Quantitative Colocalisation Imaging: Concepts, Measurements, and Pitfalls. In: Shorte SL, Frischknecht F, editors. Imaging Cellular and Molecular Biological Functions. Berlin: Springer Berlin Heidelberg; 2007. pp. 117-55. [DOI: 10.1007/978-3-540-71331-9_5]

Mapping densely packed αIIbβ3 receptors in murine blood platelets with expansion microscopy

Interrogating platelets and their densely packed, highly abundant receptor landscape is key to understand platelet clotting, a process that can save lives when stopping blood loss after an injury, but also kill when causing heart attack, stroke, or pulmonary embolism. The underlying key receptor distributions and interactions, in particular the relevance of integrin clustering, are not fully understood is because of highly abundant and densely distributed αIIbβ3 receptors. This makes receptor distributions difficult to assess even by super-resolution fluorescence microscopy. Here, we combine dual-color expansion and confocal microscopy with colocalization analysis to assess platelet receptor organization without the need of a super-resolution microscope. We show that 4x expansion is highly straight-forward for super-resolution microscopy of platelets, while 10x expansion provides higher precision at the price of increased efforts in sample preparation and imaging. Quantifying various receptor colocalization scenarios we demonstrate that expansion microscopy can pinpoint receptor distributions and interactions in resting and activated platelets being superior to conventional methods that fail in such dense 3D scenarios with highly abundant receptors. We reveal the presence of αIIbβ3 clusters in resting platelets, as well as in activated platelets, indicating that they contribute to the rapid platelet response during platelet clotting.

Colocalization Estimation Using Graphical Modeling and Variational Bayesian Expectation Maximization: Towards a Parameter-Free Approach

In microscopy imaging, colocalization between two biological entities (e.g., protein-protein or protein-cell) refers to the (stochastic) dependencies between the spatial locations of the two entities in the biological specimen. Measuring colocalization between two entities relies on fluorescence imaging of the specimen using two fluorescent chemicals, each of which indicates the presence/absence of one of the entities at any pixel location. State-of-the-art methods for estimating colocalization rely on post-processing image data using an adhoc sequence of algorithms with many free parameters that are tuned visually. This leads to loss of reproducibility of the results. This paper proposes a brand-new framework for estimating the nature and strength of colocalization directly from corrupted image data by solving a single unified optimization problem that automatically deals with noise, object labeling, and parameter tuning. The proposed framework relies on probabilistic graphical image modeling and a novel inference scheme using variational Bayesian expectation maximization for estimating all model parameters, including colocalization, from data. Results on simulated and real-world data demonstrate improved performance over the state of the art.

A practical guide to evaluating colocalization in biological microscopy

Fluorescence microscopy is one of the most powerful tools for elucidating the cellular functions of proteins and other molecules. In many cases, the function of a molecule can be inferred from its association with specific intracellular compartments or molecular complexes, which is typically determined by comparing the distribution of a fluorescently labeled version of the molecule with that of a second, complementarily labeled probe. Although arguably the most common application of fluorescence microscopy in biomedical research, studies evaluating the "colocalization" of two probes are seldom quantified, despite a diversity of image analysis tools that have been specifically developed for that purpose. Here we provide a guide to analyzing colocalization in cell biology studies, emphasizing practical application of quantitative tools that are now widely available in commercial and free image analysis software.

Live cell fluorescence microscopy to study microbial pathogenesis

Advances in microscopy and fluorescent probes provide new insight into the nanometer-scale biochemistry governing the interactions between eukaryotic cells and pathogens. When combined with mathematical modelling, these new technologies hold the promise of qualitative, quantitative and predictive descriptions of these pathways. Using the light microscope to study the spatial and temporal relationships between pathogens, host cells and their respective biochemical machinery requires an appreciation for how fluorescent probes and imaging devices function. This review summarizes how live cell fluorescence microscopy with common instruments can provide quantitative insight into the cellular and molecular functions of hosts and pathogens.