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The use of freeze-dried allograft as a bone graft substitute for pediatric spine surgery has been found to be safe and efficacious in the thoracic and lumbar spines in several conditions, namely idiopathic scoliosis. Large series of instrumented fusions for pediatric cervical spine in the literature confirm a high degree of fusion, although all include autogenous grafting, mostly from the iliac crest. The use of allograft for subaxial instrumented fusions in the cervical spine has …

Fluorescence microscopy is growing dramatically both in terms of technical capabilities and the volume of images generated. A number of online repositories have been created to begin providing public access to images and opportunities for joint research to many scientists.1 This has reintroduced challenges faced when sequence and structure databases were being established: developing fast and effective means of searching for records (images) either by context (e.g., what protein is labeled) or content (e.g., what pattern it displays). Image databases normally contain context descriptors in the form of annotations describing the source of the sample, the protocol used to prepare it, the instrumental settings used, and the laboratory that produced it. Searches can readily be done on one or more of these annotations, although incomplete or inconsistent annotation remains a problem. Searching for images based on their contents is much less developed. Some content annotations may be provided in the form of labels (such as Gene Ontology terms) resulting from either visual or automated analysis, and therefore images can be retrieved using them in the same way as context terms. However, these are limited by the “resolution” of the terms used and do not facilitate discovery of new patterns or similarities between known patterns that were not previously recognized. Content-based image retrieval (also known as Query-by-image content) was proposed many years ago to address this issue; this method takes one or more images as a query and retrieves the most similar images in terms of numerically computed features.2 However, current fluorescence microscopy image databases do not provide these methods. Here we present a content-based image searcher for microscope images, OMERO.searcher (http://murphylab.web.cmu.edu/software/searcher), that can be used with any OMERO database (http://openmicroscopy.org).3 The two requirements for content-based retrieval are a set of numerical features to describe each image and a method for combining them to measure similarity. OMERO.searcher by default uses the subcellular location features (SLFs)4 which have previously been successfully used to identify protein location patterns in fluorescence microscope images, but these can be replaced with any numerical feature set the user devises for their own purposes (one of the advantages of the SLFs is that they are designed to be applicable to images taken at different resolutions or with different modalities). Images are ranked by their similarity to one or more query images using a modified implementation of the FALCON algorithm5 that has been used in the Protein Subcellular Location Image Database (http://pslid.org).6 The searcher is implemented on top of the OMERO web client service with minimum modification of its default web pages. The features for individual images are stored as an attached HDF5 file; the code can be configured to automatically calculate and store these features when a new image is uploaded to the server (or they can be calculated on demand through the web interface). The features for the entire database are also stored in one master file to facilitate fast searches. For each query, the searcher retrieves the features for the query images as well as the features for the entire database and performs a similarity measurement. Both positive and negative examples can be included in a query. A typical work flow using OMERO.searcher is shown in Supplementary Fig. 1. After uploading images, features are calculated and stored in the database. These features are calculated at different image resolutions. A search can then be done simply by selecting one or more images and clicking the magnifying glass icon. The system automatically chooses, based on the resolution of the query images, the set of features to use. The query information is displayed on the left side of the resulting web page, and the most similar images retrieved are shown on the right. A user can refine the search by choosing images from the results, marking them as positive (retrieve more images similar to these) and negative (exclude images similar to these), and repeating until satisfied. A standalone client that does not require a local copy of OMERO is also available. It permits users to choose images on their local computer, calculate features, and find similar images in remote databases that have OMERO.searcher installed (Supplementary Note). (The next release of OMERO.searcher will support searching across multiple OMERO databases at different locations, assuming access rights.) To test how well the searcher retrieves relevant images, we performed tests using two distinct fluorescence microscopy databases. Classes of images with the same content annotations were created, and images were ranked by similarity to one or more query images drawn from one of those classes (Supplementary Methods). Success was measured using the area under a receiver operating characteristic curve, in which retrieval rates for images from the desired class are compared to those for images from undesired classes. Good results were obtained for many different patterns from both databases (Fig. 1). Note that this was true even though the Cell Library contained images of different resolutions and microscope types. The quality of the results was improved by increasing the number of images in the query, and using both positive and negative examples improved the results for the same total number of labeled images (Fig. 1b). The images used in this second test were collected at 40× magnification. Very similar results were obtained when searching with downsampled versions to simulate a query with images collected at 10× magnification (Supplementary Fig. 2). Feature sets are also available to permit searching with three-dimensional images and time series. Fig. 1 Results of retrieval performance tests. (a) Images from The Cell Library were grouped by their annotations and used to search for similar images. The area under receiver operating characteristic curves (AUC) was calculated, where a value of 1 means that ... In conclusion, OMERO.searcher is an open-source content-based image search tool for the cell and computational biology community. It has a number of useful applications, such as asking if someone has previously observed a pattern similar to an unrecognized one, or finding examples of a particular pattern in other cell types or different modes of microscopy.

Representative members of the bioimage informatics community review the computational steps and some of the primary software tools available to biologists who are acquiring and analyzing microscopy-based digital image data, with a focus on open-source options. Few technologies are more widespread in modern biological laboratories than imaging. Recent advances in optical technologies and instrumentation are providing hitherto unimagined capabilities. Almost all these advances have required the development of software to enable the acquisition, management, analysis and visualization of the imaging data. We review each computational step that biologists encounter when dealing with digital images, the inherent challenges and the overall status of available software for bioimage informatics, focusing on open-source options.

Despite the clinical success of platinum-containing drugs in the treatment of solid tumors, acquired resistance remains a major obstacle. We previously identified a group of novel transplanaramine or transplatinum compounds based on distinct activity profiles in the NCI-60 panel. In the present study, parental KB-3.1 cells with wild-type p53 and its cisplatin- and oxaliplatin-resistant sublines harboring mutant p53 proteins were used to contrast several transplatinum compounds with cisplatin and oxaliplatin. The transplatinum compounds retained cytotoxic activity in the resistant cell lines. While intracellular accumulation and DNA platination of cisplatin and oxaliplatin was decreased in the resistant cells, the transplatinum compounds both accumulated intracellularly and platinated DNA at comparable levels in all cell lines. Cytoflow analysis confirmed that cisplatin and oxaliplatin alter the cell cycle distribution and result in apoptosis; however, at comparably toxic concentrations, the transplatinum compounds did not alter the cell cycle distribution. Analysis of the cytoplasmic fraction treated with acetone showed that cisplatin and oxaliplatin readily bound to macromolecules in the pellet, whereas a larger percentage of the transplatinum compounds remained in the supernatant. We concluded that, distinct from platinum compounds currently in use, transplatinum compounds accumulate intracellularly in resistant cells at levels comparable to those in drug-sensitive cells, do not affect the cell cycle and thus retain cytotoxicity independent of p53 status and likely have cytoplasmic targets that are important in their activity.

This chapter describes approaches for learning models of subcellular organization from images. The primary utility of these models is expected to be from incorporation into complex simulations of cell behaviors. Most current cell simulations do not consider spatial organization of proteins at all, or treat each organelle type as a single, idealized compartment. The ability to build generative models for all proteins in a proteome and use them for spatially accurate simulations is expected to improve the accuracy of models of cell behaviors. A second use, of potentially equal importance, is expected to be in testing and comparing software for analyzing cell images. The complexity and sophistication of algorithms used in cell-image-based screens and assays (variously referred to as high-content screening, high-content analysis, or high-throughput microscopy) is continuously increasing, and generative models can be used to produce images for testing these algorithms in which the expected answer is known.

Characterizing the spatial distribution of proteins directly from microscopy images is a difficult problem with numerous applications in cell biology (e.g. identifying motor-related proteins) and clinical research (e.g. identification of cancer biomarkers). Here we describe the design of a system that provides automated analysis of punctate protein patterns in microscope images, including quantification of their relationships to microtubules. We constructed the system using confocal immunofluorescence microscopy images from the Human Protein Atlas project for 11 punctate proteins in three cultured cell lines. These proteins have previously been characterized as being primarily located in punctate structures, but their images had all been annotated by visual examination as being simply “vesicular”. We were able to show that these patterns could be distinguished from each other with high accuracy, and we were able to assign to one of these subclasses hundreds of proteins whose subcellular localization had not previously been well defined. In addition to providing these novel annotations, we built a generative approach to modeling of punctate distributions that captures the essential characteristics of the distinct patterns. Such models are expected to be valuable for representing and summarizing each pattern and for constructing systems biology simulations of cell behaviors., Author Summary Determining the subcellular location of all proteins is a critical but daunting task for systems biologists, especially when variation between different cell types is considered. Fluorescence microscopy is the main source of information about subcellular location, but large collections of fluorescence images for many proteins are frequently annotated visually and result in assignment only to broad categories. In this paper, we describe automated methods for analyzing images from the Human Protein Atlas to identify nine specific punctate patterns and assign these more specific annotations to 550 proteins many of which previously had little information about subcellular location. We also describe building models of these patterns that will be useful for carrying out systems biology simulations of cellular reactions using accurate spatial distributions.

Biological shape modeling is an essential task that is required for systems biology efforts to simulate complex cell behaviors. Statistical learning methods have been used to build generative shape models based on reconstructive shape parameters extracted from microscope image collections. However, such parametric modeling approaches are usually limited to simple shapes and easily-modeled parameter distributions. Moreover, to maximize the reconstruction accuracy, significant effort is required to design models for specific datasets or patterns. We have therefore developed an instance-based approach to model biological shapes within a shape space built upon diffeomorphic measurement. We also designed a recursive interpolation algorithm to probabilistically synthesize new shape instances using the shape space model and the original instances. The method is quite generalizable and therefore can be applied to most nuclear, cell and protein object shapes, in both 2D and 3D.

Location proteomics is concerned with the systematic analysis of the subcellular location of proteins. In order to perform comprehensive analysis of all protein location patterns, automated methods are needed. With the goal of extending automated subcellular location pattern analysis methods to high resolution images of tissues, 3D confocal microscope images of polarized CaCo 2 cells immunostained for various proteins were collected. A three-color staining protocol was developed that permits parallel imaging of proteins of interest as well as DNA and the actin cytoskeleton. The collection is composed of 11 to 21 images for each of the 9 proteins that depict major subcellular patterns. A classifier was trained to recognize the subcellular location pattern of segmented cells with an accuracy of 89.2%. Using the Prior Updating method allowed improvement of this accuracy to 99.6%. This study demonstrates the benefit of using a graphical model approach for improving the pattern classification in tissue images.

While basic principles of microtubule organization are well understood, much remains to be learned about the extent and significance of variation in that organization among cell types and conditions. Large numbers of images of microtubule distributions for many cell types can be readily obtained by high throughput fluorescence microscopy but direct estimation of the parameters underlying the organization is problematic because it is difficult to resolve individual microtubules present at the microtubule-organizing center or at regions of high crossover. Previously, we developed an indirect, generative model-based approach that can estimate such spatial distribution parameters as the number and mean length of microtubules. In order to validate this approach, we have applied it to 3D images of NIH 3T3 cells expressing fluorescently-tagged tubulin in the presence and absence of the microtubule depolymerizing drug nocodazole. We describe here the first application of our inverse modeling approach to live cell images and demonstrate that it yields estimates consistent with expectations.

The Human Protein Atlas (HPA) is a repository of location patterns of about 11000 proteins within tissues and cell lines. In this work we summarize some of our current work on analyzing immunohistochemical images of proteins within 7 distinct tissues. Firstly, we present our efforts to analyze spatial point patterns of protein staining and determine protein subcellular location from image-derived proximity features. Next, we present a method to identify proteins that have differential location between normal and cancer tissue. These methods provide a principled way to characterize location diversity of the proteome across normal and cancer tissues.

Proper subcellular localization is critical for proteins to perform their roles in cellular functions. Proteins are transported by different cellular sorting pathways, some of which take a protein through several intermediate locations until reaching its final destination. The pathway a protein is transported through is determined by carrier proteins that bind to specific sequence motifs. In this paper we present a new method that integrates sequence, motif and protein interaction data to model how proteins are sorted through these targeting pathways. We use a hidden Markov model (HMM) to represent protein targeting pathways. The model is able to determine intermediate sorting states and to assign carrier proteins and motifs to the sorting pathways. In simulation studies, we show that the method can accurately recover an underlying sorting model. Using data for yeast, we show that our model leads to accurate prediction of subcellular localization. We also show that the pathways learned by our model recover many known sorting pathways and correctly assign proteins to the path they utilize. The learned model identified new pathways and their putative carriers and motifs and these may represent novel protein sorting mechanisms. Supplementary results and software implementation are available from http://murphylab.web.cmu.edu/software/2010_RECOMB_pathways/

We have previously described the isolation of a Chinese hamster ovary cell line, TfT1.11, that has a pleiotropic, temperature-conditional defect in receptor recycling (Cain, C. C., Wilson, R. B., and Murphy, R. F. (1991) J. Biol. Chem. 266, 11746-11752). These cells show a rapid loss of cell surface receptors upon temperature shift due to a reduction in the rate of receptor recycling. We show here that, in addition to altered receptor recycling, TfT1.11 cells show three defects in lysosome biogenesis. At the nonpermissive temperature, they 1) redistribute at least one lysosomal enzyme from lysosomes to endosomes, 2) fail to transfer fluid-phase material from early endosomes to later compartments, and 3) fail to accumulate fluid-phase markers due to increased efflux of internalized material. The results suggest that the processes of recycling from the endosome and movement of material from endosomes to lysosomes are tightly linked.

The thiopurines have a wide array of effects on purine metabolism, but the primary mechanism of cytotoxicity for both 6-mercaptopurine (6-MP) and 6-thioguanine (6-TG) appears to be incorporation of drug into DNA following conversion to the thioguanylate form. In murine leukemic cell lines exposed to a range of thiopurine concentrations in vitro, cell survival curves have displayed a phenomenon termed paradoxical cytotoxicity, defined as a decrease in cytotoxicity with increasing drug concentration. The paradoxical cytotoxicity of thiopurines has usually been attributed to concentration-dependent perturbations in the cell cycle. The present study assessed whether the paradoxical cytotoxicity of 6-MP occurred in cultured human leukemic cells, and investigated the biochemical and cell-cycle alterations occurring in these lines at thiopurine concentrations associated with the reverse of cytotoxicity. Paradoxical cytotoxicity was observed in the two human leukemic cell lines examined, but only when 6-MP concentrations exceeded 100 microM. The extent of incorporation of 6-MP metabolites into DNA as thiol-versus non-thiol-containing metabolites was analyzed by performing parallel experiments with 14C- and 35S-radiolabeled drug. With 5 microM 6-MP, approximately 50% of drug was incorporated into DNA as a thionucleotide; however, with increasing drug concentrations, the degree of thionucleotide incorporation remained unchanged or decreased, and the amount incorporated as the desulfurated metabolite (presumably adenylate or guanylate) increased. With 500 microM 6-MP, less than 10% of the drug was incorporated as the thionucleotide. Perturbations in cell cycle reflected the relative amounts of thiol- and non-thiol-containing nucleotide formed at various concentrations of 6-MP. These results suggest that thiopurines may be vulnerable to a unique mechanism of detoxification, in which a human cell can metabolize a cytotoxic drug to a comparatively potent "self-rescue" agent.

Purpose To assess the role of the recombinant bacterial enzyme, glucarpidase (carboxypeptidase-G2), leucovorin, and thymidine in the management and outcome of patients with high-dose methotrexate (HDMTX) –induced nephrotoxicity. Methods Patients with HDMTX-induced nephrotoxicity received one to three doses of intravenous (IV) glucarpidase and leucovorin rescue. The initial cohort (n = 35) also received thymidine by continuous IV infusion. Subsequently, thymidine was restricted to patients with prolonged exposure (> 96 hours) to methotrexate (MTX) or with substantial MTX toxicity at study entry. Plasma MTX, leucovorin, and 5-methyltetrahydrofolate (5-mTHF) concentrations were measured pre- and postglucarpidase. Toxicities were monitored, and logistic regression analysis was used to assess the relationship of baseline characteristics to the development of severe toxicity and death. Results Glucarpidase was administered at a median of 96 hours (receiving thymidine, n = 44) and 66 hours (not receiving thymidine, n = 56) after the start of the MTX infusion. Plasma MTX concentrations decreased within 15 minutes of glucarpidase by 98.7%. Plasma 5-mTHF concentrations also decreased more than 98% after administration of glucarpidase. Of 12 deaths, six were directly attributed to irreversible MTX toxicity. Presence of grade 4 toxicity before administration of glucarpidase, inadequate initial increase in leucovorin dosing, and administration of glucarpidase more than 96 hours after the start of the MTX infusion were associated with development of grade 4 and 5 toxicity. Conclusion Early intervention with the combination of leucovorin and glucarpidase is highly effective in patients who develop HDMTX-induced renal dysfunction. Severe toxicity and mortality occurred in patients in whom glucarpidase rescue was delayed and occurred despite thymidine administration.

The SLIF project combines text-mining and image processing to extract structured information from biomedical literature. SLIF extracts images and their captions from published papers. The captions are automatically parsed for relevant biological entities (protein and cell type names), while the images are classified according to their type (e.g., micrograph or gel). Fluorescence microscopy images are further processed and classified according to the depicted subcellular localization. The results of this process can be queried online using either a user-friendly web-interface or an XML-based web-service. As an alternative to the targeted query paradigm, SLIF also supports browsing the collection based on latent topic models which are derived from both the annotated text and the image data. The SLIF web application, as well as labeled datasets used for training system components, is publicly available at http://slif.cbi.cmu.edu.

In order to build more accurate models of cells and tissues, the ability to accurate information on the distributions of proteins (and other macromolecules) will become increasingly important. This review describes current progress towards determining and representing protein subcellular patterns so that the information can be used as part of systems biology efforts. Approaches to decomposing an image of the subcellular pattern of a protein give critical information about the fraction of that protein in each of a number of fundamental patterns (e.g., organelles). Methods for learning generative models from images provide a means of capturing the essential properties, and variation in those properties, of cell shape and organelle patterns. The combination of models of fundamental patterns and vectors specifying the fraction of a protein in each of them provide a much better means of communicating subcellular patterns than the descriptive terms that are currently used. Communicating information about subcellular patterns is important not only for systems biology simulations but also for representing results from microscopy experiments, including high content screening and imaging flow cytometry, in a transportable and generalizable manner.

The field of high-content screening and analysis consists of a set of methodologies for automated discovery in cell biology and drug development using large amounts of image data. In most cases, imaging is carried out by automated microscopes, often assisted by automated liquid handling and cell culture. Image processing, computer vision, and machine learning are used to automatically process high-dimensional image data into meaningful cell biological results. The key is creating automated analysis pipelines typically consisting of 4 basic steps: (1) image processing (normalization, segmentation, tracing, tracking), (2) spatial transformation to bring images to a common reference frame (registration), (3) computation of image features, and (4) machine learning for modeling and interpretation of data. An overview of these image analysis tools is presented here, along with brief descriptions of a few applications.

Motivation: Proteins exhibit complex subcellular distributions, which may include localizing in more than one organelle and varying in location depending on the cell physiology. Estimating the amount of protein distributed in each subcellular location is essential for quantitative understanding and modeling of protein dynamics and how they affect cell behaviors. We have previously described automated methods using fluorescent microscope images to determine the fractions of protein fluorescence in various subcellular locations when the basic locations in which a protein can be present are known. As this set of basic locations may be unknown (especially for studies on a proteome-wide scale), we here describe unsupervised methods to identify the fundamental patterns from images of mixed patterns and estimate the fractional composition of them. Methods: We developed two approaches to the problem, both based on identifying types of objects present in images and representing patterns by frequencies of those object types. One is a basis pursuit method (which is based on a linear mixture model), and the other is based on latent Dirichlet allocation (LDA). For testing both approaches, we used images previously acquired for testing supervised unmixing methods. These images were of cells labeled with various combinations of two organelle-specific probes that had the same fluorescent properties to simulate mixed patterns of subcellular location. Results: We achieved 0.80 and 0.91 correlation between estimated and underlying fractions of the two probes (fundamental patterns) with basis pursuit and LDA approaches, respectively, indicating that our methods can unmix the complex subcellular distribution with reasonably high accuracy. Availability: http://murphylab.web.cmu.edu/software Contact: murphy@cmu.edu

The microtubule network plays critical roles in many cellular processes, and quantitative models of how its organization varies across cell types and conditions are required for understanding those roles and as input to cell simulations. High-throughput image acquisition technologies are potentially valuable for this purpose, but do not provide sufficient resolution for current analysis methods that rely on tracing of individual microtubules. We describe a parametric conditional model of microtubule distribution that can generate a microtubule network in intact cells using a persistent random walk approach. The model parameters are physically meaningful as they directly describe the spatial distribution of microtubules and include the number of microtubules as well as the mean of the length distribution. We also present an indirect method for estimating the parameters of the model from three-dimensional fluorescence microscope images of cells that relies on comparing acquired images with simulated images generated from the model. Our results show that our method can reasonably recover parameters for a given query image, and we present the distributions of parameters estimated by our method for a collection of HeLa cell images. (c) 2010 International Society for Advancement of Cytometry.

The field of bioimage informatics concerns the development and use of methods for computational analysis of biological images. Traditionally, analysis of such images has been done manually. Manual annotation is, however, slow, expensive, and often highly variable from one expert to another. Furthermore, with modern automated microscopes, hundreds to thousands of images can be collected per hour, making manual analysis infeasible. This field borrows from the pattern recognition and computer vision literature (which contain many techniques for image processing and recognition), but has its own unique challenges and tradeoffs. Fluorescence microscopy images represent perhaps the largest class of biological images for which automation is needed. For this modality, typical problems include cell segmentation, classification of phenotypical response, or decisions regarding differentiated responses (treatment vs. control setting). This overview focuses on the problem of subcellular location determination as a running example, but the techniques discussed are often applicable to other problems.

PURPOSE Because there is a compelling need to develop new agents for intrathecal use, we investigated the safety, efficacy, and CSF pharmacokinetics of diaziquone (AZQ) following intrathecal administration in patients with refractory meningeal malignancies. PATIENTS AND METHODS Thirty-nine patients received 45 courses of intrathecal AZQ. Two schedules were studied; twice-weekly administration of a 1- or 2-mg dose and "concentration times time" (C x T) administration of 0.5 mg every 6 hours for three doses, administered once weekly. RESULTS Dose-limiting toxicity consisting of headache, nausea, or vomiting occurred in only three patients and only at the 2-mg, twice weekly dose. The schedules of 1 mg twice-weekly and 0.5 mg every 6 hours for three doses were well tolerated. Thirty-seven courses were assessable for response. The overall response rate was 62%. Complete responses (CRs) occurred in 14 of 37 courses (38%) and partial responses (PRs) occurred in nine of 37 courses (24%). Among patients with meningeal leukemia, CRs were observed in 11 of 26 courses (42%) and PRs in nine of 26 courses (35%). There was no difference in response rate related to dose or schedule. The pharmacokinetic behavior of intrathecally administered AZQ was characterized by biexponential disappearance from ventricular CSF, with mean half-lives of 18.2 and 78.6 minutes. The mean clearance rate was 0.37 mL/min. CONCLUSION Intrathecal AZQ is safe, well tolerated, and highly active against refractory meningeal malignancies.

Herpes simplex virus (HSV) remains an important cause of meningitis and encephalitis in adults, and polymerase chain reaction (PCR)-based tests are the "gold standard" for diagnosis. The objective of this study was to assess differences in semiquantitative PCR values, based on the crossing threshold (Ct) value for cases of HSV meningitis and encephalitis. A retrospective chart review was performed for 29 patients with a documented diagnosis of HSV meningitis or encephalitis during the course of 5 years from a large urban academic hospital. Demographic information, cerebrospinal fluid (CSF) laboratory findings, and PCR data were obtained for each patient. There was no statistically significant difference in the Ct values in patients with meningitis or encephalitis. In addition, no differences were found in CSF chemistry and hematology parameters. There is a broad range in relative concentrations of HSV DNA in the CSF in cases of meningitis and encephalitis.

Protein subcellular location is one of the most important determinants of protein function during cellular processes. Changes in protein behavior during the cell cycle are expected to be involved in cellular reprogramming during disease and development, and there is therefore a critical need to understand cell-cycle dependent variation in protein localization which may be related to aberrant pathway activity. With this goal, it would be useful to have an automated method that can be applied on a proteomic scale to identify candidate proteins showing cell-cycle dependent variation of location. Fluorescence microscopy, and especially automated, high-throughput microscopy, can provide images for tens of thousands of fluorescently-tagged proteins for this purpose. Previous work on analysis of cell cycle variation has traditionally relied on obtaining time-series images over an entire cell cycle; these methods are not applicable to the single time point images that are much easier to obtain on a large scale. Hence a method that can infer cell cycle-dependence of proteins from asynchronous, static cell images would be preferable. In this work, we demonstrate such a method that can associate protein pattern variation in static images with cell cycle progression. We additionally show that a one-dimensional parameterization of cell cycle progression and protein feature pattern is sufficient to infer association between localization and cell cycle.

A major source of information (often the most crucial and informative part) in scholarly articles from scientific journals, proceedings and books are the figures that directly provide images and other graphical illustrations of key experimental results and other scientific contents. In biological articles, a typical figure often comprises multiple panels, accompanied by either scoped or global captioned text. Moreover, the text in the caption contains important semantic entities such as protein names, gene ontology, tissues labels, etc., relevant to the images in the figure. Due to the avalanche of biological literature in recent years, and increasing popularity of various bio-imaging techniques, automatic retrieval and summarization of biological information from literature figures has emerged as a major unsolved challenge in computational knowledge extraction and management in the life science. We present a new structured probabilistic topic model built on a realistic figure generation scheme to model the structurally annotated biological figures, and we derive an efficient inference algorithm based on collapsed Gibbs sampling for information retrieval and visualization. The resulting program constitutes one of the key IR engines in our SLIF system that has recently entered the final round (4 out 70 competing systems) of the Elsevier Grand Challenge on Knowledge Enhancement in the Life Science. Here we present various evaluations on a number of data mining tasks to illustrate our method.

The Human Protein Atlas is a rich source of location proteomics data. In this work, we present an automated approach for processing and classifying major subcellular patterns in the Atlas images. We demonstrate that two different classification frameworks (support vector machine and random forest) are effective at determining subcellular locations; we can analyze over 3500 Atlas images with a high degree of accuracy, up to 87.5% for all of the samples and 98.5% when only considering samples in whose classification assignments we are most confident. Moreover, the features obtained in both of these frameworks are observed to be highly consistent and generalizable. Additionally, we observe that the features relating the proteins to cell markers are especially important in automated learning approaches.

Image segmentation is an essential step in many image analysis pipelines and many algorithms have been proposed to solve this problem. However, they are often evaluated subjectively or based on a small number of examples. To fill this gap, we hand-segmented a set of 97 fluorescence microscopy images (a total of 4009 cells) and objectively evaluated some previously proposed segmentation algorithms.We focus on algorithms appropriate for high-throughput settings, where only minimal user intervention is feasible.The hand-labeled dataset (and all software used to compare methods) is publicly available to enable others to use it as a benchmark for newly proposed algorithms.

The detection of protein–protein interactions through two-hybrid assays has revolutionized our understanding of biology. The remarkable impact of two-hybrid assay platforms derives from their speed, simplicity, and broad applicability. Yet for many organisms, the need to express test proteins in Saccharomyces cerevisiae or Escherichia coli presents a substantial barrier because variations in codon specificity or bias may result in aberrant protein expression. In particular, nonstandard genetic codes are characteristic of several eukaryotic pathogens, for which there are currently no genetically based systems for detection of protein–protein interactions. We have developed a protein–protein interaction assay that is carried out in native host cells by using GFP as the only foreign protein moiety, thus circumventing these problems. We show that interaction can be detected between two protein pairs in both the model yeast S. cerevisiae and the fungal pathogen Candida albicans. We use computational analysis of microscopic images to provide a quantitative and automated assessment of confidence.

Proteomics seeks the systematic and comprehensive understanding of all aspects of proteins, and location proteomics is the relatively new subfield of proteomics concerned with the location of proteins within cells. This review provides a guide to the widening selection of methods for studying location proteomics and integrating the results into systems biology. Automated and objective methods for determining protein subcellular location have been described based on extracting numerical features from fluorescence microscope images and applying machine learning approaches to them. Systems to recognize all major protein subcellular location patterns in both two-dimensional and three-dimensional HeLa cell images with high accuracy (over 95% and 98%, respectively) have been built. The feasibility of objectively grouping proteins into subcellular location families, and in the process of discovering new subcellular patterns, has been demonstrated using cluster analysis of images from a library of randomly tagged protein clones. Generative models can be built to effectively capture and communicate the patterns in these families. While automated methods for high-resolution determination of subcellular location are now available, the task of applying these methods to all expressed proteins in many different cell types under many conditions represents a very significant challenge.

The usefulness and efficacy of cisplatin, a chemotherapeutic drug, are limited by its toxicity to normal tissues and organs, including the kidneys. The uptake of cisplatin in renal tubular cells is high, leading to cisplatin accumulation and tubular cell injury and death, culminating in acute renal failure. While extensive investigations have been focused on the signaling pathways of cisplatin nephrotoxicity, much less is known about the mechanism of cisplatin uptake by renal cells and tissues. In this regard, evidence has been shown for the involvement of organic cation transporters (OCT), specifically OCT2. The copper transporter Ctr1 is highly expressed in the renal tubular cells; however, its role in cisplatin nephrotoxicity is not known. In this study, we demonstrate that Ctr1 is mainly expressed in both proximal and distal tubular cells in mouse kidneys. We further show that Ctr1 is mainly localized on the basolateral side of these cells, a proposed site for cisplatin uptake. Importantly, downregulation of Ctr1 by small interfering RNA or copper pretreatment results in decreased cisplatin uptake. Consistently, downregulation of Ctr1 suppresses cisplatin toxicity, including cell death by both apoptosis and necrosis. Cimetidine, a pharmacological inhibitor of OCT2, can also partially attenuate cisplatin uptake. Notably, cimetidine can further reduce cisplatin uptake and cisplatin toxicity in Ctr1-downregulated cells. The results have demonstrated the first evidence for a role of Ctr1 in cisplatin uptake and nephrotoxicity.

Proteomics seeks the systematic and comprehensive understanding of all aspects of proteins, and location proteomics is the relatively new subfield of proteomics concerned with the location of proteins within cells. This review provides a guide to the widening selection of methods for studying location proteomics and integrating the results into systems biology. Automated and objective methods for determining protein subcellular location have been described based on extracting numerical features from fluorescence microscope images and applying machine learning approaches to them. Systems to recognize all major protein subcellular location patterns in both two-dimensional and three-dimensional HeLa cell images with high accuracy (over 95% and 98%, respectively) have been built. The feasibility of objectively grouping proteins into subcellular location families, and in the process of discovering new subcellular patterns, has been demonstrated using cluster analysis of images from a library of randomly tagged protein clones. Generative models can be built to effectively capture and communicate the patterns in these families. While automated methods for high-resolution determination of subcellular location are now available, the task of applying these methods to all expressed proteins in many different cell types under many conditions represents a very significant challenge.

The flow cytometry data file standard provides the specifications needed to completely describe flow cytometry data sets within the confines of the file containing the experimental data. In 1984, the first Flow Cytometry Standard format for data files was adopted as FCS 1.0. This standard was modified in 1990 as FCS 2.0 and again in 1997 as FCS 3.0. We report here on the next generation flow cytometry standard data file format. FCS 3.1 is a minor revision based on suggested improvements from the community. The unchanged goal of the standard is to provide a uniform file format that allows files created by one type of acquisition hardware and software to be analyzed by any other type.The FCS 3.1 standard retains the basic FCS file structure and most features of previous versions of the standard. Changes included in FCS 3.1 address potential ambiguities in the previous versions and provide a more robust standard. The major changes include simplified support for international characters and improved support for storing compensation. The major additions are support for preferred display scale, a standardized way of capturing the sample volume, information about originality of the data file, and support for plate and well identification in high throughput, plate based experiments. Please see the normative version of the FCS 3.1 specification in Supporting Information for this manuscript (or at http://www.isac-net.org/ in the Current standards section) for a complete list of changes.

Thirty-one assessable patients with metastatic adenocarcinoma of the gastrointestinal tract were entered onto a pilot study designed to assess the impact of recombinant interferon alpha-2a (rIFN alpha-2a) on the toxicity and pharmacokinetics of fluorouracil (5-FU) and leucovorin (LV). Patients received an initial cycle of 5-FU (370 or 425 mg/m2/d) with LV (500 mg/m2/d) for 5 days. If tolerated, the patient received the same dose of 5-FU/LV for the second cycle on days 2 to 6, with rIFN alpha-2a at 5 x 10(6) or 10 x 10(6) U/m2/d on days 1 to 7, or with 3 x 10(6) U/m2/d on days 1 to 14. In 26 matched cycles, rIFN alpha-2a administration was associated with an increased incidence of dose-limiting mucositis and diarrhea and a significantly lower median platelet nadir; rIFN alpha-2a did not significantly affect the median WBC or granulocyte nadir. Dose-limiting toxicity occurred in all six patients entered at 425 mg/m2/d of 5-FU/LV within two cycles. The majority of patients treated with 370 mg/m2/d of 5-FU/LV and 10 x 10(6) U/m2/d rIFN alpha-2a experienced grade 3 to 4 mucositis and diarrhea, whereas patients receiving 3 x 10(6) and 5 x 10(6) U/m2/d rIFN alpha-2a had acceptable toxicity. Administration of rIFN alpha-2a was associated with a dose-dependent decrease in 5-FU clearance. The increase in the area under the 5-FU concentration-time curve (AUC) was 1.3-fold and 1.5-fold in patients receiving 5 x 10(6) and 10 x 10(6) U/m2/d rIFN alpha-2a, respectively. Thus, the increase in 5-FU toxicity with rIFN alpha-2a may be explained by alterations in 5-FU pharmacokinetics. In 22 patients without prior 5-FU therapy, three complete (13.6%) and seven partial (31.8%) responses were seen, for an overall response rate of 45.4% (95% confidence interval, 24.4% to 67.8%). Since the 5 x 10(6) U/m2/d dose of rIFN alpha-2a increased the 5-FU drug exposure and was associated with acceptable toxicity, we recommend its further evaluation as given on days 1 to 7 in combination with 5-FU 370 mg/m2/d, with high-dose LV given on days 2 to 6.

We have isolated several Chinese hamster ovary cell lines with temperature-sensitive defects in the recycling of receptors after endocytosis. These cell lines were selected using fluorescence-activated cell sorting for retention of a pulse of labeled transferrin after a chase in the presence of unlabeled transferrin. One of these cell lines, TfT1.11, was selected for further characterization. In TfT1.11 the trapping of transferrin within the cells is paralleled by a loss of cell surface transferrin receptors. Within 4 h after the shift from 33 to 41 degrees C the surface binding of transferrin is reduced to 18% of parental cells at 41 degrees C. The trapping of transferrin and the loss of transferrin receptor from the cell surface are caused by a temperature-conditional 5.5-fold decrease in the initial rate of transferrin recycling. TfT1.11 cells also rapidly lose 89% of their ability to take up alpha 2-macroglobulin after the temperature shift to 41 degrees C. These data indicate that the TfT1.11 cell line has a pleiotropic defect in receptor recycling.

In order to better understand the cellular delivery of iron from serum transferrin (Tf), we compared iron release from receptor-bound and free Tf. While free Tf did not release all iron until below pH 4.6, receptor-bound Tf released significantly more iron at mildly acidic pH, with essentially all iron released between pH 5.6 and 6.0. Since Tf is acidified to a minimum pH of 5.4 in K562 cells, this result accounts for the nearly complete extraction of iron from Tf by these cells. Comparison of fluorescence from Tf conjugated with lissamine rhodamine sulfonyl chloride (LRSC-Tf) free in solution and bound to receptor provides further evidence that the Tf receptor modulates low pH-mediated conformational changes in Tf. As pH was decreased from neutrality, the fluorescence of free LRSC-Tf began to increase below pH 6.2; the fluorescence of LRSC-Tf bound to human receptors did not increase until below pH 5.6. Binding to the Tf receptor, while facilitating iron release from Tf, appears to partially inhibit a conformational change that causes the increase in LRSC-Tf fluorescence at low pH. The fluorescence of human LRSC-Tf bound to murine receptors increases at a higher pH, 6.0, indicating that there are differences in conformational stabilization of Tf by receptors of different species. The results suggest that the Tf receptor, in addition to providing a means by which cells may internalize Tf, functions to increase the release of iron from Tf in the endosome.

Transferrin (Tf) acidification has been shown to be limited to pH 6 in murine Balb/c 3T3 fibroblasts, human A549 epidermoid carcinoma cells, and Chinese hamster ovary cells and is followed by alkalinization during recycling. In contrast, Tf acidification in the human erythroleukemic cell line K562 proceeds to below pH 5.5, and alkalinization of internal Tf during recycling is not observed. To explore the regulation of endosomal pH in K562 cells, we determined whether the existence of an early endosome of pH 6 could be demonstrated in K562 cells. The kinetics of Tf internalization, acidification, and recycling were determined at temperatures which block recycling of Tf in 3T3 cells. As in 3T3, Tf recycling in K562 was inhibited at 24 degrees C and below. At these temperatures, Tf internalization and acidification were delayed relative to 37 degrees C, yet the minimum pH achieved was below 5.5. Temperatures at or below 19 degrees C resulted in a complete block in recycling (at least over 40 min), which was rapidly reversible by incubation at 37 degrees C. Ouabain (a specific inhibitor of the Na+,K(+)-ATPase) had no effect on K562 Tf acidification, indicating that K562 endosomal pH is probably not regulated by the Na+,K(+)-ATPase. The results suggest that differentiation of the early endocytic pathway in erythroid cells involves changes such that the pH of Tf-containing compartments is not limited to 6 by the Na+,K(+)-ATPase.

One of the goals of Current Protocols is to provide information from the very basic level to the very advanced. This unit is an excellent example of providing explanation and application for a complex area. Building upon several other units on data analysis, it provides a detailed explanation of how multivariate analytical techniques are applied to flow cytometry data. The unit deals with the covariance matrix, principal component analysis, and cluster analysis and provides sources for software as well as example data files. This material will be particularly valuable to the graduate student trying to obtain a good understanding of statistical methods in flow cytometry, as well as to the experienced investigator who uses these tools on a regular basis without really understanding how they operate. Keywords: principal components analysis; cluster analysis; FCS file format One of the goals of Current Protocols is to provide information from the very basic level to the very advanced

Our group has previously used machine learning techniques to develop computational systems to automatically analyse fluorescence microscope images and classify the location of the depicted protein. Based on this work, we developed a system, the Subcellular Location Image Finder (slif), which mines images from scientific journals for analysis.

Early cancer diagnosis and evaluation of cancer progression during treatment are two important factors for clinical therapy. In this study we propose a novel approach which automatically compares the subcellular location of proteins between normal and cancerous tissues in order to identify proteins whose distribution is modified by oncogenesis. This study analyzes 258 proteins in 14 different cancer tissues and their corresponding normal tissues using images provided by the tissue microarray collection of the Human Protein Atlas. Using texture features automatically extracted from the tissue images, 14 machine classifiers were trained to recognize the patterns of eight major organelles in each tissue. For each tissue-protein combination, the results of the classifier for normal and cancerous tissues were compared. Eleven proteins were identified as showing differences in location; these proteins may have potential as biomarkers.

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.

� Abstract The empirical characterization of nuclear shape distributions is an important unsolved problem with many applications in biology and medicine. Numerous genetic diseases and cancers have alterations in nuclear morphology, and methods for characterization of morphology could aid in both diagnoses and fundamental understanding of these disorders. Automated approaches have been used to measure features related to the size and shape of the cell nucleus, and statistical analysis of these features has often been performed assuming an underlying Euclidean (linear) vector space. We discuss the difficulties associated with the analysis of nuclear shape in light of the fact that shape spaces are nonlinear, and demonstrate methods for characterizing nuclear shapes and shape distributions based on spatial transformations that map one nucleus to another. By combining large deformation metric mapping with multidimensional scaling we offer a flexible approach for elucidating the intrinsic nonlinear degrees of freedom of a distribution of nuclear shapes. More specifically, we demonstrate approaches for nuclear shape interpolation and computation of mean nuclear shape. We also provide a method for estimating the number of free parameters that contribute to shape as well as an approach for visualizing most representative shape variations within a distribution of nuclei. The proposed methodology can be completely automated, is independent of the dimensionality of the images, and can handle complex shapes. Results obtained by analyzing two sets of images of HeLa cells are shown. In addition to identifying the modes of variation in normal HeLa nuclei, the effects of lamin A/C on nuclear morphology are quantitatively described. ' 2007 International Society for Analytical Cytology

Modern cultural theory suffers from a diffuseness so great that it seems to subvert discourse within our scholarly community. Our theories range from sociobiology, at one extreme, through various kinds of materialism and old-fashioned functionalism to theories, at the other extreme, that locate the study of culture in language and meaning. One might hope that the protagonists of each doxology would illuminate our profession with constructive debate, but they speak past each other in a confusion of tongues; there is much talking but little dialogue. Offhand, one might say that our future seems very bad, yet we can draw some comfort from our one tried and tested anthropological truth, which is that: Things are never the way they seem. Indeed, we make our livings by showing that what seems to be one thing is actually something else, an alchemy that allows us to find in ritual either sublimated sex or a source of fertilizer. In this critical spirit, I will attempt to locate within the chaos of contemporary theory a different direction that, paradoxically, will call anthropology back to its center. One axis of distinction between anthropological schools is the degree to which they take account of the human mind, and I will confine my remarks to two persuasions at polar extremes of difference on this subject: materialism and certain directions in postmodernism and interpretation. Some materialists say that there is no such thing as "mind." There is, rather, only the human brain, into which external stimuli flow and from which behavioral responses emanate. As for this brain, one doesn't have to know what's inside it or how it works-just what goes in and what comes out. What comes out is whatever gives the biggest bang for the least effort, which is more elegantly known as "operant conditioning." And since culture is defined by the same materialists as "learned and shared behavior," it follows that when enough people get the same bangs and respond in the same way, culture is born-but John Stuart Mill said it better in his views on the greatest good for the greatest number 150 years ago. And 42 years ago, I took experimental psychology with my old friend and Columbia College classmate, Marvin Harris; he became a believer in the Gospel according to Thorndike, and I, reacting against behaviorism, became a devotee of Sigmund Freud. Isaiah Berlin sorted career thinkers into two types: Foxes and Hedgehogs. The Foxes are people who have many ideas, folks like Clifford Geertz and myself. Our trouble is that after a while nobody knows where we stand, which only be

Previous studies have shown that catecholamine secretion patterns have been imperfect predictors of clinical behavior of neuroblastomas. Recently, studies of nuclear DNA content in neuroblastoma have shown that an aneuploid DNA content predicts favorable clinical behavior. To determine if a correlation exists between these tumor biologic indicators, the authors analyzed both in a series of 39 patients with neuroblastoma. Flow cytometric DNA analysis performed on paraffin blocks determined that 23 patients had tumors with aneuploid DNA content (aneuploid tumors) and 16 patients showed no demonstrable anomalies of tumor DNA content (nonaneuploid tumors). Comparison of catecholamine levels in urine and tumor homogenates with DNA content data indicate that nonaneuploid neuroblastomas include a significant number (P less than 0.02) of biochemically primitive tumors which secrete high levels of 3,4 dihydroxyphenylalanine (DOPA), dopamine and homovanillic acid (HVA). This suggests a dopamine-norepinephrine pathway block, which supports previous reports of deficiency of dopamine beta-hydroxylase activity in some neuroblastomas. The study shows that in contrast to aneuploid tumors, nonaneuploid neuroblastomas secrete higher levels of early pathway catecholamine metabolites and are more likely to present in higher (unfavorable) clinical stages of disease.

The kinetics of exposure of endocytosed material to two lysosomal enzymes were determined for a number of cultured cell lines using fluorogenic substrates. Hydrolysis of endocytosed substrates for cathepsin B and acid phosphatase was observed to begin within 3-10 min of substrate addition and to proceed linearly for up to 60 min thereafter. Hydrolysis of the cathepsin B substrate was not affected by inhibition of protein synthesis with cycloheximide, indicating that the enzymes present in early endosomes are not exclusively newly synthesized. As had been observed previously for a cathepsin B substrate (Roederer, M., Bowser, R., and Murphy, R. F., J. Cell. Physiol., 131:200-209, 1987), hydrolysis of the acid phosphatase substrate was not blocked at temperatures below 20 degrees C. The results suggest that the endosome is the primary site of initial exposure of endocytosed material to hydrolytic enzymes.

The goal of location proteomics is the systematic and comprehensive study of protein subcellular location. We have previously developed automated, quantitative methods to identify protein subcellular location families, but there have been no effective means of communicating their patterns to integrate them with other information for building cell models. We built generative models of subcellular location that are learned from a collection of images so that they not only represent the pattern, but also capture its variation from cell to cell. Our models contain three components: a nuclear model, a cell shape model and a protein-containing object model. We built models for six patterns that consist primarily of discrete structures. To validate the generated images, we showed that they are recognized with reasonable accuracy by a classifier trained on real images. We also showed that the model parameters themselves can be used as features to discriminate the classes. The models allow the synthesis of images with the expectation that they are drawn from the same underlying statistical distribution as the images used to train them. They can potentially be combined for many proteins to yield a high resolution location map in support of systems biology. © 2007 International Society for Analytical Cytology

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 Contact: murphy@cmu.edu

We have previously built a subcellular location image finder (SLIP) system, which extracts information regarding protein subcellular location patterns from both text and images in journal articles. One important task in SLIP is to identify fluorescence microscope images. To improve the performance of this binary classification problem, a set of 7 edge features extracted from images and a set of "bag of words" text features extracted from text have been introduced in addition to the 64 intensity histogram features we have used previously. An overall accuracy of 88.6% has been achieved with an SVM classifier. A co-training algorithm has also been applied to the problem to utilize the unlabeled dataset and it substantially increases the accuracy when the training set is very small but can contribute very little when the training set is large.

articles. Associating the information from the text and image requires matching sub-flgures with the sentences in the text. We introduce a stacked graphical model, a meta-learning scheme to augment a base learner by expanding features based on related instances, to match the labels of sub-flgures with labels of sentences. The experimental results show a signiflcant improvement in the matching accuracy of the stacked graphical model (81.3%) as compared with a relational dependency network (70.8%) or the current algorithm in SLIF (64.3%).

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.