Design and Applications of a Multimodality Image Data Warehouse Framework

Journal of the American Medical Informatics Association : JAMIA. May-Jun, 2002  |  Pubmed ID: 11971885

A comprehensive data warehouse framework is needed, which encompasses imaging and non-imaging information in supporting disease management and research. The authors propose such a framework, describe general design principles and system architecture, and illustrate a multimodality neuroimaging data warehouse system implemented for clinical epilepsy research. The data warehouse system is built on top of a picture archiving and communication system (PACS) environment and applies an iterative object-oriented analysis and design (OOAD) approach and recognized data interface and design standards. The implementation is based on a Java CORBA (Common Object Request Broker Architecture) and Web-based architecture that separates the graphical user interface presentation, data warehouse business services, data staging area, and backend source systems into distinct software layers. To illustrate the practicality of the data warehouse system, the authors describe two distinct biomedical applications--namely, clinical diagnostic workup of multimodality neuroimaging cases and research data analysis and decision threshold on seizure foci lateralization. The image data warehouse framework can be modified and generalized for new application domains.

Workflow-enabled Distributed Component-based Information Architecture for Digital Medical Imaging Enterprises

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society. Sep, 2003  |  Pubmed ID: 14518730

Few information systems today offer a flexible means to define and manage the automated part of radiology processes, which provide clinical imaging services for the entire healthcare organization. Even fewer of them provide a coherent architecture that can easily cope with heterogeneity and inevitable local adaptation of applications and can integrate clinical and administrative information to aid better clinical, operational, and business decisions. We describe an innovative enterprise architecture of image information management systems to fill the needs. Such a system is based on the interplay of production workflow management, distributed object computing, Java and Web techniques, and in-depth domain knowledge in radiology operations. Our design adapts the approach of "4+1" architectural view. In this new architecture, PACS and RIS become one while the user interaction can be automated by customized workflow process. Clinical service applications are implemented as active components. They can be reasonably substituted by applications of local adaptations and can be multiplied for fault tolerance and load balancing. Furthermore, the workflow-enabled digital radiology system would provide powerful query and statistical functions for managing resources and improving productivity. This paper will potentially lead to a new direction of image information management. We illustrate the innovative design with examples taken from an implemented system.

Towards Effective and Rewarding Data Sharing

Neuroinformatics. 2003  |  Pubmed ID: 15046250

A Web-based Federated Neuroinformatics Model for Surgical Planning and Clinical Research Applications in Epilepsy

Neuroinformatics. 2004  |  Pubmed ID: 15067170

There is an increasing need to efficiently share diverse clinical and image data among different clinics, labs, and departments of a medical center enterprise to facilitate better quality care and more effective clinical research. In this paper, we describe a web-based, federated information model as a viable technical solution with applications in medical refractory epilepsy and other neurological disorders. We describe four such online applications developed in a federated system prototype: surgical planning, image analysis, statistical data analysis, and dynamic extraction, transforming, and loading (ETL) of data from a heterogeneous collection of data sources into an epilepsy multimedia data warehouse (EMDW). The federated information system adopts a three-tiered architecture, consisting of a user-interface layer, an application logic layer, and a data service layer. We implemented two complementary federated information technologies, i.e., XML (eXtensible Markup Language) and CORBA (Common Object Request Broker Architecture), in the prototype to enable multimedia data exchange and brain images transmission. The preliminary results show that the federated prototype system provides a uniform interface, heterogeneous information integration and efficient data sharing for users in our institution who are concerned with the care of patients with epilepsy and who pursue research in this area.

Cancer Classification and Prediction Using Logistic Regression with Bayesian Gene Selection

Journal of Biomedical Informatics. Aug, 2004  |  Pubmed ID: 15465478

In microarray-based cancer classification and prediction, gene selection is an important research problem owing to the large number of genes and the small number of experimental conditions. In this paper, we propose a Bayesian approach to gene selection and classification using the logistic regression model. The basic idea of our approach is in conjunction with a logistic regression model to relate the gene expression with the class labels. We use Gibbs sampling and Markov chain Monte Carlo (MCMC) methods to discover important genes. To implement Gibbs Sampler and MCMC search, we derive a posterior distribution of selected genes given the observed data. After the important genes are identified, the same logistic regression model is then used for cancer classification and prediction. Issues for efficient implementation for the proposed method are discussed. The proposed method is evaluated against several large microarray data sets, including hereditary breast cancer, small round blue-cell tumors, and acute leukemia. The results show that the method can effectively identify important genes consistent with the known biological findings while the accuracy of the classification is also high. Finally, the robustness and sensitivity properties of the proposed method are also investigated.

DBMap: a Space-conscious Data Visualization and Knowledge Discovery Framework for Biomedical Data Warehouse

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society. Sep, 2004  |  Pubmed ID: 15484440

Advances in digital imaging modalities as well as other diagnosis and therapeutic techniques have generated a massive amount of diverse data for clinical research. The purpose of this study is to investigate and implement a new intuitive and space-conscious visualization framework, called DBMap, to facilitate efficient multidimensional data visualization and knowledge discovery against the large-scale data warehouses of integrated image and nonimage data. The DBMap framework is built upon the TreeMap concept. TreeMap is a space constrained graphical representation of large hierarchical data sets, mapped to a matrix of rectangles, whose size and color represent interested database fields. It allows the display of a large amount of numerical and categorical information in limited real estate of the computer screen with an intuitive user interface. DBMap has been implemented and integrated into a large brain research data warehouse to support neurologic and neuroradiologic research at the University of California, San Francisco Medical Center. For imaging specialists and clinical researchers, this novel DBMap framework facilitates another way to better explore and classify the hidden knowledge embedded in medical image data warehouses.

A Neuroinformatics Database System for Disease-oriented Neuroimaging Research

Academic Radiology. Mar, 2004  |  Pubmed ID: 15035525

Clinical databases are continually growing and accruing more patient information. One of the challenges for managing this wealth of data is efficient retrieval and analysis of a broad range of image and non-image patient data from diverse data sources. This article describes the design and implementation of a new class of research data warehouse, neuroinformatics database system (NIDS), which will alleviate these problems for clinicians and researchers studying and treating patients with intractable temporal lobe epilepsy. The NIDS is a secured, multi-tier system that enables the user to gather, proofread, analyze, and store data from multiple underlying sources. In addition to data management, the NIDS provides several key functions including image analysis and processing, free text search of patient reports, construction of general queries, and on-line statistical analysis. The establishment of this integrated research database will serve as a foundation for future hypothesis-driven experiments, which could uncover previously unsuspected correlations and perhaps help to identify new and accurate predictors for image diagnosis.

Boosting Alternating Decision Trees Modeling of Disease Trait Information

BMC Genetics. 2005  |  Pubmed ID: 16451591

We applied the alternating decision trees (ADTrees) method to the last 3 replicates from the Aipotu, Danacca, Karangar, and NYC populations in the Problem 2 simulated Genetic Analysis Workshop dataset. Using information from the 12 binary phenotypes and sex as input and Kofendrerd Personality Disorder disease status as the outcome of ADTrees-based classifiers, we obtained a new quantitative trait based on average prediction scores, which was then used for genome-wide quantitative trait linkage (QTL) analysis. ADTrees are machine learning methods that combine boosting and decision trees algorithms to generate smaller and easier-to-interpret classification rules. In this application, we compared four modeling strategies from the combinations of two boosting iterations (log or exponential loss functions) coupled with two choices of tree generation types (a full alternating decision tree or a classic boosting decision tree). These four different strategies were applied to the founders in each population to construct four classifiers, which were then applied to each study participant. To compute average prediction score for each subject with a specific trait profile, such a process was repeated with 10 runs of 10-fold cross validation, and standardized prediction scores obtained from the 10 runs were averaged and used in subsequent expectation-maximization Haseman-Elston QTL analyses (implemented in GENEHUNTER) with the approximate 900 SNPs in Hardy-Weinberg equilibrium provided for each population. Our QTL analyses on the basis of four models (a full alternating decision tree and a classic boosting decision tree paired with either log or exponential loss function) detected evidence for linkage (Z >or= 1.96, p < 0.01) on chromosomes 1, 3, 5, and 9. Moreover, using average iteration and abundance scores for the 12 phenotypes and sex as their relevancy measurements, we found all relevant phenotypes for all four populations except phenotype b for the Karangar population, with suggested subgroup structure consistent with latent traits used in the model. In conclusion, our findings suggest that the ADTrees method may offer a more accurate representation of the disease status that allows for better detection of linkage evidence.

Multiclass Cancer Classification by Using Fuzzy Support Vector Machine and Binary Decision Tree with Gene Selection

Journal of Biomedicine & Biotechnology. Jun, 2005  |  Pubmed ID: 16046822

We investigate the problems of multiclass cancer classification with gene selection from gene expression data. Two different constructed multiclass classifiers with gene selection are proposed, which are fuzzy support vector machine (FSVM) with gene selection and binary classification tree based on SVM with gene selection. Using F test and recursive feature elimination based on SVM as gene selection methods, binary classification tree based on SVM with F test, binary classification tree based on SVM with recursive feature elimination based on SVM, and FSVM with recursive feature elimination based on SVM are tested in our experiments. To accelerate computation, preselecting the strongest genes is also used. The proposed techniques are applied to analyze breast cancer data, small round blue-cell tumors, and acute leukemia data. Compared to existing multiclass cancer classifiers and binary classification tree based on SVM with F test or binary classification tree based on SVM with recursive feature elimination based on SVM mentioned in this paper, FSVM based on recursive feature elimination based on SVM can find most important genes that affect certain types of cancer with high recognition accuracy.

Parameters Selection in Gene Selection Using Gaussian Kernel Support Vector Machines by Genetic Algorithm

Journal of Zhejiang University. Science. B. Oct, 2005  |  Pubmed ID: 16187409

In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear statistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two representative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method performs well in selecting genes and achieves high classification accuracies with these genes.

76-space Analysis of Grey Matter Diffusivity: Methods and Applications

Medical Image Computing and Computer-assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 2005  |  Pubmed ID: 16685840

Diffusion Weighted Imaging (DWI) and Diffusion Tensor Imaging (DTI) are widely used in the study and diagnosis of neurological diseases involving the White Matter (WM). However, many neurological and neurodegenerative diseases (e.g., Alzheimer's disease and Creutzfeldt-Jakob disease) are generally considered to involve the Grey Matter (GM). Investigation of GM diffusivity of normal aging and pathological brains has both scientific significance and clinical applications. Most of previous research reports on quantification of GM diffusivity were based on the manually labeled Region of Interests (ROI) analysis of specific neuroanatomic regions. The well-known drawbacks of ROI analysis include inter-rater variations, irreproducible results, tediousness, and requirement of a priori definition of interested regions. In this paper, we present a new framework of automated 76-space analysis of GM diffusivity using DWI/DTI. The framework will be evaluated using clinical data, and applied for study of normal brain, Creutzfeldt-Jakob disease and Schizophrenia.

Towards Automated Cellular Image Segmentation for RNAi Genome-wide Screening

Medical Image Computing and Computer-assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 2005  |  Pubmed ID: 16685930

The Rho family of small GTPases is essential for morphological changes during normal cell development and migration, as well as during disease states such as cancer. Our goal is to identify novel effectors of Rho proteins using a cell-based assay for Rho activity to perform genome-wide functional screens using double stranded RNA (dsRNAs) interference. We aim to discover genes could cause the cell phenotype changed dramatically. Biologists currently attempt to perform the genome-wide RNAi screening to identify various image phenotypes. RNAi genome-wide screening, however, could easily generate more than a million of images per study, manual analysis is thus prohibitive. Image analysis becomes a bottleneck in realizing high content imaging screens. We propose a two-step segmentation approach to solve this problem. First, we determine the center of a cell using the information in the DNA-channel by segmenting the DNA nuclei and the dissimilarity function is employed to attenuate the over-segmentation problem, then we estimate a rough boundary for each cell using a polygon. Second, we apply fuzzy c-means based multi-threshold segmentation and sharpening technology; for isolation of touching spots, marker-controlled watershed is employed to remove touching cells. Furthermore, Voronoi diagrams are employed to correct the segmentation errors caused by overlapping cells. Image features are extracted for each cell. K-nearest neighbor classifier (KNN) is employed to perform cell phenotype classification. Experimental results indicate that the proposed approach can be used to identify cell phenotypes of RNAi genome-wide screens.

Repulsive Force Based Snake Model to Segment and Track Neuronal Axons in 3D Microscopy Image Stacks

NeuroImage. Oct, 2006  |  Pubmed ID: 16861006

The branching patterns of axons and dendrites are fundamental structural properties that affect the synaptic connectivity of axons. Although today three-dimensional images of fluorescently labeled processes can be obtained to study axonal branching, there are no robust methods of tracing individual axons. This paper describes a repulsive force based snake model to segment and track axonal profiles in 3D images. This new method segments all the axonal profiles in a 2D image and then uses the results obtained from that image as prior information to help segment the adjacent 2D image. In this way, the segmentation successfully connects axonal profiles over hundreds of images in a 3D image stack. Individual axons can then be extracted based on the segmentation results. The utility and performance of the method are demonstrated using 3D axonal images obtained from transgenic mice that express fluorescent protein.

Computerized Image Analysis for Quantitative Neuronal Phenotyping in Zebrafish

Journal of Neuroscience Methods. Jun, 2006  |  Pubmed ID: 16364449

An integrated microscope image analysis pipeline is developed for automatic analysis and quantification of phenotypes in zebrafish with altered expression of Alzheimer's disease (AD)-linked genes. We hypothesize that a slight impairment of neuronal integrity in a large number of zebrafish carrying the mutant genotype can be detected through the computerized image analysis method. Key functionalities of our zebrafish image processing pipeline include quantification of neuron loss in zebrafish embryos due to knockdown of AD-linked genes, automatic detection of defective somites, and quantitative measurement of gene expression levels in zebrafish with altered expression of AD-linked genes or treatment with a chemical compound. These quantitative measurements enable the archival of analyzed results and relevant meta-data. The structured database is organized for statistical analysis and data modeling to better understand neuronal integrity and phenotypic changes of zebrafish under different perturbations. Our results show that the computerized analysis is comparable to manual counting with equivalent accuracy and improved efficacy and consistency. Development of such an automated data analysis pipeline represents a significant step forward to achieve accurate and reproducible quantification of neuronal phenotypes in large scale or high-throughput zebrafish imaging studies.

76-space Analysis of Grey Matter Diffusivity: Methods and Applications

NeuroImage. May, 2006  |  Pubmed ID: 16434215

Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) allow in vivo investigation of molecular motion of tissue water at a microscopic level in cerebral gray matter (GM) and white matter (WM). DWI/DTI measure of water diffusion has been proven to be invaluable for the study of many neurodegenerative diseases (e.g., Alzheimer's disease and Creutzfeldt-Jakob disease) that predominantly involve GM. Thus, quantitative analysis of GM diffusivity is of scientific interest and is promised to have a clinical impact on the investigation of normal brain aging and neuropathology. In this paper, we propose an automated framework for analysis of GM diffusivity in 76 standard anatomic subdivisions of gray matter to facilitate studies of neurodegenerative and other gray matter neurological diseases. The computational framework includes three enabling technologies: (1) automatic parcellation of structural MRI GM into 76 precisely defined neuroanatomic subregions ("76-space"), (2) automated segmentation of GM, WM and CSF based on DTI data, and (3) automatic measurement of the average apparent diffusion coefficient (ADC) in each segmented GM subregion. We evaluate and validate this computational framework for 76-space GM diffusivity analysis using data from normal volunteers and from patients with Creutzfeldt-Jakob disease.

Protein Structure Similarity from Principle Component Correlation Analysis

BMC Bioinformatics. 2006  |  Pubmed ID: 16436213

Owing to rapid expansion of protein structure databases in recent years, methods of structure comparison are becoming increasingly effective and important in revealing novel information on functional properties of proteins and their roles in the grand scheme of evolutionary biology. Currently, the structural similarity between two proteins is measured by the root-mean-square-deviation (RMSD) in their best-superimposed atomic coordinates. RMSD is the golden rule of measuring structural similarity when the structures are nearly identical; it, however, fails to detect the higher order topological similarities in proteins evolved into different shapes. We propose new algorithms for extracting geometrical invariants of proteins that can be effectively used to identify homologous protein structures or topologies in order to quantify both close and remote structural similarities.

A Computerized Cellular Imaging System for High Content Analysis in Monastrol Suppressor Screens

Journal of Biomedical Informatics. Apr, 2006  |  Pubmed ID: 16011909

In this paper, we describe a new bioimage informatics system developed for high content screening (HCS) applications with the goal to extract and analyze phenotypic features of hundreds of thousands of mitotic cells simultaneously. The system introduces the algorithm of multi-phenotypic mitotic analysis (MMA) and integrates that with algorithms of correlation analysis and compound clustering used in gene microarray studies. The HCS-MMA system combines different phenotypic information of cellular images obtained from three-channel acquisitions to distinguish and label individual cells at various phases of mitosis. The proposed system can also be used to extract and count the number of cells in each phase in cell-based assay experiments and archive the extracted data into a structured database for more sophisticated statistical and data analysis. To recognize different mitotic phases, binary patterns are set up based on a known biological mitotic spindle model to characterize cellular morphology of actin, microtubules, and DNA. To illustrate its utility, the HCS-MMA system has been applied to screen the quantitative response of 320 different drug compounds in suppressing Monastrol. The results are validated and evaluated by comparing the performance of HCS-MMA with visual analysis, as well as clustering of the drug compounds under evaluation.

Zebrafish Lacking Alzheimer Presenilin Enhancer 2 (Pen-2) Demonstrate Excessive P53-dependent Apoptosis and Neuronal Loss

Journal of Neurochemistry. Mar, 2006  |  Pubmed ID: 16464238

Gamma-secretase cleavage, mediated by a complex of presenilin, presenilin enhancer (Pen-2), nicastrin, and Aph-1, is the final proteolytic step in generating amyloid beta protein found in brains of Alzheimer's disease patients and Notch intracellular domain critical for proper neuronal development. Here, we employ the zebrafish model to study the role of Pen-2 in neuronal survival. We found that (i) knockdown of Pen-2 using antisense morpholino led to a reduction of islet-1 positive neurons, (ii) Notch signaling was reduced in embryos lacking Pen-2 or other gamma-secretase components, (iii) neuronal loss in Pen-2 knockdown embryos is not as a result of a lack of neuronal precursor cells or cell proliferation, (iv) absence of Pen-2 caused massive apoptosis in the whole animal, which could be suppressed by simultaneous knockdown of the tumor suppressor p53, (v) loss of islet-1 or acetylated tubulin positive neurons in Pen-2 knockdown embryos could be partially rescued by knockdown of p53. Our results demonstrate that knockdown of Pen-2 directly induces a p53-dependent apoptotic pathway that contributes to neuronal loss and suggest that Pen-2 plays an important role in promoting neuronal cell survival and protecting from apoptosis in vivo.

Mutual Information-based Feature Selection in Studying Perturbation of Dendritic Structure Caused by TSC2 Inactivation

Neuroinformatics. 2006  |  Pubmed ID: 16595860

In this study, the effect of protein Tuberous sclerosis 2 (TSC2) on the dendritic spine density and length was demonstrated by using TSC2-RNAinactivation. In addition, the role of rapamycin, an antagonist of the molecular target of rapamycin, in the morphological changes of spine caused by TSC2 silencing was investigated. The features were extracted from highresolution three-dimensional image stacks collected by two-photon laser scanning microscopy of green fluorescing pyramidal cells expressing TSC2-RNA interference (RNAi), or TSC2-RNAi and rapamycin treatment in rat hippocampal slice cultures. We proposed to apply the lognormal distribution method for feature extraction. The extracted features of three cases under investigation, namely, (1) green-fluorescent protein GFP vs TSC2-RNAi, (2) GFP vs TSC2-RNAi and rapamycin, and (3) TSC2-RNAi vs TSC2-RNAi and rapamycin, were analyzed by mutual information-based feature selection and evaluated by three classifiers, K-nearest neighbor, Perceptron, and two-layer neural networks. The results showed that both the spine density and length have significant morphological changes after TSC2-RNAi treatment. However, rapamycin treatment could reverse the effect of TSC2-RNAi on spine length but not on spine density. These results are consistent with the results reported in the scientific literature. Finally, we explored the application of pattern recognition method in a small sample with richer feature properties, namely bootstrap mutual information estimation and a mutual information- based feature selection method.

Bayesian Variable Selection for Gene Expression Modeling with Regulatory Motif Binding Sites in Neuroinflammatory Events

Neuroinformatics. 2006  |  Pubmed ID: 16595861

Multiple transcription factors (TFs) coordinately control transcriptional regulation of genes in eukaryotes. Although numerous computational methods focus on the identification of individual TF-binding sites (TFBSs), very few consider the interdependence among these sites. In this article, we studied the relationship between TFBSs and microarray gene expression levels using both family-wise and memberspecific motifs, under various combination of regression models with Bayesian variable selection, as well as motif scoring and sharing conditions, in order to account for the coordination complexity of transcription regulation. We proposed a three-step approach to model the relationship. In the first step, we preprocessed microarray data and used p-values and expression ratios to preselect upregulated and downregulated genes. The second step aimed to identify and score individual TFBSs within DNA sequence of each gene. A method based on the degree of similarity and the number of TFBSs was employed to calculate the score of each TFBS in each gene sequence. In the last step, linear regression and probit regression were used to build a predictive model of gene expression outcomes using these TFBSs as predictors. Given a certain number of predictors to be used, a full search of all possible predictor sets is usually combinatorially prohibitive. Therefore, this article considered the Bayesian variable selection for prediction using either of the regression models. The Bayesian variable selection has been applied in the context of gene selection, missing value estimation, and regulatory motif identification. In our modeling, the regressor was approximated as a linear combination of the TFBSs and a Gibbs sampler was employed to find the strongest TFBSs. We applied these regression models with the Bayesian variable selection on spinal cord injury gene expression data set. These TFs demonstrated intricate regulatory roles either as a family or as individual members in neuroinflammatory events. Our analysis can be applied to create plausible hypotheses for combinatorial regulation by TFBSs and avoiding false-positive candidates in the modeling process at the same time. Such a systematic approach provides the possibility to dissect transcription regulation, from a more comprehensive perspective, through which phenotypical events at cellular and tissue levels are moved forward by molecular events at gene transcription and translation levels.

Automated Segmentation, Classification, and Tracking of Cancer Cell Nuclei in Time-lapse Microscopy

IEEE Transactions on Bio-medical Engineering. Apr, 2006  |  Pubmed ID: 16602586

Quantitative measurement of cell cycle progression in individual cells over time is important in understanding drug treatment effects on cancer cells. Recent advances in time-lapse fluorescence microscopy imaging have provided an important tool to study the cell cycle process under different conditions of perturbation. However, existing computational imaging methods are rather limited in analyzing and tracking such time-lapse datasets, and manual analysis is unreasonably time-consuming and subject to observer variances. This paper presents an automated system that integrates a series of advanced analysis methods to fill this gap. The cellular image analysis methods can be used to segment, classify, and track individual cells in a living cell population over a few days. Experimental results show that the proposed method is efficient and effective in cell tracking and phase identification.

Automated Neurite Labeling and Analysis in Fluorescence Microscopy Images

Cytometry. Part A : the Journal of the International Society for Analytical Cytology. Jun, 2006  |  Pubmed ID: 16680708

To investigate the intricate nervous processes involved in many biological activities by computerized image analysis, accurate and reproducible labeling and measurement of neurites are prerequisite. We have developed an automated neurite analysis method to assist this task.

Least-square Conformal Brain Mapping with Spring Energy

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society. Dec, 2007  |  Pubmed ID: 17950575

The human brain cortex is a highly convoluted sheet. Mapping of the cortical surface into a canonical coordinate space is an important tool for the study of the structure and function of the brain. Here, we present a technique based on least-square conformal mapping with spring energy for the mapping of the cortical surface. This method aims to reduce the metric and area distortion while maintaining the conformal map and computation efficiency. We demonstrate through numerical results that this method effectively controls metric and area distortion, and is computational efficient. This technique is particularly useful for fast visualization of the brain cortex.

A Novel Tracing Algorithm for High Throughput Imaging Screening of Neuron-based Assays

Journal of Neuroscience Methods. Feb, 2007  |  Pubmed ID: 16987551

High throughput neuron image processing is an important method for drug screening and quantitative neurobiological studies. The method usually includes detection of neurite structures, feature extraction, quantification, and statistical analysis. In this paper, we present a new algorithm for fast and automatic extraction of neurite structures in microscopy neuron images. The algorithm is based on novel methods for soma segmentation, seed point detection, recursive center-line detection, and 2D curve smoothing. The algorithm is fully automatic without any human interaction, and robust enough for usage on images with poor quality, such as those with low contrast or low signal-to-noise ratio. It is able to completely and accurately extract neurite segments in neuron images with highly complicated neurite structures. Robustness comes from the use of 2D smoothening techniques and the idea of center-line extraction by estimating the surrounding edges. Efficiency is achieved by processing only pixels that are close enough to the line structures, and by carefully chosen stopping conditions. These make the proposed approach suitable for demanding image processing tasks in high throughput screening of neuron-based assays. Detailed results on experimental validation of the proposed method and on its comparative performance with other proposed schemes are included.

Context Based Mixture Model for Cell Phase Identification in Automated Fluorescence Microscopy

BMC Bioinformatics. 2007  |  Pubmed ID: 17263881

Automated identification of cell cycle phases of individual live cells in a large population captured via automated fluorescence microscopy technique is important for cancer drug discovery and cell cycle studies. Time-lapse fluorescence microscopy images provide an important method to study the cell cycle process under different conditions of perturbation. Existing methods are limited in dealing with such time-lapse data sets while manual analysis is not feasible. This paper presents statistical data analysis and statistical pattern recognition to perform this task.

Automated Neurite Extraction Using Dynamic Programming for High-throughput Screening of Neuron-based Assays

NeuroImage. May, 2007  |  Pubmed ID: 17363284

High-throughput screening (HTS) of cell-based assays has recently emerged as an important tool of drug discovery. The analysis and modeling of HTS microscopy neuron images, however, is particularly challenging. In this paper we present a novel algorithm for extraction and quantification of neurite segments from HTS neuron images. The algorithm is designed to be able to detect and link neurites even with complex neuronal structures and of poor imaging quality. Our proposed algorithm automatically detects initial seed points on a set of grid lines and estimates the ending points of the neurite by iteratively tracing the centerline points along the line path representing the neurite segment. The live-wire method is then applied to link the seed points and the corresponding ending points using dynamic programming techniques, thus enabling the extraction of the centerlines of the neurite segments accurately and robustly against noise, discontinuity, and other image artifacts. A fast implementation of our algorithm using dynamic programming is also provided in the paper. Any thin neurite and its segments with low intensity contrast can be well preserved by detecting the starting and ending points of the neurite. All these properties make the proposed algorithm attractive for high-throughput screening of neuron-based assays.

Detection of Molecular Particles in Live Cells Via Machine Learning

Cytometry. Part A : the Journal of the International Society for Analytical Cytology. Aug, 2007  |  Pubmed ID: 17431884

Clathrin-coated pits play an important role in removing proteins and lipids from the plasma membrane and transporting them to the endosomal compartment. It is, however, still unclear whether there exist "hot spots" for the formation of Clathrin-coated pits or the pits and arrays formed randomly on the plasma membrane. To answer this question, first of all, many hundreds of individual pits need to be detected accurately and separated in live-cell microscope movies to capture and monitor how pits and vesicles were formed. Because of the noisy background and the low contrast of the live-cell movies, the existing image analysis methods, such as single threshold, edge detection, and morphological operation, cannot be used. Thus, this paper proposes a machine learning method, which is based on Haar features, to detect the particle's position. Results show that this method can successfully detect most of particles in the image. In order to get the accurate boundaries of these particles, several post-processing methods are applied and signal-to-noise ratio analysis is also performed to rule out the weak spots.

Characteristic of Hypothalamic Kisspeptin Expression in the Pubertal Development of Precocious Female Rats

Neuroscience Letters. Jun, 2007  |  Pubmed ID: 17442487

To investigate the potential role of kisspeptin in the advance onset of puberty in precocious puberty, model rats induced by danazol were used to study the developmental expression of hypothalamic kisspeptin. Kisspeptin immunoreactive cells were observed in the arcuate nucleus (ARC), periventricular nucleus (PeN) and preoptic area (POA) in model rats on the day of onset-puberty. On the day of post-puberty, however, the number of kisspeptin immunoreactive cells in ARC and PeN decreased while the number of those cells in POA increased. Kisspeptin immunoreactive cells were not detected in hypothalamus in both normal and model rats at their pre-puberty stages. Furthermore, the expression of hypothalamic Kiss-1 mRNA reached top on the day of onset-puberty in both of the normal and model rats, and the expression of Kiss-1 mRNA increased significantly in the model rats compared with those in the normal ones. Our results indicated that kisspeptin might involve in the advance onset of puberty in danazol induced female precocious model rats.

An Automated Feedback System with the Hybrid Model of Scoring and Classification for Solving Over-segmentation Problems in RNAi High Content Screening

Journal of Microscopy. May, 2007  |  Pubmed ID: 17444941

High content screening (HCS) via automated fluorescence microscopy is a powerful technology for generating cellular images that are rich in phenotypic information. RNA interference is a revolutionary approach for silencing gene expression and has become an important method for studying genes through RNA interference-induced cellular phenotype analysis. The convergence of the two technologies has led to large-scale, image-based studies of cellular phenotypes under systematic perturbations of RNA interference. However, existing high content screening image analysis tools are inadequate to extract content regarding cell morphology from the complex images, thus they limit the potential of genome-wide RNA interference high content screening screening for simple marker readouts. In particular, over-segmentation is one of the persistent problems of cell segmentation; this paper describes a new method to alleviate this problem.

Dendritic Spine Detection Using Curvilinear Structure Detector and LDA Classifier

NeuroImage. Jun, 2007  |  Pubmed ID: 17448688

Dendritic spines are small, bulbous cellular compartments that carry synapses. Biologists have been studying the biochemical pathways by examining the morphological and statistical changes of the dendritic spines at the intracellular level. In this paper a novel approach is presented for automated detection of dendritic spines in neuron images. The dendritic spines are recognized as small objects of variable shape attached or detached to multiple dendritic backbones in the 2D projection of the image stack along the optical direction. We extend the curvilinear structure detector to extract the boundaries as well as the centerlines for the dendritic backbones and spines. We further build a classifier using Linear Discriminate Analysis (LDA) to classify the attached spines into valid and invalid types to improve the accuracy of the spine detection. We evaluate the proposed approach by comparing with the manual results in terms of backbone length, spine number, spine length, and spine density.

Tracking Molecular Particles in Live Cells Using Fuzzy Rule-based System

Cytometry. Part A : the Journal of the International Society for Analytical Cytology. Aug, 2007  |  Pubmed ID: 17542029

Recent development of detection techniques of molecular particles in live cells has stimulated interest in developing the new powerful techniques to track the molecular particles in live cells. One special type of cellular microscopy images is about the formation and transportation of clathrin-coated pits and vesicles. Clathrin-coated pits are very important in studying the behavior of proteins and lipids in live cells. To answer the question, whether there exist "hot spots" for the formation of Clathrin-coated pits or the pits and arrays formed randomly on the plasma membrane, it is necessary to track many hundreds of individual pits dynamically in live-cell microscope movies to capture and monitor how pits and vesicles were formed. Therefore, a motion correspondence algorithm based on fuzzy rule-based system is proposed to resolve the problem of ambiguous association encountered in these dynamic, live-cell images of clathrin assemblies. Results show that this method can accurately track most of the particles in the high volume images.

Automatic Dendritic Spine Analysis in Two-photon Laser Scanning Microscopy Images

Cytometry. Part A : the Journal of the International Society for Analytical Cytology. Oct, 2007  |  Pubmed ID: 17654649

Dendritic spine expression plays an important role in the central nervous system. Modern fluorescence microscopy and green fluorescent protein technology have facilitated the research on spines. To quantitatively analyze the spines in fluorescence microscopy images, an automatic dendritic spine analysis method is proposed. Because of the limit of axial resolution, our method is designed to process the projection image along the z-axis and analyze the lateral spines. The method can automatically extract the dendrite centerlines and segment the spines along the dendrites according to width-based criteria. The criteria utilize a common morphological feature of the spines. It can detect some shapes of spines missed by previous methods. In addition, the proposed method is automatic once a few parameters are set. Spine numbers, lengths, and densities, which biologists are interested in, are analyzed both manually and automatically. The results of the two methods match well. The proposed method provides automatic and accurate dendritic spine analysis. It can serve as a useful tool for spine image analysis to avoid tedious manual labor.

Detection of Blob Objects in Microscopic Zebrafish Images Based on Gradient Vector Diffusion

Cytometry. Part A : the Journal of the International Society for Analytical Cytology. Oct, 2007  |  Pubmed ID: 17654652

The zebrafish has become an important vertebrate animal model for the study of developmental biology, functional genomics, and disease mechanisms. It is also being used for drug discovery. Computerized detection of blob objects has been one of the important tasks in quantitative phenotyping of zebrafish. We present a new automated method that is able to detect blob objects, such as nuclei or cells in microscopic zebrafish images. This method is composed of three key steps. The first step is to produce a diffused gradient vector field by a physical elastic deformable model. In the second step, the flux image is computed on the diffused gradient vector field. The third step performs thresholding and nonmaximum suppression based on the flux image. We report the validation and experimental results of this method using zebrafish image datasets from three independent research labs. Both sensitivity and specificity of this method are over 90%. This method is able to differentiate closely juxtaposed or connected blob objects, with high sensitivity and specificity in different situations. It is characterized by a good, consistent performance in blob object detection.

3D Cell Nuclei Segmentation Based on Gradient Flow Tracking

BMC Cell Biology. 2007  |  Pubmed ID: 17784958

Reliable segmentation of cell nuclei from three dimensional (3D) microscopic images is an important task in many biological studies. We present a novel, fully automated method for the segmentation of cell nuclei from 3D microscopic images. It was designed specifically to segment nuclei in images where the nuclei are closely juxtaposed or touching each other. The segmentation approach has three stages: 1) a gradient diffusion procedure, 2) gradient flow tracking and grouping, and 3) local adaptive thresholding.

Brain Tissue Segmentation Based on DTI Data

NeuroImage. Oct, 2007  |  Pubmed ID: 17804258

We present a method for automated brain tissue segmentation based on the multi-channel fusion of diffusion tensor imaging (DTI) data. The method is motivated by the evidence that independent tissue segmentation based on DTI parametric images provides complementary information of tissue contrast to the tissue segmentation based on structural MRI data. This has important applications in defining accurate tissue maps when fusing structural data with diffusion data. In the absence of structural data, tissue segmentation based on DTI data provides an alternative means to obtain brain tissue segmentation. Our approach to the tissue segmentation based on DTI data is to classify the brain into two compartments by utilizing the tissue contrast existing in a single channel. Specifically, because the apparent diffusion coefficient (ADC) values in the cerebrospinal fluid (CSF) are more than twice that of gray matter (GM) and white matter (WM), we use ADC images to distinguish CSF and non-CSF tissues. Additionally, fractional anisotropy (FA) images are used to separate WM from non-WM tissues, as highly directional white matter structures have much larger fractional anisotropy values. Moreover, other channels to separate tissue are explored, such as eigenvalues of the tensor, relative anisotropy (RA), and volume ratio (VR). We developed an approach based on the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm that combines these two-class maps to obtain a complete tissue segmentation map of CSF, GM, and WM. Evaluations are provided to demonstrate the performance of our approach. Experimental results of applying this approach to brain tissue segmentation and deformable registration of DTI data and spoiled gradient-echo (SPGR) data are also provided.

Automated Axon Tracking of 3D Confocal Laser Scanning Microscopy Images Using Guided Probabilistic Region Merging

Neuroinformatics. 2007  |  Pubmed ID: 17917130

This paper presents a new algorithm for extracting the centerlines of the axons from a 3D data stack collected by a confocal laser scanning microscope. Recovery of neuronal structures from such datasets is critical for quantitatively addressing a range of neurobiological questions such as the manner in which the branching pattern of motor neurons change during synapse elimination. Unfortunately, the data acquired using fluorescence microscopy contains many imaging artifacts, such as blurry boundaries and non-uniform intensities of fluorescent radiation. This makes the centerline extraction difficult. We propose a robust segmentation method based on probabilistic region merging to extract the centerlines of individual axons with minimal user interaction. The 3D model of the extracted axon centerlines in three datasets is presented in this paper. The results are validated with the manual tracking results while the robustness of the algorithm is compared with the published repulsive snake algorithm.

High Content Image Analysis for Human H4 Neuroglioma Cells Exposed to CuO Nanoparticles

BMC Biotechnology. 2007  |  Pubmed ID: 17925027

High content screening (HCS)-based image analysis is becoming an important and widely used research tool. Capitalizing this technology, ample cellular information can be extracted from the high content cellular images. In this study, an automated, reliable and quantitative cellular image analysis system developed in house has been employed to quantify the toxic responses of human H4 neuroglioma cells exposed to metal oxide nanoparticles. This system has been proved to be an essential tool in our study.

Comparison of Reversible-jump Markov-chain-Monte-Carlo Learning Approach with Other Methods for Missing Enzyme Identification

Journal of Biomedical Informatics. Apr, 2008  |  Pubmed ID: 17950040

Computational identification of missing enzymes plays a significant role in accurate and complete reconstruction of metabolic network for both newly sequenced and well-studied organisms. For a metabolic reaction, given a set of candidate enzymes identified according to certain biological evidences, a powerful mathematical model is required to predict the actual enzyme(s) catalyzing the reactions. In this study, several plausible predictive methods are considered for the classification problem in missing enzyme identification, and comparisons are performed with an aim to identify a method with better performance than the Bayesian model used in previous work. In particular, a regression model consisting of a linear term and a nonlinear term is proposed to apply to the problem, in which the reversible jump Markov-chain-Monte-Carlo (MCMC) learning technique (developed in [Andrieu C, Freitas Nando de, Doucet A. Robust full Bayesian learning for radial basis networks 2001;13:2359-407.]) is adopted to estimate the model order and the parameters. We evaluated the models using known reactions in Escherichia coli, Mycobacterium tuberculosis, Vibrio cholerae and Caulobacter cresentus bacteria, as well as one eukaryotic organism, Saccharomyces Cerevisiae. Although support vector regression also exhibits comparable performance in this application, it was demonstrated that the proposed model achieves favorable prediction performance, particularly sensitivity, compared with the Bayesian method.

Novel Cell Segmentation and Online SVM for Cell Cycle Phase Identification in Automated Microscopy

Bioinformatics (Oxford, England). Jan, 2008  |  Pubmed ID: 17989093

Automated identification of cell cycle phases captured via fluorescent microscopy is very important for understanding cell cycle and for drug discovery. In this article, we propose a novel cell detection method that utilizes both the intensity and shape information of the cell for better segmentation quality. In contrast to conventional off-line learning algorithms, an Online Support Vector Classifier (OSVC) is thus proposed, which removes support vectors from the old model and assigns new training examples weighted according to their importance to accommodate the ever-changing experimental conditions.

ZFIQ: a Software Package for Zebrafish Biology

Bioinformatics (Oxford, England). Feb, 2008  |  Pubmed ID: 18089619

Rapid development, transparency and small size are the outstanding features of zebrafish that make it as an increasingly important vertebrate system for developmental biology, functional genomics, disease modeling and drug discovery. Zebrafish has been regarded as ideal animal specie for studying the relationship between genotype and phenotype, for pathway analysis and systems biology. However, the tremendous amount of data generated from large numbers of embryos has led to the bottleneck of data analysis and modeling. The zebrafish image quantitator (ZFIQ) software provides streamlined data processing and analysis capability for developmental biology and disease modeling using zebrafish model. AVAILABILITY: ZFIQ is available for download at http://www.cbi-platform.net.

Cellular Phenotype Recognition for High-content RNA Interference Genome-wide Screening

Journal of Biomolecular Screening. Jan, 2008  |  Pubmed ID: 18227224

Genome-wide, cell-based screens using high-content screening (HCS) techniques and automated fluorescence microscopy generate thousands of high-content images that contain an enormous wealth of cell biological information. Such screens are key to the analysis of basic cell biological principles, such as control of cell cycle and cell morphology. However, these screens will ultimately only shed light on human disease mechanisms and potential cures if the analysis can keep up with the generation of data. A fundamental step toward automated analysis of high-content screening is to construct a robust platform for automatic cellular phenotype identification. The authors present a framework, consisting of microscopic image segmentation and analysis components, for automatic recognition of cellular phenotypes in the context of the Rho family of small GTPases. To implicate genes involved in Rac signaling, RNA interference (RNAi) was used to perturb gene functions, and the corresponding cellular phenotypes were analyzed for changes. The data used in the experiments are high-content, 3-channel, fluorescence microscopy images of Drosophila Kc167 cultured cells stained with markers that allow visualization of DNA, polymerized actin filaments, and the constitutively activated Rho protein Rac(V12). The performance of this approach was tested using a cellular database that contained more than 1000 samples of 3 predefined cellular phenotypes, and the generalization error was estimated using a cross-validation technique. Moreover, the authors applied this approach to analyze the whole high-content fluorescence images of Drosophila cells for further HCS-based gene function analysis.

An Automated Method for Cell Detection in Zebrafish

Neuroinformatics. 2008  |  Pubmed ID: 18288618

Quantification of cells is a critical step towards the assessment of cell fate in neurological disease or developmental models. Here, we present a novel cell detection method for the automatic quantification of zebrafish neuronal cells, including primary motor neurons, Rohon-Beard neurons, and retinal cells. Our method consists of four steps. First, a diffused gradient vector field is produced. Subsequently, the orientations and magnitude information of diffused gradients are accumulated, and a response image is computed. In the third step, we perform non-maximum suppression on the response image and identify the detection candidates. In the fourth and final step the detected objects are grouped into clusters based on their color information. Using five different datasets depicting zebrafish cells, we show that our method consistently displays high sensitivity and specificity of over 95%. Our results demonstrate the general applicability of this method to different data samples, including nuclear staining, immunohistochemistry, and cell death detection.

Reconstruction of Central Cortical Surface from Brain MRI Images: Method and Application

NeuroImage. Apr, 2008  |  Pubmed ID: 18289879

Reconstruction of the central surface representation of the cerebral cortex is an important means to study the structure and function of the human brain. In this paper, we propose a novel method based on an elastic transform vector field to drive a deformable model for the reconstruction of the central cortical surface. Both simulated brain cortexes and real brain images are used to evaluate this approach. We applied the surface reconstruction method and a hybrid volumetric and surface registration algorithm to detect simulated brain atrophy. Experimental results show that the central cortical surface representation has better performance in detecting simulated atrophy than the traditionally used inner or outer cortical surface representations.

MR Analysis of Regional Brain Volume in Adolescent Idiopathic Scoliosis: Neurological Manifestation of a Systemic Disease

Journal of Magnetic Resonance Imaging : JMRI. Apr, 2008  |  Pubmed ID: 18302230

To investigate whether regional brain volumes in adolescent idiopathic scoliosis (AIS) patients differ from matched control subjects as AIS subjects are reported to have poor performance on combined visual and proprioceptive testing and impaired postural balance in previous studies.

3D Axon Structure Extraction and Analysis in Confocal Fluorescence Microscopy Images

Neural Computation. Aug, 2008  |  Pubmed ID: 18336075

The morphological properties of axons, such as their branching patterns and oriented structures, are of great interest for biologists in the study of the synaptic connectivity of neurons. In these studies, researchers use triple immunofluorescent confocal microscopy to record morphological changes of neuronal processes. Three-dimensional (3D) microscopy image analysis is then required to extract morphological features of the neuronal structures. In this article, we propose a highly automated 3D centerline extraction tool to assist in this task. For this project, the most difficult part is that some axons are overlapping such that the boundaries distinguishing them are barely visible. Our approach combines a 3D dynamic programming (DP) technique and marker-controlled watershed algorithm to solve this problem. The approach consists of tracking and updating along the navigation directions of multiple axons simultaneously. The experimental results show that the proposed method can rapidly and accurately extract multiple axon centerlines and can handle complicated axon structures such as cross-over sections and overlapping objects.

Using Nonlinear Diffusion and Mean Shift to Detect and Connect Cross-sections of Axons in 3D Optical Microscopy Images

Medical Image Analysis. Dec, 2008  |  Pubmed ID: 18440853

The morphology of neuronal axons has been actively investigated by researchers to understand functionalities of neuronal networks, for example, in developmental neurology. Today's optical microscope and labeling techniques allow us to obtain high-resolution images about axons in three dimensions (3D), however, it remains challenging to segment and reconstruct the 3D morphology of axons. These include differentiating adjacent axons and detecting the axon branches. In this paper we present a method to track axons in 3D by identifying cross-sections of axons on 2D images and connecting the cross-sections over a series of 2D images to reconstruct the 3D morphology. The method can separate adjacent axons and detect the split and merge of axons. The method consists of three steps, modified nonlinear diffusion to remove noise and enhance edges in 2D, morphological operations to detect edges of the cross-sections of axons in 2D, and mean shift to track the cross-sections of axons in 3D. Performance of the method is demonstrated by processing real data acquired by confocal laser scanning microscopy.

A Quantitative Study of Factors Affecting in Vivo Bioluminescence Imaging

Luminescence : the Journal of Biological and Chemical Luminescence. Sep-Oct, 2008  |  Pubmed ID: 18452141

In vivo bioluminescence imaging (BLI) has the advantages of high sensitivity and low background. By counting the number of photons emitted from a specimen, BLI can quantify biological events such as tumour growth, gene expression and drug response. The intensities and kinetics of the BL signal are affected by many factors and may confound the quantitative results acquired from consecutive imaging sessions or different specimens. We used three different mouse models of tumours to examine whether anaesthetics, positioning and tumour growth may affect the consistency of the BL signal. The results showed that BLI signal could be affected by different anaesthetics and repetitive positioning. Using the same anaesthetics produced consistent peak times, while other factors were held constant. However, as the tumours grew the peak times shifted and the time course of BL signals had different shapes, depending on the positioning of the mice. The data indicate that a carefully designed BLI experiment is required to generate optimal and consistent results.

Workflow and Methods of High-content Time-lapse Analysis for Quantifying Intracellular Calcium Signals

Neuroinformatics. 2008  |  Pubmed ID: 18506641

Calcium ions (Ca2+) play a fundamental role in a variety of physiological functions in many cell types by acting as a secondary messenger. Variation of intracellular Ca2+ concentration ([Ca2+]i) is often observed when the cell is stimulated. However, it is a challenging task to automatically quantify intracellular [Ca2+]i in a population of cells. In this study, we present a workflow including specific algorithms for the automated intracellular calcium signal analysis using high-content, time-lapse cellular images. The experimental validations indicate the effectiveness of the proposed workflow and algorithms. We applied the workflow to analyze the intracellular calcium signals induced by different concentrations of H2O2 in the cell lines transfected by presenilin-1 (PS-1) that is known to be closely related to the familial Alzheimer's disease (FAD). The analysis results imply an important role of mutant PS-1, but not normal human PS-1 and mutant human amyloid precursor protein (APP), in enhancing intracellular calcium signaling induced by H2O2.

MeCP2, a Key Contributor to Neurological Disease, Activates and Represses Transcription

Science (New York, N.Y.). May, 2008  |  Pubmed ID: 18511691

Mutations in the gene encoding the transcriptional repressor methyl-CpG binding protein 2 (MeCP2) cause the neurodevelopmental disorder Rett syndrome. Loss of function as well as increased dosage of the MECP2 gene cause a host of neuropsychiatric disorders. To explore the molecular mechanism(s) underlying these disorders, we examined gene expression patterns in the hypothalamus of mice that either lack or overexpress MeCP2. In both models, MeCP2 dysfunction induced changes in the expression levels of thousands of genes, but unexpectedly the majority of genes (approximately 85%) appeared to be activated by MeCP2. We selected six genes and confirmed that MeCP2 binds to their promoters. Furthermore, we showed that MeCP2 associates with the transcriptional activator CREB1 at the promoter of an activated target but not a repressed target. These studies suggest that MeCP2 regulates the expression of a wide range of genes in the hypothalamus and that it can function as both an activator and a repressor of transcription.

Using Iterative Cluster Merging with Improved Gap Statistics to Perform Online Phenotype Discovery in the Context of High-throughput RNAi Screens

BMC Bioinformatics. 2008  |  Pubmed ID: 18534020

The recent emergence of high-throughput automated image acquisition technologies has forever changed how cell biologists collect and analyze data. Historically, the interpretation of cellular phenotypes in different experimental conditions has been dependent upon the expert opinions of well-trained biologists. Such qualitative analysis is particularly effective in detecting subtle, but important, deviations in phenotypes. However, while the rapid and continuing development of automated microscope-based technologies now facilitates the acquisition of trillions of cells in thousands of diverse experimental conditions, such as in the context of RNA interference (RNAi) or small-molecule screens, the massive size of these datasets precludes human analysis. Thus, the development of automated methods which aim to identify novel and biological relevant phenotypes online is one of the major challenges in high-throughput image-based screening. Ideally, phenotype discovery methods should be designed to utilize prior/existing information and tackle three challenging tasks, i.e. restoring pre-defined biological meaningful phenotypes, differentiating novel phenotypes from known ones and clarifying novel phenotypes from each other. Arbitrarily extracted information causes biased analysis, while combining the complete existing datasets with each new image is intractable in high-throughput screens.

Reversible Jump MCMC Approach for Peak Identification for Stroke SELDI Mass Spectrometry Using Mixture Model

Bioinformatics (Oxford, England). Jul, 2008  |  Pubmed ID: 18586741

Mass spectrometry (MS) has shown great potential in detecting disease-related biomarkers for early diagnosis of stroke. To discover potential biomarkers from large volume of noisy MS data, peak detection must be performed first. This article proposes a novel automatic peak detection method for the stroke MS data. In this method, a mixture model is proposed to model the spectrum. Bayesian approach is used to estimate parameters of the mixture model, and Markov chain Monte Carlo method is employed to perform Bayesian inference. By introducing a reversible jump method, we can automatically estimate the number of peaks in the model. Instead of separating peak detection into substeps, the proposed peak detection method can do baseline correction, denoising and peak identification simultaneously. Therefore, it minimizes the risk of introducing irrecoverable bias and errors from each substep. In addition, this peak detection method does not require a manually selected denoising threshold. Experimental results on both simulated dataset and stroke MS dataset show that the proposed peak detection method not only has the ability to detect small signal-to-noise ratio peaks, but also greatly reduces false detection rate while maintaining the same sensitivity.

The Knowledge-integrated Network Biomarkers Discovery for Major Adverse Cardiac Events

Journal of Proteome Research. Sep, 2008  |  Pubmed ID: 18665624

The mass spectrometry (MS) technology in clinical proteomics is very promising for discovery of new biomarkers for diseases management. To overcome the obstacles of data noises in MS analysis, we proposed a new approach of knowledge-integrated biomarker discovery using data from Major Adverse Cardiac Events (MACE) patients. We first built up a cardiovascular-related network based on protein information coming from protein annotations in Uniprot, protein-protein interaction (PPI), and signal transduction database. Distinct from the previous machine learning methods in MS data processing, we then used statistical methods to discover biomarkers in cardiovascular-related network. Through the tradeoff between known protein information and data noises in mass spectrometry data, we finally could firmly identify those high-confident biomarkers. Most importantly, aided by protein-protein interaction network, that is, cardiovascular-related network, we proposed a new type of biomarkers, that is, network biomarkers, composed of a set of proteins and the interactions among them. The candidate network biomarkers can classify the two groups of patients more accurately than current single ones without consideration of biological molecular interaction.

Registration of 3-D CT and 2-D Flat Images of Mouse Via Affine Transformation

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society. Sep, 2008  |  Pubmed ID: 18779071

It is difficult to directly coregister the 3-D fluorescence molecular tomography (FMT) image of a small tumor in a mouse whose maximal diameter is only a few millimeters with a larger CT image of the entire animal that spans about 10 cm. This paper proposes a new method to register 2-D flat and 3-D CT image first to facilitate the registration between small 3-D FMT images and large 3-D CT images. A novel algorithm combining differential evolution and improved simplex method for the registration between the 2-D flat and 3-D CT images is introduced and validated with simulated images and real images of mice. The visualization of the alignment of the 3-D FMT and CT image through 2-D registration shows promising results.

Computational Prediction Models for Early Detection of Risk of Cardiovascular Events Using Mass Spectrometry Data

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society. Sep, 2008  |  Pubmed ID: 18779078

Early prediction of the risk of cardiovascular events in patients with chest pain is critical in order to provide appropriate medical care for those with positive diagnosis. This paper introduces a computational methodology for predicting such events in the context of robust computerized classification using mass spectrometry data of blood samples collected from patients in emergency departments. We applied the computational theories of statistical and geostatistical linear prediction models to extract effective features of the mass spectra and a simple decision logic to classify disease and control samples for the purpose of early detection. While the statistical and geostatistical techniques provide better results than those obtained from some other methods, the geostatistical approach yields superior results in terms of sensitivity and specificity in various designs of the data set for validation, training, and testing. The proposed computational strategies are very promising for predicting major adverse cardiac events within six months.

A Novel Method for Cortical Sulcal Fundi Extraction

Medical Image Computing and Computer-assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 2008  |  Pubmed ID: 18979757

Sulcal fundi are 3D curves along the bottom of sulcal regions of the human cerebral cortex. In this paper, we propose a novel automatic method for extraction of sulcal fundi from triangulated cortical surface. Compared to existing methods, the proposed method can find accurate sulcal fundi using curvatures and curvature derivatives without manual interaction. Given a triangulated cortical surface, our method is composed of four steps: estimating curvatures and curvature derivatives for each vertex, detecting the sulcal fundi segments in each triangle, linking the sulcal fundi segments and combining of adjacent sulcal fundi, and connecting breaking sulcal fundi and smoothing using the fast marching method on the cortical surface. The proposed sulcal fundi extraction method is applied to ten normal brain inner cortical surfaces. We quantitatively validated the proposed method of sulcal fundi extraction using manually labeled sulcal fundi by experts as the ground truth.

Computational Systems Bioinformatics and Bioimaging for Pathway Analysis and Drug Screening

Proceedings of the IEEE. Institute of Electrical and Electronics Engineers. Aug, 2008  |  Pubmed ID: 20011613

The premise of today's drug development is that the mechanism of a disease is highly dependent upon underlying signaling and cellular pathways. Such pathways are often composed of complexes of physically interacting genes, proteins, or biochemical activities coordinated by metabolic intermediates, ions, and other small solutes and are investigated with molecular biology approaches in genomics, proteomics, and metabonomics. Nevertheless, the recent declines in the pharmaceutical industry's revenues indicate such approaches alone may not be adequate in creating successful new drugs. Our observation is that combining methods of genomics, proteomics, and metabonomics with techniques of bioimaging will systematically provide powerful means to decode or better understand molecular interactions and pathways that lead to disease and potentially generate new insights and indications for drug targets. The former methods provide the profiles of genes, proteins, and metabolites, whereas the latter techniques generate objective, quantitative phenotypes correlating to the molecular profiles and interactions. In this paper, we describe pathway reconstruction and target validation based on the proposed systems biologic approach and show selected application examples for pathway analysis and drug screening.

Progress of Engineered Antibody-targeted Molecular Imaging for Solid Tumors (Review)

Molecular Medicine Reports. Jan-Feb, 2008  |  Pubmed ID: 21479389

Engineered antibodies, with their high specificity and affinity for their target antigens, as well as their reduced size and multivalent design, can be tailored to carry radionuclide magnetic, luciferase or fluorescent probes for specific attachment to tissue cells, extracellularly or intracellularly, for PET, SPECT, MRI, optical and ultrasonic imaging. The antigen-specific imaging agents of engineered antibodies have deep tissue penetration, high tissue retention and fast blood clearance, which are desirable properties for the rapid imaging of tumors with high specificity and resolution in pre-clinical or clinical imaging studies.

ONLINE THREE-DIMENSIONAL DENDRITIC SPINES MOPHOLOGICAL CLASSIFICATION BASED ON SEMI-SUPERVISED LEARNING

Proceedings / IEEE International Symposium on Biomedical Imaging: from Nano to Macro. IEEE International Symposium on Biomedical Imaging. Jun, 2009  |  Pubmed ID: 21922077

Recent studies on neuron imaging show that there is a strong relationship between the functional properties of a neuron and its morphology, especially its dendritic spine structures. However, most of the current methods for morphological spine classification only concern features in two-dimensional (2D) space, which consequently decreases the accuracy of dendritic spine analysis. In this paper, we propose a semi-supervised learning (SSL) framework, in which spine phenotypes in three-dimensional (3D) space are considered. With training only on a few pre-classified inputs, the rest of the spines can be identified effectively. We also derived a new scheme using an affinity matrix between features to further improve the accuracy. Our experimental results indicate that a small training dataset is sufficient to classify detected dendritic spines.

An Image Driven Systems Biology Approach for Neurodegenerative Disease Studies in the TSC-mTOR Pathway

IEEE/NIH Life Science Systems and Applications Workshop. IEEE/NIH Life Science Systems and Applications Workshop. May, 2009  |  Pubmed ID: 21922078

In this brief paper we present an overview of the TSC-mTOR pathway and its importance in neurodegenerative disease (ND). We illustrate the influence of ND on dendritic spine morphology. Then we discuss some details of functional gene networks (FGN) and use this information to propose an image driven systems biology approach for the construction of a FGN for ND. We conclude on its importance and the prospective outcome of our study.

INTEGRATING MULTI-SCALE BLOB/CURVILINEAR DETECTOR TECHNIQUES AND MULTI-LEVEL SETS FOR AUTOMATED SEGMENTATION OF STEM CELL IMAGES

Proceedings / IEEE International Symposium on Biomedical Imaging: from Nano to Macro. IEEE International Symposium on Biomedical Imaging. 2009  |  Pubmed ID: 20585412

Studies of differentiation abilities of stem cells have been attracting a lot of attention over the last years. Microscopy can be used to record details of the differentiation process of stem cells under different perturbations and is an important tool for studying stem cell differentiation. Since it is infeasible to quantitatively analyze a huge amount of image data manually, automated image analysis systems are urgently needed. However, the complicated morphological appearances of stem cells are challenging to the existing segmentation methods. Herein, we propose a new, automated scheme for stem cell segmentation. This scheme first uses the multi-scale blob and curvilinear structure detectors to delineate the skeletons of stem cells quickly and then segment out stem cells by refining the skeletons to the cell boundaries using multi-level sets. The initial experimental results indicate the effectiveness of the proposed scheme.

An Image Based System Biology Approach for Alzheimer's Disease Pathway Analysis

IEEE/NIH Life Science Systems and Applications Workshop. IEEE/NIH Life Science Systems and Applications Workshop. Apr, 2009  |  Pubmed ID: 20585413

We report multifactorial analysis of candidate mechanisms of Alzheimer's disease utilizing high content analysis, gene expression microarray, and linear regression model to integrate neuronal imaging data with hippocampal gene expression data. Our analysis led to the identification of several genes that may contribute to different image traits or phenotypes in the amyloid-beta (Aβ) injured neurons. Gene network and biological pathways analysis for those genes were further analyzed and led to several novel pathways that may contribute to amyloid plaque triggered neurite loss.

Simultaneous Consideration of Spatial Deformation and Tensor Orientation in Diffusion Tensor Image Registration Using Local Fast Marching Patterns

Information Processing in Medical Imaging : Proceedings of the ... Conference. 2009  |  Pubmed ID: 19694253

Diffusion tensor imaging (DTI) plays increasingly important roles in surgical planning, neurological disease diagnosis, and follow-up studies in recent years. In order to compare the tractography obtained from different subjects or the same subject at different timepoints, a key step is to spatially align DTI images. Different from scalar or multi-channel image registration, tensor orientation should be considered in DTI registration. Several DTI registration methods have been proposed before, and some of them are based on first extracting the orientation-invariant features and then registering images using traditional scalar or multi-channel registration techniques followed by tensor reorientation. They essentially do not fully use the tensor information. Other methods such as the piece-wise affine transformation and the diffeomorphic non-linear registration algorithms use analytical gradients of the registration objective functions by considering the reorientation of tensor during the registration. However, only local tensor information such as voxel tensor similarity is utilized in these algorithms, which can be regarded as a counterpart of the traditional intensity similarity-based image registration in the DTI case. This paper proposes a novel DTI image registration algorithm, called fast marching-based simultaneous registration. It not only considers the orientation of tensors but also utilizes the neighborhood tensor information of each voxel, which is extracted from a local fast marching algorithm around voxels of interest. Compared to the voxel-wise tensor similarity-based registration, richer and more distinctive tensor features are used in this algorithm to better define correspondences between DTI images. Thus, more robust and accurate registration results can be obtained. In the experiments, comparative results using the real DTI data show the advantages of the proposed algorithm.

A NOVEL SURFACE-BASED GEOMETRIC APPROACH FOR 3D DENDRITIC SPINE DETECTION FROM MULTI-PHOTON EXCITATION MICROSCOPY IMAGES

Proceedings / IEEE International Symposium on Biomedical Imaging: from Nano to Macro. IEEE International Symposium on Biomedical Imaging. Jun, 2009  |  Pubmed ID: 20046805

Determining the relationship between the dendritic spine morphology and its functional properties is a fundamental while challenging problem in neurobiology research. In particular, how to accurately and automatically analyze meaningful structural information from a large microscopy image dataset is far away from being resolved. In this paper, we propose a novel method for the automated neuron reconstruction and spine detection from fluorescence microscopy images. After image processing, backbone of the neuron is obtained and the neuron is represented as a 3D surface. Based on the analysis of geometric features on the surface, spines are detected by a novel hybrid of two segmentation methods. Besides the automated detection of spines, our algorithm is able to extract accurate 3D structures of spines. Comparison results between our approach and the state of the art shows that our algorithm is more accurate and robust, especially for detecting and separating touching spines.

A Novel Peak Detection Approach with Chemical Noise Removal Using Short-time FFT for PrOTOF MS Data

Proteomics. Aug, 2009  |  Pubmed ID: 19681055

Peak detection is a pivotal first step in biomarker discovery from MS data and can significantly influence the results of downstream data analysis steps. We developed a novel automatic peak detection method for prOTOF MS data, which does not require a priori knowledge of protein masses. Random noise is removed by an undecimated wavelet transform and chemical noise is attenuated by an adaptive short-time discrete Fourier transform. Isotopic peaks corresponding to a single protein are combined by extracting an envelope over them. Depending on the S/N, the desired peaks in each individual spectrum are detected and those with the highest intensity among their peak clusters are recorded. The common peaks among all the spectra are identified by choosing an appropriate cut-off threshold in the complete linkage hierarchical clustering. To remove the 1 Da shifting of the peaks, the peak corresponding to the same protein is determined as the detected peak with the largest number among its neighborhood. We validated this method using a data set of serial peptide and protein calibration standards. Compared with MoverZ program, our new method detects more peaks and significantly enhances S/N of the peak after the chemical noise removal. We then successfully applied this method to a data set from prOTOF MS spectra of albumin and albumin-bound proteins from serum samples of 59 patients with carotid artery disease compared to vascular disease-free patients to detect peaks with S/N> or =2. Our method is easily implemented and is highly effective to define peaks that will be used for disease classification or to highlight potential biomarkers.

Online Phenotype Discovery Based on Minimum Classification Error Model

Pattern Recognition. Apr, 2009  |  Pubmed ID: 20161245

Identifying and validating novel phenotypes from images inputting online is a major challenge against high-content RNA interference (RNAi) screening. Newly discovered phenotypes should be visually distinct from existing ones and make biological sense. An online phenotype discovery method featuring adaptive phenotype modeling and iterative cluster merging using improved gap statistics is proposed. Clustering results based on compactness criteria and Gaussian mixture models (GMM) for existing phenotypes iteratively modify each other by multiple hypothesis test and model optimization based on minimum classification error (MCE). The method works well on discovering new phenotypes adaptively when applied to both of synthetic datasets and RNAi high content screen (HCS) images with ground truth labels.

Cell Segmentation Using Front Vector Flow Guided Active Contours

Medical Image Computing and Computer-assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 2009  |  Pubmed ID: 20426162

Phase-contrast microscopy is a common approach for studying the dynamics of cell behaviors, such as cell migration. Cell segmentation is the basis of quantitative analysis of the immense cellular images. However, the complicated cell morphological appearance in phase-contrast microscopy images challenges the existing segmentation methods. This paper proposes a new cell segmentation method for cancer cell migration studies using phase-contrast images. Instead of segmenting cells directly based on commonly used low-level features, e.g., intensity and gradient, we first identify the leading protrusions, a high level feature, of cancer cells. Based on the identified cell leading protrusions, we introduce a front vector flow guided active contour, which guides the initial cell boundaries to the real boundaries. The experimental validation on a set of breast cancer cell images shows that the proposed method demonstrates fast, stable, and accurate segmentation for breast cancer cells with wide range of sizes and shapes.

Multiple Distinct Clones May Co-exist in Different Lineages in Myelodysplastic Syndromes

Leukemia Research. Jun, 2009  |  Pubmed ID: 19084271

Using single nucleotide polymorphism (SNP) microarray with unfractionized bone marrow specimens, recent studies have demonstrated that multiple cytogenetically cryptic genomic aberrations, uniparental disomy (UPD) and/or copy number (CN) aberration, are present in patients with myelodysplastic syndromes (MDS). We hypothesize that various hematopoietic lineages in MDS may carry different cytogenetically cryptic genomic aberrations leading to lineage-specific manifestations of MDS. Flow cytometry sorting was performed to sort 12 MDS marrow samples into blastic, erythroid, immature myeloid and lymphoid fractions. The fractions with enough DNA underwent 250K SNP microarray analysis. Of importance, different chromosomal regions of UPD, deletions and/or gains were present in different fractions of same patients in all samples. Only small percentages (6.7%) of genomic aberrations were present in all fractions from same patients. These results suggest that multiple distinct clones may co-exist in different lineages in MDS and may contribute to cytopenias in specific lineages and the significant clinical heterogeneity observed in these patients. Further studies are warranted to confirm our findings and to investigate the lineage specific genomic lesions in MDS.

Improved Residue Function and Reduced Flow Dependence in MR Perfusion Using Least-absolute-deviation Regularization

Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine. Feb, 2009  |  Pubmed ID: 19161133

Cerebral blood flow (CBF) estimates derived from singular value decomposition (SVD) of time intensity curves from Gadolinium bolus perfusion-weighted imaging are known to underestimate CBF, especially at high flow rates. We report the development of a model-independent delay-invariant deconvolution technique using least-absolute-deviation (LAD) regularization to improve the CBF estimation accuracy. Computer simulations were performed to compare the accuracy of CBF estimates derived from LAD, reformulated SVD (rSVD) and standard SVD (sSVD) techniques. Simulations were performed at image signal-to-noise ratios ranging from 20 to 400, cerebral blood volumes from 1% to 10%, and CBF from 2.5 mL/100 g/min to 176.5 mL/100 g/min to estimate the effect of these parameters on the accuracy of CBF estimation. The LAD method improved the CBF estimation accuracy by up to 32% in gray matter and 23% in white matter compared with rSVD and sSVD methods. LAD method also reduces the systematic bias of rSVD and sSVD methods to baseline SNR while producing more accurate and reproducible residue function calculation than either rSVD or sSVD method. Initial clinical implementation of the method on six representative clinical cases confirm the advantages of the LAD method over rSVD and sSVD methods.

A Screening Platform for Glioma Growth and Invasion Using Bioluminescence Imaging. Laboratory Investigation

Journal of Neurosurgery. Aug, 2009  |  Pubmed ID: 19199503

The study of tumor cell growth and invasion in cancer biology is often limited by the inability to visualize tumor cell behavior in real time in animal models. The authors provide evidence that glioma cells are heterogeneous,with a subset responsible for increased invasiveness. The use of bioluminescence (BL) imaging to investigate dynamic aspects of glioma progression are discussed.

A Novel Cell Segmentation Method and Cell Phase Identification Using Markov Model

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society. Mar, 2009  |  Pubmed ID: 19272857

Optical microscopy is becoming an important technique in drug discovery and life science research. The approaches used to analyze optical microscopy images are generally classified into two categories: automatic and manual approaches. However, the existing automatic systems are rather limited in dealing with large volume of time-lapse microscopy images because of the complexity of cell behaviors and morphological variance. On the other hand, manual approaches are very time-consuming. In this paper, we propose an effective automated, quantitative analysis system that can be used to segment, track, and quantize cell cycle behaviors of a large population of cells nuclei effectively and efficiently. We use adaptive thresholding and watershed algorithm for cell nuclei segmentation followed by a fragment merging method that combines two scoring models based on trend and no trend features. Using the context information of time-lapse data, the phases of cell nuclei are identified accurately via a Markov model. Experimental results show that the proposed system is effective for nuclei segmentation and phase identification.

Introduction to the Special Issue of "Advances in Molecular Imaging"

European Journal of Radiology. May, 2009  |  Pubmed ID: 19261413

Identification of Biomarkers for Risk Stratification of Cardiovascular Events Using Genetic Algorithm with Recursive Local Floating Search

Proteomics. Apr, 2009  |  Pubmed ID: 19337989

Conventional biomarker discovery focuses mostly on the identification of single markers and thus often has limited success in disease diagnosis and prognosis. This study proposes a method to identify an optimized protein biomarker panel based on MS studies for predicting the risk of major adverse cardiac events (MACE) in patients. Since the simplicity and concision requirement for the development of immunoassays can only tolerate the complexity of the prediction model with a very few selected discriminative biomarkers, established optimization methods, such as conventional genetic algorithm (GA), thus fails in the high-dimensional space. In this paper, we present a novel variant of GA that embeds the recursive local floating enhancement technique to discover a panel of protein biomarkers with far better prognostic value for prediction of MACE than existing methods, including the one approved recently by FDA (Food and Drug Administration). The new pragmatic method applies the constraints of MACE relevance and biomarker redundancy to shrink the local searching space in order to avoid heavy computation penalty resulted from the local floating optimization. The proposed method is compared with standard GA and other variable selection approaches based on the MACE prediction experiments. Two powerful classification techniques, partial least squares logistic regression (PLS-LR) and support vector machine classifier (SVMC), are deployed as the MACE predictors owing to their ability in dealing with small scale and binary response data. New preprocessing algorithms, such as low-level signal processing, duplicated spectra elimination, and outliner patient's samples removal, are also included in the proposed method. The experimental results show that an optimized panel of seven selected biomarkers can provide more than 77.1% MACE prediction accuracy using SVMC. The experimental results empirically demonstrate that the new GA algorithm with local floating enhancement (GA-LFE) can achieve the better MACE prediction performance comparing with the existing techniques. The method has been applied to SELDI/MALDI MS datasets to discover an optimized panel of protein biomarkers to distinguish disease from control.

Pattern-selection Based Power Analysis and Discrimination of Low- and High-grade Myelodysplastic Syndromes Study Using SNP Arrays

PloS One. 2009  |  Pubmed ID: 19352488

Copy Number Aberration (CNA) in myelodysplastic syndromes (MDS) study using single nucleotide polymorphism (SNP) arrays have been received increasingly attentions in the recent years. In the current study, a new Constraint Moving Average (CMA) algorithm is adopted to determine the regions of CNA regions first. In addition to large regions of CNA, using the proposed CMA algorithm, small regions of CNA can also be detected. Real-time Polymerase Chain Reaction (qPCR) results prove that the CMA algorithm presents an insightful discovery of both large and subtle regions. Based on the results of CMA, two independent applications are studied. The first one is power analysis for sample estimation. An accurate estimation of sample size needed for the desired purpose of an experiment will be important for effort-efficiency and cost-effectiveness. The power analysis is performed to determine the minimum sample size required for ensuring at least (0

An Automated Pipeline for Dendrite Spine Detection and Tracking of 3D Optical Microscopy Neuron Images of in Vivo Mouse Models

Neuroinformatics. Jun, 2009  |  Pubmed ID: 19434521

The variations in dendritic branch morphology and spine density provide insightful information about the brain function and possible treatment to neurodegenerative disease, for example investigating structural plasticity during the course of Alzheimer's disease. Most automated image processing methods aiming at analyzing these problems are developed for in vitro data. However, in vivo neuron images provide real time information and direct observation of the dynamics of a disease process in a live animal model. This paper presents an automated approach for detecting spines and tracking spine evolution over time with in vivo image data in an animal model of Alzheimer's disease. We propose an automated pipeline starting with curvilinear structure detection to determine the medial axis of the dendritic backbone and spines connected to the backbone. We, then, propose the adaptive local binary fitting (aLBF) energy level set model to accurately locate the boundary of dendritic structures using the central line of curvilinear structure as initialization. To track the growth or loss of spines, we present a maximum likelihood based technique to find the graph homomorphism between two image graph structures at different time points. We employ dynamic programming to search for the optimum solution. The pipeline enables us to extract dynamically changing information from real time in vivo data. We validate our proposed approach by comparing with manual results generated by neurologists. In addition, we discuss the performance of 3D based segmentation and conclude that our method is more accurate in identifying weak spines. Experiments show that our approach can quickly and accurately detect and quantify spines of in vivo neuron images and is able to identify spine elimination and formation.

Automated Brain Tumor Segmentation Using Spatial Accuracy-weighted Hidden Markov Random Field

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society. Sep, 2009  |  Pubmed ID: 19446435

A variety of algorithms have been proposed for brain tumor segmentation from multi-channel sequences, however, most of them require isotropic or pseudo-isotropic resolution of the MR images. Although co-registration and interpolation of low-resolution sequences, such as T2-weighted images, onto the space of the high-resolution image, such as T1-weighted image, can be performed prior to the segmentation, the results are usually limited by partial volume effects due to interpolation of low-resolution images. To improve the quality of tumor segmentation in clinical applications where low-resolution sequences are commonly used together with high-resolution images, we propose the algorithm based on Spatial accuracy-weighted Hidden Markov random field and Expectation maximization (SHE) approach for both automated tumor and enhanced-tumor segmentation. SHE incorporates the spatial interpolation accuracy of low-resolution images into the optimization procedure of the Hidden Markov Random Field (HMRF) to segment tumor using multi-channel MR images with different resolutions, e.g., high-resolution T1-weighted and low-resolution T2-weighted images. In experiments, we evaluated this algorithm using a set of simulated multi-channel brain MR images with known ground-truth tissue segmentation and also applied it to a dataset of MR images obtained during clinical trials of brain tumor chemotherapy. The results show that more accurate tumor segmentation results can be obtained by comparing with conventional multi-channel segmentation algorithms.

Bioluminescence Imaging Reveals Inhibition of Tumor Cell Proliferation by Alzheimer's Amyloid Beta Protein

Cancer Cell International. 2009  |  Pubmed ID: 19480719

Cancer and Alzheimer's disease (AD) are two seemingly distinct diseases and rarely occur simultaneously in patients. To explore molecular determinants differentiating pathogenic routes towards AD or cancer, we investigate the role of amyloid beta protein (Abeta) on multiple tumor cell lines that are stably expressing luciferase (human glioblastoma U87; human breast adenocarcinoma MDA-MB231; and mouse melanoma B16F).

Conference Scene: Wake-up Call for the Engineering and Biomedical Science Communities in Nanomedicine

Nanomedicine (London, England). Jul, 2009  |  Pubmed ID: 19572817

The IEEE-NIH 4th Life Science Systems and Applications Workshop 2009 (LiSSA '09) was jointly organized by the IEEE LiSSA Technical Committee and the NIH Nano Task Force. It was endorsed by the NIH Biomedical Information Science and Technology Initiative (BISTI) and the National Library of Medicine. The workshop was held in the Natcher Conference Center on the NIH campus in Bethesda, MD, USA. It took place on the 9-10 April, 2009, during the NIH NanoWeek and had approximately 200 delegates from around the globe (including North America, Europe, Asia and Australia) from both engineering and biomedical science disciplines. The conference featured around 40 talks, including nine plenary speakers emphasizing current state-of-the-art nanotechnology practices, developments and industry applications. All talks were scheduled in three oral and seven special sessions, as well as three breakout sessions. In addition, the interactive poster sessions hosted over 30 abstracts and attracted much attention from the audience; these sessions were readily used by many attendees to connect with colleagues of similar interest. In this article, we provide some of the highlights from the workshop.

Robust 3D Reconstruction and Identification of Dendritic Spines from Optical Microscopy Imaging

Medical Image Analysis. Feb, 2009  |  Pubmed ID: 18819835

In neurobiology, the 3D reconstruction of neurons followed by the identification of dendritic spines is essential for studying neuronal morphology, function and biophysical properties. Most existing methods suffer from problems of low reliability, poor accuracy and require much user interaction. In this paper, we present a method to reconstruct dendrites using a surface representation of the neuron. The skeleton of the dendrite is extracted by a procedure based on the medial geodesic function that is robust and topology preserving, and it is used to accurately identify spines. The sensitivity of the algorithm on the various parameters is explored in detail and the method is shown to be robust.

Conditional Random Pattern Algorithm for LOH Inference and Segmentation

Bioinformatics (Oxford, England). Jan, 2009  |  Pubmed ID: 18974074

Loss of heterozygosity (LOH) is one of the most important mechanisms in the tumor evolution. LOH can be detected from the genotypes of the tumor samples with or without paired normal samples. In paired sample cases, LOH detection for informative single nucleotide polymorphisms (SNPs) is straightforward if there is no genotyping error. But genotyping errors are always unavoidable, and there are about 70% non-informative SNPs whose LOH status can only be inferred from the neighboring informative SNPs.

An Image Score Inference System for RNAi Genome-wide Screening Based on Fuzzy Mixture Regression Modeling

Journal of Biomedical Informatics. Feb, 2009  |  Pubmed ID: 18547870

With recent advances in fluorescence microscopy imaging techniques and methods of gene knock down by RNA interference (RNAi), genome-scale high-content screening (HCS) has emerged as a powerful approach to systematically identify all parts of complex biological processes. However, a critical barrier preventing fulfillment of the success is the lack of efficient and robust methods for automating RNAi image analysis and quantitative evaluation of the gene knock down effects on huge volume of HCS data. Facing such opportunities and challenges, we have started investigation of automatic methods towards the development of a fully automatic RNAi-HCS system. Particularly important are reliable approaches to cellular phenotype classification and image-based gene function estimation. We have developed a HCS analysis platform that consists of two main components: fluorescence image analysis and image scoring. For image analysis, we used a two-step enhanced watershed method to extract cellular boundaries from HCS images. Segmented cells were classified into several predefined phenotypes based on morphological and appearance features. Using statistical characteristics of the identified phenotypes as a quantitative description of the image, a score is generated that reflects gene function. Our scoring model integrates fuzzy gene class estimation and single regression models. The final functional score of an image was derived using the weighted combination of the inference from several support vector-based regression models. We validated our phenotype classification method and scoring system on our cellular phenotype and gene database with expert ground truth labeling. We built a database of high-content, 3-channel, fluorescence microscopy images of Drosophila Kc(167) cultured cells that were treated with RNAi to perturb gene function. The proposed informatics system for microscopy image analysis is tested on this database. Both of the two main components, automated phenotype classification and image scoring system, were evaluated. The robustness and efficiency of our system were validated in quantitatively predicting the biological relevance of genes.

Multiple Nuclei Tracking Using Integer Programming for Quantitative Cancer Cell Cycle Analysis

IEEE Transactions on Medical Imaging. Jan, 2010  |  Pubmed ID: 19643704

Automated cell segmentation and tracking are critical for quantitative analysis of cell cycle behavior using time-lapse fluorescence microscopy. However, the complex, dynamic cell cycle behavior poses new challenges to the existing image segmentation and tracking methods. This paper presents a fully automated tracking method for quantitative cell cycle analysis. In the proposed tracking method, we introduce a neighboring graph to characterize the spatial distribution of neighboring nuclei, and a novel dissimilarity measure is designed based on the spatial distribution, nuclei morphological appearance, migration, and intensity information. Then, we employ the integer programming and division matching strategy, together with the novel dissimilarity measure, to track cell nuclei. We applied this new tracking method for the tracking of HeLa cancer cells over several cell cycles, and the validation results showed that the high accuracy for segmentation and tracking at 99.5% and 90.0%, respectively. The tracking method has been implemented in the cell-cycle analysis software package, DCELLIQ, which is freely available.

An Automatic and Robust Algorithm of Reestablishment of Digital Dental Occlusion

IEEE Transactions on Medical Imaging. Sep, 2010  |  Pubmed ID: 20529735

In the field of craniomaxillofacial (CMF) surgery, surgical planning can be performed on composite 3-D models that are generated by merging a computerized tomography scan with digital dental models. Digital dental models can be generated by scanning the surfaces of plaster dental models or dental impressions with a high-resolution laser scanner. During the planning process, one of the essential steps is to reestablish the dental occlusion. Unfortunately, this task is time-consuming and often inaccurate. This paper presents a new approach to automatically and efficiently reestablish dental occlusion. It includes two steps. The first step is to initially position the models based on dental curves and a point matching technique. The second step is to reposition the models to the final desired occlusion based on iterative surface-based minimum distance mapping with collision constraints. With linearization of rotation matrix, the alignment is modeled by solving quadratic programming. The simulation was completed on 12 sets of digital dental models. Two sets of dental models were partially edentulous, and another two sets have first premolar extractions for orthodontic treatment. Two validation methods were applied to the articulated models. The results show that using our method, the dental models can be successfully articulated with a small degree of deviations from the occlusion achieved with the gold-standard method.

Reconstruction of the Neuromuscular Junction Connectome

Bioinformatics (Oxford, England). Jun, 2010  |  Pubmed ID: 20529938

MOTIVATION: Unraveling the structure and behavior of the brain and central nervous system (CNS) has always been a major goal of neuroscience. Understanding the wiring diagrams of the neuromuscular junction connectomes (full connectivity of nervous system neuronal components) is a starting point for this, as it helps in the study of the organizational and developmental properties of the mammalian CNS. The phenomenon of synapse elimination during developmental stages of the neuronal circuitry is such an example. Due to the organizational specificity of the axons in the connectomes, it becomes important to label and extract individual axons for morphological analysis. Features such as axonal trajectories, their branching patterns, geometric information, the spatial relations of groups of axons, etc. are of great interests for neurobiologists in the study of wiring diagrams. However, due to the complexity of spatial structure of the axons, automatically tracking and reconstructing them from microscopy images in 3D is an unresolved problem. In this article, AxonTracker-3D, an interactive 3D axon tracking and labeling tool is built to obtain quantitative information by reconstruction of the axonal structures in the entire innervation field. The ease of use along with accuracy of results makes AxonTracker-3D an attractive tool to obtain valuable quantitative information from axon datasets. AVAILABILITY: The software is freely available for download at http://www.cbi-tmhs.org/AxonTracker/.

A Computational Framework for Studying Neuron Morphology from in Vitro High Content Neuron-based Screening

Journal of Neuroscience Methods. Jul, 2010  |  Pubmed ID: 20580743

High content neuron image processing is considered as an important method for quantitative neurobiological studies. The main goal of analysis in this paper is to provide automatic image processing approaches to process neuron images for studying neuron mechanism in high content screening. In the nuclei channel, all nuclei are segmented and detected by applying the gradient vector field based watershed. Then the neuronal nuclei are selected based on the soma region detected in neurite channel. In neurite images, we propose a novel neurite centerline extraction approach using the improved line-pixel detection technique. The proposed neurite tracing method can detect the curvilinear structure more accurately compared with the current existing methods. An interface called NeuriteIQ based on the proposed algorithms is developed finally for better application in high content screening.

Catch the Wave--nanotechnology, the Future is Now

IEEE Engineering in Medicine and Biology Magazine : the Quarterly Magazine of the Engineering in Medicine & Biology Society. Jan-Feb, 2010  |  Pubmed ID: 20209671

A Local Fast Marching-based Diffusion Tensor Image Registration Algorithm by Simultaneously Considering Spatial Deformation and Tensor Orientation

NeuroImage. Aug, 2010  |  Pubmed ID: 20382233

It is a key step to spatially align diffusion tensor images (DTI) to quantitatively compare neural images obtained from different subjects or the same subject at different timepoints. Different from traditional scalar or multi-channel image registration methods, tensor orientation should be considered in DTI registration. Recently, several DTI registration methods have been proposed in the literature, but deformation fields are purely dependent on the tensor features not the whole tensor information. Other methods, such as the piece-wise affine transformation and the diffeomorphic non-linear registration algorithms, use analytical gradients of the registration objective functions by simultaneously considering the reorientation and deformation of tensors during the registration. However, only relatively local tensor information such as voxel-wise tensor-similarity is utilized. This paper proposes a new DTI image registration algorithm, called local fast marching (FM)-based simultaneous registration. The algorithm not only considers the orientation of tensors during registration but also utilizes the neighborhood tensor information of each voxel to drive the deformation, and such neighborhood tensor information is extracted from a local fast marching algorithm around the voxels of interest. These local fast marching-based tensor features efficiently reflect the diffusion patterns around each voxel within a spherical neighborhood and can capture relatively distinctive features of the anatomical structures. Using simulated and real DTI human brain data the experimental results show that the proposed algorithm is more accurate compared with the FA-based registration and is more efficient than its counterpart, the neighborhood tensor similarity-based registration.

Conditional Random Pattern Model for Copy Number Aberration Detection

BMC Bioinformatics. 2010  |  Pubmed ID: 20412592

DNA copy number aberration (CNA) is very important in the pathogenesis of tumors and other diseases. For example, CNAs may result in suppression of anti-oncogenes and activation of oncogenes, which would cause certain types of cancers. High density single nucleotide polymorphism (SNP) array data is widely used for the CNA detection. However, it is nontrivial to detect the CNA automatically because the signals obtained from high density SNP arrays often have low signal-to-noise ratio (SNR), which might be caused by whole genome amplification, mixtures of normal and tumor cells, experimental noise or other technical limitations. With the reduction in SNR, many false CNA regions are often detected and the true CNA regions are missed. Thus, more sophisticated statistical models are needed to make the CNAs detection, using the low SNR signals, more robust and reliable.

A Hybrid Approach to Automatic Clustering of White Matter Fibers

NeuroImage. Jan, 2010  |  Pubmed ID: 19683061

Recently, the tract-based white matter (WM) fiber analysis has been recognized as an effective framework to study the diffusion tensor imaging (DTI) data of human brain. This framework can provide biologically meaningful results and facilitate the tract-based comparison across subjects. However, due to the lack of quantitative definition of WM bundle boundaries, the complexity of brain architecture and the variability of WM shapes, clustering WM fibers into anatomically meaningful bundles is nontrivial. In this paper, we propose a hybrid top-down and bottom-up approach for automatic clustering and labeling of WM fibers, which utilizes both brain parcellation results and similarities between WM fibers. Our experimental results show reasonably good performance of this approach in clustering WM fibers into anatomically meaningful bundles.

Computer-assisted Quantitative Evaluation of Therapeutic Responses for Lymphoma Using Serial PET/CT Imaging

Academic Radiology. Apr, 2010  |  Pubmed ID: 20060747

Molecular imaging modalities such as positron emission tomography (PET)/computed tomography (CT) have emerged as an essential diagnostic tool for monitoring treatment response in lymphoma patients. However, quantitative assessment of treatment outcomes from serial scans is often difficult, laborious, and time consuming. Automatic quantization of longitudinal PET/CT scans provides more efficient and comprehensive quantitative evaluation of cancer therapeutic responses. This study develops and validates a Longitudinal Image Navigation and Analysis (LINA) system for this quantitative imaging application.

A Neurocomputational Method for Fully Automated 3D Dendritic Spine Detection and Segmentation of Medium-sized Spiny Neurons

NeuroImage. May, 2010  |  Pubmed ID: 20100579

Acquisition and quantitative analysis of high resolution images of dendritic spines are challenging tasks but are necessary for the study of animal models of neurological and psychiatric diseases. Currently available methods for automated dendritic spine detection are for the most part customized for 2D image slices, not volumetric 3D images. In this work, a fully automated method is proposed to detect and segment dendritic spines from 3D confocal microscopy images of medium-sized spiny neurons (MSNs). MSNs constitute a major neuronal population in striatum, and abnormalities in their function are associated with several neurological and psychiatric diseases. Such automated detection is critical for the development of new 3D neuronal assays which can be used for the screening of drugs and the studies of their therapeutic effects. The proposed method utilizes a generalized gradient vector flow (GGVF) with a new smoothing constraint and then detects feature points near the central regions of dendrites and spines. Then, the central regions are refined and separated based on eigen-analysis and multiple shape measurements. Finally, the spines are segmented in 3D space using the fast marching algorithm, taking the detected central regions of spines as initial points. The proposed method is compared with three popular existing methods for centerline extraction and also with manual results for dendritic spine detection in 3D space. The experimental results and comparisons show that the proposed method is able to automatically and accurately detect, segment, and quantitate dendritic spines in 3D images of MSNs.

Treating Triple-negative Breast Cancer by a Combination of Rapamycin and Cyclophosphamide: an in Vivo Bioluminescence Imaging Study

European Journal of Cancer (Oxford, England : 1990). Apr, 2010  |  Pubmed ID: 20156674

Rapamycin, a mammalian target of rapamycin (mTOR) inhibitor, has been shown to inhibit the growth of oestrogen positive breast cancer. However, triple-negative (TN) breast cancer is resistant to rapamycin treatment in vitro. We set to test a combination treatment of rapamycin with DNA-damage agent, cyclophosphamide, in a TN breast cancer model. By binding to and disrupting cellular DNA, cyclophosphamide kills cells via interfering with their normal functions. We assessed the responses of nude mice bearing tumour xenografts of TN MDA-MB-231 cells to the combination of rapamycin and cyclophosphamide in both orthotopic mammary and lung-metastasis models. We tracked tumour growth and metastasis by bioluminescent imaging and examined the expression of Ki67, CD34 and HIF-1alpha in tumour tissues by immunohistochemistry and apoptosis index with TUNEL assay, and found that MDA-MB-231 cells are sensitive to rapamycin therapy in orthotopic mammary, but not in lung with metastasis. Rapamycin when combined with cyclophosphamide is found to have a more significant effect in reducing tumour volume and metastasis with a much improved survival rate. Our data also show that the sensitivity of TN tumours to rapamycin is associated with the microenvironment of the tumour cells. The data indicate that in a relatively hypoxic environment HIF-1alpha may play a role in mediating the anti-cancer effect of rapamycin and cyclophosphamide may prevent the feedback activation of Akt by rapamycin. Overall our results show that rapamycin plus cyclophosphamide can achieve an improved efficacy in suppressing tumour growth and metastasis, suggesting that the combination therapy can be a promising treatment option for TN cancer.

Joint Registration and Segmentation of Serial Lung CT Images for Image-guided Lung Cancer Diagnosis and Therapy

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society. Jan, 2010  |  Pubmed ID: 19709855

In image-guided diagnosis and treatment of small peripheral lung lesions the alignment of the pre-procedural lung CT images and the intra-procedural images is an important step to accurately guide and monitor the interventional procedure. Registering the serial images often relies on correct segmentation of the images and, on the other hand, the segmentation results can be further improved by temporal alignment of the serial images. This paper presents a joint serial image registration and segmentation algorithm. In this algorithm, serial images are segmented based on the current deformations, and the deformations among the serial images are iteratively refined based on the updated segmentation results. No temporal smoothness about the deformation fields is enforced so that the algorithm can tolerate larger or discontinuous temporal changes that often appear during image-guided therapy. Physical procedure models could also be incorporated to our framework to better handle the temporal changes of the serial images during intervention. In experiments, we apply the proposed algorithm to align serial lung CT images. Results using both simulated and clinical images show that the new algorithm is more robust compared to the method that only uses deformable registration.

Using Genetic and Clinical Factors to Predict Tacrolimus Dose in Renal Transplant Recipients

Pharmacogenomics. Oct, 2010  |  Pubmed ID: 21047202

Tacrolimus has a narrow therapeutic window and shows significant interindividual difference in dose requirement. In this study we aim to first identify genetic factors that impact tacrolimus dose using a candidate gene association approach, and then generate a personalized algorithm combining identified genetic and clinical factors to predict individualized tacrolimus dose.

MicroRNA-integrated and Network-embedded Gene Selection with Diffusion Distance

PloS One. 2010  |  Pubmed ID: 21060785

Gene network information has been used to improve gene selection in microarray-based studies by selecting marker genes based both on their expression and the coordinate expression of genes within their gene network under a given condition. Here we propose a new network-embedded gene selection model. In this model, we first address the limitations of microarray data. Microarray data, although widely used for gene selection, measures only mRNA abundance, which does not always reflect the ultimate gene phenotype, since it does not account for post-transcriptional effects. To overcome this important (critical in certain cases) but ignored-in-almost-all-existing-studies limitation, we design a new strategy to integrate together microarray data with the information of microRNA, the major post-transcriptional regulatory factor. We also handle the challenges led by gene collaboration mechanism. To incorporate the biological facts that genes without direct interactions may work closely due to signal transduction and that two genes may be functionally connected through multi paths, we adopt the concept of diffusion distance. This concept permits us to simulate biological signal propagation and therefore to estimate the collaboration probability for all gene pairs, directly or indirectly-connected, according to multi paths connecting them. We demonstrate, using type 2 diabetes (DM2) as an example, that the proposed strategies can enhance the identification of functional gene partners, which is the key issue in a network-embedded gene selection model. More importantly, we show that our gene selection model outperforms related ones. Genes selected by our model 1) have improved classification capability; 2) agree with biological evidence of DM2-association; and 3) are involved in many well-known DM2-associated pathways.

Multi Scale and Slice-based Approach for Automatic Spine Detection

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference. 2010  |  Pubmed ID: 21096249

Dendritic spines play an essential role in the central nervous system. Recent experiments have revealed that neuron functional properties are highly correlated with the statistical and morphological changes of the dendritic spines. In this paper, we propose a new multi scale approach for detecting dendritic spines in a 2D Maximum Intensity Projection (MIP) image of the 3D neuron data stacks collected from a 2-photon laser scanning confocal microscope. The proposed method utilizes the curvilinear structure detector in conjunction with the multi scale spine detection algorithm which automatically and accurately extracts and segments the spines with variational sizes along the dendrite. In addition, a slice-based spine detection algorithm is also proposed to detect spines which are hidden from the MIP image within the dendrite area. Experimental results show that our proposed method is effective for automatic spine detection and is able to accurately segment dendrite.

Coronary Artery Segmentation Using Geometric Moments Based Tracking and Snake-driven Refinement

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference. 2010  |  Pubmed ID: 21096589

Automatic or semi-automatic segmentation and tracking of artery trees from computed tomography angiography (CTA) is an important step to improve the diagnosis and treatment of artery diseases, but it still remains a significant challenging problem. In this paper, we present an artery extraction method to address the challenge. The proposed method consists of two steps: (1) a geometric moments based tracking to secure a rough centerline, and (2) a fully automatic generalized cylinder structure-based snake method to refine the centerlines and estimate the radii of the arteries. In this method, a new line direction based on first and second order geometric moments is adopted while both gradient and intensity information are used in the snake model to improve the accuracy. The approach has been evaluated on synthetic images as well as 8 clinical coronary CTA images with 32 coronary arteries. Our method achieves 94.7% overlap tracking ability within an average distance inside the vessel of 0.36 mm.

Oriented Markov Random Field Based Dendritic Spine Segmentation for Fluorescence Microscopy Images

Neuroinformatics. Oct, 2010  |  Pubmed ID: 20585900

Dendritic spines have been shown to be closely related to various functional properties of the neuron. Usually dendritic spines are manually labeled to analyze their morphological changes, which is very time-consuming and susceptible to operator bias, even with the assistance of computers. To deal with these issues, several methods have been recently proposed to automatically detect and measure the dendritic spines with little human interaction. However, problems such as degraded detection performance for images with larger pixel size (e.g. 0.125 μm/pixel instead of 0.08 μm/pixel) still exist in these methods. Moreover, the shapes of detected spines are also distorted. For example, the "necks" of some spines are missed. Here we present an oriented Markov random field (OMRF) based algorithm which improves spine detection as well as their geometric characterization. We begin with the identification of a region of interest (ROI) containing all the dendrites and spines to be analyzed. For this purpose, we introduce an adaptive procedure for identifying the image background. Next, the OMRF model is discussed within a statistical framework and the segmentation is solved as a maximum a posteriori estimation (MAP) problem, whose optimal solution is found by a knowledge-guided iterative conditional mode (KICM) algorithm. Compared with the existing algorithms, the proposed algorithm not only provides a more accurate representation of the spine shape, but also improves the detection performance by more than 50% with regard to reducing both the misses and false detection.

Delivery of Picosecond Lasers in Multimode Fibers for Coherent Anti-Stokes Raman Scattering Imaging

Optics Express. Jun, 2010  |  Pubmed ID: 20588430

We investigated the possibility of using standard commercial multimode fibers (MMF), Corning SMF28 fibers, to deliver picosecond excitation lasers for coherent anti-Stokes Raman scattering (CARS) imaging. We theoretically and/or experimentally analyzed issues associated with the fiber delivery, such as dispersion length, walk-off length, nonlinear length, average threshold power for self-phase modulations, and four-wave mixing (FWM). These analyses can also be applied to other types of fibers. We found that FWM signals are generated in MMF, but they can be filtered out using a long-pass filter for CARS imaging. Finally, we demonstrated that MMF can be used for delivery of picosecond excitation lasers in the CARS imaging system without any degradation of image quality.

Semi-supervised Drug-protein Interaction Prediction from Heterogeneous Biological Spaces

BMC Systems Biology. 2010  |  Pubmed ID: 20840733

Predicting drug-protein interactions from heterogeneous biological data sources is a key step for in silico drug discovery. The difficulty of this prediction task lies in the rarity of known drug-protein interactions and myriad unknown interactions to be predicted. To meet this challenge, a manifold regularization semi-supervised learning method is presented to tackle this issue by using labeled and unlabeled information which often generates better results than using the labeled data alone. Furthermore, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data.

Image-based Chemical Screening Identifies Drug Efflux Inhibitors in Lung Cancer Cells

Cancer Research. Oct, 2010  |  Pubmed ID: 20841476

Cancer cells with active drug efflux capability are multidrug resistant and pose a significant obstacle for the efficacy of chemotherapy. Moreover, recent evidence suggests that high drug efflux cancer cells (HDECC) may be selectively enriched with stem-like cancer cells, which are believed to be the cause for tumor initiation and recurrence. There is a great need for therapeutic reagents that are capable of eliminating HDECCs. We developed an image-based high-content screening (HCS) system to specifically identify and analyze the HDECC population in lung cancer cells. Using the system, we screened 1,280 pharmacologically active compounds that identified 12 potent HDECC inhibitors. It is shown that these inhibitors are able to overcome multidrug resistance (MDR) and sensitize HDECCs to chemotherapeutic drugs, or directly reduce the tumorigenicity of lung cancer cells possibly by affecting stem-like cancer cells. The HCS system we established provides a new approach for identifying therapeutic reagents overcoming MDR. The compounds identified by the screening may potentially be used as potential adjuvant to improve the efficacy of chemotherapeutic drugs.

Online 4-D CT Estimation for Patient-specific Respiratory Motion Based on Real-time Breathing Signals

Medical Image Computing and Computer-assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 2010  |  Pubmed ID: 20879424

In image-guided lung intervention, the electromagnetic (EM) tracked needle can be visualized in a pre-procedural CT by registering the EM tracking and the CT coordinate systems. However, there exist discrepancies between the static pre-procedural CT and the patient due to respiratory motion. This paper proposes an online 4-D CT estimation approach to patient-specific respiratory motion compensation. First, the motion patterns between 4-D CT data and respiratory signals such as fiducials from a number of patients are trained in a template space after image registration. These motion patterns can be used to estimate the patient-specific serial CTs from a static 3-D CT and the real-time respiratory signals of that patient, who do not generally take 4-D CTs. Specifically, the respiratory lung field motion vectors are projected onto the Kernel Principal Component Analysis (K-PCA) space, and a motion estimation model is constructed to estimate the lung field motion from the fiducial motion using the ridge regression method based on the least squares support vector machine (LS-SVM). The algorithm can be performed onsite prior to the intervention to generate the serial CT images according to the respiratory signals in advance, and the estimated CTs can be visualized in real-time during the intervention. In experiments, we evaluated the algorithm using leave-one-out strategy on 30 4-D CT data, and the results showed that the average errors of the lung field surfaces are 1.63 mm.

Motion Artifact Correction of Multi-photon Imaging of Awake Mice Models Using Speed Embedded HMM

Medical Image Computing and Computer-assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 2010  |  Pubmed ID: 20879434

Multi-photon fluorescence microscopy (MFM) captures high-resolution anatomical and functional fluorescence image sequences and can be used for the intact brain imaging of small animals. Recently, it has been extended from imaging anesthetized and head-stabilized animals to awake and head-restrained ones for in vivo neurological study. In these applications, motion correction is an important pre-processing step since brain pulsation and tiny body movement can cause motion artifacts and prevent stable serial image acquisition at such a high spatial resolution. This paper proposes a speed embedded hidden Markov model (SEHMM) for motion correction in MFM imaging of awake head-restrained mice. The algorithm extends the traditional HMM method by embedding a motion prediction model to better estimate the state transition probability. SEHMM is a line-by-line motion correction algorithm, which is implemented within the in-focal-plane 2-D videos and can operate directly on the motion-distorted imaging data without external signal measurements such as the movement, heartbeat, respiration, or muscular tension. In experiments, we demonstrat that SEHMM is more accurate than traditional HMM using both simulated and real MFM image sequences.

Fabrication of Localized Surface Plasmon Resonance Fiber Probes Using Ionic Self-assembled Gold Nanoparticles

Sensors (Basel, Switzerland). 2010  |  Pubmed ID: 22163561

An nm-thickness composite gold thin film consisting of gold nanoparticles and polyelectrolytes is fabricated through ionic self-assembled multilayers (ISAM) technique and is deposited on end-faces of optical fibers to construct localized surface plasmon resonance (LSPR) fiber probes. We demonstrate that the LSPR spectrum induced by ISAM gold films can be fine-tuned through the ISAM procedure. We investigate variations of reflection spectra of the probe with respect to the layer-by-layer adsorption of ISAMs onto end-faces of fibers, and study the spectral variation mechanism. Finally, we demonstrated using this fiber probe to detect the biotin-streptavidin bioconjugate pair. ISAM adsorbed on optical fibers potentially provides a simple, fast, robust, and low-cost, platform for LSPR biosensing applications.

Aurora-B Mediated ATM Serine 1403 Phosphorylation is Required for Mitotic ATM Activation and the Spindle Checkpoint

Molecular Cell. Nov, 2011  |  Pubmed ID: 22099307

The ATM kinase plays a critical role in the maintenance of genetic stability. ATM is activated in response to DNA damage and is essential for cell-cycle checkpoints. Here, we report that ATM is activated in mitosis in the absence of DNA damage. We demonstrate that mitotic ATM activation is dependent on the Aurora-B kinase and that Aurora-B phosphorylates ATM on serine 1403. This phosphorylation event is required for mitotic ATM activation. Further, we show that loss of ATM function results in shortened mitotic timing and a defective spindle checkpoint, and that abrogation of ATM Ser1403 phosphorylation leads to this spindle checkpoint defect. We also demonstrate that mitotically activated ATM phosphorylates Bub1, a critical kinetochore protein, on Ser314. ATM-mediated Bub1 Ser314 phosphorylation is required for Bub1 activity and is essential for the activation of the spindle checkpoint. Collectively, our data highlight mechanisms of a critical function of ATM in mitosis.

On-the-spot Lung Cancer Differential Diagnosis by Label-free, Molecular Vibrational Imaging and Knowledge-based Classification

Journal of Biomedical Optics. Sep, 2011  |  Pubmed ID: 21950918

We report the development and application of a knowledge-based coherent anti-Stokes Raman scattering (CARS) microscopy system for label-free imaging, pattern recognition, and classification of cells and tissue structures for differentiating lung cancer from non-neoplastic lung tissues and identifying lung cancer subtypes. A total of 1014 CARS images were acquired from 92 fresh frozen lung tissue samples. The established pathological workup and diagnostic cellular were used as prior knowledge for establishment of a knowledge-based CARS system using a machine learning approach. This system functions to separate normal, non-neoplastic, and subtypes of lung cancer tissues based on extracted quantitative features describing fibrils and cell morphology. The knowledge-based CARS system showed the ability to distinguish lung cancer from normal and non-neoplastic lung tissue with 91% sensitivity and 92% specificity. Small cell carcinomas were distinguished from nonsmall cell carcinomas with 100% sensitivity and specificity. As an adjunct to submitting tissue samples to routine pathology, our novel system recognizes the patterns of fibril and cell morphology, enabling medical practitioners to perform differential diagnosis of lung lesions in mere minutes. The demonstration of the strategy is also a necessary step toward in vivo point-of-care diagnosis of precancerous and cancerous lung lesions with a fiber-based CARS microendoscope.

A Time-series Method for Automated Measurement of Changes in Mitotic and Interphase Duration from Time-lapse Movies

PloS One. 2011  |  Pubmed ID: 21966537

Automated time-lapse microscopy can visualize proliferation of large numbers of individual cells, enabling accurate measurement of the frequency of cell division and the duration of interphase and mitosis. However, extraction of quantitative information by manual inspection of time-lapse movies is too time-consuming to be useful for analysis of large experiments.

Label-free High-resolution Imaging of Prostate Glands and Cavernous Nerves Using Coherent Anti-Stokes Raman Scattering Microscopy

Biomedical Optics Express. 2011  |  Pubmed ID: 21483613

A custom built coherent anti-Stokes Raman scattering (CARS) microscope was used to image prostatic glands and nerve structures from 17 patients undergoing radical prostatectomy. Imaging of glandular and nerve structures showed distinctive cellular features that correlated to histological stains. Segmentation of cell nucleus was performed to establish a cell feature-based model to separate normal glands from cancer glands. In this study, we use a single parameter, average cell neighbor distance based on CARS imaging, to characterize normal and cancerous glandular structures. By combining CARS with our novel classification model, we are able to characterize prostate glandular and nerve structures in a manner that potentially enables real-time, intra-operative assessment of surgical margins and neurovascular bundles. As such, this method could potentially improve outcomes following radical prostatectomy.

Peak Tree: a New Tool for Multiscale Hierarchical Representation and Peak Detection of Mass Spectrometry Data

IEEE/ACM Transactions on Computational Biology and Bioinformatics / IEEE, ACM. Jul-Aug, 2011  |  Pubmed ID: 21566254

Peak detection is one of the most important steps in mass spectrometry (MS) analysis. However, the detection result is greatly affected by severe spectrum variations. Unfortunately, most current peak detection methods are neither flexible enough to revise false detection results nor robust enough to resist spectrum variations. To improve flexibility, we introduce peak tree to represent the peak information in MS spectra. Each tree node is a peak judgment on a range of scales, and each tree decomposition, as a set of nodes, is a candidate peak detection result. To improve robustness, we combine peak detection and common peak alignment into a closed-loop framework, which finds the optimal decomposition via both peak intensity and common peak information. The common peak information is derived and loopily refined from the density clustering of the latest peak detection result. Finally, we present an improved ant colony optimization biomarker selection method to build a whole MS analysis system. Experiment shows that our peak detection method can better resist spectrum variations and provide higher sensitivity and lower false detection rates than conventional methods. The benefits from our peak-tree-based system for MS disease analysis are also proved on real SELDI data.

Coherent Anti-Stokes Raman Scattering Microscopy Imaging with Suppression of Four-wave Mixing in Optical Fibers

Optics Express. Apr, 2011  |  Pubmed ID: 21643045

We demonstrated an optical fiber delivered coherent anti-Stokes Raman scattering (CARS) microscopy imaging system with a polarization-based mechanism for suppression of four-wave mixing (FWM) signals in delivery fiber. Polarization maintaining fibers (PMF) were used as the delivery fiber to ensure stability of the state of polarization (SOP) of lasers. The pump and Stokes waves were coupled into PMFs at orthogonal SOPs along the slow and fast axes of PMFs, respectively, resulting in a significant reduction of FWM signals generated in the fiber. At the output end of PMFs, a dual-wavelength waveplate was used to realign the SOPs of the two waves into identical SOPs prior to their entrance into the CARS microscope. Therefore, it allows the pump and Stokes waves with identical SOPs to excite samples at highest excitation efficiency. Our experimental results showed that this polarization-based FWM-suppressing mechanism can dramatically reduce FWM signals generated in PMFs up to approximately 99%. Meanwhile, the PMF-delivered CARS microscopy system with this mechanism can still produce high-quality CARS images. Consequently, our PMF-delivered CARS microscopy imaging system with the polarization-based FWM-suppressing mechanism potentially offers a new strategy for building fiber-based CARS endoscopes with effective suppression of FWM background noises.

An Enhanced Petri-net Model to Predict Synergistic Effects of Pairwise Drug Combinations from Gene Microarray Data

Bioinformatics (Oxford, England). Jul, 2011  |  Pubmed ID: 21685086

Prediction of synergistic effects of drug combinations has traditionally been relied on phenotypic response data. However, such methods cannot be used to identify molecular signaling mechanisms of synergistic drug combinations. In this article, we propose an enhanced Petri-Net (EPN) model to recognize the synergistic effects of drug combinations from the molecular response profiles, i.e. drug-treated microarray data.

Real-time Monitoring of Cell Viability Using Direct Electrical Measurement with a Patch-clamp Microchip

Biomedical Microdevices. Oct, 2011  |  Pubmed ID: 21698381

Real-time tagless monitoring of cell viability using patch-clamp microchips is reported and validated by using fluorescence imaging techniques for the first time. Specifically, four human breast cancer cell lines (MDA-MB231, MDA-MB231-brain metastatic subline (abbreviated as MB231-BR), MB231-BR over-expressing HER2 gene (MB231-BR-HER2), and MB231-BR-vector control for the HER2 (MB231-BR-vector)) have been used for these studies. Systematic experiments on these cells found that the seal impedance/resistance of cells captured by the micro-pipettes always decreases during the process when the cell loses its viability, and therefore it is a valid indicator of live or dead cells. Systematic experiments also found that the Mega-seal of patch-clamp microchip is sufficient for monitoring cell viability. Given its simplicity of direct electrical measurement of cells without fluorescence labeling, this technology may provide an efficient technical platform to monitor the drug effects on cells, thereby significantly benefiting high throughput drug screening and discovery process.

Use of Multimode Optical Fibers for Fiber-based Coherent Anti-Stokes Raman Scattering Microendoscopy Imaging

Optics Letters. Aug, 2011  |  Pubmed ID: 21808374

A multimode fiber (MMF) was used for both delivery of excitation lasers and collection of returned coherent anti-Stokes Raman scattering (CARS) signals in a CARS microendoscopy prototype imaging system. We demonstrated a polarization-based scheme for suppression of four-wave mixing (FWM) signals in delivery fibers. Our experimental results showed that this polarization-based FWM-suppressing scheme can dramatically reduce FWM signals generated in MMFs, and MMFs can be used to produce CARS images in this microendoscopy system. The proposed MMF-based CARS microendoscopy imaging system with the polarization-based FWM-suppressing scheme offers a potential platform for building fiber-based CARS microendoscopes that can effectively suppress FWM background noises.

Differential Diagnosis of Breast Cancer Using Quantitative, Label-free and Molecular Vibrational Imaging

Biomedical Optics Express. Aug, 2011  |  Pubmed ID: 21833355

We present a label-free, chemically-selective, quantitative imaging strategy to identify breast cancer and differentiate its subtypes using coherent anti-Stokes Raman scattering (CARS) microscopy. Human normal breast tissue, benign proliferative, as well as in situ and invasive carcinomas, were imaged ex vivo. Simply by visualizing cellular and tissue features appearing on CARS images, cancerous lesions can be readily separated from normal tissue and benign proliferative lesion. To further distinguish cancer subtypes, quantitative disease-related features, describing the geometry and distribution of cancer cell nuclei, were extracted and applied to a computerized classification system. The results show that in situ carcinoma was successfully distinguished from invasive carcinoma, while invasive ductal carcinoma (IDC) and invasive lobular carcinoma were also distinguished from each other. Furthermore, 80% of intermediate-grade IDC and 85% of high-grade IDC were correctly distinguished from each other. The proposed quantitative CARS imaging method has the potential to enable rapid diagnosis of breast cancer.

Motion Correction for Cellular-resolution Multi-photon Fluorescence Microscopy Imaging of Awake Head-restrained Mice Using Speed Embedded HMM

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society. Sep, 2011  |  Pubmed ID: 21890321

Multi-photon fluorescence microscopy (MFM) captures high-resolution fluorescence image sequences and can be used for the intact brain imaging of small animals. Recently, it has been extended from anesthetized and head-stabilized mice to awake and head-restrained ones for in vivo neurological study. In these applications, motion correction is an important pre-processing step since brain pulsation and body movement can cause motion artifact and prevent stable serial image acquisition at such high spatial resolution. This paper proposes a speed embedded Hidden Markov model (SEHMM) for motion correction in MFM imaging of awake head-restrained mice. The algorithm extends the traditional Hidden Markov model (HMM) method by embedding a motion prediction model to better estimate the state transition probability. The novelty of the method lies in using adaptive probability to estimate the linear motion, while the state-of-the-art method assumes that the highest probability is assigned to the case with no motion. In experiments we demonstrated that SEHMM is more accurate than the traditional HMM using both simulated and real MFM image sequences.

Diffusion Tensor-based Fast Marching for Modeling Human Brain Connectivity Network

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society. Apr, 2011  |  Pubmed ID: 21035304

Diffusion tensor imaging (DTI) is an effective modality in studying the connectivity of the brain. To eliminate possible biases caused by fiber extraction approaches due to low spatial resolution of DTI and the number of fibers obtained, the fast marching (FM) algorithm based on the whole diffusion tensor information is proposed to model and study the brain connectivity network. Our observation is that the connectivity extracted from the whole tensor field would be more robust and reliable for constructing brain connectivity network using DTI data. To construct the connectivity network, in this paper, the arrival time map and the velocity map generated by the FM algorithm are combined to define the connectivity strength among different brain regions. The conventional fiber tracking-based and the proposed tensor-based FM connectivity methods are compared, and the results indicate that the connectivity features obtained using the FM-based method agree better with the neuromorphical studies of the human brain.

Cortical and Frontal Atrophy Are Associated with Cognitive Impairment in Age-related Confluent White-matter Lesion

Journal of Neurology, Neurosurgery, and Psychiatry. Jan, 2011  |  Pubmed ID: 20826875

Although age-related confluent white-matter lesion (WML) is an important substrate for cognitive impairment, the mechanisms whereby WML induces cognitive impairment are uncertain. The authors investigated cognitive predictors in patients with confluent WML.

Characterization of a Human Tumorsphere Glioma Orthotopic Model Using Magnetic Resonance Imaging

Journal of Neuro-oncology. Sep, 2011  |  Pubmed ID: 21240539

Magnetic resonance imaging (MRI) is the imaging modality of choice by which to monitor patient gliomas and treatment effects, and has been applied to murine models of glioma. However, a major obstacle to the development of effective glioma therapeutics has been that widely used animal models of glioma have not accurately recapitulated the morphological heterogeneity and invasive nature of this very lethal human cancer. This deficiency is being alleviated somewhat as more representative models are being developed, but there is still a clear need for relevant yet practical models that are well-characterized in terms of their MRI features. Hence we sought to chronicle the MRI profile of a recently developed, comparatively straightforward human tumor stem cell (hTSC) derived glioma model in mice using conventional MRI methods. This model reproduces the salient features of gliomas in humans, including florid neoangiogenesis and aggressive invasion of normal brain. Accordingly, the variable, invasive morphology of hTSC gliomas visualized on MRI duplicated that seen in patients, and it differed considerably from the widely used U87 glioma model that does not invade normal brain. After several weeks of tumor growth the hTSC model exhibited an MRI contrast enhancing phenotype having variable intensity and an irregular shape, which mimicked the heterogeneous appearance observed with human glioma patients. The MRI findings reported here support the use of the hTSC glioma xenograft model combined with MRI, as a test platform for assessing candidate therapeutics for glioma, and for developing novel MR methods.

A Global Spatial Similarity Optimization Scheme to Track Large Numbers of Dendritic Spines in Time-lapse Confocal Microscopy

IEEE Transactions on Medical Imaging. Mar, 2011  |  Pubmed ID: 21047709

Dendritic spines form postsynaptic contact sites in the central nervous system. The rapid and spontaneous morphology changes of spines have been widely observed by neurobiologists. Determining the relationship between dendritic spine morphology change and its functional properties such as memory learning is a fundamental yet challenging problem in neurobiology research. In this paper, we propose a novel algorithm to track the morphology change of multiple spines simultaneously in time-lapse neuronal images based on nonrigid registration and integer programming. We also propose a robust scheme to link disappearing-and-reappearing spines. Performance comparisons with other state-of-the-art cell and spine tracking algorithms, and the ground truth show that our approach is more accurate and robust, and it is capable of tracking a large number of neuronal spines in time-lapse confocal microscopy images.

The Effect of MTOR Inhibition Alone or Combined with MEK Inhibitors on Brain Metastasis: an in Vivo Analysis in Triple-negative Breast Cancer Models

Breast Cancer Research and Treatment. Jan, 2012  |  Pubmed ID: 21394501

mTOR inhibitor rapamycin and its analogs are lipophilic, demonstrate blood-brain barrier penetration, and have shown promising antitumor effects in several types of refractory tumors. We thus try to explore the therapeutic effects of mTOR inhibitors on brain metastasis models. We examined the effects of different dose of mTOR inhibitors (rapamycin, Temsirolimus-CCI-779) on cell invasion in two brain metastatic breast cancer cell lines (MDA-MB231-BR and CN34-BrM2). Antibody microarray and immunoblotting were applied to detect signaling pathways underlying the dose differential drug effects. The in vivo effects of single drug (CCI-779), and drug combination of CCI-779 with SL327 (a brain penetrant MEK inhibitor) to eliminate the unfavorable activation of MAPK pathway were evaluated in MDA-MB231-BR brain metastases xenograft mice. The two mTOR inhibitors, rapamycin and CCI-779, inhibited the invasion of brain metastatic cells only at a moderate concentration level, which was lost at higher concentrations secondary to activation of the MAPK signaling pathway. Pharmacological inhibition of ERK1/2 by PD98059 and SL327 restored the anti-invasion effects of mTOR inhibition in vitro. In vivo, a significant decrease was noted in the average number of micro and large metastatic lesions as well as the whole brain GFP expression in the CCI-779 1 mg/kg/day treated group compared with that in the vehicle group (P < 0.05). However, 10 mg/kg CCI-779 treatment did not show significant anti-metastasis effect on the animal model. High-dose CCI-779 eliciting the ERK MAPK activation in the brain metastatic lesion was corroborated. Combined with the brain penetrant MEK inhibitor SL327, high-dose CCI-779 significantly reduces the brain metastasis, and the combination treatment prohibited perivascular invasion of tumor cells and inhibits tumor angiogenesis in vivo. This study provides evidence on the potential value of CCI-779 as well as CCI-779 + SL327 in prohibiting breast cancer brain metastasis.

The Kinetochore Protein Bub1 Participates in the DNA Damage Response

DNA Repair. Feb, 2012  |  Pubmed ID: 22071147

The DNA damage response (DDR) and the spindle assembly checkpoint (SAC) are two critical mechanisms by which mammalian cells maintain genome stability. There is a growing body of evidence that DDR elements and SAC components crosstalk. Here we report that Bub1 (budding uninhibited by benzimidazoles 1), one of the critical kinetochore proteins essential for SAC, is required for optimal DDRs. We found that knocking-down Bub1 resulted in prolonged H2AX foci and comet tail formation as well as hypersensitivity in response to ionizing radiation (IR). Further, we found that Bub1-mediated Histone H2A Threonine 121 phosphorylation was induced after IR in an ATM-dependent manner. We demonstrated that ATM phosphorylated Bub1 on serine 314 in response to DNA damage in vivo. Finally, we showed that ATM-mediated Bub1 serine 314 phosphorylation was required for IR-induced Bub1 activation and for the optimal DDR. Together, we elucidate the molecular mechanism of DNA damage-induced Bub1 activation and highlight a critical role of Bub1 in DDR.

A Novel Method of Transcriptional Response Analysis to Facilitate Drug Repositioning for Cancer Therapy

Cancer Research. Jan, 2012  |  Pubmed ID: 22108825

Little research has been done to address the huge opportunities that may exist to reposition existing approved or generic drugs for alternate uses in cancer therapy. In addition, there has been little work on strategies to reposition experimental cancer agents for testing in alternate settings that could shorten their clinical development time. Progress in each area has lagged, in part, because of the lack of systematic methods to define drug off-target effects (OTE) that might affect important cancer cell signaling pathways. In this study, we addressed this critical gap by developing an OTE-based method to repurpose drugs for cancer therapeutics, based on transcriptional responses made in cells before and after drug treatment. Specifically, we defined a new network component called cancer-signaling bridges (CSB) and integrated it with a Bayesian factor regression model (BFRM) to form a new hybrid method termed CSB-BFRM. Proof-of-concept studies were conducted in breast and prostate cancer cells and in promyelocytic leukemia cells. In each system, CSB-BFRM analysis could accurately predict clinical responses to more than 90% of drugs approved by the U.S. Food and Drug Administration and more than 75% of experimental clinical drugs that were tested. Mechanistic investigation of OTEs for several high-ranking drug-dose pairs suggested repositioning opportunities for cancer therapy, based on the ability to enforce retinoblastoma-dependent repression of important E2F-dependent cell-cycle genes. Together, our findings establish new methods to identify opportunities for drug repositioning or to elucidate the mechanisms of action of repositioned drugs.

Identification of Small Molecule Inhibitors of Neurite Loss Induced by Aβ Peptide Using High-content Screening

The Journal of Biological Chemistry. Jan, 2012  |  Pubmed ID: 22277654

Multiple lines of evidence indicate a strong relationship between Aβ peptide-induced neurite degeneration and the progressive loss of cognitive functions in Alzheimer's disease (AD) patients and in AD animal models. This prompted us to develop a high content screening assay (HCS) and Neurite Image Quantitator (NeuriteIQ) software to quantify the loss of neuronal projections induced by Aβ peptide neurons and enable us to identify new classes of neurite-protective small molecules, which may represent new leads for AD drug discovery. We identified thirty-six inhibitors of Aβ-induced neurite loss in the 1,040 compound National Institute of Neurological Disorders and Stroke (NINDS) custom collection of known bioactives and FDA approved drugs. Activity clustering showed that non-steroidal anti-inflammatory drugs (NSAIDs) were significantly enriched among the hits. Notably, NSAIDs have previously attracted significant attention as potential drugs for AD, however their mechanism of action remains controversial. Our data revealed that cyclooxygenase-2 (COX-2) expression was increased following Aβ treatment. Furthermore, multiple distinct classes of COX inhibitors efficiently blocked neurite loss in primary neurons, suggesting that increased COX activity contributes to Aβ peptide-induced neurite loss. Finally, we discovered that the detrimental effect of COX activity on neurite integrity may be mediated through the inhibition of peroxisome proliferator-activated receptor γ (PPARγ activity. Overall, our work establishes the feasibility of identifying small molecule inhibitors of Aβ induced neurite loss using the NeuriteIQ pipeline and provides novel insights into the mechanisms of neuroprotection by NSAIDs.