dc.creator | Vittoraki A.G., Fylaktou A., Tarassi K., Tsinaris Z., Petasis G.C., Gerogiannis D., Kheav V.-D., Carmagnat M., Lehmann C., Doxiadis I., Iniotaki A.G., Theodorou I. | en |
dc.date.accessioned | 2023-01-31T11:36:53Z | |
dc.date.available | 2023-01-31T11:36:53Z | |
dc.date.issued | 2020 | |
dc.identifier | 10.3389/fimmu.2020.01667 | |
dc.identifier.issn | 16643224 | |
dc.identifier.uri | http://hdl.handle.net/11615/80633 | |
dc.description.abstract | Allele specific antibody response against the polymorphic system of HLA is the allogeneic response marker determining the immunological risk for graft acceptance before and after organ transplantation and therefore routinely studied during the patient's workup. Experimentally, bead bound antigen- antibody reactions are detected using a special multicolor flow cytometer (Luminex). Routinely for each sample, antibody responses against 96 different HLA antigen groups are measured simultaneously and a 96-dimensional immune response vector is created. Under a common experimental protocol, using unsupervised clustering algorithms, we analyzed these immune intensity vectors of anti HLA class II responses from a dataset of 1,748 patients before or after renal transplantation residing in a single country. Each patient contributes only one serum sample in the analysis. A population view of linear correlations of hierarchically ordered fluorescence intensities reveals patterns in human immune responses with striking similarities with the previously described CREGs but also brings new information on the antigenic properties of class II HLA molecules. The same analysis affirms that “public” anti-DP antigenic responses are not correlated to anti DR and anti DQ responses which tend to cluster together. Principal Component Analysis (PCA) projections also demonstrate ordering patterns clearly differentiating anti DP responses from anti DR and DQ on several orthogonal planes. We conclude that a computer vision of human alloresponse by use of several dimensionality reduction algorithms rediscovers proven patterns of immune reactivity without any a priori assumption and might prove helpful for a more accurate definition of public immunogenic antigenic structures of HLA molecules. Furthermore, the use of Eigen decomposition on the Immune Response generates new hypotheses that may guide the design of more effective patient monitoring tests. © Copyright © 2020 Vittoraki, Fylaktou, Tarassi, Tsinaris, Petasis, Gerogiannis, Kheav, Carmagnat, Lehmann, Doxiadis, Iniotaki and Theodorou. | en |
dc.language.iso | en | en |
dc.source | Frontiers in Immunology | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089349174&doi=10.3389%2ffimmu.2020.01667&partnerID=40&md5=17646c3c6e72f4c2e108319c8330c9b7 | |
dc.subject | adolescent | en |
dc.subject | algorithm | en |
dc.subject | antibody response | en |
dc.subject | Article | en |
dc.subject | CD8+ T lymphocyte | en |
dc.subject | cluster analysis | en |
dc.subject | enzyme linked immunosorbent assay | en |
dc.subject | female | en |
dc.subject | follow up | en |
dc.subject | human | en |
dc.subject | immune response | en |
dc.subject | immunofluorescence test | en |
dc.subject | immunology | en |
dc.subject | immunoreactivity | en |
dc.subject | immunosuppressive treatment | en |
dc.subject | kidney transplantation | en |
dc.subject | machine learning | en |
dc.subject | major clinical study | en |
dc.subject | male | en |
dc.subject | organ transplantation | en |
dc.subject | principal component analysis | en |
dc.subject | risk factor | en |
dc.subject | vaccination | en |
dc.subject | adult | en |
dc.subject | adverse event | en |
dc.subject | automated pattern recognition | en |
dc.subject | blood | en |
dc.subject | flow cytometry | en |
dc.subject | graft rejection | en |
dc.subject | graft survival | en |
dc.subject | histocompatibility | en |
dc.subject | histocompatibility test | en |
dc.subject | immunology | en |
dc.subject | kidney transplantation | en |
dc.subject | middle aged | en |
dc.subject | treatment outcome | en |
dc.subject | alloantibody | en |
dc.subject | alloantigen | en |
dc.subject | HLA antigen | en |
dc.subject | immunosuppressive agent | en |
dc.subject | Adult | en |
dc.subject | Cluster Analysis | en |
dc.subject | Female | en |
dc.subject | Flow Cytometry | en |
dc.subject | Graft Rejection | en |
dc.subject | Graft Survival | en |
dc.subject | Histocompatibility | en |
dc.subject | Histocompatibility Testing | en |
dc.subject | HLA Antigens | en |
dc.subject | Humans | en |
dc.subject | Immunosuppressive Agents | en |
dc.subject | Isoantibodies | en |
dc.subject | Isoantigens | en |
dc.subject | Kidney Transplantation | en |
dc.subject | Machine Learning | en |
dc.subject | Male | en |
dc.subject | Middle Aged | en |
dc.subject | Pattern Recognition, Automated | en |
dc.subject | Principal Component Analysis | en |
dc.subject | Treatment Outcome | en |
dc.subject | Frontiers Media S.A. | en |
dc.title | Patterns of 1,748 Unique Human Alloimmune Responses Seen by Simple Machine Learning Algorithms | en |
dc.type | journalArticle | en |