We report that two classes of membrane nanotubes between human monocyte-derived macrophages can be distinguished by their cytoskeletal structure and their functional properties. Thin membrane nanotubes contained only F-actin, whereas thicker nanotubes, i.e., those > approximately 0.7 microm in diameter, contained both F-actin and microtubules. Bacteria could be trapped and surf along thin, but not thick, membrane nanotubes toward connected macrophage cell bodies. Once at the cell body, bacteria could then be phagocytosed. The movement of bacteria is aided by a constitutive flow of the nanotube surface because streptavidin-coated beads were similarly able to traffic along nanotubes between surface-biotinylated macrophages. Mitochondria and intracellular vesicles, including late endosomes and lysosomes, could be detected within thick, but not thin, membrane nanotubes. Analysis from kymographs demonstrated that vesicles moved in a stepwise, bidirectional manner at approximately 1 microm/s, consistent with their traffic being mediated by the microtubules found only in thick nanotubes. Vesicular traffic in thick nanotubes and surfing of beads along thin nanotubes were both stopped upon the addition of azide, demonstrating that both processes require ATP. However, microtubule destabilizing agents colchicine or nocodazole abrogated vesicular transport but not the flow of the nanotube surface, confirming that distinct cytoskeletal structures of nanotubes give rise to different functional properties. Thus, membrane nanotubes between macrophages are more complex than unvarying ubiquitous membrane tethers and facilitate several means for distal interactions between immune cells.

Super-resolution 3D imaging reveals remodeling of the cortical actin meshwork at the natural killer cell immune synapse, which is likely to be important for secretion of lytic granules.

In fluorescence microscopy, the fluorescence emission can be characterised not only by intensity and position, but also by lifetime, polarization and wavelength. Fluorescence lifetime imaging (FLIM) can report on photophysical events that are difficult or impossible to observe by fluorescence intensity imaging, and time-resolved fluorescence anisotropy imaging (TR-FAIM) can measure the rotational mobility of a fluorophore in its environment. We compare different FLIM methods: a chief advantage of wide-field time-gating and phase modulation methods is the speed of acquisition whereas for time-correlated single photon counting (TCSPC) based confocal scanning it is accuracy in the fluorescence decay. FLIM has been used to image interactions between proteins such as receptor oligomerisation and to reveal protein phosphorylation by detecting fluorescence resonance energy transfer (FRET). In addition, FLIM can also probe the local environment of fluorophores, reporting, for example, on the local pH, refractive index, ion or oxygen concentration without the need for ratiometric measurements.

An emerging theme in cell biology is that cell surface receptors need to be considered as part of supramolecular complexes of proteins and lipids facilitating specific receptor conformations and distinct distributions, e.g., at the immunological synapse. Thus, a new goal is to develop bioimaging that not only locates proteins in live cells but can also probe their environment. Such a technique is demonstrated here using fluorescence lifetime imaging of green fluorescent protein (GFP). We first show, by time-correlated single-photon counting, that the fluorescence decay of GFP depends on the local refractive index. This is in agreement with the Strickler Berg formula, relating the Einstein A and B coefficients for absorption and spontaneous emission in molecules. We then quantitatively image, by wide-field time-gated fluorescence lifetime imaging, the refractive index of the environment of GFP. This novel approach paves the way for imaging the biophysical environment of specific GFP-tagged proteins in live cells.

Fluorescence lifetime imaging (FLIM) is widely applied to obtain quantitative information from fluorescence signals, particularly using Förster Resonant Energy Transfer (FRET) measurements to map, for example, protein-protein interactions. Extracting FRET efficiencies or population fractions typically entails fitting data to complex fluorescence decay models but such experiments are frequently photon constrained, particularly for live cell or in vivo imaging, and this leads to unacceptable errors when analysing data on a pixel-wise basis. Lifetimes and population fractions may, however, be more robustly extracted using global analysis to simultaneously fit the fluorescence decay data of all pixels in an image or dataset to a multi-exponential model under the assumption that the lifetime components are invariant across the image (dataset). This approach is often considered to be prohibitively slow and/or computationally expensive but we present here a computationally efficient global analysis algorithm for the analysis of time-correlated single photon counting (TCSPC) or time-gated FLIM data based on variable projection. It makes efficient use of both computer processor and memory resources, requiring less than a minute to analyse time series and multiwell plate datasets with hundreds of FLIM images on standard personal computers. This lifetime analysis takes account of repetitive excitation, including fluorescence photons excited by earlier pulses contributing to the fit, and is able to accommodate time-varying backgrounds and instrument response functions. We demonstrate that this global approach allows us to readily fit time-resolved fluorescence data to complex models including a four-exponential model of a FRET system, for which the FRET efficiencies of the two species of a bi-exponential donor are linked, and polarisation-resolved lifetime data, where a fluorescence intensity and bi-exponential anisotropy decay model is applied to the analysis of live cell homo-FRET data. A software package implementing this algorithm, FLIMfit, is available under an open source licence through the Open Microscopy Environment.