FRAP Analysis: Accounting for Bleaching during Image Capture

The analysis of Fluorescence Recovery After Photobleaching (FRAP) experiments involves mathematical modeling of the fluorescence recovery process. An important feature of FRAP experiments that tends to be ignored in the modeling is that there can be a significant loss of fluorescence due to bleaching during image capture. In this paper, we explicitly include the effects of bleaching during image capture in the model for the recovery process, instead of correcting for the effects of bleaching using reference measurements. Using experimental examples, we demonstrate the usefulness of such an approach in FRAP analysis.Molecular and Cellular Biolog

Colloidal diffusion and hydrodynamic screening near boundaries

The hydrodynamic interactions between colloidal particles in small ensembles are measured at varying distances from a no-slip surface over a range of inter-particle separations. The diffusion tensor for motion parallel to the wall of each ensemble is calculated by analyzing thousands of particle trajectories generated by blinking holographic optical tweezers and by dynamic simulation. The Stokesian Dynamics simulations predict similar particle dynamics. By separating the dynamics into three classes of modes: self, relative and collective diffusion, we observe qualitatively different behavior depending on the relative magnitudes of the distance of the ensemble from the wall and the inter-particle separation. A simple picture of the pair-hydrodynamic interactions is developed, while many-body-hydrodynamic interactions give rise to more complicated behavior. The results demonstrate that the effect of many-body hydrodynamic interactions in the presence of a wall is much richer than the single particle behavior and that the multiple-particle behavior cannot be simply predicted by a superposition of pair interactions

Roadmap on emerging concepts in the physical biology of bacterial biofilms: from surface sensing to community formation

Bacterial biofilms are communities of bacteria that exist as aggregates that can adhere to surfaces or be free-standing. This complex, social mode of cellular organization is fundamental to the physiology of microbes and often exhibits surprising behavior. Bacterial biofilms are more than the sum of their parts: single-cell behavior has a complex relation to collective community behavior, in a manner perhaps cognate to the complex relation between atomic physics and condensed matter physics. Biofilm microbiology is a relatively young field by biology standards, but it has already attracted intense attention from physicists. Sometimes, this attention takes the form of seeing biofilms as inspiration for new physics. In this roadmap, we highlight the work of those who have taken the opposite strategy: we highlight the work of physicists and physical scientists who use physics to engage fundamental concepts in bacterial biofilm microbiology, including adhesion, sensing, motility, signaling, memory, energy flow, community formation and cooperativity. These contributions are juxtaposed with microbiologists who have made recent important discoveries on bacterial biofilms using state-of-the-art physical methods. The contributions to this roadmap exemplify how well physics and biology can be combined to achieve a new synthesis, rather than just a division of labor

Switching and Torque Generation in Swarming E. coli

Escherichia coli swarm on semi-solid surfaces with the aid of flagella. It has been hypothesized that swarmer cells overcome the increased viscous drag near surfaces by developing higher flagellar thrust and by promoting surface wetness with the aid of a flagellar switch. The switch enables reversals between clockwise (CW) and counterclockwise (CCW) directions of rotation of the flagellar motor. Here, we measured the behavior of flagellar motors in swarmer cells. Results indicated that although the torque was similar to that in planktonic cells, the tendency to rotate CCW was higher in swarmer cells. This suggested that swarmers likely have a smaller pool of phosphorylated CheY. Results further indicated that the upregulation of the flagellin gene was not critical for flagellar thrust or swarming. Consistent with earlier reports, moisture added to the swarm surface restored swarming in a CCW-only mutant, but not in a FliG mutant that rotated motors CW-only (FliGCW). Fluorescence assays revealed that FliGCW cells grown on agar surfaces carried fewer flagella than planktonic FliGCW cells. The surface-dependent reduction in flagella correlated with a reduction in the number of putative flagellar preassemblies. These results hint toward a possibility that the conformational dynamics of switch proteins play a role in the proper assembly of flagellar complexes and flagellar export, thereby aiding bacterial swarming

Roadmap on emerging concepts in the physical biology of bacterial biofilms: from surface sensing to community formation

Bacterial biofilms are communities of bacteria that exist as aggregates that can adhere to surfaces or be free-standing. This complex, social mode of cellular organization is fundamental to the physiology of microbes and often exhibits surprising behavior. Bacterial biofilms are more than the sum of their parts: single-cell behavior has a complex relation to collective community behavior, in a manner perhaps cognate to the complex relation between atomic physics and condensed matter physics. Biofilm microbiology is a relatively young field by biology standards, but it has already attracted intense attention from physicists. Sometimes, this attention takes the form of seeing biofilms as inspiration for new physics. In this roadmap, we highlight the work of those who have taken the opposite strategy: we highlight the work of physicists and physical scientists who use physics to engage fundamental concepts in bacterial biofilm microbiology, including adhesion, sensing, motility, signaling, memory, energy flow, community formation and cooperativity. These contributions are juxtaposed with microbiologists who have made recent important discoveries on bacterial biofilms using state-of-the-art physical methods. The contributions to this roadmap exemplify how well physics and biology can be combined to achieve a new synthesis, rather than just a division of labor

FRAP Analysis: Accounting for Bleaching during Image Capture

<div><p>The analysis of Fluorescence Recovery After Photobleaching (FRAP) experiments involves mathematical modeling of the fluorescence recovery process. An important feature of FRAP experiments that tends to be ignored in the modeling is that there can be a significant loss of fluorescence due to bleaching during image capture. In this paper, we explicitly include the effects of bleaching during image capture in the model for the recovery process, instead of correcting for the effects of bleaching using reference measurements. Using experimental examples, we demonstrate the usefulness of such an approach in FRAP analysis.</p> </div

Protein expression-independent response of intensity-based pH-sensitive fluorophores in Escherichia coli.

Fluorescent proteins that modulate their emission intensities when protonated serve as excellent probes of the cytosolic pH. Since the total fluorescence output fluctuates significantly due to variations in the fluorophore levels in cells, eliminating the dependence of the signal on protein concentration is crucial. This is typically accomplished with the aid of ratiometric fluorescent proteins such as pHluorin. However, pHluorin is excited by blue light, which can complicate pH measurements by adversely impacting bacterial physiology. Here, we characterized the response of intensity-based, pH-sensitive fluorescent proteins that excite at longer wavelengths where the blue light effect is diminished. The pH-response was interpreted in terms of an analytical model that assumed two emission states for each fluorophore: a low intensity protonated state and a high intensity deprotonated state. The model suggested a scaling to eliminate the dependence of the signal on the expression levels as well as on the illumination and photon-detection settings. Experiments successfully confirmed the scaling predictions. Thus, the internal pH can be readily determined with intensity-based fluorophores with appropriate calibrations irrespective of the fluorophore concentration and the signal acquisition setup. The framework developed in this work improves the robustness of intensity-based fluorophores for internal pH measurements in E. coli, potentially extending their applications

Solutions to Eq. 9a and 9b that account for the presence of an actual immobile fraction.

<p>(A) Observed recovery curves with  = 0.4,  = 0.3 (immobile fraction),  = 0.2, <<1 (i.e. negligible photobleaching of the cytoplasmic molecules such that ) and  = 1 (*), 0.5 (<b>◊</b>), 0.25 (<b>□</b>), 0.1 (<b>○</b>) (from top to bottom). (B) Illustration of behavior of mobile (dashed curve) and immobile fractions (dotted curve) during recovery for  = 1 (* indicates total intensity). The immobile fraction can be seen to decay due to bleaching during image capture, resulting in a decrease in the total fluorescent intensity. (C) Effect of the immobile fraction on the observed recovery curves.  = 0.4,  = 0.2, <<1,  = 1 and  = 0.2 (*), 0.4 (<b>◊</b>), 0.6 (<b>□</b>), 0.8 (<b>○</b>) (from top to bottom). Pronounced transients are observed in the recovery. (D) Effect of the bleaching function on recovery. Observed recovery curves with  = 0.4,  = 0.3, <<1,  = 1, and  = 10⁻⁶ (*), 0.2 (<b>◊</b>), 0.46 (<b>□</b>), 1.1 (<b>○</b>) (from top to bottom). Solid triangles at in all figures indicate the normalized initial intensity before the photobleaching.</p

Effect of bleaching due to image capture on measured fluorescence.

<p>(A) shows calculations of Eq (2) for and (as the actual concentration is not measured in experiments, the value of is not relevant). The dotted lines indicate the actual dynamics including the decay of the fluorescence due to bleaching during image capture. * indicates the averaged concentration in an image. B) Dotted curves are normalized concentrations calculated from Eq (3). Normalizing average concentrations with the concentration in the first image yields similar dynamics, except the effect of averaging on the measured concentration is cancelled (this is discussed more in the text) such that the normalized average fluorescence is equal to . (C) Hypothetical effect of bleaching during image capture on FRAP recovery. The dotted curve is the actual dynamics consisting of (unobserved) recovery interspersed by bleaching during image capture, * indicates measured intensity. The solid triangle at indicates the normalized initial intensity before photobleaching.</p

Solutions to Eq. 6 showing how bleaching during image capture can give the erroneous impression of an ‘immobile’ fraction.

<p>Recovery curves are shown with  = 0.4,  = 0.2,  = 1 (*), 0.5 (◊), 0.25 (□) and 0.1 (○) (from top to bottom). For plotting purposes, is assumed to be /10. The solid triangle at indicates the normalized initial intensity before photobleaching.</p