Supplemental Material. Estimation of lipid thickness by high resolution color microscopy.
An advantage of using a color camera with the microscope, rather than the monochrome camera used
in a previous study (Ref. 22) is that unambiguous values of lipid thickness may be obtained. A
disadvantage of the monochrome camera is that a certain response from a pixel may correspond to
more than one possible lipid thickness, whereas these different thickness values can be distinguished
with a color camera by comparison of responses from red, green and blue pixels.
This is illustrated in Figure S1 where the size of tiny red spots represent the frequency of combinations
of red response (x axis) and blue response (y axis) derived from 9 images from cholesteryl nervonate
films (as in Figure 5) plus one image of bare saline surface. The black curve is a fit to these data based on
the spectral response functions (derived as in Ref. 22, including the effects of spectral distribution of the
source, transmission and reflectance of optical components and spectral sensitivity of the red, green
and blue pixels) of the three types of pixels; the fit was based on Equations 2, 7, 11 and 20 of Reference
34. Zero thickness is given at the lower left of this curve and small and large black spots correspond to
thickness increments of 10 and 50 nm up to a maximum of 500 nm. Each cluster of red points
corresponds to a thickness of an integral number of layers of cholesteryl nervonate. The fit of this curve
to the data was optimized by adjusting the assumed refractive index of cholesteryl nervonate for the red
and blue pixels (1.489 and 1.496 respectively). Similar plots were made for red versus green and for
green versus blue responses. The inset shows responses versus lipid thickness for red, green and blue
pixels used to generate the fits.
150
100
50
response
blue response
200
250
200
150
100
50
0
100
200
300
400
500
lipid thickness, nm
0
100
150
200
red response
Figure S1. Red spots represent the frequency of combinations of red and blue pixel responses, based on
10 images for cholesteryl nervonate. The black line is a theoretical fit to these data based on increasing
lipid thickness starting at zero thickness at the lower left; small and large black spots correspond to
intervals of 10 and 50 nm up to 500 nm. The inset shows the red, green and blue pixel responses as a
function of lipid thickness.
The lipid thickness for any pixel was estimated by comparing its red, green and blue responses to the
theoretical curve of Figure S1 (extended into three dimensions) and finding the closest point on that
curve and the corresponding thickness. Thickness estimates were rejected if the distance to the closest
point on the curve was above a criterion value. Histograms of thickness estimates for two images of
cholesteryl nervonate are shown in Figure S2. It is seen that each shows a number of sharp peaks which
tend to be spaced at regular intervals of about 4.4 nm
number of observations
10000
1000
100
10
0
50
100
150
200
250
lipid thickness, nm
Figure S2. Histograms of cholesteryl nervonate thickness for two images.
A program was written to find the optimum correspondence between thickness of each peak and the
number of layers of cholesteryl nervonate. Peaks were accepted for analysis if they were sufficiently
sharp and large. Figure S3 shows a plot of thickness of cholesteryl nervonate as a function of number of
layers; this is based on 132 peaks from 12 images. A good correlation is observed (r2 = 0.999973). The
slope of the curve gives the estimated thickness of a single layer of cholesteryl nervonate of 4.38 nm.
thickness, nm
400
300
200
100
0
0
20
40
60
80
number of layers
Figure S3. Thickness of cholesteryl nervonate as a function of number of layers.