A label-free proteome analysis strategy for identifying quantitative changes in erythrocyte membranes induced by red cell disorders☆
Graphical abstract
Highlights
► Direct proteome analysis of red cell ghosts was superior to ghost membrane extracts. ► Excellent depth of analysis with separation of red cell ghosts into 30 fractions ► Use of 1D SDS gels provided insight into protein size heterogeneity. ► Label-free quantitation methods to address unsolved questions in red cell disorders
Introduction
The red blood cell (RBC), unlike other eukaryotic cells, lacks a nucleus, other organelles, and the capacity to synthesize proteins. The RBC membrane and associated membrane skeleton proteins provide the flexibility that is required for red cells to pass through small capillaries for gas transport and exchange. The ease of obtaining RBC, the readily obtained high purity of plasma membrane preparations, and the relatively simple protein composition make the RBC membrane one of the best-characterized membrane systems. Yet, despite years of research, there are many unanswered questions regarding RBC membrane properties and pathophysiology. In this context, unbiased proteomic studies provide a valuable platform for understanding how the red cell proteome is altered in erythrocyte disorders.
Previous red blood cell proteomic studies have used fractionation techniques to lower the complexity of the sample and enable more protein identifications. These techniques include white ghost (WG) analysis on 1- [1] or 2-DE gels [2], in-solution digestion of four RBC fractions (white ghosts, cytoplasmic proteins, inside out vesicles (IOV), and membrane skeletal proteins) [3], and membrane protein extraction with detergents followed by in-solution digestion for multidimensional protein identification technology analysis (MudPIT) [4]. Several studies used 2-DE as the preferred fractionation method for LC–MS/MS analysis of the RBC proteome [5], [6], [7], [8]. However, 2-DE analysis of hydrophobic membrane proteins is not ideal and detection can vary significantly depending upon individual protein structures and characteristics [9]. Conversely, 1-D SDS gels provide the advantage of separation by protein size without the risk of losing highly hydrophobic proteins. Additionally, 1-D SDS gels ensure consistent protein loads for LC–MS/MS analysis and provide insight into size variations of individual proteins prior to digestion.
Our current approach was adapted from an in-depth analysis of the RBC proteome in which a stringent protocol was used to eliminate contaminating non-red cells [10]. In this robust study by Pasini et al., the peripheral blood was stored to allow maturation of reticulocytes, and then RBCs were passed through a leukocyte depletion filter, density filter, and nylon nets prior to RBC washing and lysis. The white ghosts were then subjected to several processes such as a high pH extraction with sodium carbonate, ethanol solubilization, and membrane skeleton extraction to maximize protein identifications while obtaining information on interactions with the lipid bilayer. While such rigorous processing was successful in verifying the RBC proteome, a more streamlined approach is desirable when comparing red cell proteomes in multiple clinical specimens. Additionally, when studying rare hematological diseases, particularly those that affect young children, it is critical to generate methods that are compatible with small sample volumes and are highly consistent, so that label-free quantitative methods can be used. This study investigates RBC extraction, differing levels of fractionation, and label-free quantitative methods to determine optimized conditions for proteomic analysis for the comparison of clinical blood samples.
Section snippets
RBC sample preparation
Typically, 5–10 mL of whole blood was obtained with informed consent and collected in K2EDTA. Samples and buffers were kept at 0–4 °C throughout the procedure to minimize proteolysis. The procedure was modified from previously published methods [10]. RBCs were isolated from the plasma by centrifugation for 10 min at 150 ×g before being resuspended and passed through a leukocyte depletion filter (Plasmodipur®, Accurate Chemical & Scientific Corp., Westbury, NY). Red cells were washed four times with
Proteome coverage of RBC membrane extracts
Efficient, reproducible RBC processing and sample handling are critical for obtaining a consistent and comprehensive proteome with minimal contamination from other cell types. When working with small volumes of peripheral blood it is important to find a balance that maximizes protein yield while minimizing proteolysis and contaminants. In the current workflow, proteolysis was reduced by keeping all reagents and samples at 0–4 °C during processing and using non-protein protease inhibitors, i.e.,
Discussion
One important consideration for RBC proteomics is the presence of contaminating non-red cells and the detection of proteins that are not native to the RBC. Hence, even if contaminating cells are present at very low levels, such as one macrophage or platelet per million red cells, abundant macrophage or platelet proteins may be detected in the RBC proteome analysis as low-abundance proteins. A recent review listed 50 blood-cell-type-specific proteins that were only found in purified constituents
Conclusion
Low ionic strength extraction of WG with separate analysis of the resulting pellet and supernatant was not superior to direct proteome analysis of WG. The high abundance Band 3 and spectrin proteins, present in the IOV and DCS fractions, limit the gel protein load and therefore do not provide an advantage over the WG analysis. Separation of WG into 30 fractions using 1-D SDS gels yielded an excellent depth of analysis and provided insights into protein size heterogeneity. The combination of
Acknowledgments
We gratefully acknowledge the administrative assistance of Mea Fuller and the assistance of The Wistar Institute Proteomics Core Facility. This work was supported in part by NIH grants HL38794 (to D. W. S.) and DK084188 to (M. B.), and the Institutional Cancer Center Support grant CA010815.
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This article is part of a Special Issue entitled: Integrated omics.