Roles for endocytosis and low pH in herpes simplex virus entry into HeLa and Chinese hamster ovary cells

Herpes simplex virus (HSV) infection of many cultured cells, e.g., Vero cells, can be initiated by receptor binding and pH-neutral fusion with the cell surface. Here we report that a major pathway for HSV entry into the HeLa and CHO-K1 cell lines is dependent on endocytosis and exposure to a low pH. Enveloped virions were readily detected in HeLa or receptor-expressing CHO cell vesicles by electron microscopy at <30 min postinfection. As expected, images of virus fusion with the Vero cell surface were prevalent. Treatment with energy depletion or hypertonic medium, which inhibits endocytosis, prevented uptake of HSV from the HeLa and CHO cell surface relative to uptake from the Vero cell surface. Incubation of HeLa and CHO cells with the weak base ammonium chloride or the ionophore monensin, which elevate the low pH of organelles, blocked HSV entry in a dose-dependent manner. Noncytotoxic concentrations of these agents acted at an early step during infection by HSV type 1 and 2 strains. Entry mediated by the HSV receptor HveA, nectin-1, or nectin-2 was also blocked. As analyzed by fluorescence microscopy, lysosomotropic agents such as the vacuolar H(+)-ATPase inhibitor bafilomycin A1 blocked the delivery of virus capsids to the nuclei of the HeLa and CHO cell lines but had no effect on capsid transport in Vero cells. The results suggest that HSV can utilize two distinct entry pathways, depending on the type of cell encountered.     

A framework for the automated analysis of subcellular patterns in human protein atlas images

The systematic study of subcellular location patterns is required to fully characterize the human proteome, as subcellular location provides critical context necessary for understanding a protein's function. The analysis of tens of thousands of expressed proteins for the many cell types and cellular conditions under which they may be found creates a need for automated subcellular pattern analysis. We therefore describe the application of automated methods, previously developed and validated by our laboratory on fluorescence micrographs of cultured cell lines, to analyze subcellular patterns in tissue images from the Human Protein Atlas. The Atlas currently contains images of over 3000 protein patterns in various human tissues obtained using immunohistochemistry. We chose a 16 protein subset from the Atlas that reflects the major classes of subcellular location. We then separated DNA and protein staining in the images, extracted various features from each image, and trained a support vector machine classifier to recognize the protein patterns. Our results show that our system can distinguish the patterns with 83% accuracy in 45 different tissues, and when only the most confident classifications are considered, this rises to 97%. These results are encouraging given that the tissues contain many different cell types organized in different manners, and that the Atlas images are of moderate resolution. The approach described is an important starting point for automatically assigning subcellular locations on a proteome-wide basis for collections of tissue images such as the Atlas.     

Automated image analysis of protein localization in budding yeast

MOTIVATION: The yeast Saccharomyces cerevisiae is the first eukaryotic organism to have its genome completely sequenced. Since then, several large-scale analyses of the yeast genome have provided extensive functional annotations of individual genes and proteins. One fundamental property of a protein is its subcellular localization, which provides critical information about how this protein works in a cell. An important project therefore was the creation of the yeast GFP fusion localization database by the University of California, San Francisco, USA (UCSF). This database provides localization data for 75% of the proteins believed to be encoded by the yeast genome. These proteins were classified into 22 distinct subcellular location categories by visual examination. Based on our past success at building automated systems to classify subcellular location patterns in mammalian cells, we sought to create a similar system for yeast. RESULTS: We developed computational methods to automatically analyze the images created by the UCSF yeast GFP fusion localization project. The system was trained to recognize the same location categories that were used in that study. We applied the system to 2640 images, and the system gave the same label as the previous assignments to 2139 images (81%). When only the highest confidence assignments were considered, 94.7% agreement was observed. Visual examination of the proteins for which the two approaches disagree suggests that at least some of the automated assignments may be more accurate. The automated method provides an objective, quantitative and repeatable assignment of protein locations that can be applied to new collections of yeast images (e.g. for different strains or the same strain under different conditions). It is also important to note that this performance could be achieved without requiring colocalization with any marker proteins. AVAILABILITY: The original images analyzed in this article are available at http://yeastgfp.ucsf.edu, and source code and results are available at http://murphylab.web.cmu.edu/software.     

Adherence of intestinal and extraintestinalPseudomonas aeruginosa to tissue culture cells

The adherence pattern ofPseudomonas aeruginosa strains to HeLa, Vero and CHO cells was studied. The diffuse type of adherence was found to prevail on HeLa cells. It was characteristic for intestinal and environmental strains. Urinary strains revealed more often a localized adherence. A similar pattern was obtained with CHO cells. Experiments with Vero cells showed an equal distribution of intestinal strains regarding the diffuse, localized and mixed adherence. Urinary strains revealed mostly a localized adherence of a similar pattern as was observed on HeLa and CHO cells.     

Automated analysis of high‐content microscopy data with deep learning

Existing computational pipelines for quantitative analysis of high‐content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization. We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone‐arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. We offer an open‐source implementation that enables updating DeepLoc on new microscopy datasets. This study highlights deep learning as an important tool for the expedited analysis of high‐content microscopy data.     

Evaluation of cytotoxic activity of Vibrio cholerae non-01 culture filtrate on established cell lines and human diploid cells

The cell-destroying effect of cell free filtrates of 90 V. cholerae non-01 cultures was measured by titration method in 3 established cell lines: CHO, HeLa and Vero and in 3 human diploid cells cultures: MRC-5, WI-38 and PZ. The vibrio strains differed in the titre of toxic effect. Most sensitive was CHO cell line, least sensitive were human diploid cell cultures. It was found that bacterial strains produced different substances toxic for various cell lines. Among them NAG-ST toxin produced by 41% of examined strains was identified and hemolysins/cytolysins activity was evaluated. Both may play a role in the pathogenicity of those strains for humans.     

Three critical factors affecting automated image species recognition performance for camera traps

Ecological camera traps are increasingly used by wildlife biologists to unobtrusively monitor an ecosystems animal population. However, manual inspection of the images produced is expensive, laborious, and time‐consuming. The success of deep learning systems using camera trap images has been previously explored in preliminary stages. These studies, however, are lacking in their practicality. They are primarily focused on extremely large datasets, often millions of images, and there is little to no focus on performance when tasked with species identification in new locations not seen during training. Our goal was to test the capabilities of deep learning systems trained on camera trap images using modestly sized training data, compare performance when considering unseen background locations, and quantify the gradient of lower bound performance to provide a guideline of data requirements in correspondence to performance expectations. We use a dataset provided by Parks Canada containing 47,279 images collected from 36 unique geographic locations across multiple environments. Images represent 55 animal species and human activity with high‐class imbalance. We trained, tested, and compared the capabilities of six deep learning computer vision networks using transfer learning and image augmentation: DenseNet201, Inception‐ResNet‐V3, InceptionV3, NASNetMobile, MobileNetV2, and Xception. We compare overall performance on “trained” locations where DenseNet201 performed best with 95.6% top‐1 accuracy showing promise for deep learning methods for smaller scale research efforts. Using trained locations, classifications with <500 images had low and highly variable recall of 0.750 ± 0.329, while classifications with over 1,000 images had a high and stable recall of 0.971 ± 0.0137. Models tasked with classifying species from untrained locations were less accurate, with DenseNet201 performing best with 68.7% top‐1 accuracy. Finally, we provide an open repository where ecologists can insert their image data to train and test custom species detection models for their desired ecological domain.     

Induction of histone H1(0) differs with different treatments among different cell lines

Histone H1(0) is an H1 subfraction whose level on the chromatin is inversely correlated with the mitotic index of the cell. We investigated the increase in H1(0) in 6 cell lines when cell division was blocked by 4 methods: 5 mM butyrate, 2% DMSO, serum withdrawal or density inhibition. The 6 cell lines (HeLa S1 and S3, CHO, 3T6, NIE-115 and Vero) responded differently to the treatments as regards the amount of increase in H1(0) and comparisons among the lines reveal no obvious similarities between the lines in the differential effects of the various treatments on H1(0) levels.     

Automated detection of tunneling nanotubes in 3D images.

BACKGROUND: This paper presents an automated method for the identification of thin membrane tubes in 3D fluorescence images. These tubes, referred to as tunneling nanotubes (TNTs), are newly discovered intercellular structures that connect living cells through a membrane continuity. TNTs are 50-200 nm in diameter, crossing from one cell to another at their nearest distance. In microscopic images, they are seen as straight lines. It now emerges that the TNTs represent the underlying structure of a new type of cell-to-cell communication. METHODS: Our approach for the identification of TNTs is based on a combination of biological cell markers and known image processing techniques. Watershed segmentation and edge detectors are used to find cell borders, TNTs, and image artifacts. Mathematical morphology is employed at several stages of the processing chain. Two image channels are used for the calculations to improve classification of watershed regions into cells and background. One image channel displays cell borders and TNTs, the second is used for cell classification and displays the cytoplasmic compartments of the cells. The method for cell segmentation is 3D, and the TNT detection incorporates 3D information using various 2D projections. RESULTS: The TNT- and cell-detection were applied to numerous 3D stacks of images. A success rate of 67% was obtained compared with manual identification of the TNTs. The digitalized results were used to achieve statistical information of selected properties of TNTs. CONCLUSION: To further explore these structures, automated detection and quantification is desirable. Consequently, this automated recognition tool will be useful in biological studies on cell-to-cell communication where TNT quantification is essential.     

Using a novel AdaBoost algorithm and Chou's Pseudo amino acid composition for predicting protein subcellular localization

For a protein, an important characteristic is its location or compartment in a cell. This is because a protein has to be located in its proper position in a cell to perform its biological functions. Therefore, predicting protein subcellular location is an important and challenging task in current molecular and cellular biology. In this paper, based on AdaBoost.ME algorithm and Chou's PseAAC (pseudo amino acid composition), a new computational method was developed to identify protein subcellular location. AdaBoost.ME is an improved version of AdaBoost algorithm that can directly extend the original AdaBoost algorithm to deal with multi-class cases without the need to reduce it to multiple two-class problems. In some previous studies the conventional amino acid composition was applied to represent protein samples. In order to take into account the sequence order effects, in this study we use Chou's PseAAC to represent protein samples. To demonstrate that AdaBoost.ME is a robust and efficient model in predicting protein subcellular locations, the same protein dataset used by Cedano et al. (Journal of Molecular Biology, 1997, 266: 594-600) is adopted in this paper. It can be seen from the computed results that the accuracy achieved by our method is better than those by the methods developed by the previous investigators.     

Intracellular vacuolation induced by culture filtrates of Plesiomonas shigelloides isolated from environmental sources

AIMS: Potential virulence factors produced by culture filtrates of Plesiomonas shigelloides isolated from water were investigated. METHODS AND RESULTS: Culture filtrates of P. shigelloides strains were assayed for cytotoxic activity in CHO (Chinese hamster ovary), Vero (African green monkey kidney), HeLa (human cervix), HT29 (human epithelial intestinal) and SK6 (swine epithelial kidney) cells. Microscopic analyses revealed intensive cytoplasmic vacuolation including cell rounding and swelling, with gradual destruction of the monolayer in filtrate-treated cells. Neutral red assays showed that CHO, HeLa and Vero cells were the most sensitive to the vacuolating activity, which was evident within 30 min of culture filtrate exposure. This activity was inactived by heating at 56 degrees C for 15 min and partially neutralized by antiserum to the cytotoxin of Aeromonas hydrophila. All P. shigelloides strains had a cell-associated haemolysin in the agar plate assay. Three isolates were found to produce a cell-free haemolytic activity at 37 degrees C. In the suckling mouse test, two P. shigelloides culture supernatants were positive for enterotoxic activity. CONCLUSIONS: P. shigelloides culture filtrates isolated from aquatic environment cause intracellular vacuolation on mammalian cells, and produce haemolytic and enterotoxic activities. SIGNIFICANCE AND IMPACT OF THE STUDY: This work revealed the presence of putative virulence factors that could be associated with human infections involving Plesiomonas strains.     

Toward the virtual cell: Automated approaches to building models of subcellular organization "learned" from microscopy images

We review state-of-the-art computational methods for constructing, from image data, generative statistical models of cellular and nuclear shapes and the arrangement of subcellular structures and proteins within them. These automated approaches allow consistent analysis of images of cells for the purposes of learning the range of possible phenotypes, discriminating between them, and informing further investigation. Such models can also provide realistic geometry and initial protein locations to simulations in order to better understand cellular and subcellular processes. To determine the structures of cellular components and how proteins and other molecules are distributed among them, the generative modeling approach described here can be coupled with high throughput imaging technology to infer and represent subcellular organization from data with few a priori assumptions. We also discuss potential improvements to these methods and future directions for research.     

Quantification of the Heterogeneity of Prognostic Cellular Biomarkers in Ewing Sarcoma Using Automated Image and Random Survival Forest Analysis

Driven by genomic somatic variation, tumour tissues are typically heterogeneous, yet unbiased quantitative methods are rarely used to analyse heterogeneity at the protein level. Motivated by this problem, we developed automated image segmentation of images of multiple biomarkers in Ewing sarcoma to generate distributions of biomarkers between and within tumour cells. We further integrate high dimensional data with patient clinical outcomes utilising random survival forest (RSF) machine learning. Using material from cohorts of genetically diagnosed Ewing sarcoma with EWSR1 chromosomal translocations, confocal images of tissue microarrays were segmented with level sets and watershed algorithms. Each cell nucleus and cytoplasm were identified in relation to DAPI and CD99, respectively, and protein biomarkers (e.g. Ki67, pS6, Foxo3a, EGR1, MAPK) localised relative to nuclear and cytoplasmic regions of each cell in order to generate image feature distributions. The image distribution features were analysed with RSF in relation to known overall patient survival from three separate cohorts (185 informative cases). Variation in pre-analytical processing resulted in elimination of a high number of non-informative images that had poor DAPI localisation or biomarker preservation (67 cases, 36%). The distribution of image features for biomarkers in the remaining high quality material (118 cases, 10⁴ features per case) were analysed by RSF with feature selection, and performance assessed using internal cross-validation, rather than a separate validation cohort. A prognostic classifier for Ewing sarcoma with low cross-validation error rates (0.36) was comprised of multiple features, including the Ki67 proliferative marker and a sub-population of cells with low cytoplasmic/nuclear ratio of CD99. Through elimination of bias, the evaluation of high-dimensionality biomarker distribution within cell populations of a tumour using random forest analysis in quality controlled tumour material could be achieved. Such an automated and integrated methodology has potential application in the identification of prognostic classifiers based on tumour cell heterogeneity.     

Visual Recognition Software for Binary Classification and Its Application to Spruce Pollen Identification

Discriminating between black and white spruce (Picea mariana and Picea glauca) is a difficult palynological classification problem that, if solved, would provide valuable data for paleoclimate reconstructions. We developed an open-source visual recognition software (ARLO, Automated Recognition with Layered Optimization) capable of differentiating between these two species at an accuracy on par with human experts. The system applies pattern recognition and machine learning to the analysis of pollen images and discovers general-purpose image features, defined by simple features of lines and grids of pixels taken at different dimensions, size, spacing, and resolution. It adapts to a given problem by searching for the most effective combination of both feature representation and learning strategy. This results in a powerful and flexible framework for image classification. We worked with images acquired using an automated slide scanner. We first applied a hash-based “pollen spotting” model to segment pollen grains from the slide background. We next tested ARLO’s ability to reconstruct black to white spruce pollen ratios using artificially constructed slides of known ratios. We then developed a more scalable hash-based method of image analysis that was able to distinguish between the pollen of black and white spruce with an estimated accuracy of 83.61%, comparable to human expert performance. Our results demonstrate the capability of machine learning systems to automate challenging taxonomic classifications in pollen analysis, and our success with simple image representations suggests that our approach is generalizable to many other object recognition problems.     

Multi-resolution correlative focused ion beam scanning electron microscopy: Applications to cell biology

Efficient correlative imaging of small targets within large fields is a central problem in cell biology. Here, we demonstrate a series of technical advances in focused ion beam scanning electron microscopy (FIB–SEM) to address this issue. We report increases in the speed, robustness and automation of the process, and achieve consistent z slice thickness of ∼3 nm. We introduce “keyframe imaging” as a new approach to simultaneously image large fields of view and obtain high-resolution 3D images of targeted sub-volumes. We demonstrate application of these advances to image post-fusion cytoplasmic intermediates of the HIV core. Using fluorescently labeled cell membranes, proteins and HIV cores, we first produce a “target map” of an HIV infected cell by fluorescence microscopy. We then generate a correlated 3D EM volume of the entire cell as well as high-resolution 3D images of individual HIV cores, achieving correlative imaging across a volume scale of 10⁹ in a single automated experimental run.     

Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images

Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells.     

Computer‐automated bird detection and counts in high‐resolution aerial images: a review

Bird surveys conducted using aerial images can be more accurate than those using airborne observers, but can also be more time‐consuming if images must be analyzed manually. Recent advances in digital cameras and image‐analysis software offer unprecedented potential for computer‐automated bird detection and counts in high‐resolution aerial images. We review the literature on this subject and provide an overview of the main image‐analysis techniques. Birds that contrast sharply with image backgrounds (e.g., bright birds on dark ground) are generally the most amenable to automated detection, in some cases requiring only basic image‐analysis software. However, the sophisticated analysis capabilities of modern object‐based image analysis software provide ways to detect birds in more challenging situations based on a variety of attributes including color, size, shape, texture, and spatial context. Some techniques developed to detect mammals may also be applicable to birds, although the prevalent use of aerial thermal‐infrared images for detecting large mammals is of limited applicability to birds because of the low pixel resolution of thermal cameras and the smaller size of birds. However, the increasingly high resolution of true‐color cameras and availability of small unmanned aircraft systems (drones) that can fly at very low altitude now make it feasible to detect even small shorebirds in aerial images. Continued advances in camera and drone technology, in combination with increasingly sophisticated image analysis software, now make it possible for investigators involved in monitoring bird populations to save time and resources by increasing their use of automated bird detection and counts in aerial images. We recommend close collaboration between wildlife‐monitoring practitioners and experts in the fields of remote sensing and computer science to help generate relevant, accessible, and readily applicable computer‐automated aerial photographic census techniques.     

Herpes simplex virus immediate-early promoters are responsive to virus and cell trans-acting factors.

The promoters for each of the immediate-early genes from herpes simplex virus type 1 were cloned and fused to a chloramphenicol acetyltransferase cassette. These chimeric genes were used as targets in a transient expression assay to determine how the immediate-early gene products ICP4 and ICP0 and the virion-associated stimulatory protein Vmw65 affected their expression in HeLa and Vero cells. The basal level of expression from these cassettes differed significantly depending on the extent of 5'-flanking sequence and the cell line that served as host. The promoters from IE-4 and IE-0 behaved in a qualitatively similar fashion independent of the host cell. However, the promoter for ICP27 had a unique response pattern: in Vero cells it acted as an alpha gene promoter, whereas in HeLa cells its response was more like that of a beta gene promoter. The promoter sequences for ICP22 and ICP47 behaved as the IE-4 and IE-0 promoters did in HeLa cells, but their response to the effector molecules in Vero cells was unlike that of other alpha gene promoters we have studied. Evidence is also presented for a role for ICP27 in autoregulation.     

Expression cloning of functional receptor used by SARS coronavirus

We have expressed a series of truncated spike (S) glycoproteins of SARS-CoV and found that the N-terminus 14-502 residuals were sufficient to bind to SARS-CoV susceptible Vero E6 cells. With this soluble S protein fragment as an affinity ligand, we screened HeLa cells transduced with retroviral cDNA library from Vero E6 cells and obtained a HeLa cell clone which could bind with the S protein. This cell clone was susceptible to HIV/SARS pseudovirus infection and the presence of a functional receptor for S protein in this cell clone was confirmed by the cell-cell fusion assay. Further studies showed the susceptibility of this cell was due to the expression of endogenous angiotensin-converting enzyme 2 (ACE2) which was activated by inserted LTR from retroviral vector used for expression cloning. When human ACE2 cDNA was transduced into NIH3T3 cells, the ACE2 expressing NIH3T3 cells could be infected with HIV/SARS pseudovirus. These data clearly demonstrated that ACE2 was the functional receptor for SARS-CoV.     

Cytotoxic activity of Serratia marcescens clinical isolates

Twenty Serratia marcescens isolates from clinical specimens were examined for their cytotoxic activity on four cell lines (HEp-2, Vero, CHO, J774). Most of the isolates were found to be cytotoxic to CHO (70%), Vero (75%) and HEp-2 cells (90%). CHO cells were the most sensitive to cell-free supernatants, followed by HEp-2 and Vero cells. Two strains produced cytotonic toxins which caused elongation of CHO cells. Moreover, twelve isolates (60%) revealed cytotoxic potential to macrophage cell line J774. The results indicate that these bacteria may destroy phagocytes and epithelial cells, which may lead to spread within the host.