Using multi-channel level sets to measure the cytoplasmic localization of HCMV pUL97 in GFP-B-gal fusion constructs.

Human cytomegalovirus UL97-encoded protein kinase (pUL97) phosphorylates cellular and viral proteins and is critical for viral replication. To quantify the efficiency of nuclear translocation and to elucidate the role of putative nuclear localization signal (NLS) elements, immunofluorescence analysis of different pUL97 expression constructs was performed. Since manual quantitation of respective expression levels lacks objectivity and reproducibility, and is time-consuming as well, a computer-based model is established. This model enables objective quantitation of the degree of cytoplasmic localization λ. To determine the degree of cytoplasmic localization of different pUL97-GFP-ß-gal fusion proteins automatically, a multi-channel segmentation of the nucleus and cytoplasm of transfected HeLa cells is performed in DAPI and GFP micrographs. A watershed transform-based segmentation scheme is used for the segmentation of the cell nuclei. Subsequently, the cytoplasm is segmented using a fast marching level set method. Based on the segmentation of cell nuclei and cytoplasm, λ can be determined for each HeLa cell by quantitation of the ratio of average signal intensity outside and inside the nucleus. The degree of cytoplasmic localization of an individual construct is then determined by evaluating the average and standard deviation of λ for the corresponding HeLa cells. Evaluation demonstrates that nuclear transport of pUL97 is a multilayered mechanism resulting in different efficiencies of nuclear translocation between a small and a large isoform and objective quantitation of the cytoplasmic localization is possible with a high accuracy (96.7% and 94.3%).

Approaches to automatic parameter fitting in a microscopy image segmentation pipeline: An exploratory parameter space analysis.

INTRODUCTION: Research and diagnosis in medicine and biology often require the assessment of a large amount of microscopy image data. Although on the one hand, digital pathology and new bioimaging technologies find their way into clinical practice and pharmaceutical research, some general methodological issues in automated image analysis are still open. METHODS: In this study, we address the problem of fitting the parameters in a microscopy image segmentation pipeline. We propose to fit the parameters of the pipeline's modules with optimization algorithms, such as, genetic algorithms or coordinate descents, and show how visual exploration of the parameter space can help to identify sub-optimal parameter settings that need to be avoided. RESULTS: This is of significant help in the design of our automatic parameter fitting framework, which enables us to tune the pipeline for large sets of micrographs. CONCLUSION: The underlying parameter spaces pose a challenge for manual as well as automated parameter optimization, as the parameter spaces can show several local performance maxima. Hence, optimization strategies that are not able to jump out of local performance maxima, like the hill climbing algorithm, often result in a local maximum.

Enhancing automated micrograph-based evaluation of LPS-stimulated macrophage spreading.

To evaluate macrophage spreading in immunofluorescence images of macrophages for surface protein CD11b and nuclear counterstaining with DAPI, it is necessary to measure the size of the macrophages at different time points after stimulation. Manual evaluation of fluorescent micrographs is usually a time-consuming and error-prone task, with poor reproducibility. Automatic image analysis methods can be used to improve the results. The quality of the analysis with these methods mainly depends on the quality of the image segmentation. A segmentation and quantification scheme based on shading correction, k-means clustering, and fast marching level sets has been developed for the purpose. An initial application of this approach showed that separating touching and overlapping cells in particular suffers severely in the inevitably blurred conditions, leading to partly erroneous measurements of macrophage spreading. An alternative method of segmentation in fluorescent micrographs was therefore investigated and evaluated in this study. The proposed approach uses a methodology that separates foreground objects from background objects on the basis of Boykov's graph cuts. In this process, a rough estimation of background pixels is used for background seeds. To identify foreground seeds, a difference of Gaussian band pass filter based workflow is developed. Information on foreground and background seeds is then used for a gradient magnitude based graph cut resulting in a robust figure-ground separation method. In addition, a fast marching level set approach is used in the post-processing step, which makes it possible to split touching cells by incorporating information about the cell nuclei. An evaluation based on a total of 553 manually labeled macrophages depicted in 21 micrographs showed that the proposed method significantly improves segmentation and splitting performance for fluorescent micrographs of LPS-stimulated macrophages and reduces the rate of error in automated analysis of macrophage spreading in comparison with alternative methods.

A bottom-up approach to estimating cost elements of REDD+ pilot projects in Tanzania.

BACKGROUND: Several previous global REDD+ cost studies have been conducted, demonstrating that payments for maintaining forest carbon stocks have significant potential to be a cost-effective mechanism for climate change mitigation. These studies have mostly followed highly aggregated top-down approaches without estimating the full range of REDD+ costs elements, thus underestimating the actual costs of REDD+. Based on three REDD+ pilot projects in Tanzania, representing an area of 327,825 ha, this study explicitly adopts a bottom-up approach to data assessment. By estimating opportunity, implementation, transaction and institutional costs of REDD+ we develop a practical and replicable methodological framework to consistently assess REDD+ cost elements. RESULTS: Based on historical land use change patterns, current region-specific economic conditions and carbon stocks, project-specific opportunity costs ranged between US$ -7.8 and 28.8 tCOxxxx for deforestation and forest degradation drivers such as agriculture, fuel wood production, unsustainable timber extraction and pasture expansion. The mean opportunity costs for the three projects ranged between US$ 10.1 - 12.5 tCO2. Implementation costs comprised between 89% and 95% of total project costs (excluding opportunity costs) ranging between US$ 4.5 - 12.2 tCO2 for a period of 30 years. Transaction costs for measurement, reporting, verification (MRV), and other carbon market related compliance costs comprised a minor share, between US$ 0.21 - 1.46 tCO2. Similarly, the institutional costs comprised around 1% of total REDD+ costs in a range of US$ 0.06 - 0.11 tCO2. CONCLUSIONS: The use of bottom-up approaches to estimate REDD+ economics by considering regional variations in economic conditions and carbon stocks has been shown to be an appropriate approach to provide policy and decision-makers robust economic information on REDD+. The assessment of opportunity costs is a crucial first step to provide information on the economic baseline situation of deforestation and forest degradation agents and on the economic incentives required to halt unsustainable land use. Since performance based REDD+ carbon payments decrease over time (as deforestation rates drop and for each saved ha of forest payments occur once), investments in REDD+ implementation have a crucial role in triggering sustainable land use systems by investing in the underlying assets and the generation of sustainable revenue streams to compensate for opportunity costs of land use change. With a potential increase in the land value due to effective REDD+ investments, expenditures in an enabling institutional environment for REDD+ policies are crucial to avoid higher deforestation pressure on natural forests.

Nuclear import of isoforms of the cytomegalovirus kinase pUL97 is mediated by differential activity of NLS1 and NLS2 both acting through classical importin-α binding.

The multifunctional protein kinase pUL97 of human cytomegalovirus (HCMV) strongly determines the efficiency of virus replication. Previously, the existence of two pUL97 isoforms that arise from alternative translational initiation and show a predominant nuclear localization was described. Two bipartite nuclear localization sequences, NLS1 and NLS2, were identified in the N terminus of the large isoform, whilst the small isoform exclusively contained NLS2. The current study found the following: (i) pUL97 nuclear localization in HCMV-infected primary fibroblasts showed accumulations in virus replication centres and other nuclear sections; (ii) in a quantitative evaluation system for NLS activity, the large isoform showed higher efficiency of nuclear translocation than the small isoform; (iii) NLS1 was mapped to aa 6-35 and NLS2 to aa 190-213; (iv) using surface plasmon resonance spectroscopy, the binding of both NLS1 and NLS2 to human importin-α was demonstrated, stressing the importance of individual arginine residues in the bipartite consensus motifs; (v) nuclear magnetic resonance spectroscopy of pUL97 peptides confirmed an earlier statement about the functional requirement of NLS1 embedding into an intact α-helical structure; and (vi) a bioinformatics investigation of the solvent-accessible surface suggested a high accessibility of NLS1 and an isoform-specific, variable accessibility of NLS2 for interaction with importin-α. Thus, the nucleocytoplasmic transport mechanism of the isoforms appeared to be differentially regulated, and this may have consequences for isoform-dependent functions of pUL97 during virus replication.

Quantitative multichannel NC-AFM data analysis of graphene growth on SiC(0001).

Noncontact atomic force microscopy provides access to several complementary signals, such as topography, damping, and contact potential. The traditional presentation of such data sets in adjacent figures or in colour-coded pseudo-three-dimensional plots gives only a qualitative impression. We introduce two-dimensional histograms for the representation of multichannel NC-AFM data sets in a quantitative fashion. Presentation and analysis are exemplified for topography and contact-potential data for graphene grown epitaxially on 6H-SiC(0001), as recorded by Kelvin probe force microscopy in ultrahigh vacuum. Sample preparations by thermal decomposition in ultrahigh vacuum and in an argon atmosphere are compared and the respective growth mechanisms discussed.

Measurement of TLR-Induced Macrophage Spreading by Automated Image Analysis: Differential Role of Myd88 and MAPK in Early and Late Responses.

Sensing of infectious danger by toll-like receptors (TLRs) on macrophages causes not only a reprogramming of the transcriptome but also changes in the cytoskeleton important for cell spreading and motility. Since manual determination of cell contact areas from fluorescence micrographs is very time-consuming and prone to bias, we have developed and tested algorithms for automated measurement of macrophage spreading. The two-step method combines identification of cells by nuclear staining with DAPI and cell surface staining of the integrin CD11b. Automated image analysis correlated very well with manual annotation in resting macrophages and early after stimulation, whereas at later time points the automated cell segmentation algorithm and manual annotation showed slightly larger variation. The method was applied to investigate the impact of genetic or pharmacological inhibition of known TLR signaling components. Deficiency in the adapter protein Myd88 strongly reduced spreading activity at the late time points, but had no impact early after LPS-stimulation. A similar effect was observed upon pharmacological inhibition of MEK1, the kinase activating the mitogen-activated protein kinases (MAPK) ERK1/2, indicating that ERK1/2 mediates Myd88-dependent macrophages spreading. In contrast, macrophages lacking the MAPK p38 were impaired in the initial spreading response but responded normally 8-24 h after stimulation. The dichotomy of p38 and ERK1/2 MAPK effects on early and late macrophage spreading raises the question which of the respective substrate proteins mediate(s) cytoskeletal remodeling and spreading. The automated measurement of cell spreading described here increases the objectivity and greatly reduces the time required for such investigations and is therefore expected to facilitate larger throughput analysis of macrophage spreading, e.g., in siRNA knockdown screens.

Comparison of parameter-adapted segmentation methods for fluorescence micrographs.

Interpreting images from fluorescence microscopy is often a time-consuming task with poor reproducibility. Various image processing routines that can help investigators evaluate the images are therefore useful. The critical aspect for a reliable automatic image analysis system is a robust segmentation algorithm that can perform accurate segmentation for different cell types. In this study, several image segmentation methods were therefore compared and evaluated in order to identify the most appropriate segmentation schemes that are usable with little new parameterization and robustly with different types of fluorescence-stained cells for various biological and biomedical tasks. The study investigated, compared, and enhanced four different methods for segmentation of cultured epithelial cells. The maximum-intensity linking (MIL) method, an improved MIL, a watershed method, and an improved watershed method based on morphological reconstruction were used. Three manually annotated datasets consisting of 261, 817, and 1,333 HeLa or L929 cells were used to compare the different algorithms. The comparisons and evaluations showed that the segmentation performance of methods based on the watershed transform was significantly superior to the performance of the MIL method. The results also indicate that using morphological opening by reconstruction can improve the segmentation of cells stained with a marker that exhibits the dotted surface of cells.

Two isoforms of the protein kinase pUL97 of human cytomegalovirus are differentially regulated in their nuclear translocation.

The pUL97 protein kinase encoded by human cytomegalovirus is a multifunctional determinant of the efficiency of viral replication and phosphorylates viral as well as cellular substrate proteins. Here, we report that pUL97 is expressed in two isoforms with molecular masses of approximately 90 and 100 kDa. ORF UL97 comprises an unusual coding strategy in that five in-frame ATG start codons are contained within the N-terminal 157 aa. Site-directed mutagenesis, transient expression of point and deletion mutants and proteomic analyses accumulated evidence that the formation of the large and small isoforms result from alternative initiation of translation, with the start points being at amino acids 1 and 74, respectively. In vitro kinase assays demonstrated that catalytic activity, in terms of autophosphorylation and histone substrate phosphorylation, was indistinguishable for the two isoforms. An analysis of the intracellular distribution of pUL97 by confocal laser-scanning microscopy demonstrated that both isoforms have a pronounced nuclear localization. Surprisingly, mapping experiments performed to identify the nuclear localization signal (NLS) of pUL97 strongly suggest that the mechanism of nuclear transport is distinct for the two isoforms. While the extreme N terminus (large isoform) comprises a highly efficient, bipartite NLS (amino acids 6-35), a second sequence apparently conferring a less efficient mode of nuclear translocation was identified downstream of amino acid 74 (small and large isoforms). Taken together, the findings argue for a complex mechanism of nuclear translocation for pUL97 which might be linked with fine-regulatory differences between the two isoforms.

Using multimodal information for the segmentation of fluorescent micrographs with application to virology and microbiology.

In order to improve reproducibility and objectivity of fluorescence microscopy based experiments and to enable the evaluation of large datasets, flexible segmentation methods are required which are able to adapt to different stainings and cell types. This adaption is usually achieved by the manual adjustment of the segmentation methods parameters, which is time consuming and challenging for biologists with no knowledge on image processing. To avoid this, parameters of the presented methods automatically adapt to user generated ground truth to determine the best method and the optimal parameter setup. These settings can then be used for segmentation of the remaining images. As robust segmentation methods form the core of such a system, the currently used watershed transform based segmentation routine is replaced by a fast marching level set based segmentation routine which incorporates knowledge on the cell nuclei. Our evaluations reveal that incorporation of multimodal information improves segmentation quality for the presented fluorescent datasets.

Contour tracing for segmentation of mammographic masses.

CADx systems have the potential to support radiologists in the difficult task of discriminating benign and malignant mammographic lesions. The segmentation of mammographic masses from the background tissue is an important module of CADx systems designed for the characterization of mass lesions. In this work, a novel approach to this task is presented. The segmentation is performed by automatically tracing the mass' contour in-between manually provided landmark points defined on the mass' margin. The performance of the proposed approach is compared to the performance of implementations of three state-of-the-art approaches based on region growing and dynamic programming. For an unbiased comparison of the different segmentation approaches, optimal parameters are selected for each approach by means of tenfold cross-validation and a genetic algorithm. Furthermore, segmentation performance is evaluated on a dataset of ROI and ground-truth pairs. The proposed method outperforms the three state-of-the-art methods. The benchmark dataset will be made available with publication of this paper and will be the first publicly available benchmark dataset for mass segmentation.