The HDL proteome has been widely recognized as an important mediator of HDL function. While a variety of HDL isolation methods exist, their impact on the HDL proteome and its associated function remain largely unknown. Here, we compared three of the most common methods for HDL isolation, namely immunoaffinity (IA), density gradient ultracentrifugation (UC), and dextran-sulfate precipitation (DS), in terms of their effects on the HDL proteome and associated functionalities. We used state-of-the-art mass spectrometry to identify 171 proteins across all three isolation methods. IA-HDL contained higher levels of paraoxonase 1, apoB, clusterin, vitronectin, and fibronectin, while UC-HDL had higher levels of apoA2, apoC3, and α-1-antytrypsin. DS-HDL was enriched with apoA4 and complement proteins, while the apoA2 content was very low. Importantly, size-exclusion chromatography analysis showed that IA-HDL isolates contained subspecies in the size range above 12 nm, which were entirely absent in UC-HDL and DS-HDL isolates. Analysis of these subspecies indicated that they primarily consisted of apoA1, IGκC, apoC1, and clusterin. Functional analysis revealed that paraoxonase 1 activity was almost completely lost in IA-HDL, despite high paraoxonase content. We observed that the elution conditions, using 3M thiocyanate, during IA resulted in an almost complete loss of paraoxonase 1 activity. Notably, the cholesterol efflux capacity of UC-HDL and DS-HDL was significantly higher compared to IA-HDL. Together, our data clearly demonstrate that the isolation procedure has a substantial impact on the composition, subclass distribution, and functionality of HDL. In summary, our data show that the isolation procedure has a significant impact on the composition, subclass distribution and functionality of HDL. Our data can be helpful in the comparison, replication and analysis of proteomic datasets of HDL.

The association between high-density lipoprotein (HDL) cholesterol and breast cancer (BC) remains controversial due to the high complexity of the HDL particle and its functionality. The HDL proteome was determined in newly diagnosed BC classified according to the molecular type [luminal A or B (LA or LB), HER2, and triple-negative (TN)] and clinical stage of the disease. Women (n = 141) aged between 18 and 80 years with BC, treatment-naïve, and healthy women [n = 103; control group (CT)], matched by age and body mass index, were included. Data-independent acquisition (DIA) proteomics was performed in isolated HDL (D = 1.063–1.21 g/mL). Results: Paraoxonase1, carnosine dipeptidase1, immunoglobulin mMu heavy chain constant region (IGHM), apoA-4, and transthyretin were reduced, and serum amyloid A2 and tetranectin were higher in BC compared to CT. In TNBC, apoA-1, apoA-2, apoC-2, and apoC-4 were reduced compared to LA, LB, and HER2, and apoA-4 compared to LA and HER2. ComplementC3, lambda immunoglobulin2/3, serpin3, IGHM, complement9, alpha2 lysine rich-glycoprotein1, and complement4B were higher in TNBC in comparison to all other types; complement factor B and vitamin D-binding protein were in contrast to LA and HER2, and plasminogen compared to LA and LB. In grouped stages III + IV, tetranectin and alpha2-macroglobulin were reduced, and haptoglobin-related protein; lecithin cholesterol acyltransferase, serum amyloid A1, and IGHM were increased compared to stages I + II. Conclusions: A differential proteomic profile of HDL in BC based on tumor molecular classification and the clinical stage of the disease may contribute to a better understanding of the association of HDL with BC pathophysiology, treatment, and outcomes.

The introduction of mass spectrometry-based proteomics has revolutionized the high-density lipoprotein (HDL) field, with the description, characterization, and implication of HDL-associated proteins in an array of pathologies. However, acquiring robust, reproducible data is still a challenge in the quantitative assessment of HDL proteome. Data-independent acquisition (DIA) is a mass spectrometry methodology that allows the acquisition of reproducible data, but data analysis remains a challenge in the field. To date, there is no consensus on how to process DIA-derived data for HDL proteomics. Here, we developed a pipeline aiming to standardize HDL proteome quantification. We optimized instrument parameters and compared the performance of four freely available, user-friendly software tools (DIA-NN, EncyclopeDIA, MaxDIA, and Skyline) in processing DIA data. Importantly, pooled samples were used as quality controls throughout our experimental setup. A careful evaluation of precision, linearity, and detection limits, first using E. coli background for HDL proteomics and second using HDL proteome and synthetic peptides, was undertaken. Finally, as a proof of concept, we employed our optimized and automated pipeline to quantify the proteome of HDL and apolipoprotein B-containing lipoproteins. Our results show that determination of precision is key to confidently and consistently quantifying HDL proteins. Taking this precaution, any of the available software tested here would be appropriate for quantification of HDL proteome, although their performance varied considerably.