Non-parametric Heat Map Representation of Flow Cytometry Data: Identifying Cellular Changes Associated With Genetic Immunodeficiency Disorders
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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-09-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fimmu.2019.02134/full |
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| author | Julia I. Ellyard Julia I. Ellyard Robert Tunningley Robert Tunningley Ayla May Lorenzo Ayla May Lorenzo Simon H. Jiang Simon H. Jiang Simon H. Jiang Amelia Cook Amelia Cook Rochna Chand Rochna Chand Rochna Chand Dipti Talaulikar Ann-Maree Hatch Anastasia Wilson Carola G. Vinuesa Carola G. Vinuesa Matthew C. Cook Matthew C. Cook Matthew C. Cook David A. Fulcher David A. Fulcher |
| author_facet | Julia I. Ellyard Julia I. Ellyard Robert Tunningley Robert Tunningley Ayla May Lorenzo Ayla May Lorenzo Simon H. Jiang Simon H. Jiang Simon H. Jiang Amelia Cook Amelia Cook Rochna Chand Rochna Chand Rochna Chand Dipti Talaulikar Ann-Maree Hatch Anastasia Wilson Carola G. Vinuesa Carola G. Vinuesa Matthew C. Cook Matthew C. Cook Matthew C. Cook David A. Fulcher David A. Fulcher |
| author_sort | Julia I. Ellyard |
| collection | DOAJ |
| description | 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 TNFRSF13B (encoding TACI), CTLA4, and CARD11. In PAD, loss of switched memory B cells (B-SM) was readily demonstrated, but as a continuous, not dichotomous, variable. Expansion of CXCR5+/CD45RA- CD4+ T cells (X5-Th cells) was a prominent feature in PAD, particularly in TACI mutants, and patients with expansion in CD21-lo B cells or transitional B cells were readily apparent. We observed differences between unaffected and affected TACI mutants (increased B cells and CD8+ T-effector memory cells, loss of B-SM cells and non-classical monocytes), cellular signatures that distinguished CTLA4 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 CARD11 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, identifying X5-Th cells (and derivatives), naïve, 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. |
| first_indexed | 2024-04-12T19:18:57Z |
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| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-04-12T19:18:57Z |
| publishDate | 2019-09-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Immunology |
| spelling | doaj.art-0b6ff66eb1ef4b62bb850289385bfead2022-12-22T03:19:39ZengFrontiers Media S.A.Frontiers in Immunology1664-32242019-09-011010.3389/fimmu.2019.02134460172Non-parametric Heat Map Representation of Flow Cytometry Data: Identifying Cellular Changes Associated With Genetic Immunodeficiency DisordersJulia I. Ellyard0Julia I. Ellyard1Robert Tunningley2Robert Tunningley3Ayla May Lorenzo4Ayla May Lorenzo5Simon H. Jiang6Simon H. Jiang7Simon H. Jiang8Amelia Cook9Amelia Cook10Rochna Chand11Rochna Chand12Rochna Chand13Dipti Talaulikar14Ann-Maree Hatch15Anastasia Wilson16Carola G. Vinuesa17Carola G. Vinuesa18Matthew C. Cook19Matthew C. Cook20Matthew C. Cook21David A. Fulcher22David A. Fulcher23Department of Immunology and Infectious Diseases, Australian National University, Canberra, ACT, AustraliaCentre for Personalised Immunology, John Curtin School of Medical Research, Australian National University, Canberra, ACT, AustraliaDepartment of Immunology and Infectious Diseases, Australian National University, Canberra, ACT, AustraliaCentre for Personalised Immunology, John Curtin School of Medical Research, Australian National University, Canberra, ACT, AustraliaDepartment of Immunology and Infectious Diseases, Australian National University, Canberra, ACT, AustraliaCentre for Personalised Immunology, John Curtin School of Medical Research, Australian National University, Canberra, ACT, AustraliaDepartment of Immunology and Infectious Diseases, Australian National University, Canberra, ACT, AustraliaCentre for Personalised Immunology, John Curtin School of Medical Research, Australian National University, Canberra, ACT, AustraliaDepartment of Nephrology, The Canberra Hospital, Canberra, ACT, AustraliaDepartment of Immunology and Infectious Diseases, Australian National University, Canberra, ACT, AustraliaCentre for Personalised Immunology, John Curtin School of Medical Research, Australian National University, Canberra, ACT, AustraliaDepartment of Immunology and Infectious Diseases, Australian National University, Canberra, ACT, AustraliaCentre for Personalised Immunology, John Curtin School of Medical Research, Australian National University, Canberra, ACT, AustraliaDepartment of Immunology, The Canberra Hospital, Canberra, ACT, AustraliaDepartment of Hematology, The Canberra Hospital, Canberra, ACT, AustraliaCentre for Personalised Immunology, John Curtin School of Medical Research, Australian National University, Canberra, ACT, AustraliaCentre for Personalised Immunology, John Curtin School of Medical Research, Australian National University, Canberra, ACT, AustraliaDepartment of Immunology and Infectious Diseases, Australian National University, Canberra, ACT, AustraliaCentre for Personalised Immunology, John Curtin School of Medical Research, Australian National University, Canberra, ACT, AustraliaDepartment of Immunology and Infectious Diseases, Australian National University, Canberra, ACT, AustraliaCentre for Personalised Immunology, John Curtin School of Medical Research, Australian National University, Canberra, ACT, AustraliaDepartment of Immunology, The Canberra Hospital, Canberra, ACT, AustraliaDepartment of Immunology and Infectious Diseases, Australian National University, Canberra, ACT, AustraliaCentre for Personalised Immunology, John Curtin School of Medical Research, Australian National University, Canberra, ACT, AustraliaGenetic 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 TNFRSF13B (encoding TACI), CTLA4, and CARD11. In PAD, loss of switched memory B cells (B-SM) was readily demonstrated, but as a continuous, not dichotomous, variable. Expansion of CXCR5+/CD45RA- CD4+ T cells (X5-Th cells) was a prominent feature in PAD, particularly in TACI mutants, and patients with expansion in CD21-lo B cells or transitional B cells were readily apparent. We observed differences between unaffected and affected TACI mutants (increased B cells and CD8+ T-effector memory cells, loss of B-SM cells and non-classical monocytes), cellular signatures that distinguished CTLA4 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 CARD11 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, identifying X5-Th cells (and derivatives), naïve, 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.https://www.frontiersin.org/article/10.3389/fimmu.2019.02134/fullflow cytometryimmunodeficiencycommon variable immunodeficiencyTACICTLA4TNFSF13B |
| spellingShingle | Julia I. Ellyard Julia I. Ellyard Robert Tunningley Robert Tunningley Ayla May Lorenzo Ayla May Lorenzo Simon H. Jiang Simon H. Jiang Simon H. Jiang Amelia Cook Amelia Cook Rochna Chand Rochna Chand Rochna Chand Dipti Talaulikar Ann-Maree Hatch Anastasia Wilson Carola G. Vinuesa Carola G. Vinuesa Matthew C. Cook Matthew C. Cook Matthew C. Cook David A. Fulcher David A. Fulcher Non-parametric Heat Map Representation of Flow Cytometry Data: Identifying Cellular Changes Associated With Genetic Immunodeficiency Disorders Frontiers in Immunology flow cytometry immunodeficiency common variable immunodeficiency TACI CTLA4 TNFSF13B |
| title | Non-parametric Heat Map Representation of Flow Cytometry Data: Identifying Cellular Changes Associated With Genetic Immunodeficiency Disorders |
| title_full | Non-parametric Heat Map Representation of Flow Cytometry Data: Identifying Cellular Changes Associated With Genetic Immunodeficiency Disorders |
| title_fullStr | Non-parametric Heat Map Representation of Flow Cytometry Data: Identifying Cellular Changes Associated With Genetic Immunodeficiency Disorders |
| title_full_unstemmed | Non-parametric Heat Map Representation of Flow Cytometry Data: Identifying Cellular Changes Associated With Genetic Immunodeficiency Disorders |
| title_short | Non-parametric Heat Map Representation of Flow Cytometry Data: Identifying Cellular Changes Associated With Genetic Immunodeficiency Disorders |
| title_sort | non parametric heat map representation of flow cytometry data identifying cellular changes associated with genetic immunodeficiency disorders |
| topic | flow cytometry immunodeficiency common variable immunodeficiency TACI CTLA4 TNFSF13B |
| url | https://www.frontiersin.org/article/10.3389/fimmu.2019.02134/full |
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