Imaging of Lipid Bilayer Mixtures and Actual Cell Membrane Fragments by Nanosims

Abstract

Lipids, proteins, and cholesterol are the major components of biological membranes; however, little is known about their lateral organization and how it changes during cell-cell interactions, signaling, development and division on the nanometer length scale that is relevant for function. These studies develop a new method for determining the spatial composition and organization of model membranes using secondary ion mass spectrometry (SIMS) imaging in an effort to explain the rich phase behavior of complex biological membranes. Furthermore, these experiments set the stage for measuring the native distribution of cholesterol and proteins within membrane fragments taken from cells. Advanced fluorescence and atomic force microscopies (FM and AFM, respectively) used thus far to interrogate model cell membranes have limited molecular specificity (can only detect fluorescently labeled molecules in FM), spatial resolution (governed by optics in FM or tips in AFM) and both lack compositional information. SIMS imaging, in particular using the NanoSIMS by Cameca, offers imaging capability with high sensitivity (sub-ppm), specificity (based on isotopic labeling), and spatial resolution (∼50nm). Using this unique technique, the composition and organization of cholesterol, lipids and proteins within the plane of the lipid bilayer of model and actual cell membranes with nanometer resolution is investigated. Cholesterol in plasma membranes is responsible for modulating acyl chain order, membrane elasticity and lateral organization and its level is tightly regulated. In order to investigate the spatial distribution of cholesterol in actual cell membranes, supported cell membrane fragments are imaged using the NanoSIMS. These measurements are the first example of the direct analysis of the organization of a cell membrane at the nanometer length scale that is relevant for function setting the stage for quantitative analysis of systems of increasing complexity and direct biological relevance.