ssDNA antibodies, which are commercially available, react with random sequences of ssDNA of all species (21-25). ssDNA is conventionally used in immunohistochemical apoptosis assays because anti-ssDNA binds ssDNA produced during apoptosis but it will not bind to DNA/protein complexes generated during S-phase (26-28).
To demonstrate ssDNA antibody specificity and the absence of RNA cross-reactivity we printed RPMAs with ssDNA, rRNA, bovine serum albumin (BSA), ssDNA spiked into BSA, rRNA spiked into BSA, tissue protein lysates, lysates derived from a known number of cells, and tissue and cell protein lysates devoid of DNA (DNA precipitated out by ethanol). ssDNA signal was detectable only in those samples containing ssDNA when the array was probed with anti-ssDNA antibody. No staining was observed in RNA samples either alone or in BSA, in BSA alone, or in lysates lacking DNA (Figure 1A). Moreover, we demonstrated that ssDNA staining intensity is directly proportional to DNA amount and cell number in the sample. Samples with increasing amounts of spiked-in ssDNA or larger numbers of cell input exhibited increasing relative intensity when probed with ssDNA antibody (Figure 1B). A comparison of ssDNA levels between RPMI 8226 cells lines that were heat treated to induce apoptosis or untreated cells revealed no difference between total ssDNA on the RPMA, indicating that our sample extraction and RPMA processing produced ssDNA independent of the state of the cell (data not shown).
Inter-slide ssDNA staining reproducibility was assessed using 50 sequentially printed arrays comprised of cellular control lysates, ssDNA positive controls, and ribosomal RNA-negative controls. The amount of ssDNA between slide 1 (first printed) and slide 50 (last printed) showed high precision across a printing run (CV <10%). Between run precision was assessed using 15 different staining sessions for 30 identical sequentially printed protein microarrays. Each array was prepared with U266 cell lysates of known cell number, printed in 12 point, 2-fold dilution curves. One RPMA was probed with anti-ssDNA antibody and one was probed with antibody diluent instead of the primary antibody which served as a control for non-specific binding. The curve representing the antibody binding reaction for each sample for each array exhibited high reproducibility. (Figure 1C)
To demonstrate the contribution of red blood cells to total protein and β-actin levels but not ssDNA in a blood contaminated sample, we built an array with: i) RPMI 8226 lysate, as control nucleated cells; i) mixed lysate containing RPMI 8226:RBCs in a ratio 10:1; and iii) mixed lysate containing RPMI 8266:RBCs in a ratio 1:10. Triplicate arrays were stained for total protein, β-actin, or ssDNA. For an equal number of RPMI 8226 cells in the lysates, the 3 lysates showed no statistical difference in the ssDNA content, while β-actin and total protein were significantly higher in the RPMI 8226:RBCs 1:10 lysate, thus highlighting the substantial contribution of red blood cells to β-actin and total protein levels. (Figure 2)
The RBC proteome and interactome have been published in a comprehensive review by D'Alessandro et al. (29). Theoretically, contaminating RBCs may contribute proteins that correspond to analyte proteins investigated in the experimental samples. Thus, an RBC protein lysate could be used as a control sample to exclude from the study those proteins that are positive in the RBC sample. Another method for quantifying the amount of blood contamination is the use of an antibody against a specific RBC protein to measure the relative intensity in each sample for that specific RBC marker. In addition to blood-contaminated samples, peripheral blood mononuclear cell (PBMC) preparations could benefit from ssDNA normalization methods. Although efficient lymphocyte extraction from whole blood is well established, there is a certain amount of residual protein contamination due to immunoglobulins, albumin and other abundant proteins. (30-32)
Normalization of spot intensity data occurs prior to curve-fitting. Real Time RT-PCR studies commonly use two different algorithms, geNorm (7) and NormFinder (8), which use geometric averaging, for determining the most stable genes for normalization. Analogous to RT-PCR or gene microarray normalization, the optimal normalization molecule can be determined for RPMA data sets. Different RPMA data sets, such as microdissected samples, cell culture samples, and homogeneous/heterogeneous tissues, may benefit from systematic selection of normalization molecules. A single molecule selected a priori will not necessarily represent the most suitable normalization analyte for all RPMA study sets.