Establishment of normative ranges of the healthy human immune system with comprehensive polychromatic flow cytometry profiling
Existing normative flow cytometry data have several limitations including small sample sizes, incompletely described study populations, variable flow cytometry methodology, and limited depth for defining lymphocyte subpopulations. To overcome these issues, we defined high-dimensional flow cytometry...
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| Published in: | PloS one Vol. 14; no. 12; p. e0225512 |
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| Main Authors: | , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Public Library of Science
11.12.2019
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| ISSN: | 1932-6203, 1932-6203 |
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| Abstract | Existing normative flow cytometry data have several limitations including small sample sizes, incompletely described study populations, variable flow cytometry methodology, and limited depth for defining lymphocyte subpopulations. To overcome these issues, we defined high-dimensional flow cytometry reference ranges for the healthy human immune system using Human Immunology Project Consortium methodologies after carefully screening 127 subjects deemed healthy through clinical and laboratory testing. We enrolled subjects in the following age cohorts: 18-29 years, 30-39, 40-49, and 50-66 and enrolled cohorts to ensure an even gender distribution and at least 30% non-Caucasians. From peripheral blood mononuclear cells, flow cytometry reference ranges were defined for >50 immune subsets including T-cell (activation, maturation, T follicular helper and regulatory T cell), B-cell, and innate cells. We also developed a web tool for visualization of the dataset and download of raw data. This dataset provides the immunology community with a resource to compare and extract data from rigorously characterized healthy subjects across age groups, gender and race. |
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| AbstractList | Existing normative flow cytometry data have several limitations including small sample sizes, incompletely described study populations, variable flow cytometry methodology, and limited depth for defining lymphocyte subpopulations. To overcome these issues, we defined high-dimensional flow cytometry reference ranges for the healthy human immune system using Human Immunology Project Consortium methodologies after carefully screening 127 subjects deemed healthy through clinical and laboratory testing. We enrolled subjects in the following age cohorts: 18-29 years, 30-39, 40-49, and 50-66 and enrolled cohorts to ensure an even gender distribution and at least 30% non-Caucasians. From peripheral blood mononuclear cells, flow cytometry reference ranges were defined for >50 immune subsets including T-cell (activation, maturation, T follicular helper and regulatory T cell), B-cell, and innate cells. We also developed a web tool for visualization of the dataset and download of raw data. This dataset provides the immunology community with a resource to compare and extract data from rigorously characterized healthy subjects across age groups, gender and race. Existing normative flow cytometry data have several limitations including small sample sizes, incompletely described study populations, variable flow cytometry methodology, and limited depth for defining lymphocyte subpopulations. To overcome these issues, we defined high-dimensional flow cytometry reference ranges for the healthy human immune system using Human Immunology Project Consortium methodologies after carefully screening 127 subjects deemed healthy through clinical and laboratory testing. We enrolled subjects in the following age cohorts: 18-29 years, 30-39, 40-49, and 50-66 and enrolled cohorts to ensure an even gender distribution and at least 30% non-Caucasians. From peripheral blood mononuclear cells, flow cytometry reference ranges were defined for >50 immune subsets including T-cell (activation, maturation, T follicular helper and regulatory T cell), B-cell, and innate cells. We also developed a web tool for visualization of the dataset and download of raw data. This dataset provides the immunology community with a resource to compare and extract data from rigorously characterized healthy subjects across age groups, gender and race.Existing normative flow cytometry data have several limitations including small sample sizes, incompletely described study populations, variable flow cytometry methodology, and limited depth for defining lymphocyte subpopulations. To overcome these issues, we defined high-dimensional flow cytometry reference ranges for the healthy human immune system using Human Immunology Project Consortium methodologies after carefully screening 127 subjects deemed healthy through clinical and laboratory testing. We enrolled subjects in the following age cohorts: 18-29 years, 30-39, 40-49, and 50-66 and enrolled cohorts to ensure an even gender distribution and at least 30% non-Caucasians. From peripheral blood mononuclear cells, flow cytometry reference ranges were defined for >50 immune subsets including T-cell (activation, maturation, T follicular helper and regulatory T cell), B-cell, and innate cells. We also developed a web tool for visualization of the dataset and download of raw data. This dataset provides the immunology community with a resource to compare and extract data from rigorously characterized healthy subjects across age groups, gender and race. |
| Audience | Academic |
| Author | Yi, John S. Guptill, Jeffrey T. Russo, Melissa A. Dumbauld, Chelsae Rosa-Bray, Marilyn Zakroysky, Pearl Weinhold, Kent J. Staats, Janet Chan, Cliburn White, Scott Gierman, Todd |
| AuthorAffiliation | 4 Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, United States of America 2 Biomat USA–Grifols Plasma Operations, United States of America 3 Duke Clinical Research Institute, Durham, NC, United States of America Jackson Laboratory, UNITED STATES 5 Department of Neurology, Duke University School of Medicine, Durham, NC, United States of America 1 Department of Surgery, Duke University School of Medicine, Durham, NC, United States of America |
| AuthorAffiliation_xml | – name: Jackson Laboratory, UNITED STATES – name: 2 Biomat USA–Grifols Plasma Operations, United States of America – name: 5 Department of Neurology, Duke University School of Medicine, Durham, NC, United States of America – name: 4 Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, United States of America – name: 1 Department of Surgery, Duke University School of Medicine, Durham, NC, United States of America – name: 3 Duke Clinical Research Institute, Durham, NC, United States of America |
| Author_xml | – sequence: 1 givenname: John S. orcidid: 0000-0001-7777-2437 surname: Yi fullname: Yi, John S. – sequence: 2 givenname: Marilyn orcidid: 0000-0002-2209-5990 surname: Rosa-Bray fullname: Rosa-Bray, Marilyn – sequence: 3 givenname: Janet surname: Staats fullname: Staats, Janet – sequence: 4 givenname: Pearl surname: Zakroysky fullname: Zakroysky, Pearl – sequence: 5 givenname: Cliburn surname: Chan fullname: Chan, Cliburn – sequence: 6 givenname: Melissa A. surname: Russo fullname: Russo, Melissa A. – sequence: 7 givenname: Chelsae surname: Dumbauld fullname: Dumbauld, Chelsae – sequence: 8 givenname: Scott surname: White fullname: White, Scott – sequence: 9 givenname: Todd surname: Gierman fullname: Gierman, Todd – sequence: 10 givenname: Kent J. surname: Weinhold fullname: Weinhold, Kent J. – sequence: 11 givenname: Jeffrey T. orcidid: 0000-0002-0312-0716 surname: Guptill fullname: Guptill, Jeffrey T. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31825961$$D View this record in MEDLINE/PubMed |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: MR-B and TG are employees of Biomat USA – Grifols Plasma Operations. JTG has received honoraria for participation in a Grifols advisory board and educational program unrelated to this project. This does not alter our adherence to PLOS ONE policies on sharing data and materials. |
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