Home | Sign Up For Our Newsletter

CASE STUDIES

Should I Use Serum or Plasma for my HumanMAP Study?

Comparison of Serum to Three Plasma Anticoagulant Samples (EDTA, Citrate and Heparin) using the Human MAP.

Abstract:

Blood is commonly processed into serum or plasma for the measurement of analytes depending upon their stability and reproducibility in each matrix. With the development of multiplexing, it has become critical to understand the behavior of many different analytes in the four most common sample types. This study utilized the 78 antigen measurements of the Rules-Based Medicine Human MAP, version 1.5 to examine the analyte differences between the four matrices. Serum, heparin-plasma, citrated-plasma and EDTA-plasma were collected during the same blood draw using standard clinical tubes from 12 healthy volunteers. Serum and plasma were processed using standard protocols and the resulting samples were stored at -80°C until testing. As expected, serum and plasma were easily discriminated using analytes such as fibrinogen. Further delineation of the different types of plasma samples required as few as 6 analytes and as many as 15. In addition, serum to plasma ratios for the cytokines, chemokines, growth factors, and other analytes were developed as important, useful tools for investigators planning MAP studies. What was unexpected was the similarity in testing results between citrate and heparin-plasma as each of these anticoagulants possesses different mechanisms of action. Likewise, there were significant differences in the levels of 15 analytes between plasma generated using EDTA and citrate even though these two anticoagulants share a similar biochemical mechanism to block blood clotting. This case study revealed interesting biochemical differences between these various biological fluids and will help our customers to choose the best sample type for their needs.

Introduction:

At RBM, we are often asked about which type of blood sample is preferable; serum or plasma. We do not have a preference of plasma over serum or vice versa. What we constantly remind our customers is that the method used to collect and store the blood product should be consistent within a study. If different studies are to be compared, then those methods should be the same. We encourage investigators to use the methods with which they are the most familiar. If they have always used serum, we recommend that they not switch to plasma for MAP testing unless there are analytes of great interest that are sensitive to the sample difference. For example, if one is interested in fibrinogen levels plasma is a better sample than serum. To address the question of what analyte differences are seen in these various blood products, we embarked on this study.

Materials and Methods:

Blood was collected from 12 healthy human adults after overnight fasting using venipuncture and collection tubes from Becton-Dickinson. Serum was generated in "tiger-topped" SST tubes (BD Cat. # 366510) by allowing the sample to clot at room temperature for 1 hour followed by centrifugation at 2,000 x g for 10 minutes. Heparin-plasma (green-topped tubes, Cat.#366481), Citrate-plasma (blue-topped tubes, Cat. # 369714) and EDTA-plasma (purple-topped tubes, Cat. #311446) were mixed well after blood collection and spun within 30 minutes at 2,000 x g for 2 minutes to separate plasma from the blood cells. All samples were aspirated by pipette and frozen in 0.5 mL aliquots at -80°C until tested. One tube of each of the resulting 48 samples were thawed at room temperature, vortexed, spun at 13,000 x g for 5 minutes for clarification and 25 uL was removed for MAP antigen analysis into a master microtiter plate. Using automated pipetting, an aliquot of each sample was introduced into one of the capture microsphere multiplexes of the Human Antigen MAP. These mixtures of sample and capture microspheres were thoroughly mixed and incubated at room temperature for 1 hour. Multiplexed cocktails of biotinylated, reporter antibodies for each multiplex were then added robotically and after thorough mixing, were incubated for an additional hour at room temperature. Multiplexes were developed using an excess of streptavidin-phycoerythrin solution which was thoroughly mixed into each multiplex and incubated for 1 hour at room temperature. The volume of each multiplexed reaction was reduced by vacuum filtration and the volume increased by dilution into matrix buffer for analysis. Analysis was performed in a Luminex 100 instrument and the resulting data stream was interpreted using proprietary data analysis software developed at Rules-Based Medicine and licensed to Qiagen Instruments and Upstate Biotechnology. For each multiplex, both calibrators and controls were included on each microtiter plate. 8-point calibrators were run in the first and last column of each plate and 3-level controls were included in duplicate. Testing results were determined first for the high, medium and low controls for each multiplex to ensure proper assay performance. Unknown values for each of the analytes localized in a specific multiplex were determined using 4 and 5 parameter, weighted and non-weighted curve fitting algorithms included in the data analysis package. Data mining was performed using two methods. First, separation of the four sample types using non-parametric, proximity analysis was performed using OmniViz software (OmniViz, Maynard, MA). Second, analyte value means and ratios of these mean values in serum to plasma were calculated using Microsoft Excel.

Results:

We first addressed the data using OmniViz software to identify the analytes with the most significant difference between the various sample types. As you will see later in the results section and as you would expect, it was easy to separate the serum samples from the plasma using markers such as fibrinogen and factor VII. What was not anticipated was that these biomarkers of clotting were less significant than 2 other analytes in the case of fibrinogen and 8 other analytes in the case of factor VII for separating the various sample types.

Figure 1 is the proximity plot for the 48 samples using 6 analytes including fibrinogen. Although fibrinogen is extremely significant discriminating serum samples from plasma (p value < 3.0 x 10-12), this analysis identified serum glutamic oxaloacetic transaminase (SGOT) as the most significant marker for separating serum and the three types of plasma. In addition, brain-derived neurotrophic factor (BDNF) was also an extremely significant biomarker of sample type. Here are two examples of unanticipated biomarkers being revealed using Human MAP testing. Clearly, these six analytes are capable of separating serum from plasma and EDTA-plasma from citrate or heparin-plasma. To separate the two latter plasma types, additional analyte biomarkers were necessary.

Figure 2 is the proximity plot for the 48 samples using 10 analytes including VEGF (vascular endothelial growth factor), MCP-1 (monocyte chemotactic protein-type 1), eotaxin, and factor VII. These four additional analytes were highly significant in separating the citrate and heparin plasma samples. Figure 3 is the proximity plot for the 48 samples using an additional 5 analytes for a total of 15. These represent the 15 most significant analytes that separate the four types of samples. Clearly, addition of the last 5 analytes did not improve the separation. However, if one were to "validate" a pattern of biomarkers for the delineation of sample type, the 15 markers in Figure 3 would be the logical candidates to begin that process.

The mean values for each of the 78 analytes are shown in Table 1. The analytes shaded in yellow highlighted those 6 used in the first level of OmniViz data mining. There was only one analyte that was more strongly represented in all three plasma types over serum and that was fibrinogen which was not surprising. Serum glutamic oxaloacetic transaminase (SGOT) was an interesting marker that showed very different results depending upon the type of anticoagulant used. SGOT was well represented in EDTA-plasma, yet it was not detectable in heparin or citrate-plasma. Alpha-2 macroglobulin, brain derived neurotrophic factor (BDNF), epidermal growth factor (EGF) and tissue inhibitor of metalloproteinase type 1 (TIMP 1) rounded out this first group and all were better represented in serum than in plasma. This could best be seen by looking at the serum to EDTA plasma ratio listed in the last column on the right of Table 1. Lavender shading indicated an analyte's proclivity to be more strongly represented in serum than in plasma. Also note this ratio approaching zero for fibrinogen, highlighted in orange, as it was a strong marker for plasma.

The next four analytes in order of significance were highlighted in blue. These were useful for discriminating the different types of plasma. Although all four analytes were better represented in serum than EDTA-plasma, this was not true for the other two plasma types. Finally, the five additional analytes used to discriminate the plasma types are highlighted in green. Two of these five analytes, plasminogen activator inhibitor type 1 (PAI-1) and prostactic acid phosphatase (PAP), were better represented in serum than in all of the plasma samples. Again, their serum to EDTA-plasma ratio was highlighted in lavender. An interesting member of this group was matrix metalloproteinase type 3 (MMP3). MMP-3 was equally represented in serum, citrate-plasma and heparin-plasma, but it was not detected in EDTA-plasma. MMP-3, therefore, behaved in the opposite manner of SGOT.

The remaining values and ratios in Table 1 suggest how the different analytes were represented in the four sample types. The two cytokine/chemokines that did show proclivity to being found in serum vs. plasma were interleukin-8 (IL-8) and RANTES.

Conclusions:

These study results which clearly delineated the differences between serum and plasma were certainly not unexpected. One would expect more fibrinogen in plasma than in serum. However, there were some surprises with regards to specific analytes such as SGOT which was only readily measured in the EDTA-plasma. It is understandable that clotting proteins such as fibrinogen would be consumed in the clotting process, yet it is less clear why SGOT behaves so differently in EDTA-plasma as opposed to citrate or heparin. This is especially confusing when one compares the mechanisms of action of the three anticoagulants. EDTA and citrate inhibit clotting by chelation of the divalent cations Ca++ and Mg++, which inhibits several of the divalent cation-dependent proteolytic enzymes critical to the clotting cascade. Heparin is thought to exert its anticoagulation activity more specifically by preventing the proteolytic conversion of prothrombin to thrombin which is a key step in clot formation. It was only after additional analytes were included in the analysis that the citrate and heparin-plasma samples could be separated.

Conversely, alpha-2 macroglobulin, brain derived neurotrophic factor (BDNF), epidermal growth factor (EGF), epithelial neutrophil-activating protein-78 (ENA-78), interleukin-8 (IL-8), plasminogen activator inhibitor type 1 (PAI-1) and tissue inhibitor of metalloproteinase type 1 (TIMP-1) were better represented in serum than in any of the plasma preparations. The mechanisms for these individual differences are certainly not clear. The remaining analytes fall into several categories with the majority not being enriched more than 50% into either serum or plasma. The data listed in Table 1 may be of great help when comparing serum to plasma in future studies.

At the beginning of this article we posed the question: "Is serum or plasma a better sample for a MAP study?". At the conclusion of this data overview, the answer is the same. Investigators should use the sample type with which they are most comfortable and, most importantly, the sample type must be the same within a study. If SGOT or fibrinogen is an essential analyte to the study, we recommend EDTA-plasma. If MMP-3 is an essential analyte, we recommend serum or citrate-plasma. In general, however, no one sample type is better than another. With consistent sample collection, preparation, storage and shipping, Rules-Based Medicine can deliver rugged and robust biomarker patterns that can expedite your research and development program.