Timers, variability, and body-wide coordination: C. elegans as a model system for whole-animal developmental timing.

Successful development requires both precise timing of cellular processes, such as division and differentiation, and tight coordination of timing between tissues and organs. Yet, how time information is encoded with high precision and synchronized between tissues, despite inherent molecular noise, is unsolved. Here, we propose the nematode C. elegans as a unique model system for studying body-wide control of developmental timing. Recent studies combining genetics, quantitative analysis, and simulations have 1) mapped core timers controlling larval development, indicating temporal gradients as an underlying mechanism, and 2) elucidated general principles that make timing insensitive to inherent fluctuations and variation in environmental conditions. As the molecular regulators of C. elegans developmental timing are broadly conserved, these mechanisms likely apply also to higher organisms.

Organoid cell fate dynamics in space and time.

Organoids are a major new tool to study tissue renewal. However, characterizing the underlying differentiation dynamics remains challenging. Here, we developed TypeTracker, which identifies cell fates by AI-enabled cell tracking and propagating end point fates back along the branched lineage trees. Cells that ultimately migrate to the villus commit to their new type early, when still deep inside the crypt, with important consequences: (i) Secretory cells commit before terminal division, with secretory fates emerging symmetrically in sister cells. (ii) Different secretory types descend from distinct stem cell lineages rather than an omnipotent secretory progenitor. (iii) The ratio between secretory and absorptive cells is strongly affected by proliferation after commitment. (iv) Spatial patterning occurs after commitment through type-dependent cell rearrangements. This "commit-then-sort" model contrasts with the conventional conveyor belt picture, where cells differentiate by moving up the crypt-villus axis and hence raises new questions about the underlying commitment and sorting mechanisms.

Minimizing cell number fluctuations in self-renewing tissues with a stem-cell niche.

Self-renewing tissues require that a constant number of proliferating cells is maintained over time. This maintenance can be ensured at the single-cell level or the population level. Maintenance at the population level leads to fluctuations in the number of proliferating cells over time. Often, it is assumed that those fluctuations can be reduced by increasing the number of asymmetric divisions, i.e., divisions where only one of the daughter cells remains proliferative. Here, we study a model of cell proliferation that incorporates a stem-cell niche of fixed size, and explicitly model the cells inside and outside the niche. We find that in this model, fluctuations are minimized when the difference in growth rate between the niche and the rest of the tissue is maximized and all divisions are symmetric divisions, producing either two proliferating or two nonproliferating daughters. We show that this optimal state leaves visible signatures in clone size distributions and could thus be detected experimentally.

Mother cells control daughter cell proliferation in intestinal organoids to minimize proliferation fluctuations.

During renewal of the intestine, cells are continuously generated by proliferation. Proliferation and differentiation must be tightly balanced, as any bias toward proliferation results in uncontrolled exponential growth. Yet, the inherently stochastic nature of cells raises the question how such fluctuations are limited. We used time-lapse microscopy to track all cells in crypts of growing mouse intestinal organoids for multiple generations, allowing full reconstruction of the underlying lineage dynamics in space and time. Proliferative behavior was highly symmetric between sister cells, with both sisters either jointly ceasing or continuing proliferation. Simulations revealed that such symmetric proliferative behavior minimizes cell number fluctuations, explaining our observation that proliferating cell number remained constant even as crypts increased in size considerably. Proliferative symmetry did not reflect positional symmetry but rather lineage control through the mother cell. Our results indicate a concrete mechanism to balance proliferation and differentiation with minimal fluctuations that may be broadly relevant for other tissues.

The vast majority of cells lining our intestine die within three to five days. They are replaced by a small group of stem cells which divide to produce either more stem cells, or cells that stop dividing and transform, or 'differentiate', in to mature cells in the intestine. Stem cells must generate the same number of dividing and differentiated cells. If there is even a slight bias and too many stem cells are produced, this can lead to uncontrolled growth, which is the root cause of cancer. In principal, the best way to achieve this balance is for stem cells to always asymmetrically divide in to two distinct cells: one that will continue to divide, and another that will mature in to an adult cell. However, recent research suggests that this process is much more random, with stem cells also dividing symmetrically, either in to two stem cells or two differentiated cells. So, how does the random nature of stem cell divisions not cause the number of dividing cells to fluctuate unpredictably in the intestine? To investigate, Huelsz-Prince et al. studied stem cells in a miniature model of the mouse intestine, known as an organoid, which can be grown outside of the body in a laboratory. All stem cells and their progeny were tracked for over 65 hours using a microscope to see how many dividing and differentiated cells they formed. This revealed that almost all stem cells in the organoid split symmetrically rather than asymmetrically. Huelsz-Prince et al. then developed a computer model of stem cells in the model intestine and tested the impact of changing the proportion of symmetric and asymmetric divisions. The results showed that having more symmetric divisions reduced fluctuations in the number of dividing cells better than high levels of asymmetric divisions. Other organs rely on a similar system to the intestine to replenish their mature cells. Consequently, the finding that symmetric divisions control fluctuations in the number of stem cells may be applicable to other parts of the body. Further testing with human disease samples, such as cells from cancer patients, using the organoid model system may also shed light on how division is disrupted in these conditions.

Temporal scaling in C. elegans larval development.

SignificanceAn enduring mystery of development is how its timing is controlled, particularly for development after birth, where timing is highly flexible and depends on environmental conditions, such as food availability and diet. We followed timing of cell- and organism-level events in individual Caenorhabditis elegans larvae developing from hatching to adulthood, uncovering widespread variations in event timing, both between isogenic individuals in the same environment and when changing conditions and genotypes. However, in almost all cases, we found that events occurred at the same time, when time was rescaled by the duration of development measured in each individual. This observation of "temporal scaling" poses strong constraints on models to explain timing of larval development.

Mechanism of life-long maintenance of neuron identity despite molecular fluctuations.

Cell fate is maintained over long timescales, yet molecular fluctuations can lead to spontaneous loss of this differentiated state. Our simulations identified a possible mechanism that explains life-long maintenance of ASE neuron fate in Caenorhabditis elegans by the terminal selector transcription factor CHE-1. Here, fluctuations in CHE-1 level are buffered by the reservoir of CHE-1 bound at its target promoters, which ensures continued che-1 expression by preferentially binding the che-1 promoter. We provide experimental evidence for this mechanism by showing that che-1 expression was resilient to induced transient CHE-1 depletion, while both expression of CHE-1 targets and ASE function were lost. We identified a 130 bp che-1 promoter fragment responsible for this resilience, with deletion of a homeodomain binding site in this fragment causing stochastic loss of ASE identity long after its determination. Because network architectures that support this mechanism are highly conserved in cell differentiation, it may explain stable cell fate maintenance in many systems.

Cell Tracking for Organoids: Lessons From Developmental Biology.

Organoids have emerged as powerful model systems to study organ development and regeneration at the cellular level. Recently developed microscopy techniques that track individual cells through space and time hold great promise to elucidate the organizational principles of organs and organoids. Applied extensively in the past decade to embryo development and 2D cell cultures, cell tracking can reveal the cellular lineage trees, proliferation rates, and their spatial distributions, while fluorescent markers indicate differentiation events and other cellular processes. Here, we review a number of recent studies that exemplify the power of this approach, and illustrate its potential to organoid research. We will discuss promising future routes, and the key technical challenges that need to be overcome to apply cell tracking techniques to organoid biology.

OrganoidTracker: Efficient cell tracking using machine learning and manual error correction.

Time-lapse microscopy is routinely used to follow cells within organoids, allowing direct study of division and differentiation patterns. There is an increasing interest in cell tracking in organoids, which makes it possible to study their growth and homeostasis at the single-cell level. As tracking these cells by hand is prohibitively time consuming, automation using a computer program is required. Unfortunately, organoids have a high cell density and fast cell movement, which makes automated cell tracking difficult. In this work, a semi-automated cell tracker has been developed. To detect the nuclei, we use a machine learning approach based on a convolutional neural network. To form cell trajectories, we link detections at different time points together using a min-cost flow solver. The tracker raises warnings for situations with likely errors. Rapid changes in nucleus volume and position are reported for manual review, as well as cases where nuclei divide, appear and disappear. When the warning system is adjusted such that virtually error-free lineage trees can be obtained, still less than 2% of all detected nuclei positions are marked for manual analysis. This provides an enormous speed boost over manual cell tracking, while still providing tracking data of the same quality as manual tracking.

Variability in ß-catenin pulse dynamics in a stochastic cell fate decision in C. elegans.

During development, cell fate decisions are often highly stochastic, but with the frequency of the different possible fates tightly controlled. To understand how signaling networks control the cell fate frequency of such random decisions, we studied the stochastic decision of the Caenorhabditis elegans P3.p cell to either fuse to the hypodermis or assume vulva precursor cell fate. Using time-lapse microscopy to measure the single-cell dynamics of two key inhibitors of cell fusion, the Hox gene LIN-39 and Wnt signaling through the ß-catenin BAR-1, we uncovered significant variability in the dynamics of LIN-39 and BAR-1 levels. Most strikingly, we observed that BAR-1 accumulated in a single, 1-4 â€‹h pulse at the time of the P3.p cell fate decision, with strong variability both in pulse slope and time of pulse onset. We found that the time of BAR-1 pulse onset was delayed relative to the time of cell fusion in mutants with low cell fusion frequency, linking BAR-1 pulse timing to cell fate outcome. Overall, a model emerged where animal-to-animal variability in LIN-39 levels and BAR-1 pulse dynamics biases cell fate by modulating their absolute level at the time cell fusion is induced. Our results highlight that timing of cell signaling dynamics, rather than its average level or amplitude, could play an instructive role in determining cell fate.

Effect of Antifreeze Glycoproteins on Organoid Survival during and after Hypothermic Storage.

We study the effect of antifreeze glycoproteins (AFGPs) on the survival of organoids under hypothermic conditions. We find that the survival of organoids in cold conditions depends on their developmental stage. Mature organoids die within 24 h when being stored at 4 °C, while cystic organoids can survive up to 48 h. We find that in the presence of AFGPs, the organoid survival is prolonged up to 72 h, irrespective of their developmental stage. Fluorescence microscopy experiments reveal that the AFGPs predominately localize at the cell surface and cover the cell membranes. Our findings support a mechanism in which the positive effect of AFGPs on cell survival during hypothermic storage involves the direct interaction of AFGPs with the cell membrane. Our research highlights organoids as an attractive multicellular model system for studying the action of AFGPs that bridges the gap between single-cell and whole-organ studies.

Long-term expanding human airway organoids for disease modeling.

Organoids are self-organizing 3D structures grown from stem cells that recapitulate essential aspects of organ structure and function. Here, we describe a method to establish long-term-expanding human airway organoids from broncho-alveolar resections or lavage material. The pseudostratified airway organoids consist of basal cells, functional multi-ciliated cells, mucus-producing secretory cells, and CC10-secreting club cells. Airway organoids derived from cystic fibrosis (CF) patients allow assessment of CFTR function in an organoid swelling assay. Organoids established from lung cancer resections and metastasis biopsies retain tumor histopathology as well as cancer gene mutations and are amenable to drug screening. Respiratory syncytial virus (RSV) infection recapitulates central disease features, dramatically increases organoid cell motility via the non-structural viral NS2 protein, and preferentially recruits neutrophils upon co-culturing. We conclude that human airway organoids represent versatile models for the in vitro study of hereditary, malignant, and infectious pulmonary disease.

Stochastic loss and gain of symmetric divisions in the C. elegans epidermis perturbs robustness of stem cell number.

Biological systems are subject to inherent stochasticity. Nevertheless, development is remarkably robust, ensuring the consistency of key phenotypic traits such as correct cell numbers in a certain tissue. It is currently unclear which genes modulate phenotypic variability, what their relationship is to core components of developmental gene networks, and what is the developmental basis of variable phenotypes. Here, we start addressing these questions using the robust number of Caenorhabditis elegans epidermal stem cells, known as seam cells, as a readout. We employ genetics, cell lineage tracing, and single molecule imaging to show that mutations in lin-22, a Hes-related basic helix-loop-helix (bHLH) transcription factor, increase seam cell number variability. We show that the increase in phenotypic variability is due to stochastic conversion of normally symmetric cell divisions to asymmetric and vice versa during development, which affect the terminal seam cell number in opposing directions. We demonstrate that LIN-22 acts within the epidermal gene network to antagonise the Wnt signalling pathway. However, lin-22 mutants exhibit cell-to-cell variability in Wnt pathway activation, which correlates with and may drive phenotypic variability. Our study demonstrates the feasibility to study phenotypic trait variance in tractable model organisms using unbiased mutagenesis screens.

Microfabricated polyacrylamide devices for the controlled culture of growing cells and developing organisms.

The ability to spatially confine living cells or small organisms while dynamically controlling their aqueous environment is important for a host of microscopy applications. Here, we show how polyacrylamide layers can be patterned to construct simple microfluidic devices for this purpose. We find that polyacrylamide gels can be molded like PDMS into micron-scale structures that can enclose organisms, while being permeable to liquids, and transparent to allow for microscopic observation. We present a range of chemostat-like devices to observe bacterial and yeast growth, and C. elegans nematode development. The devices can integrate PDMS layers and allow for temporal control of nutrient conditions and the presence of drugs on a minute timescale. We show how spatial confinement of motile C. elegans enables for time-lapse microscopy in a parallel fashion.

Robustness and epistasis in the C. elegans vulval signaling network revealed by pathway dosage modulation.

Biological systems may perform reproducibly to generate invariant outcomes, despite external or internal noise. One example is the C. elegans vulva, in which the final cell fate pattern is remarkably robust. Although this system has been extensively studied and the molecular network underlying cell fate specification is well understood, very little is known in quantitative terms. Here, through pathway dosage modulation and single molecule fluorescence in situ hybridization, we show that the system can tolerate a 4-fold variation in genetic dose of the upstream signaling molecule LIN-3/epidermal growth factor (EGF) without phenotypic change in cell fate pattern. Furthermore, through tissue-specific dosage perturbations of the EGF and Notch pathways, we determine the first-appearing patterning errors. Finally, by combining different doses of both pathways, we explore how quantitative pathway interactions influence system behavior. Our results highlight the feasibility and significance of launching experimental studies of robustness and quantitative network analysis in genetically tractable, multicellular eukaryotes.

A mechanical bottleneck explains the variation in cup growth during FcgammaR phagocytosis.

Phagocytosis is the process by which cells internalize particulate material, and is of central importance to immunity, homeostasis and development. Here, we study the internalization of immunoglobulin G-coated particles in cells transfected with Fcgamma receptors (FcgammaRs) through the formation of an enveloping phagocytic cup. Using confocal microscopy, we precisely track the location of fluorescently tagged FcgammaRs during cup growth. Surprisingly, we found that phagocytic cups growing around identical spherical particles showed great variability even within a single cell and exhibited two eventual fates: a cup either stalled before forming a half-cup or it proceeded until the particle was fully enveloped. We explain these observations in terms of a mechanical bottleneck using a simple mathematical model of the overall process of cup growth. The model predicts that reducing F-actin concentration levels, and hence the deforming force, does not necessarily lead to stalled cups, a prediction we verify experimentally. Our analysis gives a coherent explanation for the importance of geometry in phagocytic uptake and provides a unifying framework for integrating the key processes, both biochemical and mechanical, occurring during cup growth.

An allosteric model of circadian KaiC phosphorylation.

In a recent series of ground-breaking experiments, Nakajima et al. [Nakajima M, Imai K, Ito H, Nishiwaki T, Murayama Y, Iwasaki H, Oyama T, Kondo T (2005) Science 308:414-415] showed that the three cyanobacterial clock proteins KaiA, KaiB, and KaiC are sufficient in vitro to generate circadian phosphorylation of KaiC. Here, we present a mathematical model of the Kai system. At its heart is the assumption that KaiC can exist in two conformational states, one favoring phosphorylation and the other dephosphorylation. Each individual KaiC hexamer then has a propensity to be phosphorylated in a cyclic manner. To generate macroscopic oscillations, however, the phosphorylation cycles of the different hexamers must be synchronized. We propose a novel synchronization mechanism based on differential affinity: KaiA stimulates KaiC phosphorylation, but the limited supply of KaiA dimers binds preferentially to those KaiC hexamers that are falling behind in the oscillation. KaiB sequesters KaiA and stabilizes the dephosphorylating KaiC state. We show that our model can reproduce a wide range of published data, including the observed insensitivity of the oscillation period to variations in temperature, and that it makes nontrivial predictions about the effects of varying the concentrations of the Kai proteins.

Diffusion of transcription factors can drastically enhance the noise in gene expression.

We study by Green's Function Reaction Dynamics the effect of the diffusive motion of repressor molecules on the noise in mRNA and protein levels for a gene that is under the control of a repressor. We find that spatial fluctuations due to diffusion can drastically enhance the noise in gene expression. After dissociation from the operator, a repressor can rapidly rebind to the DNA. Our results show that the rebinding trajectories are so short that, on this timescale, the RNA polymerase (RNAP) cannot effectively compete with the repressor for binding to the promoter. As a result, a dissociated repressor molecule will on average rebind many times, before it eventually diffuses away. These rebindings thus lower the effective dissociation rate, and this increases the noise in gene expression. Another consequence of the timescale separation between repressor rebinding and RNAP association is that the effect of spatial fluctuations can be described by a well-stirred, zero-dimensional, model by renormalizing the reaction rates for repressor-DNA (un) binding. Our results thus support the use of well-stirred, zero-dimensional models for describing noise in gene expression. We also show that for a fixed repressor strength, the noise due to diffusion can be minimized by increasing the number of repressors or by decreasing the rate of the open complex formation. Lastly, our results emphasize that power spectra are a highly useful tool for studying the propagation of noise through the different stages of gene expression.

Simulating biochemical networks at the particle level and in time and space: Green's function reaction dynamics.

We present a technique, called Green's function reaction dynamics (GFRD), for particle-based simulations of reaction-diffusion systems. GFRD uses a maximum time step such that only single particles or pairs of particles have to be considered. For these particles, the Smoluchowski equations are solved analytically using Green's functions, which are used to set up an event-driven algorithm. We apply the technique to a model of gene expression. Under biologically relevant conditions, GFRD is up to 5 orders of magnitude faster than conventional particle-based schemes.

Green's-function reaction dynamics: a particle-based approach for simulating biochemical networks in time and space.

We have developed a new numerical technique, called Green's-function reaction dynamics (GFRD), that makes it possible to simulate biochemical networks at the particle level and in both time and space. In this scheme, a maximum time step is chosen such that only single particles or pairs of particles have to be considered. For these particles, the Smoluchowski equation can be solved analytically using Green's functions. The main idea of GFRD is to exploit the exact solution of the Smoluchoswki equation to set up an event-driven algorithm, which combines in one step the propagation of the particles in space with the reactions between them. The event-driven nature allows GFRD to make large jumps in time and space when the particles are far apart from each other. Here, we apply the technique to a simple model of gene expression. The simulations reveal that spatial fluctuations can be a major source of noise in biochemical networks. The calculations also show that GFRD is highly efficient. Under biologically relevant conditions, GFRD is up to five orders of magnitude faster than conventional particle-based techniques for simulating biochemical networks in time and space. GFRD is not limited to biochemical networks. It can also be applied to a large number of other reaction-diffusion problems.