to BioTechniques free email alert service to receive content updates.
Immune systems biology: immunoprofiling of cells and molecules
 
Aaron B. Kantor, Susan E. Alters, Karen Cheal, and Louis J. Dietz
Full Text (PDF)





We applied our immunophenotyping platform to the pharmacodynamic profiling of oral prednisone, a common glucocorticoid steroid. Glucocorticoids, both inhaled and systemic, have broad-spectrum anti-inflammatory and immunosuppressive effects and have long been used in the treatment of asthma and other diseases. However, they are associated with both subjective and objective side effects that limit their usefulness. A more comprehensive understanding of their pharmacodynamics is desirable. Eighty subjects were enrolled in a single-blind, placebo-controlled, randomized parallel study to evaluate the effects of prednisone on cellular and soluble bioanalytical parameters after 3 days of twice-a-day treatment with 20 mg of prednisone or placebo per dose. Blood samples were analyzed before and after a 3-day treatment.

In this study, a panel of 64 three-color cellular assays and 67 immunoassays for soluble proteins was used. The cellular assays are arranged in two disposable capillary arrays and allow the identification and enumeration of hundreds of different cell types and cell-associated molecules that are relevant to immune, inflammatory, and metabolic processes. Each reagent cocktail typically contains one or two antibodies to the major cell populations—neutrophils, eosinophils, monocytes, T cells, B cells, and natural killer (NK) cells, plus one or two antibodies to subset antigens, which may indicate the functional state, activation state, or adhesion characteristics of the major cell population. Soluble proteins are measured with sandwich ELISA using matched antibody pairs and chemiluminescent detection. Analytes include immunoglobulin isotypes, cytokines and cytokine receptors, chemokines, acute phase proteins, soluble adhesion molecules, matrix metalloproteinases (MMPs), and their inhibitors.

We observed many significant changes post-prednisone treatment, but not post-placebo treatment. Paired Student's t-tests and nonparametric tests were used to evaluate the changes as appropriate for each variable. A conservative statistical procedure, the step-down Bonferroni method of Holm, was used to protect against false positive errors and to calculate an adjusted P value for all comparisons. Paired tests yielded 199 significant changes with prednisone, but only one for placebo, allowing a high degree of confidence in the results. Differences were observed using all aspects of the bioanalytical platform, cell counts, cell surface antigen levels, and soluble factors.

A distinct pattern of changes is observed with the prednisone group. Figure 2 shows a representative data set, including 120 of the 713 variables measured. Variables are sorted based on the degree of change observed with the drug, and significant changes are indicted with gray shading in the sidebar. Significant changes are observed in cell populations. For example, circulating neutrophils, neutrophil subsets, B cells, and monocytes are increased, whereas circulating eosinophils and CD4 T cell subsets are decreased. There are also significant changes in levels of cell surface expression. For example, human leukocyte antigen (HLA) class II is decreased on both B cells and monocytes. Significant changes are also observed in soluble factors, with MMP-3 (stromelysin) increased by 5-fold.





In summary, the SurroScan system is a breakthrough technology platform that enables immunoprofiling of small quantities of whole blood for drug response and disease indicators in a format that can analyze hundreds of parameters at one time. This expands by more than an order of magnitude methods currently in routine use and could have a major impact of patient diagnosis and management. The SurroScan technology is well suited to be part of an integrated bioanalytical platform for clinical research and to facilitate comprehensive differential phenotyping of patient samples for the discovery of biomarkers. This platform is complementary to and can be easily combined with other bioanalytical technologies such as proteomic and metabolomic profiling with mass spectrometry (19), gene expression arrays (20), and protein expression arrays (21,22).

Acknowledgments

We thank Harini Govindarajan, Brent Reynolds, and Chad Minks for processing clinical samples and completing cellular assays; Brad Brown, Natalia Tsekhonovskaya, and Remy Cromer for completing immunoassays; Chris Todd, Alex Puski, and Ian Walton for setting-up and maintaining SurroScan instruments; Pierre Hyun and Naomi Freiman for informatics support; and Andrea Perrone for clinical operations. We thank Dr. Alan Heller, Christy Schroeder, Dr. Theodore Chu, Kristin Chelslew, and Dr. Harold Guy for providing subject samples for the study. We thank Michael Derks, Randy Batenhorst, and Jim Snapper for contributions to the clinical protocol. We thank Mark Davis and Howard Schulman for thoughtful comments on the manuscript and Ron Krietemeyer for assistance with the figures. This work is supported, in part, by National Institute of Standards and Technology Advanced Technology Program (NIST ATP) award no. 70NANB0H3000.

References
1.) Shapiro, H.M. 2003. Practical Flow Cytometry. John Wiley and Sons, Hoboken, NJ.

2.) Groner, W., and E. Simson. 1995. Practical Guide to Modern Hematology Analyzers. John Wiley and Sons, Chichester.

3.) Tibbe, A.G., B.G. de Grooth, J. Greve, P.A. Liberti, G.J. Dolan, and L.W. Terstappen. 1999. Optical tracking and detection of immunomagnetically selected and aligned cells. Nat. Biotechnol. 17:1210-1213.

4.) De Rosa, S.C., J.M. Brenchley, and M. Roederer. 2003. Beyond six colors: a new era in flow cytometry. Nat. Med. 9:112-117.

5.) Darzynkiewicz, Z., E. Bedner, X. Li, W. Gorczyca, and M.R. Melamed. 1999. Laser-scanning cytometry: a new instrumentation with many applications. Exp. Cell Res. 249:1-12.

6.) Dietz, L.J., R.S. Dubrow, B.S. Manian, and N.L. Sizto. 1996. Volumetric capillary cytometry: a new method for absolute cell enumeration. Cytometry 23:177-186.

7.) Walton, I.D., L.J. Dietz, G. Frenzel, J. Chen, J. Winkler, S. Norton, and A.B. Kantor. 2000.Microvolume laser scanning cytometry platform for biological marker discovery. Proc.SPIE-Int.Soc.Opt.Eng. IBOS Society of Photo-Optical Instrumentation Engineers.:192-201.

8.) Mujumdar, R.B., L.A. Ernst, S.R. Mujumdar, C.J. Lewis, and A.S. Waggoner. 1993. Cyanine dye labeling reagents: sulfoindocyanine succinimidyl esters. Bioconjug. Chem. 4:105-111.

9.) Roederer, M., A.B. Kantor, D.R. Parks, and L.A. Herzenberg. 1996. Cy7PE and Cy7APC: bright new probes for immunofluorescence. Cytometry 24:191-197.

10.) Beavis, A.J., and K.J. Pennline. 1996. Allo-7: a new fluorescent tandem dye for use in flow cytometry. Cytometry 24:390-395.

11.) Holm, S. 1979. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6:65-70.

12.) Blair, R.C., J.F. Troendle, and R.W. Beck. 1996. Control of familywise errors in multiple endpoint assessments via stepwise permutation tests. Stat. Med. 15:1107-1121.

13.) Panchuk-Voloshina, N., R.P. Haugland, J. Bishop-Stewart, M.K. Bhalgat, P.J. Millard, F. Mao, and W.Y. Leung. 1999. Alexa dyes, a series of new fluorescent dyes that yield exceptionally bright, photostable conjugates. J. Histochem. Cytochem. 47:1179-1188.

14.) Berlier, J.E., A. Rothe, G. Buller, J. Bradford, D.R. Gray, B.J. Filanoski, W.G. Telford, S. Yue. 2003. Quantitative comparison of long-wavelength Alexa Fluor dyes to Cy dyes: fluorescence of the dyes and their bioconjugates. J. Histochem. Cytochem. 51:1699-1712.

15.) Zuck, P., Z. Lao, S. Skwish, J.F. Glickman, K. Yang, J. Burbaum, and J. Inglese. 1999. Ligand-receptor binding measured by laser-scanning imaging. Proc. Natl. Acad. Sci. USA 96:11122-11127.

16.) Martens, C., A. Bakker, A. Rodriguez, R.B. Mortensen, and R.W. Barrett. 1999. A generic particle-based nonradioactive homogeneous multiplex method for high-throughput screening using microvolume fluorimetry. Anal. Biochem. 273:20-31.

17.) Swartzman, E.E., S.J. Miraglia, J. Mellentin-Michelotti, L. Evangelista, and P.M. Yuan. 1999. A homogeneous and multiplexed immunoassay for high-throughput screening using fluorometric microvolume assay technology. Anal. Biochem. 271:143-151.

18.) Kamentsky, L.A. 2001. Laser scanning cytometry. Methods Cell Biol. 63:51-87.

19.) Aebersold, R., and M. Mann. 2003. Mass spectrometry-based proteomics. Nature 422:198-207.

20.) Chee, M., R. Yang, E. Hubbell, A. Berno, X.C. Huang, D. Stern, J. Winkler, D.J. Lockhart. 1996. Accessing genetic information with high-density DNA arrays. Science 274:610-614.

21.) Robinson, W.H., C. DiGennaro, W. Hueber, B.B. Haab, M. Kamachi, E.J. Dean, S. Fournel, D. Fong. 2002. Autoantigen microarrays for multiplex characterization of autoantibody responses. Nat. Med. 8:295-301.

22.) MacBeath, G., and S.L. Schreiber. 2000. Printing proteins as microarrays for high-throughput function determination. Science 289:1760-1763.

23.) Norton, S.M., J. Winkler, and L.J. Dietz. 2000.Cell enumeration and characterization in microvolume laser scanning cytometry: a multicolor image processing package. Proc.SPIE-Int.Soc.Opt.Eng. IBOS Society of Photo-Optical Instrumentation Engineers.:20-30.

  1    2    3