Cytometry Part B: Clinical CytometryVolume 80B, Issue 5 p. 269-270 Issue HighlightsFree Access Issue highlights—Clinical Cytometry (Cytometry part B) September 2011 First published: 17 August 2011 https://doi.org/10.1002/cyto.b.20615AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat DATA ANALYSIS Over the last decade, the complexity of flow cytometry data has been dramatically increased due to both availability of a significantly broader range of immunofluorescence dyes and ready availability of more complex instruments (≥3 lasers) to take advantage of this broader range of reporters. This has resulted in many clinical laboratories now routinely performing 8–10 parameter (6 to 8 fluorescence, 2 scattered light) analyses with even higher dimensionality data frequently being done in translational and basic research settings. Assessing the complex inter-relationship between these parameters is at best cumbersome when looking in the current traditional way two parameters at a time in bivariate histograms. Additional correlation between parameters is certainly gained using “gating” techniques; however, to explore all the possible associations in these complex data sets would be virtually impossible using these approaches. In addition, the associations thus found are frequently driven by preconceived notions of parameter associations and many correlations, some certainly biologically relevant, will be missed. Thus, the now prevalent, standard generation of data sets with high numbers of correlated parameters accentuates the need for more complex data analysis tools. Excitingly, in this issue, two articles explore novel application of complex data analysis techniques to clinically relevant studies. Sekiguchi et al. (this issue, page 291) applied agglomerative clustering algorithms to the analysis of peripheral blood lymphocyte subsets in a series of normal individuals. While demonstrating the potential power and utility of semiautomated, objective analysis techniques to flow cytometry immunophenotypic data, these studies highlight the narrow variation in subsets within an individual over time while documenting the differences between individuals, each having a characteristic, clustering profile compared with others. As noted by the authors, recognition of this may be quite important as monitoring of immune subsets in response to treatment is becoming standard of practice in many instances and points to the need for establishing individual baseline data for this purpose. Finn et al. (this issue, page 282) describe application of new analysis techniques based on information geometry to the analysis of myelodysplastic syndromes (MDSs) that are an evolution of earlier work by this group also reported in this Journal (1, 2). As detailed in the current article, these techniques provide an objective analysis that yielded similar conclusions to other studies of the antigen expression differences in MDS compared with normal granulopoiesis as well as the overlap between low grade MDS or dysplastic granulopoiesis and benign granulopoiesis or reactive left shifts. This is particularly important in light of the current interest in flow cytometric analysis of MDS (3, 4) and the current primarily subjective, nonstandardized, analysis approaches. ZAP-70 ZAP-70, as a highly relevant prognostic marker in chronic lymphocytic leukemia (CLL), has been of high interest over the last several years as was reflected in devotion of a special issue, “ZAP-70 in CLL: toward standardization of a biomarker for patient management,” on this topic in 2006 in Clinical Cytometry. Additional Clinical Cytometry articles on comparison of analysis techniques (5) with mutational status and on the biological relevance of ZAP-70 expression (6) have followed up to this special issue. Two companion articles by Degheidy et al. in this issue continue the theme of optimization of this important clinical assay looking at a methodological comparison of two anti-ZAP-70 antibodies (this issue, page 300) and the performance, or improvement, of the ZAP-70 assay using these two antibody clones and multiple methods of analysis (this issue, page 309). These articles are significant as a standardized, widely accepted, and highly reproducible methodology to promote the clinical utility and application of this assay has failed to materialize at this point. ANALYSIS OF CEREBROSPINAL FLUID One last highlight from this issue is a comprehensive review of flow cytometric analysis of cerebrospinal fluid (CSF) samples by de Graaf et al. (this issue, page 271) highlighting the potential utility of these assays in what has been a controversial and technically challenging arena. Also, of high relevance and correlation to this review, is a recent article from this same group (7) initiating the task of establishing normal WBC ranges in CSF to which readers interested in this topic are directed. LITERATURE CITED 1 Habiv LK, Finn WG. Unsupervised immunophenotypic profiling of chronic lymphocytic leukemia. Cytometry B Clin Cytom 2006; 70B: 124– 135. 2 Finn WG, Carter KM, Raich R, Stoolman, LM, Hero AO. Analysis of clinical flow cytometric immunophenotyping data by clustering on statistical manifolds: Treating flow cytometry data as high-dimensional objects. Cytometry B Clin Cytom 2009; 76B: 1– 7. 3 Matarraz S, López A, Barrena S, Fernandez C, Jensen E, Flores-Montero J, Rasillo A, Sayagues JM, Sánchez ML, Bárcena P, Hernandez-Rivas JM, Salvador C, Fernandez-Mosteirín N, Giralt M, Perdiguer L, Laranjeira P, Paiva A, Orfao A. Bone marrow cells from myelodysplastic syndromes show altered immunophenotypic profiles that may contribute to the diagnosis and prognostic stratification of the disease: A pilot study on a series of 56 patients. Cytometry B Clin Cytom 2010; 78B: 154– 168. 4 Della Porta MG, Lanza F, Del Vecchio L, for the Italian Society of Cytometry (GIC). Flow cytometric immunophenotyping for the evaluation of bone marrow dysplasia. Cytometry B Clin Cytom 2011; 80B: 201– 210. 5 Kern W, Dicker F, Schnittger S, Haferlach C, Haferlach T. Correlation of flow cytometrically determined expression of ZAP-70 using the SBZAP antibody with IgVH mutation status and cytogenetics in 1,229 patients with chronic lymphocytic leukemia. Cytometry B Clin Cytom 2009; 76B: 385– 393. 6 Kaplan D, Meyerson HJ, Li X, Drasny C, Liu F, Costaldi M, Barr P, Lazarus HM. Correlation between ZAP-70, phosphor-ZAP-70, and phosphor-Syk expression in leukemic cells from patients with CLL. Cytometry B Clin Cytom 2010; 78B: 115– 122. 7 de Graaf MT, Sillevis Smitt PAE, Luitwieler RL, van Velzen C, van den Broek PDM, Kraan J, Gratama JW. Central memory CD4+ T cells dominate the normal cerebrospinal fluid. Cytometry B Clin Cytom 2011; 80B: 43– 50. Volume80B, Issue5September 2011Pages 269-270 ReferencesRelatedInformation