the linear scaling imposes on such models. A central result is that the intensities of different cellular noise sources do not change independently, but are set by one single global parameter.
In chapter 5 we study the effects of a specific source of fluctuations in gene expression: the bacterial cell cycle. As cells grow and prepare for division, they copy their chromosome. With two copies of each gene and an increasing cell size, the production rate of proteins increases. We show that about half of the noise in protein production rate is caused by gene duplication. In contrast to that, protein concentration is hardly affected by the cell cycle because the exponential volume increase almost perfectly cancels the increase in protein production rate.
The fact that gene expression fluctuates has been known for many years. However, it was unclear whether such fluctuations also affect the growth rate of cells. This question is addressed in chapter 6. We show that fluctuations in enzymes can indeed propagate and cause growth fluctuations. Conversely, growth fluctuations also propagate back to disturb protein concentration. We develop an analytical model to accurately predict noise transmission. Our results indicate that fluctuations can not only propagate through gene networks but also through metabolic reactions. They also suggest that cellular metabolism is inherently stochastic.
Finally, in chapter 7 we investigate the influence of fluctuations in ribosome concentration on the growth rate. Because ribosomes are the molecular machines responsible for protein production, they play a central role in cellular growth. Each cell needs thousands of ribosomes and as producing these large numbers is costly, the ribosome production is tightly regulated and adjusted to the need. Still, their concentration in single cells fluctuates, and we here investigate whether these fluctuations affect growth rate. Our data suggests that fluctuations in ribosome concentration do not propagate to growth, which indicates that ribosomes are not dynamically limiting growth rate.