Who We Are

Who We Are

Integration of Forces and Chemical Signals 

Morphogenesis is the basis of innumerable cell functions and thus among the best studied processes in cell biology. We know probably all component processes involved in morphogenesis and the majority of their molecular parts. Missing is a quantitative understanding of how these processes are coordinated. This is the challenge our lab tackles every day with an interdisciplinary approach that welds together molecular cell biology, live cell imaging, signal processing, computer vision, and mathematics. Accordingly, we work as a team with ~20 members, big enough to establish expertise in all these disciplines, yet small enough to synergize by spontaneous self-organization and without a hierarchical overhead.  

Embracing Complexity

The processes driving cell morphogenesis are organized in a cytomechanochemical system with three key properties: i) a high level of non-linearity, i.e. feed forward and feed back interaction, between processes; ii) a high level of redundancy between processes; and iii) a separation in space and time between causative and effected processes. These three properties complicate the analysis of the system in that perturbation of any component can lead to wide-ranging adaptation. Hence, the difference between phenotype and wildtype does not necessarily inform on the actual function of the perturbed target. Moreover, the tendency for adaptation leads to heterogeneous system outputs as small genetic, epigenetic, or environmental variations can significantly rebalance the relative importance of pathways. Accordingly, cytomechanochemical system are often investigated by strong stimulation to homogenize the response, with the caveat of activating certain component processes at the expense of others and thus of obscuring their interdependences under unperturbed conditions – not in our lab.

cytomechanochemical systems
Cytomechanochemical systems integration in cell morphogenesis.

Dissecting Complexity with Minimal Experimenter Impact

Even though the limitations of molecular perturbations in dissecting nonlinear and redundant systems are acknowledged in principle, the community seems surprisingly resistant to moving away from the ‘break-and-watch’ paradigm. Many of the key controversies in our field are likely the product of variable compensation responses elicited by perturbations applied under slightly different experimental conditions. To break through this impasse we follow cytomechanochemical systems as they self-organize taking advantage of experimental heterogeneity as a source of information rather than a source of uncertainty and exploiting spontaneous fluctuations over time to track information flows between processes.

Embracing Econometric Models in Spontaneous System Fluctuations

There is precedence for this approach in other disciplines of science: the accuracy of weather forecasts is well above 90%. None of these predictions relies on experimental perturbation; and econometricians determine in split seconds the causal relations between financial markets. The last experiment a Wall Street investor would run to find out how the stock markets in Shanghai influences his/her fellow investors is blow up the index in Shanghai. Our work is inspired by these incredibly sophisticated approaches these sister disciplines pursue to build predictive models of systems with the same key features.

A fundamental difference remains, however, between cell biological data and financial data. While financial market indicators are noise-free, cell biological measurements are noisy, quire often very noisy. Therefore, we are engaged in redesigning some of the mathematical methods we borrow from econometrics for the purpose of analyzing cell biological data. 

Live Cell Imaging for Monitoring Spatially Temporally Coupled Fluctuations

intracellular forces
Reconstruction of intracellular forces. Boundary forces ‐ i.e. the tension "felt" by the growing actin network ‐ and contraction forces (top) and adhesion forces (bottom) in neighboring protruding and retracting sectors of the cell edge. These forces are mathematically derived from the strain rate fields in the observed actin network flow.

Implementing a fluctuation analysis of cytomechanochemical systems requires the means to monitor spontaneous fluctuations in relevant morphogenetic component processes. Thus, much work in our lab is devoted to measuring the dynamics of cell architecture, forces, and chemical signals with sufficient molecular detail and spatial and temporal resolution to inform predictive models of cause and effects between morphogenetic processes. Many of these imaging and image analytical tools are designed with an eye towards generalization and thus can be used in other areas of quantitative cell biology. Some of our software packages for image analysis and data modeling can be downloaded from our website. We also have a great history of collaboration where we have brought to the table our expertise in imaging and image data analysis for quite a variety of questions in cell and developmental biology. As much as we believe in team work within in the lab, we also believe in collaboration with other labs. We write this here to recruit new, attractive collaborators and to acknowledge all our past and current collaborators – too many to be listed individually; please check out who we have published with

Cell Morphogenesis in Cancer

Cancer is a genetically pluralistic disease. While the current rush to sequencing every possible cancer genome will undoubtedly reveal additional patterns of risk genes, it has become questionable whether this is the right path to potent therapy. On the other hand, it is striking how the genomic diversity of cancers converges on a few fairly stereotypic cell behaviors that are altered from normal behaviors. Our lab asks the question, is there a stereotypic program in cell morphogenesis that confers the progression of cancer, and especially metastasis, i.e. the spreading of primary tumor cells throughout other remote tissues? Obviously, cell morphogenesis is implicated in cell migration, which is a requirement for metastatic spreading. But is migration the stereotypic program that makes a metastatic cell a metastatic cell? Other cell functions essential for metastatic progression may also be linked to a stereotypic shift in cell morphogenesis. Currently, we look for links between morphogenesis and cell survival, metabolism, and even drug resistance; and we have begun to test whether we could renew the pathologist’s perspective of morphology as a prognostic marker with radically advanced measures of cell morphogenesis to complement the heterogeneous single cell genomic and molecular profiles of cancers with a stereotypical, functionally inspired marker. In some sense our search for stereotypical functional endpoints follows from Waddington’s almost a century-old canalization paradigm: ‘developmental reactions, as they occur in organisms submitted to natural selection...are adjusted so as to bring about one definite end-result regardless of minor variations in conditions during the course of the reaction’ (Waddington, C. H. Canalization of development and the inheritance of acquired characters. Nature 150, 563-565.1942). 

Our New Imaging Program

Studying cell morphogenesis in a disease context comes with new challenges. First, studying cells on glass slides with ever higher resolution and quantitative detail is not the path forward. We must conduct our experiments in environments that do not externally bias morphogenesis. However, intravital imaging approaches in mice and other model organisms may be limiting our insight into the molecular organization and dynamics of morphogenetic programs; and they offer insufficient throughput to harness spontaneous fluctuations for pathway analysis. Thus, we primarily seek the sweet spot in ex vivo models using patient-derived or isogenic oncogenically transformed cell lines cultured in engineered 3D environments. For this we lean on the shoulder of a new group of local collaborators at UTSouthwestern, who graciously introduce us to cell models and methods for generating micro-environments. Second, in 3D environments – much more than in 2D – live cell imaging is the key to accessing dynamic pathway states, as other assays are severely limited. However, even with ex vivo models the implementation of 3D imaging with the same information depth as available in 2D is an unmet challenge. We are very fortunate to having established a long-standing collaboration with Reto Fiolka’s lab, with which we share lab space, equipment, team members, and everything else. Reto’s research currently is directed towards adopting light sheet microscopy for multi-modal and multi-spectral fluorescence imaging in large 3D volumes with similar spatial and temporal resolution as achieved in 2D (check out Reto’s publications with and without our contributions). Third, even more than in 2D computer vision is the crucial technology to quantify imaging experiments in 3D. In some initial work we established image segmentation and mathematical morphology to gain insights into the relationship between cell cortex architecture and 3D cell shape at the submicron resolution, where mechanical and chemical process are integrated. Visit our Research Gallery to stay updated on the fast-evolving activities in this and other research areas in the lab.

imaging and quantifying cell morphology in 3D
Imaging and quantifying cell morphology in 3D. Left column: Maximum intensity projection of oncogenically transformed human bronchial epithelial cells imaged at fully isotropic resolution of 0.3 x 0.3 x 0.3 um by a home-built 2-photon Bessel beam light sheet illumination microscope. Cells express mEmerald-tractin labeling the actin cortex and crawl in a collagen IV gel. Middle column: Rendering of the computationally reconstructed cell surface. Note that especially in cell A the segmentation works robustly for high and low contrast (dense and scarce actin cortex) regions – a feature of advanced computational image segmentation. Right column: Color coded surface curvature as an example of numerous geometric parameters describing cell morphology. Cell A stems from a clonal population with low expression of the oncogenic mutation KrasV12; Cell B from a clonal population with high expression of KrasV12. These studies identified expression levels the constitutively active KrasV12 signal as a cell morphogenetic switch in lung cancer.