Characterization of differential Toll-like receptor responses below the optical diffraction limit

Abstract

Many membrane receptors are recruited to specific cell surface domains to form nanoscale clusters upon ligand activation. This step appears to be necessary to initiate cell signaling, including pathways in innate immune system activation. However, virulent pathogens such as Yersinia pestis (the causative agent of plague) are known to evade innate immune detection, in contrast to similar microbes (such as Escherichia coli) that elicit a robust response. This disparity has been partly attributed to the structure of lipopolysaccharides (LPS) on the bacterial cell wall, which are recognized by the innate immune receptor TLR4. It is hypothesized that nanoscale differences exist between the spatial clustering of TLR4 upon binding of LPS derived from Y. pestis and E. coli. Although optical imaging can provide exquisite details of the spatial organization of biomolecules, there is a mismatch between the scale at which receptor clustering occurs (<300 nm) and the optical diffraction limit (>400 nm). The last decade has seen the emergence of super-resolution imaging methods that effectively break the optical diffraction barrier to yield truly nanoscale information in intact biological samples. This study reports the first visualizations of TLR4 distributions on intact cells at image resolutions of <30 nm using a novel, dual-color stochastic optical reconstruction microscopy (STORM) technique. This methodology permits distinction between receptors containing bound LPS from those without at the nanoscale. Importantly, it is also shown that LPS derived from immunostimulatory bacteria result in significantly higher LPS-TLR4 cluster sizes and a nearly twofold greater ligand/receptor colocalization as compared to immunoevading LPS.

Figure 1

Schematic illustration of the localization principle behind dSTORM imaging. In each acquired image frame (left), single molecules are identified via a minimum signal-to-noise ratio (SNR). An adaptive algorithm is then used to choose an appropriate neighborhood surrounding the selected point-spread function (right). The sub-diffraction localization of the corresponding fluorophor can then be determined by employing a non-linear least squares (NLLS) regression in order to find the centroid (denoted by x₀ and y₀) of the fitting function, typically a 2D Gaussian surface.

Figure 2

Comparison between conventional TIRF (A-C) and dSTORM (D-F) images of TLR4 distributions in P388D1 macrophage cells. Cells were exposed to 100nM of either hexaacylated LPS (A and D) or tetraacylated LPS (B and E) for 30min at 37°C. Control samples were exposed to 10 μg/mL flagellin (C and F) under identical conditions before fixation and immunostaining for TLR4 using a primary antibody (eBioscience) conjugated to Atto532 dye (Attotec), which displays excellent photoswitching properties . Application of the dSTORM imaging approach increases the effective resolution nearly an order of magnitude as compared to conventional imaging (11-30nm vs. 235nm, respectively). To illustrate the apparent increase in image detail, sub-images with 2.5μm fields of view are shown in (g-l), corresponding to white boxes indicated in (A-F).

Figure 3

Ripley’s K-function analysis was used to quantitatively assess the relative level of TLR4 clustering in response to stimulatory LPS (from E. coli, shown in red), non-stimulatory (from Y. pestis, shown in blue), and TLR4 non-specific flagellin (shown in green). The analysis reveals that hexaacylated LPS binding to TLR4 results in a significant (p < 0.003, N = 10 cells for each treatment) increase in receptor clustering within the membrane, as compared to the flagellin control.

Figure 4

Incorporation of dual-color detection dSTORM imaging to visualize TLR4 and LPS distribution and colocalization. P388D1 macrophage cells were exposed to 100nM hexaacylated (A) and tetraacylated (B) LPS conjugated with Alexafluor 647 dye (Invitrogen), for 30min at 37°C. After fixation and immunolabeling for TLR4 expression, samples were imaged as described in the Experimental section. Following dSTORM analysis, the localization of TLR4 and each LPS type was compared, and relative colocalization between receptor and ligand was computed. Colocalization was assessed by the presence of both LPS and TLR signal within a radius of 50nm, corresponding to the image registration accuracy. (C-D) indicate areas of colocalization between TLR4 and either hexaacylated or tetraacylated LPS, respectively. (E) Quantification of the fraction of total LPS signal co-localized to TLR4, as described in Figure 4. Hexaacylated LPS (red), derived from E. coli was found to be significantly (p < 0.007, N = 10 cells) more associated with TLR4 than tetraacylated LPS from Y. pestis (blue), suggesting nearly two-fold higher specificity of stimulatory LPS to TLR4. (F) Ripley’s K-function analysis was applied to areas of LPS/TLR4 colocalization (shown in white in (C-D)), indicating significantly (p < 0.04) higher order clustering of TLR4 in response to stimulatory LPS over the non-stimulatory chemotype.