Comparison of serum, EDTA plasma and P100 plasma for luminex-based biomarker multiplex assays in patients with chronic obstructive pulmonary disease in the SPIROMICS study

Wanda K O'Neal, Wayne Anderson, Patricia V Basta, Elizabeth E Carretta, Claire M Doerschuk, R Graham Barr, Eugene R Bleecker, Stephanie A Christenson, Jeffrey L Curtis, Meilan K Han, Nadia N Hansel, Richard E Kanner, Eric C Kleerup, Fernando J Martinez, Bruce E Miller, Stephen P Peters, Stephen I Rennard, Mary Beth Scholand, Ruth Tal-Singer, Prescott G Woodruff, David J Couper, Sonia M Davis, SPIROMICS Investigators, Wanda K O'Neal, Wayne Anderson, Patricia V Basta, Elizabeth E Carretta, Claire M Doerschuk, R Graham Barr, Eugene R Bleecker, Stephanie A Christenson, Jeffrey L Curtis, Meilan K Han, Nadia N Hansel, Richard E Kanner, Eric C Kleerup, Fernando J Martinez, Bruce E Miller, Stephen P Peters, Stephen I Rennard, Mary Beth Scholand, Ruth Tal-Singer, Prescott G Woodruff, David J Couper, Sonia M Davis, SPIROMICS Investigators

Abstract

Background: As a part of the longitudinal Chronic Obstructive Pulmonary Disease (COPD) study, Subpopulations and Intermediate Outcome Measures in COPD study (SPIROMICS), blood samples are being collected from 3200 subjects with the goal of identifying blood biomarkers for sub-phenotyping patients and predicting disease progression. To determine the most reliable sample type for measuring specific blood analytes in the cohort, a pilot study was performed from a subset of 24 subjects comparing serum, Ethylenediaminetetraacetic acid (EDTA) plasma, and EDTA plasma with proteinase inhibitors (P100).

Methods: 105 analytes, chosen for potential relevance to COPD, arranged in 12 multiplex and one simplex platform (Myriad-RBM) were evaluated in duplicate from the three sample types from 24 subjects. The reliability coefficient and the coefficient of variation (CV) were calculated. The performance of each analyte and mean analyte levels were evaluated across sample types.

Results: 20% of analytes were not consistently detectable in any sample type. Higher reliability and/or smaller CV were determined for 12 analytes in EDTA plasma compared to serum, and for 11 analytes in serum compared to EDTA plasma. While reliability measures were similar for EDTA plasma and P100 plasma for a majority of analytes, CV was modestly increased in P100 plasma for eight analytes. Each analyte within a multiplex produced independent measurement characteristics, complicating selection of sample type for individual multiplexes.

Conclusions: There were notable detectability and measurability differences between serum and plasma. Multiplexing may not be ideal if large reliability differences exist across analytes measured within the multiplex, especially if values differ based on sample type. For some analytes, the large CV should be considered during experimental design, and the use of duplicate and/or triplicate samples may be necessary. These results should prove useful for studies evaluating selection of samples for evaluation of potential blood biomarkers.

Figures

Figure 1
Figure 1
Scatterplots of coefficient of variation (CV) and reliability score for all consistently detectible analytes for 24 subjects. Coefficient of variation (CV; left panel) and reliability (right panel) are plotted as shown for either serum and EDTA plasma (top row) or P100 and EDTA plasma (bottom row). Outliers in these figures with either CV > 20% or reliability < 0.60 are discussed further in Table 1.
Figure 2
Figure 2
Plots indicating analytes with notable differences in CV or reliability between serum and EDTA plasma for 24 subjects. A) The ratio of CV (serum:EDTA plasma) is plotted in rank order from largest to smallest by analyte. Analytes with notably better CV in EDTA plasma (ratio >1.5; lower CV in plasma) and notably better CV in serum (CV ratio <0.667; lower CV in serum) are indicated B) The difference in reliability score between serum and plasma (serum minus plasma) is is plotted in rank order from largest to smallest by analyte. Analytes with notably better reliability in serum versus plasma (difference > +0.15) and better reliability in EDTA plasma versus serum (difference < −0.015) are indicated. Horizontal lines indicate descriptive cut-points used to define notable performance.
Figure 3
Figure 3
Plot indicating analytes with notable differences in mean levels between serum and EDTA plasma for 24 subjects. The ratio of measured mean analyte levels (serum:EDTA plasma) is plotted in rank order from largest to smallest by analyte. Analytes with notably higher levels measured in serum (ratio >1.5) and EDTA plasma (< 0.667) are indicated. Horizontal lines indicate descriptive cut-points used to define notable differences.

References

    1. Couper D, LaVange LM, Han M, Barr RG, Bleecker E, Hoffman EA, Kanner R, Kleerup E, Martinez FJ, Woodruff PG, Rennard S, for the SPIROMICS Research Group. Design of the Subpopulations and Intermediate Outcomes in COPD Study (SPIROMICS) Thorax. 2013. 10.1136/thoraxjnl-2013-203897. Epub ahead of print.
    1. Biancotto A, Feng X, Langweiler M, Young NS, McCoy JP. Effect of anticoagulants on multiplexed measurement of cytokine/chemokines in healthy subjects. Cytokine. 2012;60:438–446. doi: 10.1016/j.cyto.2012.05.019.
    1. Yu Z, Kastenmuller G, He Y, Belcredi P, Moller G, Prehn C, Mendes J, Wahl S, Roemisch-Margl W, Ceglarek U, Polonikov A, Dahmen N, Prokisch H, Xie L, Li Y, Wichmann HE, Peters A, Kronenberg F, Suhre K, Adamski J, Illig T, Wang-Sattler R. Differences between human plasma and serum metabolite profiles. PLoS ONE. 2011;6:e21230. doi: 10.1371/journal.pone.0021230.
    1. Rai AJ, Gelfand CA, Haywood BC, Warunek DJ, Yi J, Schuchard MD, Mehigh RJ, Cockrill SL, Scott GB, Tammen H, Schulz-Knappe P, Speicher DW, Vitzthum F, Haab BB, Siest G, Chan DW. HUPO Plasma Proteome Project specimen collection and handling: towards the standardization of parameters for plasma proteome samples. Proteomics. 2005;5:3262–3277. doi: 10.1002/pmic.200401245.
    1. Banks RE, Stanley AJ, Cairns DA, Barrett JH, Clarke P, Thompson D, Selby PJ. Influences of blood sample processing on low-molecular-weight proteome identified by surface-enhanced laser desorption/ionization mass spectrometry. Clin Chem. 2005;51:1637–1649. doi: 10.1373/clinchem.2005.051417.
    1. Golanski J, Pietrucha T, Baj Z, Greger J, Watala C. Molecular insights into the anticoagulant-induced spontaneous activation of platelets in whole blood-various anticoagulants are not equal. Thromb Res. 1996;83:199–216. doi: 10.1016/0049-3848(96)00129-6.
    1. De JW, Bourcier K, Rijkers GT, Prakken BJ, Seyfert-Margolis V. Prerequisites for cytokine measurements in clinical trials with multiplex immunoassays. BMC Immunol. 2009;10:52. doi: 10.1186/1471-2172-10-52.
    1. Mannello F. Serum or plasma samples? The "Cinderella" role of blood collection procedures: preanalytical methodological issues influence the release and activity of circulating matrix metalloproteinases and their tissue inhibitors, hampering diagnostic trueness and leading to misinterpretation. Arterioscler Thromb Vasc Biol. 2008;28:611–614. doi: 10.1161/ATVBAHA.107.159608.
    1. Fu Q, Zhu J, Van Eyk JE. Comparison of multiplex immunoassay platforms. Clin Chem. 2010;56:314–318. doi: 10.1373/clinchem.2009.135087.
    1. Lee JW, Devanarayan V, Barrett YC, Weiner R, Allinson J, Fountain S, Keller S, Weinryb I, Green M, Duan L, Rogers JA, Millham R, O'Brien PJ, Sailstad J, Khan M, Ray C, Wagner JA. Fit-for-purpose method development and validation for successful biomarker measurement. Pharm Res. 2006;23:312–328. doi: 10.1007/s11095-005-9045-3.
    1. Ricos C, Cava F, Garcia-Lario JV, Hernandez A, Iglesias N, Jimenez CV, Minchinela J, Perich C, Simon M, Domenech MV, Alvarez V. The reference change value: a proposal to interpret laboratory reports in serial testing based on biological variation. Scand J Clin Lab Invest. 2004;64:175–184. doi: 10.1080/00365510410004885.
    1. Mosesson MW. Fibrinogen and fibrin structure and functions. J Thromb Haemost. 2005;3:1894–1904. doi: 10.1111/j.1538-7836.2005.01365.x.
    1. Thrailkill K, Cockrell G, Simpson P, Moreau C, Fowlkes J, Bunn RC. Physiological matrix metalloproteinase (MMP) concentrations: comparison of serum and plasma specimens. Clin Chem Lab Med. 2006;44:503–504.
    1. Aziz N, Nishanian P, Mitsuyasu R, Detels R, Fahey JL. Variables that affect assays for plasma cytokines and soluble activation markers. Clin Diagn Lab Immunol. 1999;6:89–95.
    1. Booth NA, Simpson AJ, Croll A, Bennett B, MacGregor IR. Plasminogen activator inhibitor (PAI-1) in plasma and platelets. Br J Haematol. 1988;70:327–333. doi: 10.1111/j.1365-2141.1988.tb02490.x.
    1. Jelkmann W. Pitfalls in the measurement of circulating vascular endothelial growth factor. Clin Chem. 2001;47:617–623.
    1. Macey M, Azam U, McCarthy D, Webb L, Chapman ES, Okrongly D, Zelmanovic D, Newland A. Evaluation of the anticoagulants EDTA and citrate, theophylline, adenosine, and dipyridamole (CTAD) for assessing platelet activation on the ADVIA 120 hematology system. Clin Chem. 2002;48:891–899.
    1. Aguilar-Mahecha A, Kuzyk MA, Domanski D, Borchers CH, Basik M. The effect of pre-analytical variability on the measurement of MRM-MS-based mid- to high-abundance plasma protein biomarkers and a panel of cytokines. PLoS ONE. 2012;7:e38290. doi: 10.1371/journal.pone.0038290.
    1. Randall SA, McKay MJ, Baker MS, Molloy MP. Evaluation of blood collection tubes using selected reaction monitoring MS: implications for proteomic biomarker studies. Proteomics. 2010;10:2050–2056. doi: 10.1002/pmic.200900517.
    1. Wildes D, Wells JA. Sampling the N-terminal proteome of human blood. Proc Natl Acad Sci USA. 2010;107:4561–4566. doi: 10.1073/pnas.0914495107.
    1. Tammen H, Schulte I, Hess R, Menzel C, Kellmann M, Mohring T, Schulz-Knappe P. Peptidomic analysis of human blood specimens: comparison between plasma specimens and serum by differential peptide display. Proteomics. 2005;5:3414–3422. doi: 10.1002/pmic.200401219.
    1. Yi J, Kim C, Gelfand CA. Inhibition of intrinsic proteolytic activities moderates preanalytical variability and instability of human plasma. J Proteome Res. 2007;6:1768–1781. doi: 10.1021/pr060550h.
    1. Thomsen M, Ingebrigtsen TS, Marott JL, Dahl M, Lange P, Vestbo J, Nordestgaard BG. Inflammatory biomarkers and exacerbations in chronic obstructive pulmonary disease. JAMA. 2013;309:2353–2361. doi: 10.1001/jama.2013.5732.
    1. Celli BR, Locantore N, Yates J, Tal-Singer R, Miller BE, Bakke P, Calverley P, Coxson H, Crim C, Edwards LD, Lomas DA, Duvoix A, MacNee W, Rennard S, Silverman E, Vestbo J, Wouters E, Agusti A. Inflammatory biomarkers improve clinical prediction of mortality in chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2012;185:1065–1072. doi: 10.1164/rccm.201110-1792OC.
    1. Gosselink JV, Hayashi S, Elliott WM, Xing L, Chan B, Yang L, Wright C, Sin D, Pare PD, Pierce JA, Pierce RA, Patterson A, Cooper J, Hogg JC. Differential expression of tissue repair genes in the pathogenesis of chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2010;181:1329–1335. doi: 10.1164/rccm.200812-1902OC.

Source: PubMed

3
订阅