Another strength is its single center design that allows uniform desensitization and immunosuppression protocols and sample processing, prospective and consistent HLA antibody monitoring, and long-term and careful patient follow-up. placement of the line of separation. G = gene expression; Pheno = CyTOF phenotyping.(TIF) pone.0153355.s003.tif (385K) GUID:?FC243131-C4F9-4A86-A369-1BAE97815C3C S1 Table: CyTOF Antibody Immunophenotyping Panel. (DOCX) pone.0153355.s004.docx (98K) GUID:?5BDBD750-4DEA-4109-BDB0-E98DE932D106 S2 Table: CyTOF Cell Subsets and Gating Pathway. (DOCX) pone.0153355.s005.docx (153K) GUID:?763CB282-F58C-4C6A-92CE-66F699FF61C0 Data Availability StatementData are available at https://immport.niaid.nih.gov, under SDY788. Abstract Background Kidney transplantation is the most effective treatment for end-stage kidney disease. Sensitization, the formation of human leukocyte antigen (HLA) antibodies, remains a major barrier to successful kidney transplantation. Despite the FGF3 implementation of desensitization strategies, many candidates fail to respond. Current progress is hindered by the lack of biomarkers to predict response and to guide therapy. Our objective was to determine whether differences in immune and gene profiles may help identify which candidates will respond to desensitization therapy. Methods and Findings Single-cell mass cytometry by time-of-flight (CyTOF) phenotyping, gene arrays, and phosphoepitope flow cytometry were performed in a study of 20 highly sensitized kidney transplant candidates undergoing desensitization therapy. Responders to desensitization therapy were defined as 5% or greater decrease in cumulative calculated panel reactive antibody (cPRA) levels, and nonresponders had 0% decrease in cPRA. Using a decision tree analysis, we found that a combination of transitional B cell and regulatory T cell (Treg) frequencies at baseline before initiation of desensitization therapy could distinguish responders from non-responders. Using a support vector AZD3988 machine (SVM) and longitudinal data, transcripts and HLA-DR-CD38+CD4+ T cells could also distinguish responders from non-responders. Combining all assays in a multivariate analysis and elastic net regression model with 72 analytes, we identified seven that were highly interrelated and eleven that predicted response to desensitization therapy. Conclusions Measuring baseline and longitudinal immune and gene profiles could provide a useful strategy to distinguish responders from non-responders to desensitization therapy. This study presents the integration of novel translational studies including CyTOF immunophenotyping in a multivariate analysis model that has potential applications to predict response to desensitization, select AZD3988 candidates, and personalize medicine to ultimately improve overall outcomes in highly sensitized kidney transplant candidates. Introduction Kidney transplantation is the most effective treatment for end-stage kidney disease (ESRD) in terms of mortality, quality of life, and health care savings [1]. Sensitization, the formation of human leukocyte antigen (HLA) antibodies against AZD3988 a transplant, remains a major barrier to successful kidney transplantation. HLA antibodies are acquired through exposure to foreign HLA antigens, most commonly from previous transplants, pregnancies, and transfusions. After implementation of the new kidney allocation system one year ago, the number of transplants increased six-fold from 2C3% transplantation rate for the highly sensitized patients with cumulative calculated panel reactive antibody (cPRA) 99C100% (http://optn.transplant.hrsa.gov). However, the majority of highly sensitized patients fail to find a compatible donor and remain on dialysis. Desensitization strategies that utilize medications to nonspecifically target both HLA antibodies and underlying immune cells have allowed successful transplantation in only a relatively small proportion of highly sensitized candidates. One limitation of desensitization therapy is that a significant number AZD3988 of candidates do not respond. Current progress is hindered by lack of in-depth immune monitoring strategies that can predict which candidates respond to therapy and can guide tailored desensitization strategies based on individual immune profiles. Furthermore, detailed mechanisms of how desensitization therapy modulates AZD3988 specific immune cell subpopulations and intracellular signaling pathways are poorly understood. Our objective was to determine whether baseline differences in immune profiles could help identify those candidates that will respond to desensitization therapy. We report the application of single-cell mass cytometry time-of-flight (CyTOF) phenotyping, gene arrays, and phosphoepitope flow cytometry to study immune and gene expression profiles in a cohort of highly sensitized candidates undergoing desensitization therapy. The CyTOF platform, which uses antibodies labeled with heavy metal isotopes, allows the ability to simultaneously measure numerous parameters per cell at one time [2]. In this study, we used.