With the development of advanced molecular tools to view and manipulate protein activity in living cells, there is an ever-growing demand for computer vision techniques that enable high-throughput analysis and interpretation of the visualized processes. At the Department of Pharmacology we aim to augment state-of-the art imaging technology with the matching power of computational tools to unveil complex organization of cellular machinery in health and disease.


Visualizing Signaling Filipodia Protrusiveact Neuron Growth Cone
Visualizing signaling in living cells Filipodia dynamics Protrusive activity Neuron growth cone


Morphodyn SignalingCellEdge PolarizationFactors Gomez Imaging
Morphodynamics Signaling at the cell edge Polarization factors Cellular motility and metastasis


Visualizing signaling circuits in living cells and animals

Our goal is to dissect dynamic signaling circuits by both activating and visualizing signaling reactions in living cells and animals. While addressing specific molecules important to motility signaling, we are developing broadly applicable imaging approaches. These include fluorescent biosensors based on different designs, capable of visualizing the conformational changes of endogenous proteins and studying multiple proteins in the same cell.

Some biosensors are genetically encoded and others, based on novel environment-sensing dyes, can be used where genetic manipulations are not accessible. We have generated biosensors for previously intractable molecules using engineered protein scaffolds, conferring specificity for a given target using high throughput screening approaches. An important goal is to develop broadly applicable approaches to modulate protein activity either with light, or through designed allosteric responses to small molecules. These tools are being used in collaboration with Denis Tsygankov, Tim Elston and Shawn Gomez at UNC, who are developing novel image analysis methods to precisely quantify and model signaling dynamics. We are currently examining rapid, spatially controlled feedback loops in GEF/GTPase/effector circuitry during protrusion and retraction, asking how the dynamics of scaffolds, including large structures such as cytoskeletal fibers, control signaling.

Hahn Imaging

Klaus M. Hahn, PhD | Webpage | Email| Publications


Protrusion Dynamics

Protrusions play a crucial role in many physiological processes including cell migration during development, wound healing and immune response. However, quantification of highly dynamic protrusion across a broad range of shapes and sizes creates a serious challenge for both imaging and statistical analysis. Here we developed a powerful computational platform CellGeo that makes precise definition and identification of protrusions possible and allows automated tracking and analysis of protrusion dynamics to be unbiased, labor efficient, and comprehensive. [Tsygankov et. al., JCB, in press]

Filopodia identification and tracking

Thin protrusions like filopodia are often hard to visualize with sufficient spatiotemporal resolution and a high signal to noise ratio. Kymograph (line scan) approaches do not work as well for cells with complex geometries and especially curving, highly dynamic filopodia. Even if the tips of filopodia are identified, it is hard to define and consistently measure filopodia width and length in an automated manner. It is also hard to define a generic distance measure for tracking multiple closely located filopodia that grow out and retract at different time points.

MovTresh module of the CellGeo software package not only automatically thresholds every frame of the movie and highlights cell outline, but also provides a convenient interactive controls to adjust threshold values and outline filopodia as accurate as the signal-to-noise ratio allows. BisectoGraph module maps an arbitrarily complex shape onto a tree-graph that allows clear definition and identification of filopodia tips, bases and central lines automatically and consistently across all geometries and sizes. FiloTrack module provides interactive controls for adjusting tracking parameters and measuring filopodia lifetime, number and length. Each tracked filopodia can be individually visualized for easy assessment of the accuracy of the results.

Tsygankov Protrusion

Broad protrusion activity

When both broad protrusions and filopodia present at the cell edge, visual perception of their individual contributions to the cell dynamics is obscured. The total change of cell area is not always an accurate indicator of protrusive or refractive activity, because the cell can undergo significant but randomly distributed protrusions and retractions with little change of the total area.

Tsygankov Broad Protrusion

The tree-graph representation of the cell outline used in the BisectoGraph module of the CellGeo software package is a perfect tool for segmenting out all filopodia and extracting the underlying cell body for further analysis. This way, thin and broad protrusions of the same cell can be analyzed simultaneously but independently of each other. ProActive module visualizes protruding and retracting parts of the cells (in different colors) and quantifies protrusive and refractive activity as a function of time using both the area and velocity measures.

Growth cone dynamics in neurons

Once again, the tree-graph representation of the shapes used in ConeTrack module of the CellGeo software package provides a way to automatically extract and track outlines of each growth cone with just two user-specified criteria.

Tsygankov Growth Cone Dynamics

Timothy Elston | WebpageEmail      Klaus Hahn | Webpage Email

(in collaboration with Mark Peifer, Colleen Bilancia, Andrei Karginov, Eric Vitriol, Denis Tsygankov)


Cellular morphodynamics

Before reaching a final state after drug treatment, cells can undergo transient morphology transformations that are significantly different from the final shape or motion type. Quantitative analysis of such transient dynamics is particularly important in dissecting functional roles of homologous cytoskeleton regulators. However, this requires a tool that is capable to classify cell motion types as the time goes.

By introducing a set of parameters that characterize major aspects of shape changes, such as the rate of area change and polarization factor, cell motion can be represented as a trajectory in parameter space with distinct regions that correspond to different types of motion. Based on this approach, our GUI SquigglyMorph classifies cell motion as uniform spreading, uniform shrinking, polarized spreading, polarized motion, or polarized shrinking at each time frame of the imported movie.

Tsygankov Cellular Morphodynamics
Timothy Elston | Webpage | Email     Klaus Hahn | Webpage | Email

(in collaboration with Pei-hsuan Chu, Denis Tsygankov)



Spatiotemporal distribution of GTPases at the cell edge

To establish correlations between the activity of a GTPase and the cell edge motion, spatiotemporal distribution of active GTPase needs to be visualized and quantify with respect to the cell edge and its velocity. However, it is important that such quantification is automated and includes the whole (or most of the) cell edge to avoid selection bias and poor statistics.

Tsygankov Spatiotemporal distribution

The GUI LineScan automatically builds scan lines of a user-defined length perpendicular to the cell edge along the whole cell outline. To calculate edge velocity, LineScan utilizes an elastic model to find the optimal correspondence between boundary points at the consecutive time frames. Finally, LineScan displays and saves biosensor activity profile as a function of edge velocity and the distance from the edge for individual time frames, but also for the whole movie by averaging activity profiles over time.

Timothy Elston| WebpageEmail |       Klaus Hahn| WebpageEmail

(in collaboration with Richard Allen, Denis Tsygankov)


Polarization factors in yeast

When using multiple fluorochromes for tracking proteins of interest and, simultaneously, for detection of individual cell in culture with membrane and nuclei markers is not an option for technical reasons, quantification of the proteins distribution in individual cells become a problem. Automated cell segmentation is still possible, but it has to be built based on other hints, such as an a priori expected shape of the cells.

Tsygankov Yeast Polarization Factors

The GUI SegmentMe is a geometry-based segmentation tool, which allows segmentation and tracking of yeast cells in tight clusters. In addition, SegmentMe provides a set of quantitative measures, such as size, velocity, MSD, etc., for the analysis of protein clusters (spots) within segmented cells in both 2D and 3D data sets.

Timothy Elston| Webpage | Email

(in collaboration with Daniel Lew, Denis Tsygankov and Hsin Chen)


Cellular Motility and Metastasis

Cellular motility is based on dynamic processes such as polarity, protrusions, cell signaling networks and many others. Focal adhesions are another cellular/molecular structure whose dynamics underlies organized cellular movement. However, the large-scale and unbiased quantification of these dynamics has proven challenging. To address these challenges and in collaboration with Klaus Hahn, Tim Elston, Denis Tsygankov, Eric Vitriol, Jim Bear and many others, we have developed methods for the quantitative characterization of these and related structures in an automated and high-throughput manner. We have similarly extended these approaches to the quantification of the dynamics of invadopodia and podosomes; structures believed to play a significant role in metastasis. Longer-term efforts seek to infer the underlying signaling networks and their dynamics based on these quantitative imaging readouts.

Shawn Gomez, PhD | Webpage | Email| Publications




Machacek, M., Hodgson, L., Welch, C., Elliot, H., Pertz, O., Nalbant, P., Abell, A., Johnson, G., Hahn, K.M.* and Danuser, G.* Coordination of Rho GTPase activities during cell protrusion. Nature 461:99-103, 2009. PMC2885353.

Wu, Y, Frey, D., Lungu, O. I., Jaehrig, A., Schlichting, I., Kuhlman, B. and Hahn, K.M. Genetically-encoded photoactivatable Rac reveals spatiotemporal coordination of Rac and Rho during cell motility. Nature 461:104-110, 2009. PMC2766670.

Karginov, A.V., Ding, F., Kota, P., Dokholyan, N.V., and Hahn, K.M. Engineered allosteric activation of kinases in living cells. Nature Biotech. 28(7): 743-7, 2010. PMC2902629.

Gulyani, A., Vitriol, E., Allen, R., Wu, J., Gremyachinskiy, D., Lewis, S., Dewar, B., Graves, L., Kay, B., Elston, T., and Hahn, K.M. A biosensor generated via high-throughput screening quantifies cell edge Src dynamics. Nature Chem Bio. 7:437-444, 2011. PMC3135387

Dagliyan, O., Shirvanyants, D., Karginov, A.V., Ding, F., Fee, L., Chandrasekaran, S.N., Freisinger, C.M., Smolen, G.A., Huttenlocher, A., Hahn, K.M.*, Dokholyan, N.V.* Rational design of a ligand-controlled protein conformational switch. Proc. Natl. Acad. Sci. U. S. A. 110(17): 6800-4, 2013.

Tsygankov D, Bilancia CG, Vitriol EA, Hahn KM, Peifer M, Elston TC. CellGeo: a computational platform for the analysis of shape changes in cells with complex geometries. J Cell Biol. 2014 Feb 3;204(3):443-60. doi: 10.1083/jcb.201306067.

Karginov, A., Tsygankov, D., Berginski, M., Chu, P-H., Trudeau, E., Yi, J.J., Gomez, Shawn, Elston, T.C. and Hahn, K.M. Dissecting motility signaling through activation of specific Src-effector complexes. Nat. Chem. Bio. 10(4):286-90, 2014. doi: 10.1038/nchembio.1477.

Bilancia CG, Winkelman JD, Tsygankov D, Nowotarski SH, Sees JA, Comber K, Evans I, Lakhani V, Wood W, Elston TC, Kovar DR, Peifer M. Enabled negatively regulates diaphanous-driven actin dynamics in vitro and in vivo. Dev Cell. 2014 Feb 24;28(4):394-408. doi: 10.1016/j.devcel.2014.01.015.

Tsygankov D., Chu P.-H., Chen H., Elston T.C., & Hahn K.M. (2014) User-friendly tools for quantifying the dynamics of cellular morphology and intracellular protein clusters. Methods in Cell Biology, in “Quantitative Imaging in Cell Biology” 123:409-427, 2014. doi: 10.1016/B978-0-12-420138-5.00022-7.

Chu P.-H., Tsygankov D., Berginski M., Gomez S., Elston T.C., Karginov A.V., Hahn K.M. Rapid and selective activation of Src family kinases reveals isoform differences in trafficking and morphodynamics. Proc. Natl. Acad. Sci. USA. 111(34):12420-12425, 2014. doi: 10.1073/pnas.1404487111. Epub 2014 Aug 12.

Gulyani, A., Vitriol, E., Allen, R., Wu, J., Gremyachinskiy, D., Lewis, S., Dewar, B., Graves, L., Kay, B., Elston, T., and Hahn, K.M. A biosensor generated via high-throughput screening quantifies cell edge Src dynamics. Nature Chem Bio. 7:437-444, 2011. PMC3135387

Allen RJ, Tsygankov D, Zawistowski JS, Elston TC, Hahn KM. Automated line scan analysis to quantify biosensor activity at the cell edge. Methods. 2014 Mar 15;66(2):162-7. doi: 10.1016/j.ymeth.2013.08.025. Epub 2013 Aug 30.

Berginski ME, Creed SJ, Cochran S, Roadcap DW, Bear JE and Gomez SM. (2014) Automated analysis of invadopodia dynamics in live cells. PeerJ PrePrints 2:e20v1 http://dx.doi.org/10.7287/peerj.preprints.320v1

Berginski ME, Gomez SM. The Focal Adhesion Analysis Server: a web tool for analyzing focal adhesion dynamics. [v1; ref status: indexed] F1000Research. 2013, 2:68 (doi: 10.3410/f1000research.2-68.v1).

Chaki SP, Barhoumi R, Berginksi ME, Sreenivasappa H, Trache A, Gomez SM and Rivera GM. Nck enables directional cell migration through the coordination of polarized membrane protrusion with adhesion dynamics. J Cell Sci. 2013 Apr 1; 126:1637-1649.

Chen Z, Lessey E, Berginski ME, Cao L, Li J, Trepat X, Itano M, Gomez SM, Kapustina M, Huang C, Burridge K, Truskey G and Jacobson K. Gleevec, an Abl Family Inhibitor, Produces a Profound Change in Cell Shape and Migration. PLoS ONE. 2013 8(1): e52233. doi:10.1371/journal.pone.0052233.

Wu C, Asokan SB, Berginski ME, Sharpless NE, Griffith JD, Gomez SM and Bear JE. Arp2/3 complex is critical for lamellipodia and organization of cell-matrix adhesion but dispensable for fibroblast chemotaxis. Cell. 2012 Mar 2;148(5):973-87.

Berginski ME, Vitriol E, Hahn KM and Gomez SM. High-resolution quantification of focal adhesion spatiotemporal dynamics in living cells. PLoS ONE. 2011;6(7):e22025.