As is common in AFM analysis, images were flattened (first order) to minimize background tilt. To identify and precisely measure individual protrusions above the lipid bilayer, a custom algorithm was used (Igor Pro 6.3). AFM images were first partitioned into 500 nm by 500 nm segments, each of which was analyzed separately to produce more consistent background conditions. A height threshold was used to separate protrusions from the background lipid bilayer. The height threshold was determined dynamically for each segment such that every pixel above the threshold was more than two SDs above the measured RMS noise level. A flood-fill algorithm was then used to identify sets of connected pixels above the height threshold, defining a protrusion and its boundary. Voids were discarded from analysis by virtue of their negative heights (as measured from the upper bilayer surface). Individual protrusions were then extracted from the full-scale image and further processed individually, first with a local first-order flattening to minimize local background tilt and then with a streak-removal algorithm. This algorithm corrected for the “parachuting” effect of the atomic force microscope tip in which the tip loses contact with the surface, generating horizontal “streak” artifacts. To identify and eliminate these artifacts, single-pixel high streaks (along a single scan line) were identified, removed, and replaced by averaging neighboring pixels. To calculate the volume of a single protrusion, a set height threshold of 500 pm above the calculated background level was used to separate protein protrusion pixels from background pixels. A fixed height threshold, as opposed to the dynamic height threshold used to originally detect the protrusion, was used to maintain consistency between volume measurements of all protrusions. The volume was then calculated by summing the height of all pixels in the protrusion and scaling by the pixel dimensions to effectively integrate under the protrusion. For height calculations, the height of a protrusion was calculated as the average height of the top 10% highest pixels in the protrusion minus the background level. The averaging was used to reduce the influence of single pixel noise on the height measurement. The entire process, including processing and segmenting of the raw image, protrusion detection, and height and volume measurements, was completed automatically. Bayesian information criterion was implemented in Igor Pro 7 to determine the optimal number of subpopulations for each dataset and avoid overfitting (49). Bootstrapping was used to estimate errors of reported population percentages. In all cases, these errors were <1%. Probability density plots were normalized to integrate to unity when units of the abscissa were expressed in meters or cubic meters (i.e., for height or volume distributions, respectively).

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