A label-free proteome analysis strategy for identifying quantitative changes in erythrocyte membranes induced by red cell disorders

Abstract

Red blood cells have been extensively studied but many questions regarding membrane properties and pathophysiology remain unanswered. Proteome analysis of red cell membranes is complicated by a very wide dynamic range of protein concentrations as well as the presence of proteins that are very large, very hydrophobic, or heterogeneously glycosylated. This study investigated the removal of other blood cell types, red cell membrane extraction, differing degrees of fractionation using 1-D SDS gels, and label-free quantitative methods to determine optimized conditions for proteomic comparisons of clinical blood samples. The results showed that fractionation of red cell membranes on 1-D SDS gels was more efficient than low-ionic-strength extractions followed by 1-D gel fractionation. When gel lanes were sliced into 30 uniform slices, a good depth of analysis that included the identification of most well-characterized, low-abundance red cell membrane proteins including those present at 500 to 10,000 copies per cell was obtained. Furthermore, the size separation enabled detection of changes due to proteolysis or in vivo protein crosslinking. A combination of Rosetta Elucidator quantitation and subsequent statistical analysis enabled the robust detection of protein differences that could be used to address unresolved questions in red cell disorders. This article is part of a Special Issue entitled: Integrated omics.

Figure 1

Schematic of processing pipeline for RBC membrane proteomics.

Figure 2

Peptide and protein identifications for WG-B, IOV-1, IOV-2, DCS, and a combined dataset of IOV-1 and DCS. A) The WG-B dataset identified more unique peptides compared to either IOV or DCS alone. The combined IOV-1 and DCS dataset identified 591 more peptides than WG alone. B) At the protein level, WG-B provides a substantial increase in unique proteins compared to IOV and DCS, with approximately 190 more proteins identified in WG-B. WG-B and the combined dataset of IOV-1 and DCS identified a similar number of proteins.

Figure 3

Number of unique proteins identified with increasing LC-MS/MS runs from gels sliced into 10, 15 or 30 segments. Increasing fractionation greatly enhances protein coverage.

Figure 4

Distribution of spectral counts across gel slices for eight abundant proteins in A) 30-slice proteome, B) 15-slice proteome, and C) 10 slice proteome.

Figure 5

Rosetta Elucidator analysis of IOV technical replicates. A) Normalized fold change ratio of protein intensities for IOV-1 compared to IOV-2. Red dashed lines define the three-fold boundary. B) Distribution of protein intensities for IOV-1 versus IOV-2, indicating the 95% confidence interval (red dotted lines) and 3-fold change (black dotted line). C) Coefficient of variation for IOV technical replicates. A maximum coefficient of variation of 5 indicates an intensity cut off of 1 × 10⁵. D) Distribution of protein intensities for IOV-1 versus IOV-2 for proteins with intensities greater than 1 × 10⁵. The red dotted lines indicate the 95% confidence interval and black dotted lines correspond to a 3-fold change.

Figure 6

Rosetta Elucidator software analysis of WG biological replicates. A) Distribution of protein intensities for WG-A versus WG-B with an intensity cut off at 1 × 10⁵. The 95% confidence interval (red dotted lines) and 3-fold change (black dotted lines) were derived from the IOV technical replicates. B) Distribution of protein intensities for WG-A versus WG-B with an intensity cut off at 1 × 10⁵. The red dotted lines correspond to 95% confidence interval and black dotted lines indicate a 3-fold change derived from the WG biological replicates alone.