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. Additionally, 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 due to the lack of systematic methods to define drug off-target effects (OTEs) 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 (CSBs) and integrated it with Bayesian Factor Regression Model (BFRM) to form a new hybrid method termed CSB-BFRM. Proof of concept studies were performed in breast and prostate cancer cells and in promyelocytic leukemia cells. In each system, CSB-BFRM analysis could accurately predict clinical responses to >90% of FDA-approved drugs and >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 Rb-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.
Off-target drug repositioning; cancer systems biology; cancer transcriptional response
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.
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.
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.
diffusion tensor imaging; fast marching; brain connectivity analysis; fiber tracking; tractography
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.
High content screening; Microscopy image; Nuclei segmentation; Neurite outgrowth; Line-pixel detection; Branch area
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.
dendritic spine; semi-supervised learning; morphological spine classification
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.
Gua Sha is a traditional Chinese folk therapy that employs skin scraping to cause subcutaneous microvascular blood extravasation and bruises. The protocol for bioluminescent optical imaging of HO-1-luciferase transgenic mice reported in this manuscript provides a rapid in vivo assay of the upregulation of the heme oxygenase-1 (HO-1) gene expression in response to the Gua Sha procedure. HO-1 has long been known to provide cytoprotection against oxidative stress. The upregulation of HO-1, assessed by the bioluminescence output, is thought to represent an antioxidative response to circulating hemoglobin products released by Gua Sha. Gua Sha was administered by repeated strokes of a smooth spoon edge over lubricated skin on the back or other targeted body part of the transgenic mouse until petechiae (splinter hemorrhages) or ecchymosis (bruises) indicative of extravasation of blood from subcutaneous capillaries was observed. After Gua Sha, bioluminescence imaging sessions were carried out daily for several days to follow the dynamics of HO-1 expression in multiple internal organs.
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.
Diffusion tensor imaging; image registration; tensor reorientation; fast marching
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.
dendritic spine; confocal microscopy image; central region extraction; gradient vector flow; fast marching; neurological disease; psychiatric disease
Rationale and Objectives
Molecular imaging modalities such as PET/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.
Materials and Methods
LINA is designed to automatically construct longitudinal correspondence along serial images of individual patients for changes in tumor volume and metabolic activity via regions of interest (ROI) segmented from a given time-point image and propagated into the space of all follow-up PET/CT images. We applied LINA retrospectively to nine lymphoma patients enrolled in an immunotherapy clinical trial conducted at the Center for Cell and Gene Therapy, Baylor College of Medicine. This methodology was compared to the readout by a diagnostic radiologist, who manually measured the ROI metabolic activity as defined by the maximal Standardized Uptake Value (SUVmax).
Quantitative results showed that the measured SUVs obtained from automatic mapping are as accurate as semi-automatic segmentation and consistent with clinical examination finding. The average of relative squared differences of SUVmax between automatic and semi-automatic segmentation was found to be 0.02.
These data support a role for LINA in facilitating quantitative analysis of serial PET/CT images to efficiently assess cancer treatment responses in a comprehensive and intuitive software platform.
Lymphoma; quantitative evaluation of treatment outcomes; PET/CT; longitudinal registration of serial images
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.
Peak detection is a pivotal first step in biomarker discovery from mass spectrometry (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 signal-to-noise ratio (SNR), 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 1Da 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 dataset of serial peptide and protein calibration standards. Compared with MoverZ program, our new method detects more peaks and significantly enhances SNR of the peak after the chemical noise removal. We then successfully applied this method to a dataset from prOTOF MS spectra of albumin and albumin-bound proteins from serum samples of 59 patients with carotid artery disease to detect peaks with SNR ≥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.
adaptive short-time discrete Fourier transform; complete linkage hierarchical clustering; peak detection; peak alignment; nudecimated wavelet transform
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.
Automated segmentation of time-lapse images is a method to facilitate the understanding of the intricate biological progression, e.g., cancer cell migration. To address this problem, we introduce a shape representation enhancement over popular snake models in the context of confident scale-space such that a higher level of interpretation can hopefully be achieved. Our proposed system consists of a hierarchical analytic framework including feedback loops, self-adaptive and demand-adaptive adjustment, incorporating a steerable boundary detail term constraint based on multiscale B-spline interpolation. To minimize the noise interference inherited from microscopy acquisition, the coarse boundary derived from the initial segmentation with refined watershed line is coupled with microscopy compensation using the mean shift filtering. A progressive approximation is applied to achieve represented as a balance between a relief function of watershed algorithm and local minima concerning multi-scale optimality, convergence, and robust constraints. Experimental results show that the proposed method overcomes problems with spurious branches, arbitrary gaps, low contrast boundaries and low signal-to-noise ratio. The proposed system has the potential to serve as an automated data processing tool for cell migration applications.
cellular image segmentation; 3T3 cell; time-lapse microscopy; snake model; multiscale detail detection; mean shift filtering
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.
stem cell differentiation; blob detection; curvilinear structure detection; cell segmentation; level set
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.
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/
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.
online phenotype discovery; RNA interference; high content screen; gap statistics; minimum classification error
In neuro-biology, 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 topologically correct, 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.
Neuron; dendrite; spine; 3D reconstruction; curve-skeleton; medial geodesic function
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.
Spine detection; geometric measurement estimation; watershed; microscopy images
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 Kc167 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.
High-content screening; Image score inference
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.
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.
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.
Materials and Methods
Twenty AIS female patients with typical right-convex thoracic curve (age range,11−18 years; mean, 14.1 years) and 26 female controls (mean age, 14.8 years) underwent three-dimensional magnetization prepared rapid acquisition gradient echo (3D-MPRAGE) MR imaging. Volumes of 99 preselected neuroanatomical regions were compared by statistical parametric mapping and atlas-based hybrid warping.
Analysis of variance statistics revealed significant mean volumetric differences in 22 brain regions between AIS and controls. Ten regions were larger in AIS including the left frontal gyri and white matter in left frontal, parietal, and temporal regions, corpus callosum and brainstem. Twelve regions were smaller in AIS, including right-sided descending white matter tracts (anterior and posterior limbs of the right internal capsule and the cerebral peduncle) and deep nucleus (caudate), bilateral perirhinal cortices, left hippocampus and amygdala, bilateral precuneus gyri, and left middle and inferior occipital gyri.
Regional brain volume difference in AIS subjects may help to explain neurological abnormalities in this group.
adolescent idiopathic scoliosis; magnetic resonance; brain morphometry