Dynamics of the DNA damage response: insights from live-cell imaging

Biophysical Approaches for the Characterization of Protein-Metabolite Interactions

With an estimate of hundred thousands of protein molecules per cell and the number of metabolites several orders of magnitude higher, protein-metabolite interactions are omnipresent. In vitro analyses are one of the main pillars on the way to establish a solid understanding of how these interactions contribute to maintaining cellular homeostasis. A repertoire of biophysical techniques is available by which protein-metabolite interactions can be quantitatively characterized in terms of affinity, specificity, and kinetics in a broad variety of solution environments. Several of those provide information on local or global conformational changes of the protein partner in response to ligand binding. This review chapter gives an overview of the state-of-the-art biophysical toolbox for the study of protein-metabolite interactions. It briefly introduces basic principles, highlights recent examples from the literature, and pinpoints promising future directions.

In Situ Analysis of DNA-Protein Complex Formation upon Radiation-Induced DNA Damage

The importance of determining at the cellular level the formation of DNA–protein complexes after radiation-induced lesions to DNA is outlined by the evidence that such interactions represent one of the first steps of the cellular response to DNA damage. These complexes are formed through recruitment at the sites of the lesion, of proteins deputed to signal the presence of DNA damage, and of DNA repair factors necessary to remove it. Investigating the formation of such complexes has provided, and will probably continue to, relevant information about molecular mechanisms and spatiotemporal dynamics of the processes that constitute the first barrier of cell defense against genome instability and related diseases. In this review, we will summarize and discuss the use of in situ procedures to detect the formation of DNA-protein complexes after radiation-induced DNA damage. This type of analysis provides important information on the spatial localization and temporal resolution of the formation of such complexes, at the single-cell level, allowing the study of heterogeneous cell populations.

Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting

Cellular microscopy images contain rich insights about biology. To extract this information, researchers use features, or measurements of the patterns of interest in the images. Here, we introduce a convolutional neural network (CNN) to automatically design features for fluorescence microscopy. We use a self-supervised method to learn feature representations of single cells in microscopy images without labelled training data. We train CNNs on a simple task that leverages the inherent structure of microscopy images and controls for variation in cell morphology and imaging: given one cell from an image, the CNN is asked to predict the fluorescence pattern in a second different cell from the same image. We show that our method learns high-quality features that describe protein expression patterns in single cells both yeast and human microscopy datasets. Moreover, we demonstrate that our features are useful for exploratory biological analysis, by capturing high-resolution cellular components in a proteome-wide cluster analysis of human proteins, and by quantifying multi-localized proteins and single-cell variability. We believe paired cell inpainting is a generalizable method to obtain feature representations of single cells in multichannel microscopy images.

To understand the cell biology captured by microscopy images, researchers use features, or measurements of relevant properties of cells, such as the shape or size of cells, or the intensity of fluorescent markers. Features are the starting point of most image analysis pipelines, so their quality in representing cells is fundamental to the success of an analysis. Classically, researchers have relied on features manually defined by imaging experts. In contrast, deep learning techniques based on convolutional neural networks (CNNs) automatically learn features, which can outperform manually-defined features at image analysis tasks. However, most CNN methods require large manually-annotated training datasets to learn useful features, limiting their practical application. Here, we developed a new CNN method that learns high-quality features for single cells in microscopy images, without the need for any labeled training data. We show that our features surpass other comparable features in identifying protein localization from images, and that our method can generalize to diverse datasets. By exploiting our method, researchers will be able to automatically obtain high-quality features customized to their own image datasets, facilitating many downstream analyses, as we highlight by demonstrating many possible use cases of our features in this study.

Quantitating repair protein accumulation at DNA lesions: Past, present, and future

All organisms must protect their genome from constantly occurring DNA damage. To this end, cells have evolved complex pathways for repairing sites of DNA lesions, and multiple in vitro and in vivo techniques have been developed to study these processes. In this review, we discuss the commonly used laser microirradiation method for monitoring the accumulation of repair proteins at DNA damage sites in cells, and we outline several strategies for deriving kinetic models from such experimental data. We discuss an example of how in vitro measurements and in vivo microirradation experiments complement each other to provide insight into the mechanism of PARP1 recruitment to DNA lesions. We also discuss a strategy to combine data obtained for the recruitment of many different proteins in a move toward fully quantitating the spatiotemporal relationships between various damage responses, and we outline potential venues for future development in the field.

Multimodal Light Microscopy Approaches to Reveal Structural and Functional Properties of Promyelocytic Leukemia Nuclear Bodies

The promyelocytic leukemia (pml) gene product PML is a tumor suppressor localized mainly in the nucleus of mammalian cells. In the cell nucleus, PML seeds the formation of macromolecular multiprotein complexes, known as PML nuclear bodies (PML NBs). While PML NBs have been implicated in many cellular functions including cell cycle regulation, survival and apoptosis their role as signaling hubs along major genome maintenance pathways emerged more clearly. However, despite extensive research over the past decades, the precise biochemical function of PML in these pathways is still elusive. It remains a big challenge to unify all the different previously suggested cellular functions of PML NBs into one mechanistic model. With the advent of genetically encoded fluorescent proteins it became possible to trace protein function in living specimens. In parallel, a variety of fluorescence fluctuation microscopy (FFM) approaches have been developed which allow precise determination of the biophysical and interaction properties of cellular factors at the single molecule level in living cells. In this report, we summarize the current knowledge on PML nuclear bodies and describe several fluorescence imaging, manipulation, FFM, and super-resolution techniques suitable to analyze PML body assembly and function. These include fluorescence redistribution after photobleaching, fluorescence resonance energy transfer, fluorescence correlation spectroscopy, raster image correlation spectroscopy, ultraviolet laser microbeam-induced DNA damage, erythrocyte-mediated force application, and super-resolution microscopy approaches. Since most if not all of the microscopic equipment to perform these techniques may be available in an institutional or nearby facility, we hope to encourage more researches to exploit sophisticated imaging tools for their research in cancer biology.

Excitability in the p53 network mediates robust signaling with tunable activation thresholds in single cells

Cellular signaling systems precisely transmit information in the presence of molecular noise while retaining flexibility to accommodate the needs of individual cells. To understand design principles underlying such versatile signaling, we analyzed the response of the tumor suppressor p53 to varying levels of DNA damage in hundreds of individual cells and observed a switch between distinct signaling modes characterized by isolated pulses and sustained oscillations of p53 accumulation. Guided by dynamic systems theory we show that this requires an excitable network structure comprising positive feedback and provide experimental evidence for its molecular identity. The resulting data-driven model reproduced all features of measured signaling responses and is sufficient to explain their heterogeneity in individual cells. We present evidence that heterogeneity in the levels of the feedback regulator Wip1 sets cell-specific thresholds for p53 activation, providing means to modulate its response through interacting signaling pathways. Our results demonstrate how excitable signaling networks can provide high specificity, sensitivity and robustness while retaining unique possibilities to adjust their function to the physiology of individual cells.

How are base excision DNA repair pathways deployed in vivo?

Since the discovery of the base excision repair (BER) system for DNA more than 40 years ago, new branches of the pathway have been revealed at the biochemical level by in vitro studies. Largely for technical reasons, however, the confirmation of these subpathways in vivo has been elusive. We review methods that have been used to explore BER in mammalian cells, indicate where there are important knowledge gaps to fill, and suggest a way to address them.

Bridging the gap between in vitro and in vivo: Dose and schedule predictions for the ATR inhibitor AZD6738

Understanding the therapeutic effect of drug dose and scheduling is critical to inform the design and implementation of clinical trials. The increasing complexity of both mono, and particularly combination therapies presents a substantial challenge in the clinical stages of drug development for oncology. Using a systems pharmacology approach, we have extended an existing PK-PD model of tumor growth with a mechanistic model of the cell cycle, enabling simulation of mono and combination treatment with the ATR inhibitor AZD6738 and ionizing radiation. Using AZD6738, we have developed multi-parametric cell based assays measuring DNA damage and cell cycle transition, providing quantitative data suitable for model calibration. Our in vitro calibrated cell cycle model is predictive of tumor growth observed in in vivo mouse xenograft studies. The model is being used for phase I clinical trial designs for AZD6738, with the aim of improving patient care through quantitative dose and scheduling prediction.

Chromatin dynamics during DNA repair revealed by pair correlation analysis of molecular flow in the nucleus

Chromatin dynamics modulate DNA repair factor accessibility throughout the DNA damage response. The spatiotemporal scale upon which these dynamics occur render them invisible to live cell imaging. Here we present a believed novel assay to monitor the in vivo structural rearrangements of chromatin during DNA repair. By pair correlation analysis of EGFP molecular flow into chromatin before and after damage, this assay measures millisecond variations in chromatin compaction with submicron resolution. Combined with laser microirradiation we employ this assay to monitor the real-time accessibility of DNA at the damage site. We find from comparison of EGFP molecular flow with a molecule that has an affinity toward double-strand breaks (Ku-EGFP) that DNA damage induces a transient decrease in chromatin compaction at the damage site and an increase in compaction to adjacent regions, which together facilitate DNA repair factor recruitment to the lesion with high spatiotemporal control.

Studying the organization of DNA repair by single-cell and single-molecule imaging

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Single-cell experiments to study stochastic events and heterogeneity in DNA repair.

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Quantifying DNA repair protein concentration, diffusion, and localization in cells.

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Direct observation of DNA repair using photoactivated single-molecule tracking.

DNA repair safeguards the genome against a diversity of DNA damaging agents. Although the mechanisms of many repair proteins have been examined separately in vitro, far less is known about the coordinated function of the whole repair machinery in vivo. Furthermore, single-cell studies indicate that DNA damage responses generate substantial variation in repair activities across cells. This review focuses on fluorescence imaging methods that offer a quantitative description of DNA repair in single cells by measuring protein concentrations, diffusion characteristics, localizations, interactions, and enzymatic rates. Emerging single-molecule and super-resolution microscopy methods now permit direct visualization of individual proteins and DNA repair events in vivo. We expect much can be learned about the organization of DNA repair by linking cell heterogeneity to mechanistic observations at the molecular level.