The test parameter value was then compared with the sorted control values and attributed the corresponding centile (0C1), with further adjustment based on interpolation between the two neighboring control values

The test parameter value was then compared with the sorted control values and attributed the corresponding centile (0C1), with further adjustment based on interpolation between the two neighboring control values. memory subpopulation gating demonstrating CD45RA over-expression in an individual bearing the variant PTPRC G77 allele (bottom) compared to an individual with the wild type allele (top). Gating on CD4 (left) or CD8 T cells (right). Image_3.TIF (5.0M) GUID:?51F1675A-7886-41BE-9A8C-8465AC10B005 Supplementary Figure 4: Coefficients of NG25 Variation (CV) for all 54 FCM parameters. Values above 30% were considered to show significant imprecision and are shaded. Image_4.TIFF (1.5M) GUID:?D60CAC44-1795-4D32-8D3A-DE01F1AFF825 Supplementary Table 1: Details of the PID patients in the four cohorts analyzed in the cited figures. Data_Sheet_1.PDF (47K) GUID:?3901EB57-5FEB-46D8-8D61-778C9BDFA91D Supplementary Table 2: Reagents used for staining cells for flow cytometry, for the four separate panels. Data_Sheet_2.PDF (78K) GUID:?6BCB3A89-07D8-49EF-99F0-AABC11B6D168 Supplementary Table 3: Raw percentages and derived centiles for each of the FCM parameters from subjects whose corresponding heatmaps are presented in Figures 4C6 (Cent. = centiles). Data_Sheet_3.PDF (59K) GUID:?DBDB4382-9377-4C9C-8D3A-647DB7F57CC0 Data Availability StatementAll datasets generated for this study are included in the manuscript and/or the Supplementary Files. Abstract Genetic primary immunodeficiency diseases are increasingly recognized, with pathogenic mutations changing the composition of circulating leukocyte subsets measured by flow cytometry (FCM). Discerning changes in multiple subpopulations is challenging, and subtle trends might be missed if traditional reference ranges derived from a control population are applied. We developed an algorithm where centiles were allocated using non-parametric comparison to controls, generating multiparameter heat maps to simultaneously represent all leukocyte subpopulations for inspection of trends within a cohort or segregation with a putative genetic mutation. To illustrate this method, we analyzed patients with Primary Antibody Deficiency (PAD) and kindreds harboring mutations in (encoding TACI), haploinsufficiency itself (expansion of plasmablasts, activated CD4+ T cells, regulatory T cells, and X5-Th cells) from its clinical expression (B-cell depletion), and those that were associated with gain-of-function mutation (decreased CD8+ T effector memory cells, B cells, CD21-lo B cells, B-SM cells, and NK cells). Co-efficients of variation exceeded 30% for 36/54 FCM parameters, but by comparing inter-assay variation with disease-related variation, we ranked each parameter in terms of laboratory precision vs. disease variability, NG25 identifying X5-Th cells (and derivatives), na?ve, activated, and central memory CD8+ T cells, transitional B cells, memory and SM-B cells, plasmablasts, activated CD4 cells, and total T cells as the 10 most useful cellular parameters. Applying these to cluster analysis of our PAD cohort, we could detect subgroups with the potential to reflect underlying genotypes. Heat mapping of normalized FCM data reveals cellular trends missed by standard reference ranges, identifies changes associating with a phenotype or genotype, and could inform hypotheses regarding pathogenesis of genetic immunodeficiency. = 77) and PAD patients for X5-Th and Tfh-effector cells. Details of PAD patients presented in Supplementary Table 2. Open in a separate window Figure 6 Analysis of cellular parameters in a CARD11 mutant kindred. (A) Heat mapping with discontinuous shading showing changes in cell populations for two unrelated patients with dominant negative CARD11 mutations, along with their relative(s) without mutation. Boxes highlight cellular changes common to the two CARD11 mutants, but differing from the family members. (B) Scatter plots showing raw values for populations identified in (A), along with representative FCM contour plots of these critical parameters (C); in the B-cell contour plot, black boxes and numbers refer to total CD19+ cells, and red boxes and numbers refer to CD19+/CD27+ memory B cells. Raw and centile data is presented in Supplementary Table 3. Written informed consent was obtained as part of the Australian Point Mutation in Systemic Lupus Erythematosus study (APOSLE), the Centre for Personalized Immunology (CPI) program, the Healthy Blood Donors register and the Hematology Research Tissue Bank (The Canberra Hospital, Canberra, Australia). This study was carried out in accordance with the recommendations of the cell activation (5, 6). Although memory cell formation and function is not affected (5), T cells of individuals carrying this allele do not downregulate CD45RA. Since many subjects in our cohort had had genetic sequencing, we were able to identify seven individuals carrying the allele who had also undergone immunophenotyping. Consistent with previous reports, we observed that all CD4+ and CD8+ T cells NG25 in these individuals expressed high levels of CD45RA (Supplementary Figure 3). The effect was more pronounced on CD8+ cells than CD4+ T Mela cells, however a true CD45RAC population was not evident in either subset of T cell. Interestingly, we did not identify any CD45RA over-expressors in our healthy controls. Description of Normalization Algorithm/Software Spreadsheet data for all FCM cellular populations from the study population were compared with the comparable data generated.