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.[19
] Using the data filters described above, only five of 41 non-erythrocyte proteins were found in our most extensive WG-A (30) dataset (plasma membrane calcium transporting ATPase and tyrosine-protein kinase SYK from monocytes, and junctional adhesion molecule A precursor, Ras-related protein Rab-6B, and Syntaxin-11 from platelets) (Supplemental Information Table S2
). Three of these five proteins (plasma membrane calcium-transporting ATPase 4, junctional adhesion molecule A precursor, and Ras-related protein Rab-6B) were also present in the comprehensive RBC proteome reported by Pasini et al in which exhaustive measures were used to eliminate contaminant cells.[10
] Moreover, we did not identify any other well-known leukocyte or platelet proteins such as lactoferrin A, elastase 2, myeloperoxidase, eosinophil peroxidase, or any histone proteins.
The drawback to eliminating low-abundance proteins from the dataset by requiring more peptides or spectral counts or by using less fractionation to minimize contaminating proteins is that lower abundance RBC proteins may be lost. We evaluated our capacity to detect low-abundance RBC proteins by comparing the WG-A 30-fraction proteome to a review by Burton et al in which they searched the literature and compiled a list of 49 well-characterized RBC membrane proteins ranging in copy number from 500 to 1.2 million per cell.[20
] We were able to identify 11 of the 13 known lower abundance proteins with less than 10,000 copies per cell, including CD99, Kell glycoprotein, complement receptor 1, CD58, CD44, Lutheran glycoprotein, LW glycoprotein, and NaK-ATPase. Importantly, these proteins were all detected in the WG-A (30) dataset by three or more peptides, further justifying the data filters used here. The exceptions were the Duffy antigen, which was detected by a single peptide and was filtered from the final dataset on this basis, as well as the XG glycoprotein, which was not found. In addition, all of the more abundant proteins in the Burton et al list were identified in our WG-A 30-fraction proteome except for several minor glycophorin isoforms (glycophorin B, D, and E) and carbonic anhydrase 4.
The depth of analysis of the current study compares favorably with prior RBC proteomics studies. Our most extensive dataset from 30 fractions of WG-A provided an excellent depth of analysis with 842 proteins identified by two peptides or more. Early proteomics studies of red cell membranes were limited by instrumental capabilities and minimal fractionation prior to LC-MS/MS, as seen in the pilot study by Kakhniashvili et al[3
] in which IOV and spectrin extracts were analyzed to identify 91 erythroid membrane proteins. An extensive study done by Pasini and coworkers[10
] utilized WG extraction and 1-D SDS-PAGE fractionation prior to LC-MS/MS to identify 340 membrane proteins. Using an alternative MudPit approach, DePalma et al[4
] identified 275 membrane proteins from WG extracts. A recent publication by van Gestel et al.[21
] utilized 2-D blue-native/SDS-PAGE to fractionate WG and identify 524 membrane proteins. Comparison of our WG-A 30 fraction protein list to these previous studies showed variable overlap ranging from 29–53% based upon protein descriptions. While these percentages may seem low, the comparison of datasets from different search engines and particularly using databases is not straightforward when the only basis for comparison is protein descriptions. The large redundancy in databases and highly variable names for the same protein or closely related isoforms limit the capacity to match hits across studies, thereby leading to apparent anomalously low overlaps.
One surprising result that emerged from this study was the observation that further fractioning of WG into a low-ionic-strength extract and the residual IOVs was not superior to direct proteome analysis of WG alone. In general, further fractionation of complex proteomes results in greater depth of analysis with identification of more proteins and greater sequence coverage. However, typically, further fractionation enables the downstream analysis of a larger percentage of the original starting sample. This is not the case for red cell membranes when using the 1-D gel approach, as is reflected by the images in . The maximum load that can be applied to a 1-D gel without extensive band broadening is limited in white ghosts by the very abundant spectrin and Band 3 proteins. Since the majority of spectrin is segregated into the DCS fraction, proportionately larger protein loads are not practical. Similarly, Band 3 is primarily separated into the IOV fraction, thus limiting larger protein loads. While the results might be somewhat different for alternative downstream fractionation methods, the advantage of being able to evaluate different molecular forms of higher abundance proteins outweighs any minor advantage that might be achieved by utilizing further fractionation. In addition, further fractionation introduces an additional variable, as the relatively crude extraction and centrifugation may lead to varying degrees of cross-contamination between these two fractions.