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Abstract

Electron cryomicroscopy was used to study the structure of human lipoprotein(a) (Lp(a)), a plasma lipoprotein implicated in cardiovascular disease. An individual Lp(a) particle consists of a neutral lipid core within a shell of phospholipid, cholesterol and glycoprotein. In principle, electron cryomicroscopy images of single particles should contain structural detail attributable to the density differences among these components and the surrounding buffer. We observed such structural detail in images of frozen, hydrated Lp(a) particles. Lp(a) particles appeared to be roughly spherical in shape with an average diameter of 210 Å. As is generally true for unstained samples in vitreous ice, imaged with a low electron dose, these images have low contrast with low signal-to-noise ratios. To increase the signal-to-noise ratio, we averaged classes of similar particles. We began with a set of 5813 randomly oriented Lp(a) particles and generated classes using a linear multivariate statistical method, followed by hierarchical ascendent classification. Our initial classification, based on only the first eight eigenvectors, separated particles on the basis of gross size and shape. After a rough reference-free alignment step, a second classification used the finer details in the images. This approach yielded class averages with structural detail only faintly visible in the raw, single images.