Optimization of the linear quantification range of an online trypsin digestion coupled liquid chromatography–tandem mass spectrometry (LC–MS/MS) platform

<p>Tandem mass spectrometry (MS/MS)-based proteomic workflows with a bottom-up approach require enzymatic digestion of proteins to peptide analytes, usually by trypsin. Online coupling of trypsin digestion of proteins, using an immobilized enzyme reactor (IMER), with liquid chromatography (LC) and MS/MS is becoming a frequently used approach. However, finding IMER digestion conditions that allow quantitative analysis of multiple proteins with wide range of endogenous concentration requires optimization of multiple interactive parameters: digestion buffer flow rate, injection volume, sample dilution, and surfactant type/concentration. In this report, we present a design of experiment approach for the optimization of an integrated IMER-LC–MS/MS platform. With bovine serum albumin as a model protein, the digestion efficacy and digestion rate were monitored based on LC–MS/MS peak area count versus protein concentration regression. The optimal parameters were determined through multivariate surface response modeling and consideration of diffusion controlled immobilized enzyme kinetics. The results may provide guidance to other users for the development of quantitative IMER-LC–MS/MS methods for other proteins.</p

Core lipid, surface lipid and apolipoprotein composition analysis of lipoprotein particles as a function of particle size in one workflow integrating asymmetric flow field-flow fractionation and liquid chromatography-tandem mass spectrometry

<div><p>Lipoproteins are complex molecular assemblies that are key participants in the intricate cascade of extracellular lipid metabolism with important consequences in the formation of atherosclerotic lesions and the development of cardiovascular disease. Multiplexed mass spectrometry (MS) techniques have substantially improved the ability to characterize the composition of lipoproteins. However, these advanced MS techniques are limited by traditional pre-analytical fractionation techniques that compromise the structural integrity of lipoprotein particles during separation from serum or plasma. In this work, we applied a highly effective and gentle hydrodynamic size based fractionation technique, asymmetric flow field-flow fractionation (AF4), and integrated it into a comprehensive tandem mass spectrometry based workflow that was used for the measurement of apolipoproteins (apos A-I, A-II, A-IV, B, C-I, C-II, C-III and E), free cholesterol (FC), cholesterol esters (CE), triglycerides (TG), and phospholipids (PL) (phosphatidylcholine (PC), sphingomyelin (SM), phosphatidylethanolamine (PE), phosphatidylinositol (PI) and lysophosphatidylcholine (LPC)). Hydrodynamic size in each of 40 size fractions separated by AF4 was measured by dynamic light scattering. Measuring all major lipids and apolipoproteins in each size fraction and in the whole serum, using total of 0.1 ml, allowed the volumetric calculation of lipoprotein particle numbers and expression of composition in molar analyte per particle number ratios. Measurements in 110 serum samples showed substantive differences between size fractions of HDL and LDL. Lipoprotein composition within size fractions was expressed in molar ratios of analytes (A-I/A-II, C-II/C-I, C-II/C-III. E/C-III, FC/PL, SM/PL, PE/PL, and PI/PL), showing differences in sample categories with combinations of normal and high levels of Total-C and/or Total-TG. The agreement with previous studies indirectly validates the AF4-LC-MS/MS approach and demonstrates the potential of this workflow for characterization of lipoprotein composition in clinical studies using small volumes of archived frozen samples.</p></div

Average exchangeable apo/apoB-100 molar ratio profiles, and protein volume vs. total particle volume ratios by sample categories.

<p>Labels indicate normolipidemic (NL, N = 25, Total-C <230 mg/dL, and Total-TG <150 mg/dL); hypercholesterolemic (HC, N = 13, Total-C >230 mg/dL and Total-TG <150 mg/dL); hyperlipidemic (HL, N = 41, Total-C >230 mg/dL and Total-TG >150 mg/dL); and hypertriglyceridemic (HT, N = 31, Total-C<230 mg/dL and Total-TG>150 mg/dL). Error bars indicate confidence intervals.</p

Sum of ApoA-I, sum of calculated Lp-P, average ApoA-I/Lp-P and other average HDL particle characteristics by diameter range in different sample categories.

<p>Labels indicate normolipidemic (NL, N = 25, Total-C <230 mg/dL, and Total-TG <150 mg/dL); hypercholesterolemic (HC, N = 13, Total-C >230 mg/dL and Total-TG <150 mg/dL); hyperlipidemic (HL, N = 41, Total-C >230 mg/dL and Total-TG >150 mg/dL); and hypertriglyceridemic (HT, N = 31, Total-C<230 mg/dL and Total-TG>150 mg/dL). Error bars indicate standard deviation.</p

Average number of apo/Lp-P vs. size profiles.

<p>Error bars indicate 95% confidence intervals based on 110 serum samples. Vertical dashed lines indicated the borders of size ranges with significantly different composition as discussed in the text. Corresponding size ranges are indicated in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0194797#pone.0194797.g010" target="_blank">Fig 10</a>.</p

Average lipid/Lp-P vs. size profiles.

<p>A: HDL particle size range, B: LDL particle size range. Error bars indicate 95% confidence intervals based on 110 serum samples. Vertical dashed lines indicated the borders of size ranges with significantly different composition as discussed in the text. Corresponding size ranges are indicated in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0194797#pone.0194797.g009" target="_blank">Fig 9</a>.</p

Overlay of size distribution profiles of selected proteins from a single sample.

<p>LC-MS/MS peak areas were normalized by the maximum peak area for overlay.</p

Size profiles from serum and corresponding fractions.

<p><b>A</b>: Size profile of representative proteins from serum (50 μL injection volume). <b>B</b>: Size profile from re-injected AF4 fractions indicated by colors (200 μL injection volume). <b>C</b>: De-convolution of the apoA-I profile in A (bottom) and predicted fraction profiles (top). <b>D</b>: De-convolution of the apoB profile in A (bottom) and predicted fraction profiles (top), generated by summing together de-convoluted Gaussian peaks. Re-injected fractions are indicated by numbers on the top and by the colors of solid vertical lines in <b>A</b>, matched with the color of the overlaid size profiles in <b>B</b>. The dotted vertical lines in <b>B</b> correspond with lines in <b>A</b>. The colors of the predicted profiles in <b>C</b> and <b>D</b> matched with the colors of the size profiles of the fractions for apoA-I and apoB in <b>B</b>.</p

Summary of mean and standard deviation of analyte levels in whole serum for non-polar lipids, polar lipids and proteins by sample categories.

<p>Summary of mean and standard deviation of analyte levels in whole serum for non-polar lipids, polar lipids and proteins by sample categories.</p

Overlay of data for six serum samples from a representative asymmetric flow field-flow fractionation run.

<p>A: Overlay of average hydrodynamic diameter measured by dynamic light scattering vs. fraction number. Continuous line shows calculated size based on quadratic fit. B: Overlay of corresponding UV signal during fractionation. C: Overlay of 92° signal from the multi-angle light scattering (MALS) detector during fractionation. Grid lines are shown for ease of visualization of fraction numbers across graphs.</p