An Integrated Platform for High-Throughput Extraction and Mass Spectrometry-Based Quantification of Cholesterol and Sphingosine

High Resolution Image Download MS PowerPoint Slide Quantification of cellular lipids in a reproducible and high-throughput manner is a key step in the development of therapeutics for lipid storage diseases. Niemann-Pick Disease Type C (NPC) is a genetic disorder characterized by the accumulation of unesterified cholesterol in late endosomes/lysosomes, which is usually measured by the filipin fluorescence assay. However, the nonspecific binding of filipin to other sterol derivatives, multiple assay steps, and difficulty in quantitation present limitations for high-throughput screening and accurate cellular cholesterol quantification. We report the development of an integrated and semiautomated protocol to extract and quantify cellular cholesterol in 384-well plates by utilizing a liquid handling platform in conjunction with a high-throughput mass spectrometry (MS) system. The 384-well plate format enables seamless lipid extraction and subsequent MS analysis in less than 2 h from a cell culture plate to final MS data. Cholesterol was extracted from neural stem cells differentiated from NPC induced pluripotent stem cells using methyl tert -butyl ether (MTBE), with 13 C-cholesterol serving as an internal standard for quantification and normalization of native cholesterol. This integrated platform showed excellent quantification linearity and reproducibility (intraday and interday, R 2 > 0.99) with a recovery rate between 83 and 107%. We employed this integrated platform to screen a collection of 241 investigational compounds at seven concentrations each, benchmarking the method as an efficient, label-free cellular cholesterol quantification assay for high-throughput applications. Furthermore, we demonstrated the capability to multiplex extraction and quantification of sphingosine/cholesterol in a single MS run, extending the applicability of this integrated workflow to other lipid storage diseases.