Automated subcellular location determination and high-throughput microscopy.
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Animals↗
Biomedical subjects
Robert F Murphy
Publications and source records attributed to Robert F Murphy.
Nutlin-3 protects kidney cells during cisplatin therapy by suppressing Bax/Bak activation.
Nutlins, the newly developed small molecule antagonists of MDM2, activate p53 and induce apoptosis in cancer cells, offering a novel strategy of chemotherapy. Recent studies have further suggested synergistic effects of nutlins with other chemotherapeutic drugs. However, it is unclear whether nutlins increase or decrease the side effects of these drugs in normal non-malignant cells or tissues. Cisplatin is a widely used chemotherapy drug, which has a major side effect of kidney injury. Here we show that Nutlin-3 protected kidney cells against cisplatin-induced apoptosis. The cytoprotective effects of Nutlin-3 were not related to its regulation of p53 or consequent gene expression during cisplatin treatment. Moreover, the protective effects were shown in MDM2-, MDM4-, or p53-deficient cells. On the other hand, Nutlin-3 suppressed mitochondrial events of apoptosis during cisplatin incubation, including Bax activation and cytochrome c release. Nutlin-3 attenuated cisplatin-induced oligomerization of Bax and Bak but not their interactions with Bcl-XL. In isolated mitochondria, Nutlin-3 inhibited cytochrome c release induced by Ca2+, Bim peptide, and recombinant tBid. Importantly, it blocked both Bax and Bak oligomerization under these conditions. Together, the results have uncovered a new pharmacological function of nutlins, i.e. suppression of Bax and Bak, two critical mediators of apoptosis.
Animals↗
From imaging to understanding: Frontiers in Live Cell Imaging, Bethesda, MD, April 19-21, 2006.
A recent meeting entitled Frontiers in Live Cell Imaging was attended by more than 400 cell biologists, physicists, chemists, mathematicians, and engineers. Unlike typical special topics meetings, which bring together investigators in a defined field primarily to review recent progress, the purpose of this meeting was to promote cross-disciplinary interactions by introducing emerging methods on the one hand and important biological applications on the other. The goal was to turn live cell imaging from a "technique" used in cell biology into a new exploratory science that combines a number of research fields.
Animals↗
A graphical model approach to automated classification of protein subcellular location patterns in multi-cell images.
BACKGROUND: Knowledge of the subcellular location of a protein is critical to understanding how that protein works in a cell. This location is frequently determined by the interpretation of fluorescence microscope images. In recent years, automated systems have been developed for consistent and objective interpretation of such images so that the protein pattern in a single cell can be assigned to a known location category. While these systems perform with nearly perfect accuracy for single cell images of all major subcellular structures, their ability to distinguish subpatterns of an organelle (such as two Golgi proteins) is not perfect. Our goal in the work described here was to improve the ability of an automated system to decide which of two similar patterns is present in a field of cells by considering more than one cell at a time. Since cells displaying the same location pattern are often clustered together, considering multiple cells may be expected to improve discrimination between similar patterns. RESULTS: We describe how to take advantage of information on experimental conditions to construct a graphical representation for multiple cells in a field. Assuming that a field is composed of a small number of classes, the classification accuracy can be improved by allowing the computed probability of each pattern for each cell to be influenced by the probabilities of its neighboring cells in the model. We describe a novel way to allow this influence to occur, in which we adjust the prior probabilities of each class to reflect the patterns that are present. When this graphical model approach is used on synthetic multi-cell images in which the true class of each cell is known, we observe that the ability to distinguish similar classes is improved without suffering any degradation in ability to distinguish dissimilar classes. The computational complexity of the method is sufficiently low that improved assignments of classes can be obtained for fields of twelve cells in under 0.04 second on a 1600 megahertz processor. CONCLUSION: We demonstrate that graphical models can be used to improve the accuracy of classification of subcellular patterns in multi-cell fluorescence microscope images. We also describe a novel algorithm for inferring classes from a graphical model. The performance and speed suggest that the method will be particularly valuable for analysis of images from high-throughput microscopy. We also anticipate that it will be useful for analyzing the mixtures of cell types typically present in images of tissues. Lastly, we anticipate that the method can be generalized to other problems.
Algorithms↗
Automated interpretation of subcellular patterns in fluorescence microscope images for location proteomics.
Proteomics, the large scale identification and characterization of many or all proteins expressed in a given cell type, has become a major area of biological research. In addition to information on protein sequence, structure and expression levels, knowledge of a protein's subcellular location is essential to a complete understanding of its functions. Currently, subcellular location patterns are routinely determined by visual inspection of fluorescence microscope images. We review here research aimed at creating systems for automated, systematic determination of location. These employ numerical feature extraction from images, feature reduction to identify the most useful features, and various supervised learning (classification) and unsupervised learning (clustering) methods. These methods have been shown to perform significantly better than human interpretation of the same images. When coupled with technologies for tagging large numbers of proteins and high-throughput microscope systems, the computational methods reviewed here enable the new subfield of location proteomics. This subfield will make critical contributions in two related areas. First, it will provide structured, high-resolution information on location to enable Systems Biology efforts to simulate cell behavior from the gene level on up. Second, it will provide tools for Cytomics projects aimed at characterizing the behaviors of all cell types before, during, and after the onset of various diseases.
Animals↗
The nonphysician provider in the gastroenterology practice.
Our nonphysician provider (NPP) experience has transformed the functioning and image of our practice. It allows the practice to care for poorly compensated or noncompensated patients and contains costs under managed care. NPPs enable our physicians to maintain reasonable workloads and focus their energy and skills on high-level diagnosis and treatment. This decreases the risk of physician burnout, and allows a diminishing supply of gastroenterologists to practice medicine until normal retirement age. NPPs greatly enhance physician productivity, revenue, and patient and physician satisfaction.
Allied Health Personnel↗
Automated interpretation of protein subcellular location patterns.
Proteomics is a major current focus of biomedical research, and location proteomics is the important branch of proteomics that systematically studies the subcellular distributions for all proteins expressed in a given cell type. Fluorescence microscopy of labeled proteins is currently the main methodology to obtain location information. Traditionally, microscope images are analyzed by visual inspection, which suffers from inefficiency and inconsistency. Automated and objective interpretation approaches are therefore needed for location proteomics. In this article, we briefly review recent advances in automated imaging interpretation tools, including supervised classification (which assigns location pattern labels to previously unseen images), unsupervised clustering (which groups proteins based on the similarity among their subcellular distributions), and additional statistical tools that can aid cell and molecular biologists who use microscopy in their work.
Automation↗
Putting proteins on the map.
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Computational Biology↗
Characterization of the TGN exit signal of the human mannose 6-phosphate uncovering enzyme.
The human mannose 6-phosphate uncovering enzyme participates in the uncovering of the mannose 6-phosphate recognition tag on lysosomal enzymes, a process that facilitates recognition of those enzymes by mannose 6-phosphate receptors to ensure delivery to lysosomes. Uncovering enzyme has been identified on the trans-Golgi network at steady state. It has been shown to traffic to the plasma membrane from where it is rapidly internalized via endosomal structures, the process being mediated by a tyrosine-based internalization motif, Y488HPL, in its cytoplasmic tail. Using immunogold electron microscopy a GFP-uncovering enzyme fusion construct was found to be colocalized with the cation-dependent mannose 6-phosphate receptor in regions of the trans-Golgi network, suggesting that uncovering enzyme might follow a similar pathway of exit from the trans-Golgi network as that of the cation-dependent mannose 6-phosphate receptor. In this study, we identified the signal sequence in the cytoplasmic tail of uncovering enzyme responsible for its exit from the trans-Golgi network. Using GFP fusion constructs of the transmembrane and cytoplasmic domains of uncovering enzyme, we could show, by automated analysis of confocal immunofluorescence images, that residues Q492EMN in the cytoplasmic tail of uncovering enzyme are involved in its exit from the trans-Golgi network. Detailed characterization of the exit signal revealed that residue Q492 is the most important to the exit function while M494 and N495 also contribute. The cytoplasmic tail of the uncovering enzyme does not possess any of the known canonical signal sequences for interaction with Golgi-associated gamma ear-containing adaptor proteins. The identification of a trans-Golgi network exit signal in its cytoplasmic tail elucidates the trafficking pathway of uncovering enzyme, a crucial player in the process of lysosomal biogenesis.
Gene Expression Regulation, Enzymologic↗
Objective clustering of proteins based on subcellular location patterns.
The goal of proteomics is the complete characterization of all proteins. Efforts to characterize subcellular location have been limited to assigning proteins to general categories of organelles. We have previously designed numerical features to describe location patterns in microscope images and developed automated classifiers that distinguish major subcellular patterns with high accuracy (including patterns not distinguishable by visual examination). The results suggest the feasibility of automatically determining which proteins share a single location pattern in a given cell type. We describe an automated method that selects the best feature set to describe images for a given collection of proteins and constructs an effective partitioning of the proteins by location. An example for a limited protein set is presented. As additional data become available, this approach can produce for the first time an objective systematics for protein location and provide an important starting point for discovering sequence motifs that determine localization.
Journal Article↗
Transferrin recycling and dextran transport to lysosomes is differentially affected by bafilomycin, nocodazole, and low temperature.
The effects of bafilomycin, nocodazole, and reduced temperature on recycling and the lysosomal pathway have been investigated in various cultured cell lines and have been shown to vary dependent on the cell type examined. However, the way in which these treatments affect recycling and transport to lysosomes within the same cell line has not been analyzed. In the current study, we used fluorophore-labeled transferrin and dextran as typical markers for the recycling and the lysosomal pathways, respectively, to explore the morphology and the intravesicular pH of endocytic compartments in HeLa cells. The V-ATPase inhibitor bafilomycin selectively inhibited the transport of marker destined for lysosomal degradation in early endosomes, whereas the transport of transferrin to the perinuclear recycling compartment (PNRC) still occurred. The kinetics of transferrin acidification was found to be biphasic, indicative of fast and slow recycling pathways via early endosomes (pH 6.0) and PNRC (pH 5.6), respectively. Furthermore, the disruption of microtubules by nocodazole blocked the transport of transferrin to the PNRC in early endosomes and of lysosome-directed marker into endosomal carrier vesicles. In contrast, incubation at 20 degrees C affected the lysosomal pathway by causing retention of internalized dextran in late endosomes and a delay in transferrin recycling. Taken together, these data clearly demonstrate, for the first time, that the transferrin recycling pathway and transport of endocytosed material to lysosomes are differentially affected by bafilomycin, nocodazole, and low temperature in HeLa cells. Consequently, these treatments can be applied to investigate whether internalized macromolecules such as viruses follow a recycling or degradative pathway.
Cold Temperature↗
Cytomics and location proteomics: automated interpretation of subcellular patterns in fluorescence microscope images.
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Animals↗
High-recall protein entity recognition using a dictionary.
SUMMARY: Protein name extraction is an important step in mining biological literature. We describe two new methods for this task: semiCRFs and dictionary HMMs. SemiCRFs are a recently-proposed extension to conditional random fields (CRFs) that enables more effective use of dictionary information as features. Dictionary HMMs are a technique in which a dictionary is converted to a large HMM that recognizes phrases from the dictionary, as well as variations of these phrases. Standard training methods for HMMs can be used to learn which variants should be recognized. We compared the performance of our new approaches with that of Maximum Entropy (MaxEnt) and normal CRFs on three datasets, and improvement was obtained for all four methods over the best published results for two of the datasets. CRFs and semiCRFs achieved the highest overall performance according to the widely-used F-measure, while the dictionary HMMs performed the best at finding entities that actually appear in the dictionary-the measure of most interest in our intended application. AVAILABILITY: Dictionary HMMs were implemented in Java. Algorithms are available through an information extraction package MINORTHIRD on http://minorthird.sourceforge.net
Algorithms↗
Object type recognition for automated analysis of protein subcellular location.
The new field of location proteomics seeks to provide a comprehensive, objective characterization of the subcellular locations of all proteins expressed in a given cell type. Previous work has demonstrated that automated classifiers can recognize the patterns of all major subcellular organelles and structures in fluorescence microscope images with high accuracy. However, since some proteins may be present in more than one organelle, this paper addresses a more difficult task: recognizing a pattern that is a mixture of two or more fundamental patterns. The approach utilizes an object-based image model, in which each image of a location pattern is represented by a set of objects of distinct, learned types. Using a two-stage approach in which object types are learned and then cell-level features are calculated based on the object types, the basic location patterns were well recognized. Given the object types, a multinomial mixture model was built to recognize mixture patterns. Under appropriate conditions, synthetic mixture patterns can be decomposed with over 80% accuracy, which, for the first time, shows that the problem of computationally decomposing subcellular patterns into fundamental organelle patterns can be solved.
Algorithms↗
Opening of size-selective pores in endosomes during human rhinovirus serotype 2 in vivo uncoating monitored by single-organelle flow analysis.
The effect of virus uncoating on endosome integrity during the early steps in viral infection was investigated. Using fluid-phase uptake of 10- and 70-kDa dextrans labeled with a pH-dependent fluorophore (fluorescein isothiocyanate [FITC]) and a pH-independent fluorophore (cyanine 5 [Cy5]), we determined the pHs of labeled compartments in intact HeLa cells by fluorescence-activated cell sorting analysis. Subsequently, the number and pH of fluorescent endosomes in cell homogenates were determined by single-organelle flow analysis. Cointernalization of adenovirus and 70-kDa FITC- and Cy5-labeled dextran (FITC/Cy5-dextran) led to virus-induced endosomal rupture, resulting in the release of the marker from the low-pH environment into the neutral cytosol. Consequently, in the presence of adenovirus, the number of fluorescent endosomes was reduced by 40% compared to that in the control. When human rhinovirus serotype 2 (HRV2) was cointernalized with 10-and 70-kDa FITC/Cy5-dextrans, the 10-kDa dextran was released, whereas the 70-kDa dextran remained within the endosomes, which also maintained their low pH. These data demonstrate that pores are generated in the membrane during HRV2 uncoating and RNA penetration into the cytosol without gross damage of the endosomes; 10-kDa dextran can access the cytosol through these pores. Whereas rhinovirus-mediated pore formation was prevented by the vacuolar ATPase inhibitor bafilomycin A1, adenovirus-mediated endosomal rupture also occurred in the presence of the inhibitor. This finding is in keeping with the low-pH requirement of HRV2 infection; for adenovirus, no pH dependence for endosomal escape was found with this drug.
Adenoviridae↗
Boosting accuracy of automated classification of fluorescence microscope images for location proteomics.
BACKGROUND: Detailed knowledge of the subcellular location of each expressed protein is critical to a full understanding of its function. Fluorescence microscopy, in combination with methods for fluorescent tagging, is the most suitable current method for proteome-wide determination of subcellular location. Previous work has shown that neural network classifiers can distinguish all major protein subcellular location patterns in both 2D and 3D fluorescence microscope images. Building on these results, we evaluate here new classifiers and features to improve the recognition of protein subcellular location patterns in both 2D and 3D fluorescence microscope images. RESULTS: We report here a thorough comparison of the performance on this problem of eight different state-of-the-art classification methods, including neural networks, support vector machines with linear, polynomial, radial basis, and exponential radial basis kernel functions, and ensemble methods such as AdaBoost, Bagging, and Mixtures-of-Experts. Ten-fold cross validation was used to evaluate each classifier with various parameters on different Subcellular Location Feature sets representing both 2D and 3D fluorescence microscope images, including new feature sets incorporating features derived from Gabor and Daubechies wavelet transforms. After optimal parameters were chosen for each of the eight classifiers, optimal majority-voting ensemble classifiers were formed for each feature set. Comparison of results for each image for all eight classifiers permits estimation of the lower bound classification error rate for each subcellular pattern, which we interpret to reflect the fraction of cells whose patterns are distorted by mitosis, cell death or acquisition errors. Overall, we obtained statistically significant improvements in classification accuracy over the best previously published results, with the overall error rate being reduced by one-third to one-half and with the average accuracy for single 2D images being higher than 90% for the first time. In particular, the classification accuracy for the easily confused endomembrane compartments (endoplasmic reticulum, Golgi, endosomes, lysosomes) was improved by 5-15%. We achieved further improvements when classification was conducted on image sets rather than on individual cell images. CONCLUSIONS: The availability of accurate, fast, automated classification systems for protein location patterns in conjunction with high throughput fluorescence microscope imaging techniques enables a new subfield of proteomics, location proteomics. The accuracy and sensitivity of this approach represents an important alternative to low-resolution assignments by curation or sequence-based prediction.
Cell Line, Tumor↗
Automated interpretation of subcellular patterns from immunofluorescence microscopy.
Immunofluorescence microscopy is widely used to analyze the subcellular locations of proteins, but current approaches rely on visual interpretation of the resulting patterns. To facilitate more rapid, objective, and sensitive analysis, computer programs have been developed that can identify and compare protein subcellular locations from fluorescence microscope images. The basis of these programs is a set of features that numerically describe the characteristics of protein images. Supervised machine learning methods can be used to learn from the features of training images and make predictions of protein location for images not used for training. Using image databases covering all major organelles in HeLa cells, these programs can achieve over 92% accuracy for two-dimensional (2D) images and over 95% for three-dimensional images. Importantly, the programs can discriminate proteins that could not be distinguished by visual examination. In addition, the features can also be used to rigorously compare two sets of images (e.g., images of a protein in the presence and absence of a drug) and to automatically select the most typical image from a set. The programs described provide an important set of tools for those using fluorescence microscopy to study protein location.
Automation↗
Automated interpretation of protein subcellular location patterns: implications for early cancer detection and assessment.
Fluorescence microscopy is a powerful tool for analyzing the subcellular distributions of proteins, but that power has not been fully utilized because most analysis of those distributions has been done by visual examination. This limitation can be overcome using automated pattern recognition methods widely used in other fields. This article summarizes work demonstrating that automated systems can recognize the patterns of major organelles in both two- and three-dimensional images of cultured cells, and that these systems can distinguish similar patterns better than visual examination. The basis for these systems are sets of Subcellular Location Features that capture the essence of subcellular patterns without being sensitive to the extensive variation that occurs in the size, shape, and orientation of cells in microscope images. These features can also be used to make sensitive, statistical comparisons of the distribution of a protein between two conditions, such as in the presence and absence of a drug. The possible use of automated pattern analysis methods for improving detection of abnormal cells in cancerous or precancerous tissues is also discussed.
Automation↗