Isolation of HDL by sequential flotation ultracentrifugation followed by size exclusion chromatography reveals size-based enrichment of HDL-associated proteins
Aug 9, 2021·,,,,,
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0 min read
Jack Jingyuan Zheng
Joanne Agus
Brian V. Hong
Xinyu Tang
Christopher Rhodes
Hannah Houts
Chenghao Zhu
Jea Woo Kang
Maurice Wong
Yixuan Xie
Carlito B. Lebrilla
Emily R. Mallick
Kenneth W. Witwer
Angela M. Zivkovic
Abstract
High-density lipoprotein (HDL) particles have multiple beneficial and cardioprotective roles, yet our understanding of their full structural and functional repertoire is limited due to challenges in separating HDL particles from contaminating plasma proteins and other lipid-carrying particles that overlap HDL in size and/or density. Here we describe a method for isolating HDL particles using a combination of sequential flotation density ultracentrifugation and fast protein liquid chromatography with a size exclusion column. Purity was visualized by polyacrylamide gel electrophoresis and verified by proteomics, while size and structural integrity were confirmed by transmission electron microscopy. This HDL isolation method can be used to isolate a high yield of purified HDL from a low starting plasma volume for functional analyses. This method also enables investigators to select their specific HDL fraction of interest: from the least inclusive but highest purity HDL fraction eluting in the middle of the HDL peak, to pooling all of the fractions to capture the breadth of HDL particles in the original plasma sample. We show that certain proteins such as lecithin cholesterol acyltransferase (LCAT), phospholipid transfer protein (PLTP), and clusterin (CLUS) are enriched in large HDL particles whereas proteins such as alpha-2HS-glycoprotein (A2HSG), alpha-1 antitrypsin (A1AT), and vitamin D binding protein (VDBP) are enriched or found exclusively in small HDL particles.
Type
Publication
Scientific Reports

Authors
Research Assistant Professor
Chenghao Zhu is a Research Assistant Professor in the NCI-designated Cancer
Center at Sanford Burnham Prebys. His research focuses on developing
computational methods and software for proteogenomics and applying
proteogenomics to cancer diagnosis, prognosis, and clinico-epidemiologic
questions.