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Traditionally, the kinetics of DNA repair have been estimated using immunocytochemistry by labeling proteins involved in the DNA damage response (DDR) with fluorescent markers in a fixed cell assay. However, detailed knowledge of DDR dynamics across multiple cell generations cannot be obtained using a limited number of fixed cell time-points. Here we report on the dynamics of 53BP1 radiation induced foci (RIF) across multiple cell generations using live cell imaging of non-malignant human mammary epithelial cells (MCF10A) expressing histone H2B-GFP and the DNA repair protein 53BP1-mCherry. Using automatic extraction of RIF imaging features and linear programming techniques, we were able to characterize detailed RIF kinetics for 24 hours before and 24 hours after exposure to low and high doses of ionizing radiation. High-content-analysis at the single cell level over hundreds of cells allows us to quantify precisely the dose dependence of 53BP1 protein production, RIF nuclear localization and RIF movement after exposure to X-ray. Using elastic registration techniques based on the nuclear pattern of individual cells, we could describe the motion of individual RIF precisely within the nucleus. We show that DNA repair occurs in a limited number of large domains, within which multiple small RIFs form, merge and/or resolve with random motion following normal diffusion law. Large foci formation is shown to be mainly happening through the merging of smaller RIF rather than through growth of an individual focus. We estimate repair domain sizes of 7.5 to 11 microm2 with a maximum number of ~15 domains per MCF10A cell. This work also highlights DDR which are specific to doses larger than 1 Gy such as rapid 53BP1 protein increase in the nucleus and foci diffusion rates that are significantly faster than for spontaneous foci movement. We hypothesize that RIF merging reflects a "stressed" DNA repair process that has been taken outside physiological conditions when too many DSB occur at once. High doses of ionizing radiation lead to RIF merging into repair domains which in turn increases DSB proximity and misrepair. Such finding may therefore be critical to explain the supralinear dose dependence for chromosomal rearrangement and cell death measured after exposure to ionizing radiation.

Cell-matrix adhesions are of great interest because of their contribution to numerous biological processes, including cell migration, differentiation, proliferation, survival, tissue morphogenesis, wound healing, and tumorigenesis. Adhesions are dynamic structures that are classically defined on two-dimensional (2D) substrates, though the need to analyze adhesions in more physiologic three-dimensional (3D) environments is being increasingly recognized. However, progress has been greatly hampered by the lack of available tools to analyze adhesions in 3D environments. To address this need, we have developed a platform for the automated analysis, segmentation, and tracking of adhesions (PAASTA) based on an open source MATLAB framework, CellAnimation. PAASTA enables the rapid analysis of adhesion dynamics and many other adhesion characteristics, such as lifetime, size, and location, in 3D environments and on traditional 2D substrates. We manually validate PAASTA and utilize it to quantify rate constants for adhesion assembly and disassembly as well as adhesion lifetime and size in 3D matrices. PAASTA will be a valuable tool for characterizing adhesions and for deciphering the molecular mechanisms that regulate adhesion dynamics in 3D environments.

In contrast to the classic view of static DNA double-strand breaks (DSBs) being repaired at the site of damage, we hypothesize that DSBs move and merge with each other over large distances (μm). As X-ray dose increases, the probability of having DSB clusters increases as does the probability of misrepair and cell death. Experimental work characterizing the X-ray dose dependence of radiation-induced foci (RIF) in nonmalignant human mammary epithelial cells (MCF10A) is used here to validate a DSB clustering model. We then use the principles of the local effect model (LEM) to predict the yield of DSBs at the submicron level. Two mechanisms for DSB clustering, namely random coalescence of DSBs versus active movement of DSBs into repair domains are compared and tested. Simulations that best predicted both RIF dose dependence and cell survival after X-ray irradiation favored the repair domain hypothesis, suggesting the nucleus is divided into an array of regularly spaced repair domains of ∼1.55 μm sides. Applying the same approach to high-linear energy transfer (LET) ion tracks, we are able to predict experimental RIF/μm along tracks with an overall relative error of 12%, for LET ranging between 30-350 keV/μm and for three different ions. Finally, cell death was predicted by assuming an exponential dependence on the total number of DSBs and of all possible combinations of paired DSBs within each simulated RIF. Relative biological effectiveness (RBE) predictions for cell survival of MCF10A exposed to high-LET showed an LET dependence that matches previous experimental results for similar cell types. Overall, this work suggests that microdosimetric properties of ion tracks at the submicron level are sufficient to explain both RIF data and survival curves for any LET, similarly to the LEM assumption. Conversely, high-LET death mechanism does not have to infer linear-quadratic dose formalism as done in the LEM. In addition, the size of repair domains derived in our model are based on experimental RIF and are three times larger than the hypothetical LEM voxel used to fit survival curves. Our model is therefore an alternative to previous approaches that provides a testable biological mechanism (i.e., RIF). In addition, we propose that DSB pairing will help develop more accurate alternatives to the linear cancer risk model (LNT) currently used for regulating exposure to very low levels of ionizing radiation.

Repair of double strand breaks (DSBs) is essential for cell survival and genome integrity. While much is known about the molecular mechanisms involved in DSB repair and checkpoint activation, the roles of nuclear dynamics of radiation-induced foci (RIF) in DNA repair are just beginning to emerge. Here, we summarize results from recent studies that point to distinct features of these dynamics in two different chromatin environments: heterochromatin and euchromatin. We also discuss how nuclear architecture and chromatin components might control these dynamics, and the need of novel quantification methods for a better description and interpretation of these phenomena. These studies are expected to provide new biomarkers for radiation risk and new strategies for cancer detection and treatment., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Three-dimensional (3D) tissue culture provides a physiologically relevant microenvironment for distinguishing malignant from non-malignant breast cell phenotypes. 3D culture assays can also be used to test novel cancer therapies and predict a differential response to radiation between normal and malignant cells in vivo. However, biological measurements in such complex models are difficult to quantify and current approaches do not allow for in-depth multifaceted assessment of individual colonies or unique sub-populations within the entire culture. This is in part due to the limitations of imaging at a range of depths in 3D culture resulting from optical aberrations and intensity attenuation. Here, we address these limitations by combining sample smearing techniques with high-throughput 2D imaging algorithms to accurately and rapidly quantify imaging features acquired from 3D cultures. Multiple high resolution imaging features especially designed to characterize 3D cultures show that non-malignant human breast cells surviving large doses of ionizing radiation acquire a "swelled acinar" phenotype with fewer and larger nuclei, loss of cell connectivity and diffused basement membrane. When integrating these imaging features into hierarchical clustering classification, we could also identify subpopulations of phenotypes from individual human tumor colonies treated with ionizing radiation or/and integrin inhibitors. Such tools have therefore the potential to further characterize cell culture populations after cancer treatment and identify novel phenotypes of resistance.

Motivation: Advances in microscopy technology have led to the creation of high-throughput microscopes that are capable of generating several hundred gigabytes of images in a few days. Analyzing such wealth of data manually is nearly impossible and requires an automated approach. There are at present a number of open-source and commercial software packages that allow the user to apply algorithms of different degrees of sophistication to the images and extract desired metrics. However, the types of metrics that can be extracted are severely limited by the specific image processing algorithms that the application implements, and by the expertise of the user. In most commercial software, code unavailability prevents implementation by the end user of newly developed algorithms better suited for a particular type of imaging assay. While it is possible to implement new algorithms in open-source software, rewiring an image processing application requires a high degree of expertise. To obviate these limitations, we have developed an open-source high-throughput application that allows implementation of different biological assays such as cell tracking or ancestry recording, through the use of small, relatively simple image processing modules connected into sophisticated imaging pipelines. By connecting modules, non-expert users can apply the particular combination of well-established and novel algorithms developed by us and others that are best suited for each individual assay type. In addition, our data exploration and visualization modules make it easy to discover or select specific cell phenotypes from a heterogeneous population., Availability: CellAnimation is distributed under the Creative Commons Attribution-NonCommercial 3.0 Unported license (http://creativecommons.org/licenses/by-nc/3.0/). CellAnimationsource code and documentation may be downloaded from www.vanderbilt.edu/viibre/software/documents/CellAnimation.zip. Sample data are available at www.vanderbilt.edu/viibre/software/documents/movies.zip., Contact: walter.georgescu@vanderbilt.edu, Supplementary Information: Supplementary data available at Bioinformatics online.

Laminin-332 (Ln-332) is an extracellular matrix molecule that regulates cell adhesion, spreading, and migration by interaction with cell surface receptors such as alpha3beta1 and alpha6beta4. Previously, we developed a function-blocking monoclonal antibody against rat Ln-332, CM6, which blocks hemidesmosome assembly induced by Ln-332-alpha6beta4 interactions. However, the location of its epitope on Ln-332 has remained unclear. In this study, we show that the CM6 epitope is located on the laminin G-like (LG)2 module of the Ln-332 alpha3 chain. To specify the residues involved in this epitope, we produced a series of GST-fused alpha3 LG2 mutant proteins in which rat-specific acids were replaced with human acids by a site-directed mutagenesis strategy. CM6 reactivity against these proteins showed that CM6 binds to the (1089)NERSVR(1094) sequence of rat Ln-332 LG2 module. In a structural model, this sequence maps to an LG2 loop sequence that is exposed to solvent according to predictions, consistent with its accessibility to antibody. CM6 inhibits integrin-dependent cell adhesion on Ln-332 and inhibits cell spreading on both Ln-332 and recombinant LG2 (rLG2; but not rLG3), suggesting the presence of an alpha3beta1 binding site on LG2. However, we were unable to show that rLG2 supports adhesion in standard assays, suggesting that LG2 may contain a "weak" integrin binding site, only detectable in spreading assays that do not require washes. These results, together with our previous findings, indicate that binding sites for alpha3beta1 and alpha6beta4 are closely spaced in the Ln-332 LG domains where they regulate alternative cell functions, namely adhesion/migration or hemidesmosome anchoring., ((c) 2010 Wiley-Liss, Inc.)

Mapping quantitative cell traits (QCT) to underlying molecular defects is a central challenge in cancer research because heterogeneity at all biological scales, from genes to cells to populations, is recognized as the main driver of cancer progression and treatment resistance. A major roadblock to a multiscale framework linking cell to signaling to genetic cancer heterogeneity is the dearth of large-scale, single-cell data on QCT-such as proliferation, death sensitivity, motility, metabolism, and other hallmarks of cancer. High-volume single-cell data can be used to represent cell-to-cell genetic and nongenetic QCT variability in cancer cell populations as averages, distributions, and statistical subpopulations. By matching the abundance of available data on cancer genetic and molecular variability, QCT data should enable quantitative mapping of phenotype to genotype in cancer. This challenge is being met by high-content automated microscopy (HCAM), based on the convergence of several technologies including computerized microscopy, image processing, computation, and heterogeneity science. In this chapter, we describe an HCAM workflow that can be set up in a medium size interdisciplinary laboratory, and its application to produce high-throughput QCT data for cancer cell motility and proliferation. This type of data is ideally suited to populate cell-scale computational and mathematical models of cancer progression for quantitatively and predictively evaluating cancer drug discovery and treatment.

Historically, it has been difficult to generate accurate and reproducible protein gradients for studies of interactions between cells and extracellular matrix. Here we demonstrate a method for rapid patterning of protein gradients using computer-driven hydrodynamic focusing in a simple microfluidic device. In contrast to published work, we are moving the complexity of gradient creation from the microfluidic hardware to dynamic computer control. Using our method, switching from one gradient profile to another requires only a few hours to devise a new control file, not days or weeks to design and build a new microfluidic device. Fitting existing protein deposition models to our data, we can extract key parameters needed for controlling protein deposition. Several protein deposition models were evaluated under microfluidic flow conditions. A mathematical model for our deposition method allows us to determine the parameters for a protein adsorption model and then predict the final shape of the surface density gradient. Simple and non-monotonic single and multi-protein gradient profiles were designed and deposited using the same device.