In biotechnology, cell growth is one of the most important properties for the characterization and optimization of microbial cultures. Novel live-cell imaging methods are leading to an ever better ...understanding of cell cultures and their development. The key to analyzing acquired data is accurate and automated cell segmentation at the single-cell level. Therefore, we present microbeSEG, a user-friendly Python-based cell segmentation tool with a graphical user interface and OMERO data management. microbeSEG utilizes a state-of-the-art deep learning-based segmentation method and can be used for instance segmentation of a wide range of cell morphologies and imaging techniques, e.g., phase contrast or fluorescence microscopy. The main focus of microbeSEG is a comprehensible, easy, efficient, and complete workflow from the creation of training data to the final application of the trained segmentation model. We demonstrate that accurate cell segmentation results can be obtained within 45 minutes of user time. Utilizing public segmentation datasets or pre-labeling further accelerates the microbeSEG workflow. This opens the door for accurate and efficient data analysis of microbial cultures.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
To exert regulatory function, miRNAs guide Argonaute (AGO) proteins to partially complementary sites on target RNAs. Crosslinking and immunoprecipitation (CLIP) assays are state-of-the-art to map AGO ...binding sites, but assigning the targeting miRNA to these sites relies on bioinformatics predictions and is therefore indirect. To directly and unambiguously identify miRNA:target site interactions, we modified our CLIP methodology in C. elegans to experimentally ligate miRNAs to their target sites. Unexpectedly, ligation reactions also occurred in the absence of the exogenous ligase. Our in vivo data set and reanalysis of published mammalian AGO-CLIP data for miRNA-chimeras yielded ∼17,000 miRNA:target site interactions. Analysis of interactions and extensive experimental validation of chimera-discovered targets of viral miRNAs suggest that our strategy identifies canonical, noncanonical, and nonconserved miRNA:targets. About 80% of miRNA interactions have perfect or partial seed complementarity. In summary, analysis of miRNA:target chimeras enables the systematic, context-specific, in vivo discovery of miRNA binding.
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•Comprehensive AGO binding map and thousands of miRNA:target interactions in C. elegans•AGO-CLIP samples contain miRNA:target chimeras generated by an endogenous ligase•From human and other systems, 17,000 miRNA interactions are largely functional•Seed sites are present in 80% of interactions; roughly half of them are imperfect
miRNAs regulate protein production by binding mRNAs. A key problem is to identify which miRNA binds to which target site. By biochemically ligating miRNAs to targets and sequencing, Grosswendt et al. identified thousands of miRNA:targets in C. elegans. Unexpectedly, ligations also occur in standard miRNA target assays. By reanalyzing published data, >17,000 miRNA:targets were detected across C. elegans, mouse, and human. About 80% of targets are explained by perfect or one-mismatch hexamer seeds.
Due to the high complexity of biological data it is difficult to disentangle cellular processes relying only on intuitive interpretation of measurements. A Systems Biology approach that combines ...quantitative experimental data with dynamic mathematical modeling promises to yield deeper insights into these processes. Nevertheless, with growing complexity and increasing amount of quantitative experimental data, building realistic and reliable mathematical models can become a challenging task: the quality of experimental data has to be assessed objectively, unknown model parameters need to be estimated from the experimental data, and numerical calculations need to be precise and efficient. Here, we discuss, compare and characterize the performance of computational methods throughout the process of quantitative dynamic modeling using two previously established examples, for which quantitative, dose- and time-resolved experimental data are available. In particular, we present an approach that allows to determine the quality of experimental data in an efficient, objective and automated manner. Using this approach data generated by different measurement techniques and even in single replicates can be reliably used for mathematical modeling. For the estimation of unknown model parameters, the performance of different optimization algorithms was compared systematically. Our results show that deterministic derivative-based optimization employing the sensitivity equations in combination with a multi-start strategy based on latin hypercube sampling outperforms the other methods by orders of magnitude in accuracy and speed. Finally, we investigated transformations that yield a more efficient parameterization of the model and therefore lead to a further enhancement in optimization performance. We provide a freely available open source software package that implements the algorithms and examples compared here.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Deep learning models achieve high-quality results in image processing. However, to robustly optimize parameters of deep neural networks, large annotated datasets are needed. Image annotation is often ...performed manually by experts without a comprehensive tool for assistance which is time- consuming, burdensome, and not intuitive. Using the here presented modular Karlsruhe Image Data Annotation (KaIDA) tool, for the first time assisted annotation in various image processing tasks is possible to support users during this process. It aims to simplify annotation, increase user efficiency, enhance annotation quality, and provide additional useful annotation-related functionalities. KaIDA is available open-source at
Supervised deep learning approaches for automated diagnosis support require datasets annotated by experts. Intra-annotator variability of a single annotator and inter-annotator variability between ...annotators can affect the quality of the diagnosis support. As medical experts will always differ in annotation details, quantitative studies concerning the annotation quality are of particular interest. A consistent and noise-free annotation of large-scale datasets by, for example, dermatologists or pathologists is a current challenge. Hence, methods are needed to automatically inspect annotations in datasets. In this paper, we categorize annotation noise in image segmentation tasks, present methods to simulate annotation noise, and examine the impact on the segmentation quality. Two novel automated methods to identify intra-annotator and inter-annotator inconsistencies based on uncertainty-aware deep neural networks are proposed. We demonstrate the benefits of our automated inspection methods such as focused re-inspection of noisy annotations or the detection of generally different annotation styles using the biomedical ISIC 2017 Melanoma image segmentation dataset.
Cancer is a devastating disease and the second leading cause of death worldwide. However, the development of resistance to current therapies is making cancer treatment more difficult. Combining the ...multi-omics data of individual tumors with information on their in-vitro Drug Sensitivity and Resistance Test (DSRT) can help to determine the appropriate therapy for each patient. Miniaturized high-throughput technologies, such as the droplet microarray, enable personalized oncology. We are developing a platform that incorporates DSRT profiling workflows from minute amounts of cellular material and reagents. Experimental results often rely on image-based readout techniques, where images are often constructed in grid-like structures with heterogeneous image processing targets. However, manual image analysis is time-consuming, not reproducible, and impossible for high-throughput experiments due to the amount of data generated. Therefore, automated image processing solutions are an essential component of a screening platform for personalized oncology. We present our comprehensive concept that considers assisted image annotation, algorithms for image processing of grid-like high-throughput experiments, and enhanced learning processes. In addition, the concept includes the deployment of processing pipelines. Details of the computation and implementation are presented. In particular, we outline solutions for linking automated image processing for personalized oncology with high-performance computing. Finally, we demonstrate the advantages of our proposal, using image data from heterogeneous practical experiments and challenges.
RNA abundance is tightly regulated in eukaryotic cells by modulating the kinetic rates of RNA production, processing, and degradation. To date, little is known about time-dependent kinetic rates ...during dynamic processes. Here, we present SLAM-Drop-seq, a method that combines RNA metabolic labeling and alkylation of modified nucleotides in methanol-fixed cells with droplet-based sequencing to detect newly synthesized and preexisting mRNAs in single cells. As a first application, we sequenced 7280 HEK293 cells and calculated gene-specific kinetic rates during the cell cycle using the novel package Eskrate. Of the 377 robust-cycling genes that we identified, only a minor fraction is regulated solely by either dynamic transcription or degradation (6 and 4%, respectively). By contrast, the vast majority (89%) exhibit dynamically regulated transcription and degradation rates during the cell cycle. Our study thus shows that temporally regulated mRNA degradation is fundamental for the correct expression of a majority of cycling genes. SLAM-Drop-seq, combined with Eskrate, is a powerful approach to understanding the underlying mRNA kinetics of single-cell gene expression dynamics in continuous biological processes.
Synopsis
A novel approach (SLAM-Drop-seq) is coupled to a robust computational analysis method (Eskrate) to discriminate new and preexisting transcripts in single cells and to calculate their time-dependent kinetic rates of synthesis and decay during the cell cycle.
SLAM-Drop-seq identifies newly transcribed and preexisting mRNAs in single cells via 4sU incubation, efficient iodoacetamide alkylation, and droplet-based single-cell RNAseq.
Eskrate is a newly developed R-package that estimates temporally resolved RNA kinetic rates from 4sU-labeled single cell data.
During the HEK293 cell cycle, oscillating mRNA levels of hundreds of cell cycle genes are regulated by time-dependent dynamic transcription, dynamic degradation, or both.
Signaling through the AKT and ERK pathways controls cell proliferation. However, the integrated regulation of this multistep process, involving signal processing, cell growth and cell cycle ...progression, is poorly understood. Here, we study different hematopoietic cell types, in which AKT and ERK signaling is triggered by erythropoietin (Epo). Although these cell types share the molecular network topology for pro‐proliferative Epo signaling, they exhibit distinct proliferative responses. Iterating quantitative experiments and mathematical modeling, we identify two molecular sources for cell type‐specific proliferation. First, cell type‐specific protein abundance patterns cause differential signal flow along the AKT and ERK pathways. Second, downstream regulators of both pathways have differential effects on proliferation, suggesting that protein synthesis is rate‐limiting for faster cycling cells while slower cell cycles are controlled at the G1‐S progression. The integrated mathematical model of Epo‐driven proliferation explains cell type‐specific effects of targeted AKT and ERK inhibitors and faithfully predicts, based on the protein abundance, anti‐proliferative effects of inhibitors in primary human erythroid progenitor cells. Our findings suggest that the effectiveness of targeted cancer therapy might become predictable from protein abundance.
Synopsis
Mathematical modeling and quantitative experiments identify the abundance of components of the AKT and ERK signaling pathways as the key determinant of the cell type‐specific regulation of Epo‐induced proliferation.
Cell type‐specific dynamics of Epo‐induced signal activation are determined by the different abundance of key signaling proteins.
A dynamic pathway model adapted to experimentally measured, cell type‐specific protein abundance can faithfully predict the Epo‐induced dynamics of AKT, ERK, and S6 activation.
Based on snapshot measurements of protein abundance, the mathematical model predicts the cell type‐specific impact of AKT and MEK inhibitors on Epo‐induced proliferation in human cells.
Mathematical modeling and quantitative experiments identify the abundance of components of the AKT and ERK signaling pathways as the key determinant of the cell type‐specific regulation of Epo‐induced proliferation.
Cell surface receptors convert extracellular cues into receptor activation, thereby triggering intracellular signaling networks and controlling cellular decisions. A major unresolved issue is the ...identification of receptor properties that critically determine processing of ligand-encoded information. We show by mathematical modeling of quantitative data and experimental validation that rapid ligand depletion and replenishment of the cell surface receptor are characteristic features of the erythropoietin (Epo) receptor (EpoR). The amount of Epo-EpoR complexes and EpoR activation integrated over time corresponds linearly to ligand input; this process is carried out over a broad range of ligand concentrations. This relation depends solely on EpoR turnover independent of ligand binding, which suggests an essential role of large intracellular receptor pools. These receptor properties enable the system to cope with basal and acute demand in the hematopoietic system.
Tightly interlinked feedback regulators control the dynamics of intracellular responses elicited by the activation of signal transduction pathways. Interferon alpha (IFNα) orchestrates antiviral ...responses in hepatocytes, yet mechanisms that define pathway sensitization in response to prestimulation with different IFNα doses remained unresolved. We establish, based on quantitative measurements obtained for the hepatoma cell line Huh7.5, an ordinary differential equation model for IFNα signal transduction that comprises the feedback regulators STAT1, STAT2, IRF9, USP18, SOCS1, SOCS3, and IRF2. The model‐based analysis shows that, mediated by the signaling proteins STAT2 and IRF9, prestimulation with a low IFNα dose hypersensitizes the pathway. In contrast, prestimulation with a high dose of IFNα leads to a dose‐dependent desensitization, mediated by the negative regulators USP18 and SOCS1 that act at the receptor. The analysis of basal protein abundance in primary human hepatocytes reveals high heterogeneity in patient‐specific amounts of STAT1, STAT2, IRF9, and USP18. The mathematical modeling approach shows that the basal amount of USP18 determines patient‐specific pathway desensitization, while the abundance of STAT2 predicts the patient‐specific IFNα signal response.
Synopsis
Mathematical modeling based on quantitative data reveals the molecular mechanisms that cause hypersensitization of interferon alpha (IFNα) signaling by pre‐exposure with a low dose of IFNα and desensitization with a high dose of IFNα.
IFNα induces the formation of the pSTAT1:pSTAT1, pSTAT1:pSTAT2 and pSTAT1:pSTAT2:IRF9 (ISGF3) transcription factor complexes in a temporal and dose‐dependent order.
IRF9 is induced with low doses of IFNα and causes pathway hypersensitization by favoring the ISGF3 transcription factor complex.
Both USP18 and SOCS1 foster desensitization of the IFNα signal transduction pathway.
The patient‐specific abundance of USP18 defines the desensitization threshold, while the abundance of STAT2 is a predictor for the antiviral response of an individual patient.
Mathematical modeling based on quantitative data reveals the molecular mechanisms that cause hypersensitization of interferon alpha (IFNα) signaling by pre‐exposure with a low dose of IFNα and desensitization with a high dose of IFNα.
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