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"Exhaustive expansion: A novel technique for analyzing complex data generated by higher-order polychromatic flow cytometry experiments," presents an exciting way to study complex flow cytometry data, across multiple donors and multiple timepoints.  The technique simultaneously supports both hypothesis-based approaches and hypothesis-generating approaches.

"Multiparameter phospho-flow analysis of lymphocytes in early rheumatoid arthritis," published in PLOSOne, discusses levels of 15 phospho-proteins across 3 cell lineages (CD3+CD4+, CD3+CD8+, and CD20+) in patients with rheumatoid arthritis.  It's a nice presentation of a "big data" study, with data from 77 donors.  There are some interesting findings about  leflunomide, systemic steroids and anti-TNF therapy reducing the levels of the phospho-proteins.

"An analyical workflow for investigating cytokine profiles," by Siebert et al., was published in the conference proceedings issue of Cytometry Part A. Ms. Siebert also presented this paper at the 2008 ISAC Congress.  Using an iterative process of population-level analysis followed by donor-level analysis, the authors identified findings that were not immediately obvious with standard analytical techniques.

In August of 2007, "Functional T cell responses to tumor antigens in breast cancer patients have a distinct phenotype and cytokine signature," by Inokuma et al. appeared in the Journal of Immunology. Ms. Siebert is a coauthor on this paper. She helped the other members of the team with some data normalization techniques that supported statistical confirmation of differences between responses to tumor associated antigens and viruses such as CMV and Flu.

All of these works build on "A rich analytical environment for flow cytometry experimental results," which  presents advantages of combining the cytometer's output with clinical data and aliquot handling information. Parsed fcs files are loaded into a relational database, where the analyst can query based on donor characteristics such as species, gender, diet type; or aliquot data such as stain type.

References

  1. Siebert JC, Walker EB. Monitoring cytokine profiles during immunotherapy. Immunotherapy 2010;2(6):799-816.
  2. Siebert JC, Wang L, Haley DP, Romer A, Zheng B, Munsil W, Gregory KW, Walker EB. Exhaustive expansion: A novel technique for analyzing complex data generated by higher-order polychromatic flow cytometry experiments. J Transl Med 2010;8:106.
  3. O'Donoghue LE, Ptitsyn AA, Kamstock DA, Siebert J, Thomas RS, Duval DL. Expression profiling in canine osteosarcoma: identification of biomarkers and pathways associated with outcome. BMC Cancer 2010;10:506.
  4. Baker PR, Baschal EE, Fain PR, Triolo TM, Nanduri P, Siebert JC, Armstrong TK, Babu SR, Rewers MJ, Gottlieb PA, Barker JM, Eisenbarth GS. Haplotype analysis discriminates genetic risk for DR3-associated endocrine autoimmunity and helps define extreme risk for Addison's disease. J. Clin. Endocrinol. Metab 2010 Oct;95(10):E263-270.
  5. Galligan CL, Siebert JC, Siminovitch KA, Keystone EC, Bykerk V, Perez OD, Fish EN. Multiparameter phospho-flow analysis of lymphocytes in early rheumatoid arthritis: implications for diagnosis and monitoring drug therapy. PLoS ONE 2009;4(8):e6703.
  6. Siebert JC, Inokuma M, Waid DM, Pennock ND, Vaitaitis GM, Disis ML, Dunne JF, Wagner DH, Maecker HT. An analytical workflow for investigating cytokine profiles. Cytometry A 2008 Apr;73(4):289-298.
  7. Inokuma M, dela Rosa C, Schmitt C, Haaland P, Siebert J, Petry D, Tang M, Suni MA, Ghanekar SA, Gladding D, Dunne JF, Maino VC, Disis ML, Maecker HT. Functional T cell responses to tumor antigens in breast cancer patients have a distinct phenotype and cytokine signature. J. Immunol. 2007 Aug;179(4):2627-2633.
  8. Siebert J, Cios KJ, Newell MK. A rich analytical environment for flow cytometry experimental results. Int. J. Bioinform. Res. Appl. 2006;2(1):52-62.
  9. Siebert J. Improving performance of data analysis in data warehouses: a methodology and case study.  In: Proceedings of the 1999 ACM SIGMETRICS international conference on Measurement and modeling of computer systems.  Atlanta, Georgia, United States: ACM; 1999 p. 234-235.

 

 
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