Highly Multiplexed Imaging Uncovers Changes in Compositional Noise within Assembling Focal Adhesions.

Integrin adhesome proteins bind each other in alternative manners, forming within the cell diverse cell-matrix adhesion sites with distinct properties. An intriguing question is how such modular assembly of adhesion sites is achieved correctly solely by self-organization of their components. Here we address this question using high-throughput multiplexed imaging of eight proteins and two phosphorylation sites in a large number of single focal adhesions. We found that during the assembly of focal adhesions the variances of protein densities decrease while the correlations between them increase, suggesting reduction in the noise levels within these structures. These changes correlate independently with the area and internal density of focal adhesions, but not with their age or shape. Artificial neural network analysis indicates that a joint consideration of multiple components improves the predictability of paxillin and zyxin levels in internally dense focal adhesions. This suggests that paxillin and zyxin densities in focal adhesions are fine-tuned by integrating the levels of multiple other components, thus averaging-out stochastic fluctuations. Based on these results we propose that increase in internal protein densities facilitates noise suppression in focal adhesions, while noise suppression enables their stable growth and further density increase-hence forming a feedback loop giving rise to a quality-controlled assembly.

Uncovering distinct protein-network topologies in heterogeneous cell populations.

BACKGROUND: Cell biology research is fundamentally limited by the number of intracellular components, particularly proteins, that can be co-measured in the same cell. Therefore, cell-to-cell heterogeneity in unmeasured proteins can lead to completely different observed relations between the same measured proteins. Attempts to infer such relations in a heterogeneous cell population can yield uninformative average relations if only one underlying biochemical network is assumed. To address this, we developed a method that recursively couples an iterative unmixing process with a Bayesian analysis of each unmixed subpopulation. RESULTS: Our approach enables to identify the number of distinct cell subpopulations, unmix their corresponding observations and resolve the network structure of each subpopulation. Using simulations of the MAPK pathway upon EGF and NGF stimulations we assess the performance of the method. We demonstrate that the presented method can identify better than clustering approaches the number of subpopulations within a mixture of observations, thus resolving correctly the statistical relations between the proteins. CONCLUSIONS: Coupling the unmixing of multiplexed observations with the inference of statistical relations between the measured parameters is essential for the success of both of these processes. Here we present a conceptual and algorithmic solution to achieve such coupling and hence to analyze data obtained from a natural mixture of cell populations. As the technologies and necessity for multiplexed measurements are rising in the systems biology era, this work addresses an important current challenge in the analysis of the derived data.