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Extracellular protein concentrations and gradients queue a wide range of cellular responses, such as cell motility and division. Spatio-temporal quantification of these concentrations as produced by cells has proven challenging. As a result, artificial gradients must be introduced to the cell culture to correlate signal and response. Here we demonstrate a label-free nanoplasmonic imaging technique that can directly map protein concentrations as secreted by single cells in real time and which integrates with standard live-cell microscopes. When used to measure the secretion of antibodies from hybridoma cells, a broad range of time-dependent concentrations was observed: from steady-state secretions of 230 pM near the cell surface to large transients which reached as high as 56 nM over several minutes and then dissipated. The label-free nature of the technique is minimally invasive and we anticipate will enable the quantification of deterministic relationships between secreted protein concentrations and their induced cellular responses.
Fluorescence Lifetime Imaging Microscopy (FLIM) using multiphoton excitation techniques is now finding an important place in quantitative imaging of protein-protein interactions and intracellular physiology. We review here the recent developments in
From the spectral plot of the (normalized) graph Laplacian, the essential qualitative properties of a network can be simultaneously deduced. Given a class of empirical networks, reconstruction schemes for elucidating the evolutionary dynamics leading
Here we present ComPPI, a cellular compartment specific database of proteins and their interactions enabling an extensive, compartmentalized protein-protein interaction network analysis (http://ComPPI.LinkGroup.hu). ComPPI enables the user to filter
Optical interferometry is amongst the most sensitive techniques for precision measurement. By increasing the light intensity a more precise measurement can usually be made. However, in some applications the sample is light sensitive. By using entangl
Motivation: Assigning statistical significance accurately has become increasingly important as meta data of many types, often assembled in hierarchies, are constructed and combined for further biological analyses. Statistical inaccuracy of meta data