Final Activity Report Summary - INTDYN (Analysis of adhesion complex dynamics in living cells)
Mortality in cancer results in 90% of cases from the metastatic dissemination of migratory tumour cells to sites distant from the primary tumour. Thus, it is crucial to clarify the mechanisms of tumour cell migration in order to understand the metastatic process. To this end, I have developed a unique data generation, integration and analysis platform to comprehensively and quantitatively characterize the cell migration process in breast carcinoma cells. This represents a new and unique approach to the study of cell migration.
There are many important elements that give this approach the capacity to substantially increase our understanding of cell migration. One is that data is derived from high resolution fluorescence microscopic imaging of cancer cells during migration in controlled cell culture environments. Fluorescence micropscopy techniques are advantageous because they provide access to quantitative spatial and temporal information, as well as to other dimensions of information such as density (eg. the density of protein machinery in different places in the cell at different times). Further, such imaging approaches allow the integration of information from different levels of biological resolution. For example, we can detect processes at the molecular level and relate them to the behaviours of entire cells, as well as addressing many levels in between these extremes. Because information about whole cell behaviours (such as migration speed) can thus be related directly to more detailed features from within the same cell, we can apply much more powerful analytical techniques to determine how cells are 'wired' together. A second major advantage of this new approach is that it produces quantitative data. This means that we can apply many mathematically based analytical approaches to accurately understand cell motility. Furthermore, because our approach is structured and based on automated software, analyses can also be automated. The combination of quantitative data and automation means that we can produce very large amounts of data, which greatly increases the strength and significance of findings.
Overall, this approach allows us to collect quantitative, spatially and temporally resolved information derived from different biological resolution levels. This produces a tightly integrated data set that will ultimately allow us to mathematically model the process of cell migration under different conditions. This will greatly accelerate the process of understanding how cells migrate, and how this may be prevented in the cancer context.
We have so far applied this approach to the analysis of a range of conditions, where we have taken cancer cells and altered specific aspects of either their internal biology or their external environment. This work has revealed the mechanisms of action and biological significance of a number of proteins, allowing us to tell what these molecular machineries do in cells, and how their manipulation can affect cell behaviour. We have also begun to study how the environment of cancer cells affects cell migration behaviours, based on the well-known observation that cancers are much more dense and contractile than equivalent healthy tissues. The modulation of cellular environments causes a complex cascade of changes within cells, and can in many cases drive the progression of cancer. By focusing on these processes, we will be able to understand the relationships between intrinsic and extrinsic factors that regulate both healthy and cancer cell motility.
There are many important elements that give this approach the capacity to substantially increase our understanding of cell migration. One is that data is derived from high resolution fluorescence microscopic imaging of cancer cells during migration in controlled cell culture environments. Fluorescence micropscopy techniques are advantageous because they provide access to quantitative spatial and temporal information, as well as to other dimensions of information such as density (eg. the density of protein machinery in different places in the cell at different times). Further, such imaging approaches allow the integration of information from different levels of biological resolution. For example, we can detect processes at the molecular level and relate them to the behaviours of entire cells, as well as addressing many levels in between these extremes. Because information about whole cell behaviours (such as migration speed) can thus be related directly to more detailed features from within the same cell, we can apply much more powerful analytical techniques to determine how cells are 'wired' together. A second major advantage of this new approach is that it produces quantitative data. This means that we can apply many mathematically based analytical approaches to accurately understand cell motility. Furthermore, because our approach is structured and based on automated software, analyses can also be automated. The combination of quantitative data and automation means that we can produce very large amounts of data, which greatly increases the strength and significance of findings.
Overall, this approach allows us to collect quantitative, spatially and temporally resolved information derived from different biological resolution levels. This produces a tightly integrated data set that will ultimately allow us to mathematically model the process of cell migration under different conditions. This will greatly accelerate the process of understanding how cells migrate, and how this may be prevented in the cancer context.
We have so far applied this approach to the analysis of a range of conditions, where we have taken cancer cells and altered specific aspects of either their internal biology or their external environment. This work has revealed the mechanisms of action and biological significance of a number of proteins, allowing us to tell what these molecular machineries do in cells, and how their manipulation can affect cell behaviour. We have also begun to study how the environment of cancer cells affects cell migration behaviours, based on the well-known observation that cancers are much more dense and contractile than equivalent healthy tissues. The modulation of cellular environments causes a complex cascade of changes within cells, and can in many cases drive the progression of cancer. By focusing on these processes, we will be able to understand the relationships between intrinsic and extrinsic factors that regulate both healthy and cancer cell motility.