The Continuing Mystery of Lipid Rafts

Abstract

Since its initial formalization nearly 20 years ago, the concept of lipid rafts has generated a tremendous amount of attention and interest and nearly as much controversy. The controversy is perhaps surprising because the notion itself is intuitive: compartmentalization in time and space is a ubiquitous theme at all scales of biology, and therefore, the partitioning of cellular membranes into lateral subdivision should be expected. Nevertheless, the physicochemical principles responsible for compartmentalization and the molecular mechanisms by which they are functionalized remain nearly as mysterious today as they were two decades ago. Herein, we review recent literature on this topic with a specific focus on the major open questions in the field including: (1) what are the best tools to assay raft behavior in living membranes? (2) what is the function of the complex lipidome of mammalian cells with respect to membrane organization? (3) what are the mechanisms that drive raft formation and determine their properties? (4) how can rafts be modulated? (5) how is membrane compartmentalization integrated into cellular signaling? Despite decades of intensive research, this compelling field remains full of fundamental questions.

Figure 1. Comparison between synthetic (GUVs) and biological (GPMVs) membrane model systems

(A) Synthetic and biological model membranes differ dramatically with respect to their lipid and protein composition. Synthetic model membranes like GUVs are typically composed of a small number of different lipid components and few, if any, proteins. Shown is a canonical “raft mixture” containing equimolar saturated (DPPC) and unsaturated (DOPC) phospholipid, and cholesterol (Chol). In contrast, biological membrane models like GPMVs contain hundreds of different lipids and likely thousands of proteins. Shown is a representative distribution of phosphatidylcholine species in a PM sample, which contains a few major species (e.g. POPC is ~25 mol% of all PC) and over 100 minor ones. (B) Despite these differences, GUVs and GPMVs are morphologically similar and both separate into coexisting ordered and disordered phases, visualized by staining vesicles with lipidic dyes that preferentially partitioning into one of the phases. Notably, GPMVs often show near-critical behavior, also achievable in GUVs at specific compositions. (C) Despite these superficial similarities, these are notable difference in domain properties between GPMVs and GUVs. For example, coexisting domains in GPMVs are much more similar than in GUVs with respect to lipid packing. Images shown are pseudocolored maps of Laurdan generalized polarization, a widely used proxy for lipid packing (for experimental details, see references^, ). (D) The relatively high disparity possibly results in most components being excluded from GUV ordered domains, whereas a number of lipids and proteins partition preferentially to this phase in GPMVs. Images show the partition of a peptide based on the transmembrane protein LAT. Yellow pseudocolor represents co-partitioning with a disordered phase marker in GUVs, while distinct red staining of one phase versus green in the other shows that the LAT-RFP protein partitions away from the disordered phase marker in GPMVs (for details, see references^, ).

Figure 2. Sample lipidomes from mammalian plasma membranes

(A) The complex composition of mammalian membranes can be represented in classes that are analogous to those often used for model membrane studies. In contrast to commonly used synthetic membrane preparations, mammalian PMs contain a high proportion of “mixed” lipid species with one saturated and one unsaturated acyl chain. Shown here is a representative lipidome of mesenchymal cell plasma membranes from unpublished studies in the IL lab. Further, it should be appreciated that most lipidomic features are cell / tissue specific. Shown are comparisons of (B) lipid classes and (C) unsaturation between the PMs of three different cell types: leukocytes, epithelial cells, and neurons.

Figure 3. Critical fluctuations in GPMVs and the 2D Ising model

(A) Membrane heterogeneity emerges over an extended temperature range in GPMVs, consistent with their passing through a critical point at the miscibility transition temperature. (B) Miscibility phase diagram for the conserved order parameter 2D Ising model or binary fluid which contains only two components, referred to here as ordered or disordered components. The left axis shows temperature in units of the critical temperature (T_C) while the right axis converts this to °C assuming that T_C is 300K (27°C). (C) Simulation snap-shots from an equilibrated 2D Ising model for the conditions indicated in part B. Simulations were conducted using standard methods as described previously. Insets are enlarged areas of the main image in the location indicated by a black box and indicate that structure on the 20nm scale is present even for conditions far removed from the critical point. Condition 6 is for an equal fraction of ordered and disordered components at a temperature of 2×T_C or T-T_C = 300°C, which is well outside of the temperature range plotted in B.

Figure 4. Predicted experimental observations for two types of membrane heterogeneity

(A) Some models of membrane heterogeneity propose that lipids are organized into small but clearly defined structures. (B) An alternate model is that membrane domains are more akin to super-critical fluctuations. For each model, several predictions can be made. (Images at top left) Possible configurations of ordered (blue) and disordered (yellow) membrane components in the two models presented. In each case, the characteristic length of structure is 20nm. (Images at top right) Green and red spots are a subset of components chosen randomly from the blue regions to mimic the finite concentration of a fluorescent probe. In these cases the label is included at 0.2% per color, or 2000 probes per μm². These spots are also blurred with a Gaussian function with standard deviation of 25nm to represent the typical localization precision of a SR measurement as indicated in the background image. Note that both under sampling and blurring acts to obscure the structures from which these images were generated which are clearly visible in the top left panels. (Plots at bottom left) Pair cross-correlation functions between the differently colored spots above for several localization precisions. These curves were tabulated from images like those shown at the top right as described previously, and indicate differently colored points are co-clustered (correlated) when g(r)>1, depleted (anti-correlated) when g(r)<1, and randomly co-distributed (uncorrelated) when g(r) = 1. Note that finite localization precision diminishes observations of the underlying structure even when localization precision is smaller than the characteristic size of structures. (Plots at bottom right) The predicted shape of mean squared displacement (MSD) vs time interval (τ) curves for both models, as discussed in the main text. Namely, single molecules that partition with the blue spots at the left will appear confined, while single molecules that partition with the blue fluctuations at the right are unconfined.