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Photosynthetic microalgae are responsible for an important fraction of CO2fixation and O2production on Earth. Three-dimensional ultrastructural characterization of these organisms in their natural environment can contribute to a deeper understanding of their cell biology. However, the low throughput of volume electron microscopy (vEM) methods, along with the complexity and heterogeneity of environmental samples, pose great technical challenges. In the present study, we used a workflow based on a specific EM sample preparation, compatible with both light and vEM imaging in order to target one cell among a complex natural community. This method revealed the 3D subcellular landscape of a photosynthetic dinoflagellate with quantitative characterization of multiple organelles. We could show that this cell contains a single convoluted chloroplast and the arrangement of the flagellar apparatus with its associated photosensitive elements. Moreover, we observed chromatin features that could shed light on how transcriptional activity takes place in organisms where chromosomes are permanently condensed. Together with providing insights in dinoflagellates biology, this proof-of-principle study illustrates an efficient tool for the targeted ultrastructural analysis of environmental microorganisms in heterogeneous mixes.

Hepatitis E virus (HEV) is a major public health problem with limited therapeutic options. Here, we engineered adeno-associated viral vectors of serotype 6 (AAV6) to express short hairpin RNAs (shRNAs) against HEV transcripts with the prospect of down-regulating HEV replication in vivo. We designed 20 different shRNAs, targeting the genome of the HEV genotype 3 (GT3) Kernow-C1 p6 strain, for delivery upon AAV6 transduction. Using an original selectable HEV GT3 reporter replicon, we identified three shRNAs that efficiently down-regulated HEV replication. We further confirmed their inhibitory potency with full-length HEV infection. Seventy-two hours following transduction, HEV replication in both systems decreased by up to 95%. The three most potent inhibitory shRNAs identified were directed against the methyltransferase domain, the junction region between the open reading frames (ORFs), and the 3´ end of ORF2. Targeting all three regions by multiplexing the shRNAs further enhanced their inhibitory potency over a prolonged period of up to 21 days following transduction. Conclusion: Combining RNA interference and AAV vector-based gene therapy has great potential for suppressing HEV replication. Our strategy to target the viral RNA with multiplexed shRNAs should help to counteract viral escape through mutations. Considering the widely documented safety of AAV vector-based gene therapies, our approach is, in principle, amenable to clinical translation.

To identify miRNAs that are involved in cell migration in human umbilical vein endothelial cells (HUVECs), we employed RNA sequencing under high glucose incubation and text mining within the databases miRWalk and TargetScanHuman using 83 genes that regulate HUVECs migration. From both databases, 307 predicted miRNAs were retrieved. Differentially expressed miRNAs were determined by exposing HUVECs to high glucose stimulation, which significantly inhibited the migratory ability of HUVECs as compared to cells cultured in normal glucose. A total of 35 miRNAs were found as differently expressed miRNAs in miRNA sequencing, and 4 miRNAs, namely miR-21-3p, miR-107, miR-143-3p, and miR-106b-5p, were identified as overlapping hits. These were subjected to hub gene analysis and pathway analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG), identifing 71 pathways which were influenced by all four miRNAs. The influence of all four miRNAs on HUVEC migration was phenomorphologically confirmed. miR21 and miR107 promoted migration in HUVECs while miR106b and miR143 inhibited migration. Pathway analysis also revealed eight shared pathways between the four miRNAs. Protein–protein interaction (PPI) network analysis was then performed to predict the functionality of interacting genes or proteins. This revealed six hub genes which could firstly be predicted to be related to HUVEC migration.

The current risk stratification in prostate cancer (PCa) is frequently insufficient to adequately predict disease development and outcome. One hallmark of cancer is telomere maintenance. For telomere maintenance, PCa cells exclusively employ telomerase, making it essential for this cancer entity. However, TERT, the catalytic protein component of the reverse transcriptase telomerase, itself does not suit as a prognostic marker for prostate cancer as it is rather low expressed. We investigated if, instead of TERT, transcription factors regulating TERT may suit as prognostic markers. To identify transcription factors regulating TERT, we developed and applied a new gene regulatory modeling strategy to a comprehensive transcriptome dataset of 445 primary PCa. Six transcription factors were predicted as TERT regulators, and most prominently, the developmental morphogenic factor PITX1. PITX1 expression positively correlated with telomere staining intensity in PCa tumor samples. Functional assays and chromatin immune-precipitation showed that PITX1 activates TERT expression in PCa cells. Clinically, we observed that PITX1 is an excellent prognostic marker, as concluded from an analysis of more than 15,000 PCa samples. PITX1 expression in tumor samples associated with (i) increased Ki67 expression indicating increased tumor growth, (ii) a worse prognosis, and (iii) correlated with telomere length.

The current risk stratification in prostate cancer (PCa) is frequently insufficient to adequately predict disease development and outcome. One hallmark of cancer is telomere maintenance. For telomere maintenance, PCa cells exclusively employ telomerase, making it essential for this cancer entity. However, TERT, the catalytic protein component of the reverse transcriptase telomerase, itself does not suit as a prognostic marker for prostate cancer as it is rather low expressed. We investigated if, instead of

In response to vascular injury vascular smooth muscle cells (VSMCs) alternate between a differentiated (contractile) and a dedifferentiated (synthetic) state or phenotype. Although parts of the signaling cascade regulating the phenotypic switch have been described, little is known on the role of miRNAs involved. To systematically address this issue, we have established a microscopy-based quantitative assay and identified 23 miRNAs that induced contractile phenotypes when over-expressed. These were then correlated to miRNAs identified from RNA-sequencing when comparing cells in the contractile and synthetic states. Using both approaches, six miRNAs (miR-132-3p, miR-138-5p, miR-141-3p, miR-145-5p, miR-150-5p, and miR-22-3p) were filtered as candidates that induce the phenotypic switch from synthetic to contractile. To identify potentially common regulatory mechanisms of these six miRNAs, their predicted targets were compared with five miRNAs sharing ZBTB20, ZNF704, and EIF4EBP2 as common potential targets and four miRNAs sharing 16 common potential targets. The interaction network consisting of these 19 targets and additional 18 hub targets were created to facilitate validation of miRNA-mRNA interactions by suggesting the most plausible pairs. Furthermore, the information on drug candidates was integrated into the network to predict novel combinatorial therapies that encompass the complexity of miRNAs-mediated regulation. This is the first study that combines phenotypic screening approach with RNA sequencing and bioinformatics to systematically identify miRNAs-mediated pathways and to identify potential drug candidates to positively influence the phenotypic switch of VSMCs.

In response to vascular injury vascular smooth muscle cells (VSMCs) alternate between a differentiated(contractile) and a dedifferentiated (synthetic) state or phenotype. Although parts of the signalingcascade regulating the phenotypic switch have been described, the role of miRNAs is still incompletelyunderstood. To systematically address this issue, we have established a microscopy‑basedquantitative assay and identified 23 miRNAs that induced contractile phenotypes when over‑expressed. These were then correlated to miRNAs identified from RNA‑sequencing when comparingcells in the contractile and synthetic states. Using both approaches, six miRNAs (miR‑132‑3p, miR‑138‑5p, miR‑141‑3p, miR‑145‑5p, miR‑150‑5p, and miR‑22‑3p) were filtered as candidates that inducethe phenotypic switch from synthetic to contractile. To identify potentially common regulatorymechanisms of these six miRNAs, their predicted targets were compared with five miRNAs sharingZBTB20, ZNF704, and EIF4EBP2 as common potential targets and four miRNAs sharing 16 commonpotential targets. The interaction network consisting of these 19 targets and additional 18 hub targetswere created to facilitate validation of miRNA‑mRNA interactions by suggesting the most plausiblepairs. Furthermore, the information on drug candidates was integrated into the network to predictnovel combinatorial therapies that encompass the complexity of miRNAs‑mediated regulation.This is the first study that combines a phenotypic screening approach with RNA sequencing andbioinformatics to systematically identify miRNA‑mediated pathway (2) (PDF) High-content analysis of microRNAs involved in the phenotype regulation of vascular smooth muscle cells. Available from: https://www.researchgate.net/publication/358990313_High-content_analysis_of_microRNAs_involved_in_the_phenotype_regulation_of_vascular_smooth_muscle_cells [accessed Nov 09 2022].

Automated analysis of microscopy image data typically requires complex pipelines that involve multiple methods for different image analysis tasks. To achieve best results of the analysis pipelines, method-dependent hyperparameters need to be optimized. However, complex pipelines often suffer from the fact that calculation of the gradient of the loss function is analytically or computationally infeasible. Therefore, first- or higher-order optimization methods cannot be applied. We developed a new framework for zero-order black-box hyperparameter optimization called HyperHyper, which has a modular architecture that separates hyperparameter sampling and optimization. We also developed a visualization of the loss function based on infimum projection to obtain further insights into the optimization problem. We applied HyperHyper in three different experiments with different imaging modalities, and evaluated in total more than 400.000 hyperparameter combinations. HyperHyper was used for optimizing two pipelines for cell nuclei segmentation in prostate tissue microscopy images and two pipelines for detection of hepatitis C virus proteins in live cell microscopy data. We evaluated the impact of separating the sampling and optimization strategy using different optimizers and employed an infimum projection for visualizing the hyperparameter space. The separation of sampling and optimization strategy of the proposed HyperHyper optimization framework improves the result of the investigated image analysis pipelines. Visualization of the loss function based on infimum projection enables gaining further insights on the optimization process.

In large scale biological experiments, like high-throughput or high-content cellular screening, the amount and the complexity of images to be analyzed are steadily increasing. To handle and process these images, well defined image processing and analysis steps need to be performed by applying dedicated workflows. Multiple software tools have emerged with the aim to facilitate creation of such workflows by integrating existing methods, tools, and routines, and by adapting them to different applications and questions, as well as making them reusable and interchangeable. In this review, we describe workflow systems for the integration of microscopy image analysis techniques with focus on KNIME and Galaxy.

Due to high associated costs and considerable time investments of cell-based screening, there is a strong demand for new technologies that enable preclinical development and tests of diverse biologicals in a cost-saving and time-efficient manner. For those reasons we developed the high-density cell array (HD-CA) platform, which miniaturizes cell-based screening in the form of preprinted and ready-to-run screening arrays. With the HD-CA technology, up to 24,576 samples can be tested in a single experiment, thereby saving costs and time for microscopy-based screening by 75%. Experiments on the scale of the entire human genome can be addressed in a real parallel manner, with screening campaigns becoming more comfortable and devoid of robotics infrastructure on the user side. The high degree of miniaturization enables working with expensive reagents and rare and difficult-to-obtain cell lines. We have also optimized an automated imaging procedure for HD-CA and demonstrate the applicability of HD-CA to CRISPR-Cas9- and RNAi-mediated phenotypic assessment of the gene function.

Segmentation of cell nuclei is essential for analyzing highcontent histological screens. Often, parameters of automatic approaches need to be optimized, which is tedious and difficult to perform manually. We propose a novel hyperparameter optimization framework, which formulates optimization as a combination of candidate sampling and an optimization strategy. We present a clustering based and a deep neural network based pipeline for nuclei segmentation, for which the parameters are optimized using state of the art optimizers as well as a novel optimizer. The pipelines were applied to challenging prostate cancer tissue images. We performed a quantitative evaluation using 28,388 parameter settings. It turned out that the deep neural network outperforms the clustering based pipeline, while the results for different optimizers vary slightly.

Cell segmentation in microscopy images is a common and challenging task. In recent years, deep neural networks achieved remarkable improvements in the field of computer vision. The dominant paradigm in segmentation is using convolutional neural networks, less common are recurrent neural networks. In this work, we propose a new deep learning method for cell segmentation, which integrates convolutional neural networks and gated recurrent neural networks over multiple image scales to exploit the strength of both types of networks. To increase the robustness of the training and improve segmentation, we introduce a novel focal loss function. We also present a distributed scheme for optimized training of the integrated neural network. We applied our proposed method to challenging data of glioblastoma cell nuclei and performed a quantitative comparison with state-of-the-art methods. Insights on how our extensions affect training and inference are also provided. Moreover, we benchmarked our method using a wide spectrum of all 22 real microscopy datasets of the Cell Tracking Challenge.

Over the last decade, the role of the assembly-activating protein (AAP) has begun to be dissected for the formation of adeno-associated virus (AAV) capsids based on different viral serotypes. Recently, the authors' group has specifically studied AAP's relevance during production of AAV gene therapy vectors in mammalian or insect cells, and AAP was found to be essential for capsid protein stabilization and generation of functional vector particles. Here, the lingering question is additionally addressed of whether molecular AAV evolution via DNA family shuffling of viral capsid genes would perturb AAP functionality due to concurrent and inadvertent recombination of the AAP open reading frame. To this end, a battery of complementary experiments was conducted in which: (1) the ability of chimeric AAP from AAVDJ, a hybrid of serotypes 2, 8, and 9, was tested to rescue AAP knockouts in the three parental serotypes; (2) the functionality of 60 chimeric AAPs extracted from five shuffled, unselected capsid libraries was measured; (3) whether production of different shuffled libraries, 10 wild-type serotypes or 25 individual chimeric capsids, can be enhanced by overexpression of AAP cocktails was assessed; and (4) the activity of 12 chimeric AAPs isolated from a shuffled library that was iteratively selected in vivo in mouse livers was studied. Collectively, the data demonstrate a remarkable tolerance of AAP for recombination via DNA family shuffling, evidenced by the findings that (1) all chimeric AAPs studied here retained at least partial activity, even in cases where the cognate hybrid capsid may be non-functional, and that (2) ectopic AAP overexpression did not enhance production of shuffled AAV chimeras or libraries, implying that the inherently encoded hybrid AAP variants are sufficiently active. Together, this work provides compelling evidence that AAP is not rate limiting during AAV capsid shuffling and thereby relieves a major concern in the field of AAV vector evolution.

Segmentation of cell nuclei is crucial for analyzing microscopy image data and understanding cellular processes. We performed a comparison of different approaches for segmentation of cell nuclei in difficult tissue microscopy image data of glioblastoma cells. We investigated nine different methods comprising thresholding, deformable models, region growing, unsupervised learning, and supervised learning using random forest. In addition, we studied two deep learning methods including a novel deep neural network based on atrous spatial pyramid pooling (ASPP), which was not yet used for cell segmentation. We used pixel-based and object-based performance metrics. The best results were obtained by machine learning methods, where deep learning models (Accuracy 91.7% and Dice 0.925) exceeded random forest classifiers (Accuracy 90.4 % and Dice 0.914). Using a deep neural network with an ASPP yielded a slightly lower segmentation performance, but was most effective in splitting merged objects.

Segmentation and quantification of cell nuclei is an important task in tissue microscopy image analysis. We introduce a deep learning method leveraging atrous spatial pyramid pooling for cell segmentation. We also present two different approaches for transfer learning using datasets with a different number of channels. A quantitative comparison with previous methods was performed on challenging glioblastoma cell tissue images. We found that our transfer learning method improves the segmentation result.

The discovery that adeno-associated virus 2 (AAV2) encodes an eighth protein, called assembly-activating protein (AAP), transformed our understanding of wild-type AAV biology. Concurrently, it raised questions about the role of AAP during production of recombinant vectors based on natural or molecularly engineered AAV capsids. Here, we show that AAP is indeed essential for generation of functional recombinant AAV2 vectors in both mammalian and insect cell-based vector production systems. Surprisingly, we observed that AAV2 capsid proteins VP1 to -3 are unstable in the absence of AAP2, likely due to rapid proteasomal degradation. Inhibition of the proteasome led to an increase of intracellular VP1 to -3 but neither triggered assembly of functional capsids nor promoted nuclear localization of the capsid proteins. Together, this underscores the crucial and unique role of AAP in the AAV life cycle, where it rapidly chaperones capsid assembly, thus preventing degradation of free capsid proteins. An expanded analysis comprising nine alternative AAV serotypes (1, 3 to 9, and rh10) showed that vector production always depends on the presence of AAP, with the exceptions of AAV4 and AAV5, which exhibited AAP-independent, albeit low-level, particle assembly. Interestingly, AAPs from all 10 serotypes could cross-complement AAP-depleted helper plasmids during vector production, despite there being distinct intracellular AAP localization patterns. These were most pronounced for AAP4 and AAP5, congruent with their inability to rescue an AAV2/AAP2 knockout. We conclude that AAP is key for assembly of genuine capsids from at least 10 different AAV serotypes, which has implications for vectors derived from wild-type or synthetic AAV capsids. IMPORTANCE Assembly of adeno-associated virus 2 (AAV2) is regulated by the assembly-activating protein (AAP), whose open reading frame overlaps with that of the viral capsid proteins. As the majority of evidence was obtained using virus-like particles composed solely of the major capsid protein VP3, AAP's role in and relevance for assembly of genuine AAV capsids have remained largely unclear. Thus, we established a trans -complementation assay permitting assessment of AAP functionality during production of recombinant vectors based on complete AAV capsids and derived from any serotype. We find that AAP is indeed a critical factor not only for AAV2, but also for generation of vectors derived from nine other AAV serotypes. Moreover, we identify a new role of AAP in maintaining capsid protein stability in mammalian and insect cells. Thereby, our study expands our current understanding of AAV/AAP biology, and it concomitantly provides insights into the importance of AAP for AAV vector production.

Single-molecule localization microscopy (SMLM) enables imaging of biological structures in the nanometre range. Long measurement times are the consequence of this kind of microscopy due to the need of acquiring thousands of images. We built a setup that automatically detects target structures using confocal microscopy and images them with SMLM. Utilizing the Konstanz Information Miner (KNIME), we were able to connect a confocal microscope with an SMLM unit for targeted screening. In this process, we developed KNIME plugins to communicate with the microscope components and combined them to a workflow. Thus, measuring biological nanometre-sized structures in a sufficient number to get statistical significance becomes feasible. For proof of principle HIV-1 assembly complexes in HeLa cells derived from transfection of replication deficient viral construct were imaged by a fully automated screen.

Applying the right acquisition method in a fluorescence imaging-based screening context is of great importance to obtain an appropriate readout and to select the right scale of the screen. In order to save imaging time and data, we have developed routines for multiscale targeted imaging, providing both a broad overview of a sample and additional in-depth information for targets of interest identified within the screen. These objects can be identified and acquired on-the-fly by an interconnection of image acquisition and image analysis.

In cancer, vulnerable breast epithelium malignance tendency correlates with number and activation of ErbB receptor tyrosine kinases. In the presented work, we observe ErbB receptors activated by irradiation-induced DNA injury or neuregulin- 1 β application, or alternatively, attenuated by a therapeutic antibody using high resolution fluorescence localization microscopy. The gap junction turnover coinciding with ErbB receptor activation and co-transport is simultaneously recorded. DNA injury caused by 4 Gray of 6 MeV photon γ -irradiation or alternatively neuregulin- 1 β application mobilized ErbB receptors in a nucleograde fashion—a process attenuated by trastuzumab antibody application. This was accompanied by increased receptor density, indicating packing into transport units. Factors mobilizing ErbB receptors also mobilized plasma membrane resident gap junction channels. The time course of ErbB receptor activation and gap junction mobilization recapitulates the time course of non-homologous end-joining DNA repair. We explain our findings under terms of DNA injury-induced membrane receptor tyrosine kinase activation and retrograde trafficking. In addition, we interpret the phenomenon of retrograde co-trafficking of gap junction connexons stimulated by ErbB receptor activation.

Fluorescence microscopy is an essential tool for imaging tagged biological structures. Due to the wave nature of light, the resolution of a conventional fluorescence microscope is limited laterally to about 200 nm and axially to about 600 nm, which is often referred to as the Abbe limit. This hampers the observation of important biological structures and dynamics in the nano-scaled range ~10 nm to ~100 nm. Consequentially, various methods have been developed circumventing this limit of resolution. Super-resolution microscopy comprises several of those methods employing physical and/or chemical properties, such as optical/instrumental modifications and specific labeling of samples. In this article, we will give a brief insight into a variety of selected optical microscopy methods reaching super-resolution beyond the Abbe limit. We will survey three different concepts in connection to biological applications in radiation research without making a claim to be complete.

Applying the right acquisition method in a fluorescence imaging-based screening context is of great importance to obtain an appropriate readout and to select the right scale of the screen. In order to save imaging time and data, we have developed routines for multiscale targeted imaging, providing both a broad overview of a sample and additional in-depth information for targets of interest identified within the screen. These objects can be identified and acquired on-the-fly by an interconnection of image acquisition and image analysis.

Scientific reports 7(1), 2265 (2017). doi:10.1038/s41598-017-02357-0, Published by Nature Publishing Group, London

Single-molecule localization microscopy (SMLM) enables imaging of biological structures in the nanometre range. Long measurement times are the consequence of this kind of microscopy due to the need of acquiring thousands of images. We built a setup that automatically detects target structures using confocal microscopy and images them with SMLM. Utilizing the Konstanz Information Miner (KNIME), we were able to connect a confocal microscope with an SMLM unit for targeted screening. In this process, we developed KNIME plugins to communicate with the microscope components and combined them to a workflow. Thus, measuring biological nanometre-sized structures in a sufficient number to get statistical significance becomes feasible. For proof of principle HIV-1 assembly complexes in HeLa cells derived from transfection of replication deficient viral construct were imaged by a fully automated screen.

Localization microscopy approaches allowing an optical resolution down to the single-molecule level in fluorescence-labeled biostructures have already found a variety of applications in cell biology, as well as in virology. Here, we focus on some perspectives of a special localization microscopy embodiment, spectral precision distance/position determination microscopy (SPDM). SPDM permits the use of conventional fluorophores or fluorescent proteins together with standard sample preparation conditions employing an aqueous buffered milieu and typically monochromatic excitation. This allowed superresolution imaging and studies on the aggregation state of modified tobacco mosaic virus particles on the nanoscale with a single-molecule localization accuracy of better than 8 nm, using standard fluorescent dyes in the visible spectrum. To gain a better understanding of cell entry mechanisms during influenza A virus infection, SPDM was used in conjunction with algorithms for distance and cluster analyses to study changes in the distribution of virus particles themselves or in the distribution of infection-related proteins, the hepatocyte growth factor receptors, in the cell membrane on the single-molecule level. Not requiring TIRF (total internal reflection) illumination, SPDM was also applied to study the molecular arrangement of gp36.5/m164 glycoprotein (essentially associated with murine cytomegalovirus infection) in the endoplasmic reticulum and the nuclear membrane inside cells with single-molecule resolution. On the basis of the experimental evidence so far obtained, we finally discuss additional application perspectives of localization microscopy approaches for the fast detection and identification of viruses by multi-color SPDM and combinatorial oligonucleotide fluorescence in situ hybridization, as well as SPDM techniques for optimization of virus-based nanotools and biodetection devices.

Novel approaches of localization microscopy have opened new insights into the molecular nano-cosmos of cells. We applied a special embodiment called spectral position determination microscopy (SPDM) that has the advantage to run with standard fluorescent dyes or proteins under standard preparation conditions. Pointillist images with a resolution in the order of 10 nm can be obtained by SPDM. Therefore, vector pEYFP-m164, encoding the murine cytomegalovirus glycoprotein gp36.5/m164 fused to enhanced yellow fluorescent protein, was transiently transfected into COS-7 cells. This protein shows exceptional intracellular trafficking dynamics, moving within the endoplasmic reticulum (ER) and outer nuclear membrane. The molecular positions of gp36.5/m164 were visualized and determined by SPDM imaging. From the position point patterns of the protein molecules, their arrangements were quantified by next neighbour distance analyses. Three different structural arrangements were discriminated: (a) a linear distribution along the membrane, (b) a highly structured distribution in the ER, and (c) a homogenous distribution in the cellular cytoplasm. The results indicate that the analysis of next neighbour distances on the nano-scale allows the identification and discrimination of different structural arrangements of molecules within their natural cellular environment.

The Golgi complex plays a central role in a number of diverse cellular processes, and numerous regulators that control these functions and/or morphology of the Golgi complex are known by now. Many of them were identified by large-scale experiments, such as RNAi-based screening. However, high-throughput experiments frequently provide only initial information that a particular protein might play a role in regulating structure and function of the Golgi complex. Multiple follow-up experiments are necessary to functionally characterize the selected hits. In order to speed up the discovery, we have established a system for correlative screening microscopy that combines rapid data collection and high-resolution imaging in one experiment. We describe here a combination of wide-field microscopy and dual-color direct stochastical optical reconstruction microscopy (dSTORM). We apply the technique to simultaneously capture and differentiate alterations of the cis- and trans-Golgi network when depleting several proteins in a singular and combinatorial manner.

We studied the nuclear topography of RNA transcription and DNA replication in mammalian cell types with super-resolution fluorescence microscopy, which offers a resolution beyond the classical Abbe/Raleigh limit. Three-dimensional structured illumination microscopy (3D-SIM) demonstrated a network of channels and wider lacunas, called the interchromatin compartment (IC). The IC starts at nuclear pores and expands throughout the nuclear space. It is demarcated from the compact interior of higher-order chromatin domains (CDs) by a 100-200-nm thick layer of decondensed chromatin, termed the perichromatin region (PR). Nascent DNA, nascent RNA, RNA polymerase II (RNA Pol II), as well as histone modifications for transcriptionally competent/active chromatin, are highly enriched in the PR, whereas splicing speckles are observed in the interior of the IC. In line with previous electron microscopic evidence, spectral precision distance/position determination microscopy (SPDM) confirmed the presence of RNA Pol II clusters indicative of transcription factories. Still, a substantial part of transcription apparently takes place outside of such factories. Previous electron microscopic evidence has suggested that the functional nuclear organization of DNA replication depends on brownian movements of chromatin between the CD interior and the PR. As an incentive for future studies, we hypothesize that such movements also take place during transcription, i.e., only the actually transcribed part of a gene may be located within the PR, whereas its major part, including previously or later transcribed sequences, is embedded in a higher-order chromatin configuration in the interior of the CD.

The microscopic analysis of telomere features provides a wealth of information on the mechanism by which tumor cells maintain their unlimited proliferative potential. Accordingly, the analysis of telomeres in tissue sections of patient tumor samples provides can be exploited to obtain diagnostic information and to define tumor subgroups. In many instances, however, analysis of the image data is conducted by manual inspection of 2D images at relatively low resolution for only a small part of the sample. As the telomere feature signal distribution is frequently heterogeneous, this approach is prone to a biased selection of the information present in the image and lacks subcellular details. Here we address these issues by using an automated high-resolution imaging and analysis workflow that quantifies individual telomere features on tissue sections for a large number of cells. The approach is particularly suited to assess telomere heterogeneity and low abundant cellular sub-populations with distinct telomere characteristics in a reproducible manner. It comprises the integration of multi-color fluorescence in situ hybridization, immunofluorescence and DNA staining with targeted automated 3D fluorescence microscopy and image analysis. We apply our method to telomeres in glioblastoma and prostate cancer samples, and describe how the imaging data can be used to derive statistically reliable information on telomere length distribution or colocalization with PML nuclear bodies. We anticipate that relating this approach to clinical outcome data will prove to be valuable for pretherapeutic patient stratification.Abbreviations3D-TIM3D targeted imagingALTalternative lengthening of telomeresAPBALT-associated PML-NBCLSMconfocal laser scanning fluorescence microscopyECTRextrachromosomal telomeric repeatFFPEformalin-fixed, paraffin-embeddedFISHfluorescence in situ hybridizationIFImmunofluorescencepedGBMpediatric glioblastomaPMLpromyelocytic leukemiaPML-NBPML nuclear bodyPNApeptide nucleic acidROIregion of interestTMAtissue microarrayTMMtelomere maintenance mechanismSMLMsingle molecule localization microscopy

The Golgi complex plays a central role in a number of diverse cellular processes, and numerous regulators that control these functions and/or morphology of the Golgi complex are known by now. Many of them were identified by large-scale experiments, such as RNAi-based screening. However, high-throughput experiments frequently provide only initial information that a particular protein might play a role in regulating structure and function of the Golgi complex. Multiple follow-up experiments are necessary to functionally characterize the selected hits. In order to speed up the discovery, we have established a system for correlative screening microscopy that combines rapid data collection and high-resolution imaging in one experiment. We describe here a combination of wide-field microscopy and dual-color direct stochastical optical reconstruction microscopy (dSTORM). We apply the technique to simultaneously capture and differentiate alterations of the cis- and trans-Golgi network when depleting several proteins in a singular and combinatorial manner.

A combined approach of 2D high-resolution localization light microscopy and statistical methods is presented to infer structural features and density fluctuations at the nuclear nanoscale. Hallmarks of nuclear nanostructure are found on the scale below 100 nm for both human fibroblast and HeLa cells. Mechanical measures were extracted as a quantitative tool from the histone density fluctuations inside the cell to obtain structural fluctuations on the scale of several micrometers. Results show that different mechanisms of expression of the same nuclear protein type lead to significantly different patterns on the nanoscale and to pronounced differences in the detected compressibility of chromatin. The observed fluctuations, including the experimental evidence for dynamic looping, are consistent with a recently proposed chromatin model.

International audience; The Dual Color Localization Microscopy (2CLM) presented here is based on the principles of Spectral Precision Distance Microscopy (SPDM) with conventional fluorochromes under special physical conditions. This technique allows us to measure the spatial distribution of single fluorescently labeled molecules in entire cells with an effective optical resolution comparable to macromolecular dimensions. Here, we describe the application of the 2CLM approach to the simultaneous nanoimaging of cellular structures using two fluorochrome types distinguished by different fluorescence emission wavelengths. The capabilities of 2CLM for studying the spatial organization of the genome in the mammalian cell nucleus are demonstrated for the relative distributions of two chromosomal proteins labeled with autofluorescent GFP and mRFP1 domains. The 2CLM images revealed quantitative information on their spatial relationships down to length-scales of 30 nm.

Far-field fluorescence techniques based on the precise determination of object positions have the potential to circumvent the optical resolution limit of direct imaging given by diffraction theory. In order to use localization to obtain structural information far below the diffraction limit, the ‘point-like’ components of the structure have to be detected independently, even if their distance is lower than the conventional optical resolution limit. This goal can be achieved by exploiting various photo-physical properties of the fluorescence labeling (‘spectral signatures’). In first experiments, spectral precision distance microscopy/spectral position determination microscopy (SPDM) was limited to a relatively small number of components to be resolved within the observation volume. Recently, the introduction of photoconvertable molecules has dramatically increased the number of components which can be independently localized. Here, we present an extension of the SPDM concept, exploiting the novel spectral signature offered by reversible photobleaching of fluorescent proteins. In combination with spatially modulated illumination (SMI) microscopy, at the present stage, we have achieved an estimated effective optical resolution of approximately 20 nm in the lateral and 50 nm in the axial direction, or about 1/25th–1/10th of the exciting wavelength.

Spatially modulated illumination (SMI) microscopy is a method of wide field fluorescence microscopy featuring interferometric illumination, which delivers structural information about nanoscale architecture in fluorescently labelled cells. The first prototype of the SMI microscope proved its applicability to a wide range of biological questions. For the SMI live cell imaging this system was enhanced in terms of the development of a completely new upright configuration. This so called Vertico-SMI transfers the advantages of SMI nanoscaling to vital biological systems, and is shown to work consistently at different temperatures using both oil- and water-immersion objective lenses. Furthermore, we increased the speed of data acquisition to minimize errors in the detection signal resulting from cellular or object movement. By performing accurate characterization, the present Vertico-SMI now offers a fully-fledged microscope enabling a complete three-dimensional (3D) SMI data stack to be acquired in less than 2 seconds. We have performed live cell measurements of a tet-operator repeat insert in U2OS cells, which provided the first in vivo signatures of subnuclear complexes. Furthermore, we have successfully implemented an optional optical configuration allowing the generation of high-resolution localization microscopy images of a nuclear pore complex distribution.

Using RNAi interference (RNAi), it is possible to study the effect of specific gene knockdowns in mammalian cells. In this protocol we present the automated preparation of "ready to transfect" multiwell plates and cell arrays, on which cells can be grown which are then reversely transfected with one type of siRNA in every individual well or spot. Additionally, different microscope types for screening approaches are compared and considerations about the information workflow are made.

Using RNAi interference (RNAi), it is possible to study the effect of specific gene knockdowns in mammalian cells. In this protocol we present the automated preparation of "ready to transfect" multiwell plates and cell arrays, on which cells can be grown which are then reversely transfected with one type of siRNA in every individual well or spot. Additionally, different microscope types for screening approaches are compared and considerations about the information workflow are made.

We have developed a method to perform microscopic temporal and spacial multi-scale experiments by imaging cellular phenotypes of interest on complementary fluorescence microscopy systems. In a low-resolution fast data acquisition screen for phenotypic cellular responses induced by small interfering RNA (siRNA), cells in spots of siRNA cell arrays showing characteristic alterations have been selected automatically by feature space analysis. These objects were imaged on a second super-resolution dSTORM microscope (direct stochastic optical reconstruction microscopy). The coordinate transfer was based on fixed cells as reference points without the use of additional fiducial markers. This procedure is suitable to combine any kind of fluorescence microscopy technique, in order to gain further insights on the observed specimen at multiple temporal or special scales.

For the improved understanding of biological systems on the nanoscale, it is necessary to enhance the resolution of light microscopy in the visible wavelength range beyond the limits of conventional epifluorescence microscopy (optical resolution of about 200 nm laterally, 600 nm axially). Recently, various far-field methods have been developed allowing a substantial increase of resolution ("superresolution microscopy", or "lightoptical nanoscopy"). This opens an avenue to 'nano-image' intact and even living cells, as well as other biostructures like viruses, down to the molecular detail. Thus, it is possible to combine light optical spatial nanoscale information with ultrastructure analyses and the molecular interaction information provided by molecular cell biology. In this review, we describe the principles of spectrally assigned localization microscopy (SALM) of biological nanostructures, focusing on a special SALM approach, spectral precision distance/position determination microscopy (SPDM) with physically modified fluorochromes (SPDM(Phymod) . Generally, this SPDM method is based on high-precision localization of fluorescent molecules, which can be discriminated using reversibly bleached states of the fluorophores for their optical isolation. A variety of application examples is presented, ranging from superresolution microscopy of membrane and cytoplasmic protein distribution to dual-color SPDM of nuclear proteins. At present, we can achieve an optical resolution of cellular structures down to the 20-nm range, with best values around 5 nm (∼1/100 of the exciting wavelength).