Monitoring nucleolar-nucleoplasmic protein shuttling in living cells by high-content microscopy and automated image analysis.

The nucleolus has core functions in ribosome biosynthesis, but also acts as a regulatory hub in a plethora of non-canonical processes, including cellular stress. Upon DNA damage, several DNA repair factors shuttle between the nucleolus and the nucleoplasm. Yet, the molecular mechanisms underlying such spatio-temporal protein dynamics remain to be deciphered. Here, we present a novel imaging platform to investigate nucleolar-nucleoplasmic protein shuttling in living cells. For image acquisition, we used a commercially available automated fluorescence microscope and for image analysis, we developed a KNIME workflow with implementation of machine learning-based tools. We validated the method with different nucleolar proteins, i.e., PARP1, TARG1 and APE1, by monitoring their shuttling dynamics upon oxidative stress. As a paradigm, we analyzed PARP1 shuttling upon H2O2 treatment in combination with a range of pharmacological inhibitors in a novel reporter cell line. These experiments revealed that inhibition of SIRT7 results in a loss of nucleolar PARP1 localization. Finally, we unraveled specific differences in PARP1 shuttling dynamics after co-treatment with H2O2 and different clinical PARP inhibitors. Collectively, this work delineates a highly sensitive and versatile bioimaging platform to investigate swift nucleolar-nucleoplasmic protein shuttling in living cells, which can be employed for pharmacological screening and in-depth mechanistic analyses.

Time and space-resolved quantification of plasma membrane sialylation for measurements of cell function and neurotoxicity.

While there are many methods to quantify the synthesis, localization, and pool sizes of proteins and DNA during physiological responses and toxicological stress, only few approaches allow following the fate of carbohydrates. One of them is metabolic glycoengineering (MGE), which makes use of chemically modified sugars (CMS) that enter the cellular biosynthesis pathways leading to glycoproteins and glycolipids. The CMS can subsequently be coupled (via bio-orthogonal chemical reactions) to tags that are quantifiable by microscopic imaging. We asked here, whether MGE can be used in a quantitative and time-resolved way to study neuronal glycoprotein synthesis and its impairment. We focused on the detection of sialic acid (Sia), by feeding human neurons the biosynthetic precursor N-acetyl-mannosamine, modified by an azide tag. Using this system, we identified non-toxic conditions that allowed live cell labeling with high spatial and temporal resolution, as well as the quantification of cell surface Sia. Using combinations of immunostaining, chromatography, and western blotting, we quantified the percentage of cellular label incorporation and effects on glycoproteins such as polysialylated neural cell adhesion molecule. A specific imaging algorithm was used to quantify Sia incorporation into neuronal projections, as potential measure of complex cell function in toxicological studies. When various toxicants were studied, we identified a subgroup (mitochondrial respiration inhibitors) that affected neurite glycan levels several hours before any other viability parameter was affected. The MGE-based neurotoxicity assay, thus allowed the identification of subtle impairments of neurochemical function with very high sensitivity.

Live cell imaging of SOS and prophage dynamics in isogenic bacterial populations.

Almost all bacterial genomes contain DNA of viral origin, including functional prophages or degenerated phage elements. A frequent but often unnoted phenomenon is the spontaneous induction of prophage elements (SPI) even in the absence of an external stimulus. In this study, we have analyzed SPI of the large, degenerated prophage CGP3 (187 kbp), which is integrated into the genome of the Gram-positive Corynebacterium glutamicum ATCC 13032. Time-lapse fluorescence microscopy of fluorescent reporter strains grown in microfluidic chips revealed the sporadic induction of the SOS response as a prominent trigger of CGP3 SPI but also displayed a considerable fraction (∼30%) of RecA-independent SPI. Whereas approx. 20% of SOS-induced cells recovered from this stress and resumed growth, the spontaneous induction of CGP3 always led to a stop of growth and likely cell death. A carbon source starvation experiment clearly emphasized that SPI only occurs in actively proliferating cells, whereas sporadic SOS induction was still observed in resting cells. These data highlight the impact of sporadic DNA damage on the activity of prophage elements and provide a time-resolved, quantitative description of SPI as general phenomenon of bacterial populations.

Vizardous: interactive analysis of microbial populations with single cell resolution.

MOTIVATION: Single cell time-lapse microscopy is a powerful method for investigating heterogeneous cell behavior. Advances in microfluidic lab-on-a-chip technologies and live-cell imaging render the parallel observation of the development of individual cells in hundreds of populations possible. While image analysis tools are available for cell detection and tracking, biologists are still confronted with the challenge of exploring and evaluating this data. RESULTS: We present the software tool Vizardous that assists scientists with explorative analysis and interpretation tasks of single cell data in an interactive, configurable and visual way. With Vizardous, lineage tree drawings can be augmented with various, time-resolved cellular characteristics. Associated statistical moments bridge the gap between single cell and the population-average level. AVAILABILITY AND IMPLEMENTATION: The software, including documentation and examples, is available as executable Java archive as well as in source form at https://github.com/modsim/vizardous. CONTACT: k.noeh@fz-juelich.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Spatiotemporal microbial single-cell analysis using a high-throughput microfluidics cultivation platform.

Cell-to-cell heterogeneity typically evolves due to a manifold of biological and environmental factors and special phenotypes are often relevant for the fate of the whole population but challenging to detect during conventional analysis. We demonstrate a microfluidic single-cell cultivation platform that incorporates several hundred growth chambers, in which isogenic bacteria microcolonies growing in cell monolayers are tracked by automated time-lapse microscopy with spatiotemporal resolution. The device was not explicitly developed for a specific organism, but has a very generic configuration suitable for various different microbial organisms. In the present study, we analyzed Corynebacterium glutamicum microcolonies, thereby generating complete lineage trees and detailed single-cell data on division behavior and morphology in order to demonstrate the platform's overall capabilities. Furthermore, the occurrence of spontaneously induced stress in individual C. glutamicum cells was investigated by analyzing strains with genetically encoded reporter systems and optically visualizing SOS response. The experiments revealed spontaneous SOS induction in the absence of any external trigger comparable to results obtained by flow cytometry (FC) analyzing cell samples from conventional shake flask cultivation. Our microfluidic setup delivers detailed single-cell data with spatial and temporal resolution; complementary information to conventional FC results.

SciJava Ops: an improved algorithms framework for Fiji and beyond.

Decades of iteration on scientific imaging hardware and software has yielded an explosion in not only the size, complexity, and heterogeneity of image datasets but also in the tooling used to analyze this data. This wealth of image analysis tools, spanning different programming languages, frameworks, and data structures, is itself a problem for data analysts who must adapt to new technologies and integrate established routines to solve increasingly complex problems. While many "bridge" layers exist to unify pairs of popular tools, there exists a need for a general solution to unify new and existing toolkits. The SciJava Ops library presented here addresses this need through two novel principles. Algorithm implementations are declared as plugins called Ops, providing a uniform interface regardless of the toolkit they came from. Users express their needs declaratively to the Op environment, which can then find and adapt available Ops on demand. By using these principles instead of direct function calls, users can write streamlined workflows while avoiding the translation boilerplate of bridge layers. Developers can easily extend SciJava Ops to introduce new libraries and more efficient, specialized algorithm implementations, even immediately benefitting existing workflows. We provide several use cases showing both user and developer benefits, as well as benchmarking data to quantify the negligible impact on overall analysis performance. We have initially deployed SciJava Ops on the Fiji platform, however it would be suitable for integration with additional analysis platforms in the future.

Integration of the ImageJ Ecosystem in the KNIME Analytics Platform.

Open-source software tools are often used for analysis of scientific image data due to their flexibility and transparency in dealing with rapidly evolving imaging technologies. The complex nature of image analysis problems frequently requires many tools to be used in conjunction, including image processing and analysis, data processing, machine learning and deep learning, statistical analysis of the results, visualization, correlation to heterogeneous but related data, and more. However, the development, and therefore application, of these computational tools is impeded by a lack of integration across platforms. Integration of tools goes beyond convenience, as it is impractical for one tool to anticipate and accommodate the current and future needs of every user. This problem is emphasized in the field of bioimage analysis, where various rapidly emerging methods are quickly being adopted by researchers. ImageJ is a popular open-source image analysis platform, with contributions from a global community resulting in hundreds of specialized routines for a wide array of scientific tasks. ImageJ's strength lies in its accessibility and extensibility, allowing researchers to easily improve the software to solve their image analysis tasks. However, ImageJ is not designed for development of complex end-to-end image analysis workflows. Scientists are often forced to create highly specialized and hard-to-reproduce scripts to orchestrate individual software fragments and cover the entire life-cycle of an analysis of an image dataset. KNIME Analytics Platform, a user-friendly data integration, analysis, and exploration workflow system, was designed to handle huge amounts of heterogeneous data in a platform-agnostic, computing environment and has been successful in meeting complex end-to-end demands in several communities, such as cheminformatics and mass spectrometry. Similar needs within the bioimage analysis community led to the creation of the KNIME Image Processing extension which integrates ImageJ into KNIME Analytics Platform, enabling researchers to develop reproducible and scalable workflows, integrating a diverse range of analysis tools. Here we present how users and developers alike can leverage the ImageJ ecosystem via the KNIME Image Processing extension to provide robust and extensible image analysis within KNIME workflows. We illustrate the benefits of this integration with examples, as well as representative scientific use cases.

Image-Based Single Cell Profiling: High-Throughput Processing of Mother Machine Experiments.

BACKGROUND: Microfluidic lab-on-chip technology combined with live-cell imaging has enabled the observation of single cells in their spatio-temporal context. The mother machine (MM) cultivation system is particularly attractive for the long-term investigation of rod-shaped bacteria since it facilitates continuous cultivation and observation of individual cells over many generations in a highly parallelized manner. To date, the lack of fully automated image analysis software limits the practical applicability of the MM as a phenotypic screening tool. RESULTS: We present an image analysis pipeline for the automated processing of MM time lapse image stacks. The pipeline supports all analysis steps, i.e., image registration, orientation correction, channel/cell detection, cell tracking, and result visualization. Tailored algorithms account for the specialized MM layout to enable a robust automated analysis. Image data generated in a two-day growth study (≈ 90 GB) is analyzed in ≈ 30 min with negligible differences in growth rate between automated and manual evaluation quality. The proposed methods are implemented in the software molyso (MOther machine AnaLYsis SOftware) that provides a new profiling tool to analyze unbiasedly hitherto inaccessible large-scale MM image stacks. CONCLUSION: Presented is the software molyso, a ready-to-use open source software (BSD-licensed) for the unsupervised analysis of MM time-lapse image stacks. molyso source code and user manual are available at https://github.com/modsim/molyso.

Non-Invasive Microbial Metabolic Activity Sensing at Single Cell Level by Perfusion of Calcein Acetoxymethyl Ester.

Phase contrast microscopy cannot give sufficient information on bacterial metabolic activity, or if a cell is dead, it has the fate to die or it is in a viable but non-growing state. Thus, a reliable sensing of the metabolic activity helps to distinguish different categories of viability. We present a non-invasive instantaneous sensing method using a fluorogenic substrate for online monitoring of esterase activity and calcein efflux changes in growing wild type bacteria. The fluorescent conversion product of calcein acetoxymethyl ester (CAM) and its efflux indicates the metabolic activity of cells grown under different conditions at real-time. The dynamic conversion of CAM and the active efflux of fluorescent calcein were analyzed by combining microfluidic single cell cultivation technology and fluorescence time lapse microscopy. Thus, an instantaneous and non-invasive sensing method for apparent esterase activity was created without the requirement of genetic modification or harmful procedures. The metabolic activity sensing method consisting of esterase activity and calcein secretion was demonstrated in two applications. Firstly, growing colonies of our model organism Corynebacterium glutamicum were confronted with intermittent nutrient starvation by interrupting the supply of iron and carbon, respectively. Secondly, bacteria were exposed for one hour to fatal concentrations of antibiotics. Bacteria could be distinguished in growing and non-growing cells with metabolic activity as well as non-growing and non-fluorescent cells with no detectable esterase activity. Microfluidic single cell cultivation combined with high temporal resolution time-lapse microscopy facilitated monitoring metabolic activity of stressed cells and analyzing their descendants in the subsequent recovery phase. Results clearly show that the combination of CAM with a sampling free microfluidic approach is a powerful tool to gain insights in the metabolic activity of growing and non-growing bacteria.

Application of a genetically encoded biosensor for live cell imaging of L-valine production in pyruvate dehydrogenase complex-deficient Corynebacterium glutamicum strains.

The majority of biotechnologically relevant metabolites do not impart a conspicuous phenotype to the producing cell. Consequently, the analysis of microbial metabolite production is still dominated by bulk techniques, which may obscure significant variation at the single-cell level. In this study, we have applied the recently developed Lrp-biosensor for monitoring of amino acid production in single cells of gradually engineered L-valine producing Corynebacterium glutamicum strains based on the pyruvate dehydrogenase complex-deficient (PDHC) strain C. glutamicum ΔaceE. Online monitoring of the sensor output (eYFP fluorescence) during batch cultivation proved the sensor's suitability for visualizing different production levels. In the following, we conducted live cell imaging studies on C. glutamicum sensor strains using microfluidic chip devices. As expected, the sensor output was higher in microcolonies of high-yield producers in comparison to the basic strain C. glutamicum ΔaceE. Microfluidic cultivation in minimal medium revealed a typical Gaussian distribution of single cell fluorescence during the production phase. Remarkably, low amounts of complex nutrients completely changed the observed phenotypic pattern of all strains, resulting in a phenotypic split of the population. Whereas some cells stopped growing and initiated L-valine production, others continued to grow or showed a delayed transition to production. Depending on the cultivation conditions, a considerable fraction of non-fluorescent cells was observed, suggesting a loss of metabolic activity. These studies demonstrate that genetically encoded biosensors are a valuable tool for monitoring single cell productivity and to study the phenotypic pattern of microbial production strains.