Integrating image analysis algorithms in a web interface for the quantification of microtubule dynamics.

We present improvements to a web interface and an integrated computational tracking algorithm for quantitative analysis of microtubule dynamics in live-cell microscopy images. Based on a previously implemented system, more new functionalities have been added to the interface. The system also integrates a computational tracking algorithm to aid the analysis. The analysis workflow of the proposed interface is made similar to the current manual analysis workflow in order to make the interface intuitive to use. We show the workflow of the computer analysis algorithm and how it is used to aid the existing analysis workflow. We also demonstrate how to re-evaluate existing data in a case study using real imaging data. Lastly, we show the added functionalities of the interface including how to share image data and analysis results.

A Web Interface for the Quantification of Microtubule Dynamics.

We propose a web interface that allows researchers to quantify and analyze microtubule confocal images online. Most analyses of microtubule confocal images are performed manually using very simple software or tools. Analysis results are stored locally within each collaborator with different styles and formats. This has limited the sharing of data and results when collaborating among different research parties. A web interface provides a simple way for users to process data online. It also allows easy sharing of both data and results among different participating groups. Analysis workflow of the interface is made similar to existing manual protocols. We demonstrate the integration of image processing algorithm in the current workflow to aid the analysis. Our design also allows integration of novel automated analysis algorithms and modules to re-evaluate existing data. This interface can provide a validation platform for new automated algorithm and allow collaboration on microtubule image analysis from different locations.

Automatic tip selection for microtubule dynamics quantification.

Microtubule (MT) dynamics quantification includes modeling of elongation, rapid shortening, and pauses. It indicates the effect of the cancer treatment drug paclitaxel because the drug causes MTs to bundle, which will in turn inhibit successful mitosis of cancerous cells. Thus, automatic MT dynamics analysis has been researched intensely because it allows for faster evaluation of potential cancer treatments and better understanding of drug effects on a cell. However, most current literatures still use manual initialization. In this work, we propose an automatic initialization algorithm that selects isolated and active tips for tracking. We use a Gaussian match filter to enhance the MT structures, and a novel technique called Pixel Nucleus Analysis (PNA) for isolated MT tip detection. To find dynamic tips, we applied a masked FFT in the temporal domain followed by K-means clustering. To evaluate the selected tips, we used a low level tip linking algorithm, and show the results of applying the algorithm to a model image and five MCF-7 breast cancer cell line images captured using fluorescent confocal microscopy. Finally, we compare tip selection criteria with existing automatic selection algorithms. We conclude that the proposed analysis is an effective technique based on three criteria which include outer region selection, separation, and MT dynamics.

Bioconjugated quantum dots for multiplexed and quantitative immunohistochemistry.

Bioconjugated quantum dots (QDs) provide a new class of biological labels for evaluating biomolecular signatures (biomarkers) on intact cells and tissue specimens. In particular, the use of multicolor QD probes in immunohistochemistry is considered one of the most important and clinically relevant applications. At present, however, clinical applications of QD-based immunohistochemistry have achieved only limited success. A major bottleneck is the lack of robust protocols to define the key parameters and steps. Here, we describe our recent experience, preliminary results and detailed protocols for QD-antibody conjugation, tissue specimen preparation, multicolor QD staining, image processing and biomarker quantification. The results demonstrate that bioconjugated QDs can be used for multiplexed profiling of molecular biomarkers, and ultimately for correlation with disease progression and response to therapy. In general, QD bioconjugation is completed within 1 day, and multiplexed molecular profiling takes 1-3 days depending on the number of biomarkers and QD probes used.

Detecting and quantifying biomarkers of risk for colorectal cancer using quantum dots and novel image analysis algorithms.

Colorectal cancer, the second leading cause of cancer deaths in the United States, is a molecular disease that is largely lifestyle determined and preventable. While heart disease has been sharply declining, in large part from widespread use of biological measurements that indicate risk ("biomarkers of risk"), such as blood cholesterol, to motivate and guide preventive treatment, colorectal cancer is a disease for which mortality rates have changed little and for which there have been no biomarkers of risk. Based on new knowledge about the molecular basis of colorectal cancer we developed and validated a panel of treatable biomarkers of risk that can be measured in rectal biopsies using automated immunohistochemistry and semi-automated image analysis. The methodology is now being made practical for clinical application through the use of 1) quantum dots, so that all of the biomarkers can be detected simultaneously on the same histologic sections (i.e., multiplexed), and 2) novel, automated image analysis algorithms to measure the quantities and tissue distributions of the biomarkers. Herein we summarize our methods, results, current directions, and progress.

Automatic microtubule tracking for QD-based in vivo cell imaging and drug efficacy study.

Microtubules (MT) are dynamic polymers that rapidly transition between states of growth, shortening, and pause. These dynamic events are critical for many microtubule functions such as intracellular trafficking and signaling. In addition, cancer chemotherapy drugs that target microtubules, such as the taxanes and the vinca alkaloids, are known to suppress microtubule dynamics at low doses, leading to mitotic arrest and cell death. Quantification of microtubule dynamics can be used as a read-out of anticancer-drug activity and can be a surrogate marker of drug sensitivity/resistance. The emerging nanotechnology such as quantum dots has provided properties such as less photo bleaching, higher probe imaging intensity, better specificity and sensitivity, which finally makes visualizing subcellular events over long enough time a possibility. But it also results in big increase in data acquisition. The traditional way of annotating MT manually is becoming a daunting task. Thus, the goal is to research and develop an efficient, reliable, and rapid MT tracking. In this paper, we describe active contour-based tracking methods to automatically track MT. We redefine the internal energy terms specifically for open snake, and examine different external energy terms for locating the end tips of a microtubule. This algorithm has been validated using simulated images, images of untreated MCF-7 breast cancer cells, and image of cells treated with the microtubule-targeting chemotherapeutic agent, Taxol.