Multiplex microsphere immunoassays for the detection of IgM and IgG to arboviral diseases
Serodiagnosis of arthropod-borne viruses (arboviruses) at the Division of Vector-Borne Diseases, CDC, employs a combination of individual enzyme-linked immunosorbent assays and microsphere immunoassays (MIAs) to test for IgM and IgG, followed by confirmatory plaque-reduction neutralization tests. Ba...
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| description | Serodiagnosis of arthropod-borne viruses (arboviruses) at the Division of Vector-Borne Diseases, CDC, employs a combination of individual enzyme-linked immunosorbent assays and microsphere immunoassays (MIAs) to test for IgM and IgG, followed by confirmatory plaque-reduction neutralization tests. Based upon the geographic origin of a sample, it may be tested concurrently for multiple arboviruses, which can be a cumbersome task. The advent of multiplexing represents an opportunity to streamline these types of assays; however, because serologic cross-reactivity of the arboviral antigens often confounds results, it is of interest to employ data analysis methods that address this issue. Here, we constructed 13-virus multiplexed IgM and IgG MIAs that included internal and external controls, based upon the Luminex platform. Results from samples tested using these methods were analyzed using 8 different statistical schemes to identify the best way to classify the data. Geographic batteries were also devised to serve as a more practical diagnostic format, and further samples were tested using the abbreviated multiplexes. Comparative error rates for the classification schemes identified a specific boosting method based on logistic regression "Logitboost" as the classification method of choice. When the data from all samples tested were combined into one set, error rates from the multiplex IgM and IgG MIAs were |
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Based upon the geographic origin of a sample, it may be tested concurrently for multiple arboviruses, which can be a cumbersome task. The advent of multiplexing represents an opportunity to streamline these types of assays; however, because serologic cross-reactivity of the arboviral antigens often confounds results, it is of interest to employ data analysis methods that address this issue. Here, we constructed 13-virus multiplexed IgM and IgG MIAs that included internal and external controls, based upon the Luminex platform. Results from samples tested using these methods were analyzed using 8 different statistical schemes to identify the best way to classify the data. Geographic batteries were also devised to serve as a more practical diagnostic format, and further samples were tested using the abbreviated multiplexes. Comparative error rates for the classification schemes identified a specific boosting method based on logistic regression "Logitboost" as the classification method of choice. When the data from all samples tested were combined into one set, error rates from the multiplex IgM and IgG MIAs were <5% for all geographic batteries. This work represents both the most comprehensive, validated multiplexing method for arboviruses to date, and also the most systematic attempt to determine the most useful classification method for use with these types of serologic tests.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0075670</identifier><identifier>PMID: 24086608</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Antibodies, Viral - blood ; Antibodies, Viral - immunology ; Antibodies, Viral - metabolism ; Antigens ; Antigens, Viral - immunology ; Arbovirus Infections - blood ; Arbovirus Infections - immunology ; Arbovirus Infections - metabolism ; Arboviruses - immunology ; Batteries ; Classification ; Cross-reactivity ; Data analysis ; Data processing ; Diagnostic systems ; Disease control ; Disease prevention ; Division ; Encephalitis ; Enzymes ; Glycoproteins ; Human subjects ; Humans ; Identification methods ; Immunization ; Immunoassay ; Immunoassay - methods ; Immunoassays ; Immunoglobulin G ; Immunoglobulin G - blood ; Immunoglobulin G - immunology ; Immunoglobulin G - metabolism ; Immunoglobulin M ; Immunoglobulin M - blood ; Immunoglobulin M - immunology ; Immunoglobulin M - metabolism ; Immunoglobulins ; Infections ; Medical laboratories ; Methods ; Microspheres ; Multiplexing ; Neutralization ; Regression analysis ; Serologic Tests - methods ; Statistical analysis ; Statistical methods ; Tropical diseases ; Vector-borne diseases ; Vectors (Biology) ; Virology ; Viruses ; West Nile virus ; Zoonoses</subject><ispartof>PloS one, 2013-09, Vol.8 (9), p.e75670-e75670</ispartof><rights>2013. This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. 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Based upon the geographic origin of a sample, it may be tested concurrently for multiple arboviruses, which can be a cumbersome task. The advent of multiplexing represents an opportunity to streamline these types of assays; however, because serologic cross-reactivity of the arboviral antigens often confounds results, it is of interest to employ data analysis methods that address this issue. Here, we constructed 13-virus multiplexed IgM and IgG MIAs that included internal and external controls, based upon the Luminex platform. Results from samples tested using these methods were analyzed using 8 different statistical schemes to identify the best way to classify the data. Geographic batteries were also devised to serve as a more practical diagnostic format, and further samples were tested using the abbreviated multiplexes. Comparative error rates for the classification schemes identified a specific boosting method based on logistic regression "Logitboost" as the classification method of choice. When the data from all samples tested were combined into one set, error rates from the multiplex IgM and IgG MIAs were <5% for all geographic batteries. 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Based upon the geographic origin of a sample, it may be tested concurrently for multiple arboviruses, which can be a cumbersome task. The advent of multiplexing represents an opportunity to streamline these types of assays; however, because serologic cross-reactivity of the arboviral antigens often confounds results, it is of interest to employ data analysis methods that address this issue. Here, we constructed 13-virus multiplexed IgM and IgG MIAs that included internal and external controls, based upon the Luminex platform. Results from samples tested using these methods were analyzed using 8 different statistical schemes to identify the best way to classify the data. Geographic batteries were also devised to serve as a more practical diagnostic format, and further samples were tested using the abbreviated multiplexes. Comparative error rates for the classification schemes identified a specific boosting method based on logistic regression "Logitboost" as the classification method of choice. When the data from all samples tested were combined into one set, error rates from the multiplex IgM and IgG MIAs were <5% for all geographic batteries. This work represents both the most comprehensive, validated multiplexing method for arboviruses to date, and also the most systematic attempt to determine the most useful classification method for use with these types of serologic tests.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>24086608</pmid><doi>10.1371/journal.pone.0075670</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2013-09, Vol.8 (9), p.e75670-e75670 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_1436781436 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Free Full-Text Journals in Chemistry; Public Library of Science (PLoS) |
| subjects | Antibodies, Viral - blood Antibodies, Viral - immunology Antibodies, Viral - metabolism Antigens Antigens, Viral - immunology Arbovirus Infections - blood Arbovirus Infections - immunology Arbovirus Infections - metabolism Arboviruses - immunology Batteries Classification Cross-reactivity Data analysis Data processing Diagnostic systems Disease control Disease prevention Division Encephalitis Enzymes Glycoproteins Human subjects Humans Identification methods Immunization Immunoassay Immunoassay - methods Immunoassays Immunoglobulin G Immunoglobulin G - blood Immunoglobulin G - immunology Immunoglobulin G - metabolism Immunoglobulin M Immunoglobulin M - blood Immunoglobulin M - immunology Immunoglobulin M - metabolism Immunoglobulins Infections Medical laboratories Methods Microspheres Multiplexing Neutralization Regression analysis Serologic Tests - methods Statistical analysis Statistical methods Tropical diseases Vector-borne diseases Vectors (Biology) Virology Viruses West Nile virus Zoonoses |
| title | Multiplex microsphere immunoassays for the detection of IgM and IgG to arboviral diseases |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-14T02%3A05%3A47IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Multiplex%20microsphere%20immunoassays%20for%20the%20detection%20of%20IgM%20and%20IgG%20to%20arboviral%20diseases&rft.jtitle=PloS%20one&rft.au=Basile,%20Alison%20J&rft.date=2013-09-25&rft.volume=8&rft.issue=9&rft.spage=e75670&rft.epage=e75670&rft.pages=e75670-e75670&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0075670&rft_dat=%3Cproquest_plos_%3E1443385056%3C/proquest_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1436781436&rft_id=info:pmid/24086608&rft_doaj_id=oai_doaj_org_article_69fd9e4dfee642c2ac786c2b6ffd4279&rfr_iscdi=true |