Christian Held

Review of free software tools for image analysis of fluorescence cell micrographs.

An increasing number of free software tools have been made available for the evaluation of fluorescence cell micrographs. The main users are biologists and related life scientists with no or little knowledge of image processing. In this review, we give an overview of available tools and guidelines about which tools the users should use to segment fluorescence micrographs. We selected 15 free tools and divided them into stand‐alone, Matlab‐based, ImageJ‐based, free demo versions of commercial tools and data sharing tools. The review consists of two parts: First, we developed a criteria catalogue and rated the tools regarding structural requirements, functionality (flexibility, segmentation and image processing filters) and usability (documentation, data management, usability and visualization). Second, we performed an image processing case study with four representative fluorescence micrograph segmentation tasks with figure‐ground and cell separation. The tools display a wide range of functionality and usability. In the image processing case study, we were able to perform figure‐ground separation in all micrographs using mainly thresholding. Cell separation was not possible with most of the tools, because cell separation methods are provided only by a subset of the tools and are difficult to parametrize and to use. Most important is that the usability matches the functionality of a tool. To be usable, specialized tools with less functionality need to fulfill less usability criteria, whereas multipurpose tools need a well‐structured menu and intuitive graphical user interface.

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.

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.

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.

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.

Semiautomatic segmentation for the computer aided diagnosis of clustered microcalcifications

Screening mammography is recognized as the most effective tool for early breast cancer detection. However, its application in clinical practice shows some of its weaknesses. While clustered microcalcifications are often an early sign of breast cancer, the discrimination of benign from malignant clusters based on their appearance in mammograms is a very difficult task. Hence, it is not surprising that typically only 15% to 30% of breast biopsies performed on calcifications will be positive for malignancy. As this low positive predictive value of mammography regarding the diagnosis of calcification clusters results in many unnecessary biopsies performed on benign calcifications, we propose a novel computer aided diagnosis (CADx) approach with the goal to improve the reliability of microcalcification classification. As effective automatic classification of microcalcification clusters relies on good segmentations of the individual calcification particles, many approaches to the automatic segmentation of individual particles have been proposed in the past. Because none of the fully automatic approaches seem to result in optimal segmentations, we propose a novel semiautomatic approach that has automatic components but also allows some interaction of the radiologist. Based on the resulting segmentations we extract a broad range of features that characterize the morphology and distribution of calcification particles. Using regions of interest containing either benign or malignant clusters extracted from the digital database for screening mammography we evaluate the performance of our approach using a support vector machine and ROC analysis. The resulting ROC performance is very promising and we show that the performance of our semiautomatic segmentation is significantly higher than that of a comparable fully automatic approach.

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.

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 Boykovs 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.

Cell Simulation for Validation of Cell Micrograph Evaluation Algorithms.

The evaluation of biological microscopy experiments is a large subfield in image processing. Many methods have been published, which are specially designed for a certain microscopy technique or even for a single experiment. Usually they are evaluated with user annotated data which is prone to errors and, thus, shows interand intraobserver variance. We present a simulation software capable to generate micrographs with known ground truth of various microspcopy techniques for an unbiased and objec-