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Multidimensional Clusters of CD4+ T Cell Dysfunction Are Primarily Associated with the CD4/CD8 Ratio in Chronic HIV Infection.

Frederiksen J, Buggert M, Noyan K, Nowak P, Sönnerborg A, Lund O, Karlsson AC - PLoS ONE (2015)

Bottom Line: HIV infection provokes a myriad of pathological effects on the immune system where many markers of CD4+ T cell dysfunction have been identified.In order to reduce the subjectivity of FLOCK, we developed an "artificial reference", using 2% of all CD4+ gated T cells from each of the HIV-infected individuals.Principle component analyses demonstrated that using an artificial reference lead to a better separation of the HIV-infected individuals from the healthy controls as compared to using a single HIV-infected subject as a reference or analyzing data manually.

View Article: PubMed Central - PubMed

Affiliation: Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark.

ABSTRACT
HIV infection provokes a myriad of pathological effects on the immune system where many markers of CD4+ T cell dysfunction have been identified. However, most studies to date have focused on single/double measurements of immune dysfunction, while the identification of pathological CD4+ T cell clusters that is highly associated to a specific biomarker for HIV disease remain less studied. Here, multi-parametric flow cytometry was used to investigate immune activation, exhaustion, and senescence of diverse maturation phenotypes of CD4+ T cells. The traditional method of manual data analysis was compared to a multidimensional clustering tool, FLOw Clustering with K (FLOCK) in two cohorts of 47 untreated HIV-infected individuals and 21 age and sex matched healthy controls. In order to reduce the subjectivity of FLOCK, we developed an "artificial reference", using 2% of all CD4+ gated T cells from each of the HIV-infected individuals. Principle component analyses demonstrated that using an artificial reference lead to a better separation of the HIV-infected individuals from the healthy controls as compared to using a single HIV-infected subject as a reference or analyzing data manually. Multiple correlation analyses between laboratory parameters and pathological CD4+ clusters revealed that the CD4/CD8 ratio was the preeminent surrogate marker of CD4+ T cells dysfunction using all three methods. Increased frequencies of an early-differentiated CD4+ T cell cluster with high CD38, HLA-DR and PD-1 expression were best correlated (Rho = -0.80, P value = 1.96×10-11) with HIV disease progression as measured by the CD4/CD8 ratio. The novel approach described here can be used to identify cell clusters that distinguish healthy from HIV infected subjects and is biologically relevant for HIV disease progression. These results further emphasize that a simple measurement of the CD4/CD8 ratio is a useful biomarker for assessment of combined CD4+ T cell dysfunction in chronic HIV disease.

No MeSH data available.


Related in: MedlinePlus

Clustering of HIV-infected and -uninfected subjects with manual and FLOCK gating principles.The top panel shows the heat map representation of the matrices containing the cell population frequencies of the manual gating results (A), the FLOCK results using one HIV infected subject that identified biologically relevant cell populations (B) and the FLOCK results using an artificial of the HIV-infected subjects as a reference (C). The bottom panel shows the principle component analysis (PCA) was performed on the matrices illustrated in A-C to investigate whether there were difference between the control, HIV-infected and AIDS subjects. The results of the PCA performed on the manually determined population frequencies is shown in (D), the results of the K-S test that compared the HIV infected individuals to the healthy controls for PC1 (P value = 0.0009, D value = 0.495) and PC2 (P value = 0.3, D value = 0.236) are shown below the biplot. The FLOCK data using one HIV infected subject that identified biologically relevant cell populations is shown in (E), the results of the K-S test that compared the HIV infected individuals to the healthy controls for PC1 (P value = 0.04, D = 0.353) and PC2 (P value = 0.02, D value = 0.378) are shown below the biplot. The FLOCK data using an artificial of the HIV-infected subjects as a reference is shown in (F), the results of the K-S test that compared the HIV infected individuals to the healthy controls for PC1 (P value = 0.02, D value = 0.384) and PC2 (P value = 0.0008, D value = 0.497) are shown below the biplot. A detailed overview of the FLOCK populations in (B) and (C) can be seen in S1 Table.
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pone.0137635.g002: Clustering of HIV-infected and -uninfected subjects with manual and FLOCK gating principles.The top panel shows the heat map representation of the matrices containing the cell population frequencies of the manual gating results (A), the FLOCK results using one HIV infected subject that identified biologically relevant cell populations (B) and the FLOCK results using an artificial of the HIV-infected subjects as a reference (C). The bottom panel shows the principle component analysis (PCA) was performed on the matrices illustrated in A-C to investigate whether there were difference between the control, HIV-infected and AIDS subjects. The results of the PCA performed on the manually determined population frequencies is shown in (D), the results of the K-S test that compared the HIV infected individuals to the healthy controls for PC1 (P value = 0.0009, D value = 0.495) and PC2 (P value = 0.3, D value = 0.236) are shown below the biplot. The FLOCK data using one HIV infected subject that identified biologically relevant cell populations is shown in (E), the results of the K-S test that compared the HIV infected individuals to the healthy controls for PC1 (P value = 0.04, D = 0.353) and PC2 (P value = 0.02, D value = 0.378) are shown below the biplot. The FLOCK data using an artificial of the HIV-infected subjects as a reference is shown in (F), the results of the K-S test that compared the HIV infected individuals to the healthy controls for PC1 (P value = 0.02, D value = 0.384) and PC2 (P value = 0.0008, D value = 0.497) are shown below the biplot. A detailed overview of the FLOCK populations in (B) and (C) can be seen in S1 Table.

Mentions: Unsupervised hierarchical clustering in conjunction with heat maps, a method for the visualization of numeric matrices to find patterns in data in an unbiased fashion, was used to analyze the three data matrices containing the cluster frequency for row (individual) i and column (cluster) j. A visualization of the results of the manual and FLOCK FCM data analysis can be seen in Fig 2A–2C. As can visually be noted, the hierarchical clustering of the manual data analysis results failed to separate the HIV-infected individuals and the healthy controls (Fig 2A) as good as sFLOCK (Fig 2B) and aFLOCK (Fig 2C). Notably, the AIDS patients, and an additional HIV-infected subject, were distinguished as outliers in all three methods. It was the same HIV-infected subject, with a low CD4 count and CD4/CD8 ratio comparable to some of the AIDS patients that clustered with the AIDS patients in the FLOCK analyses.


Multidimensional Clusters of CD4+ T Cell Dysfunction Are Primarily Associated with the CD4/CD8 Ratio in Chronic HIV Infection.

Frederiksen J, Buggert M, Noyan K, Nowak P, Sönnerborg A, Lund O, Karlsson AC - PLoS ONE (2015)

Clustering of HIV-infected and -uninfected subjects with manual and FLOCK gating principles.The top panel shows the heat map representation of the matrices containing the cell population frequencies of the manual gating results (A), the FLOCK results using one HIV infected subject that identified biologically relevant cell populations (B) and the FLOCK results using an artificial of the HIV-infected subjects as a reference (C). The bottom panel shows the principle component analysis (PCA) was performed on the matrices illustrated in A-C to investigate whether there were difference between the control, HIV-infected and AIDS subjects. The results of the PCA performed on the manually determined population frequencies is shown in (D), the results of the K-S test that compared the HIV infected individuals to the healthy controls for PC1 (P value = 0.0009, D value = 0.495) and PC2 (P value = 0.3, D value = 0.236) are shown below the biplot. The FLOCK data using one HIV infected subject that identified biologically relevant cell populations is shown in (E), the results of the K-S test that compared the HIV infected individuals to the healthy controls for PC1 (P value = 0.04, D = 0.353) and PC2 (P value = 0.02, D value = 0.378) are shown below the biplot. The FLOCK data using an artificial of the HIV-infected subjects as a reference is shown in (F), the results of the K-S test that compared the HIV infected individuals to the healthy controls for PC1 (P value = 0.02, D value = 0.384) and PC2 (P value = 0.0008, D value = 0.497) are shown below the biplot. A detailed overview of the FLOCK populations in (B) and (C) can be seen in S1 Table.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4581870&req=5

pone.0137635.g002: Clustering of HIV-infected and -uninfected subjects with manual and FLOCK gating principles.The top panel shows the heat map representation of the matrices containing the cell population frequencies of the manual gating results (A), the FLOCK results using one HIV infected subject that identified biologically relevant cell populations (B) and the FLOCK results using an artificial of the HIV-infected subjects as a reference (C). The bottom panel shows the principle component analysis (PCA) was performed on the matrices illustrated in A-C to investigate whether there were difference between the control, HIV-infected and AIDS subjects. The results of the PCA performed on the manually determined population frequencies is shown in (D), the results of the K-S test that compared the HIV infected individuals to the healthy controls for PC1 (P value = 0.0009, D value = 0.495) and PC2 (P value = 0.3, D value = 0.236) are shown below the biplot. The FLOCK data using one HIV infected subject that identified biologically relevant cell populations is shown in (E), the results of the K-S test that compared the HIV infected individuals to the healthy controls for PC1 (P value = 0.04, D = 0.353) and PC2 (P value = 0.02, D value = 0.378) are shown below the biplot. The FLOCK data using an artificial of the HIV-infected subjects as a reference is shown in (F), the results of the K-S test that compared the HIV infected individuals to the healthy controls for PC1 (P value = 0.02, D value = 0.384) and PC2 (P value = 0.0008, D value = 0.497) are shown below the biplot. A detailed overview of the FLOCK populations in (B) and (C) can be seen in S1 Table.
Mentions: Unsupervised hierarchical clustering in conjunction with heat maps, a method for the visualization of numeric matrices to find patterns in data in an unbiased fashion, was used to analyze the three data matrices containing the cluster frequency for row (individual) i and column (cluster) j. A visualization of the results of the manual and FLOCK FCM data analysis can be seen in Fig 2A–2C. As can visually be noted, the hierarchical clustering of the manual data analysis results failed to separate the HIV-infected individuals and the healthy controls (Fig 2A) as good as sFLOCK (Fig 2B) and aFLOCK (Fig 2C). Notably, the AIDS patients, and an additional HIV-infected subject, were distinguished as outliers in all three methods. It was the same HIV-infected subject, with a low CD4 count and CD4/CD8 ratio comparable to some of the AIDS patients that clustered with the AIDS patients in the FLOCK analyses.

Bottom Line: HIV infection provokes a myriad of pathological effects on the immune system where many markers of CD4+ T cell dysfunction have been identified.In order to reduce the subjectivity of FLOCK, we developed an "artificial reference", using 2% of all CD4+ gated T cells from each of the HIV-infected individuals.Principle component analyses demonstrated that using an artificial reference lead to a better separation of the HIV-infected individuals from the healthy controls as compared to using a single HIV-infected subject as a reference or analyzing data manually.

View Article: PubMed Central - PubMed

Affiliation: Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark.

ABSTRACT
HIV infection provokes a myriad of pathological effects on the immune system where many markers of CD4+ T cell dysfunction have been identified. However, most studies to date have focused on single/double measurements of immune dysfunction, while the identification of pathological CD4+ T cell clusters that is highly associated to a specific biomarker for HIV disease remain less studied. Here, multi-parametric flow cytometry was used to investigate immune activation, exhaustion, and senescence of diverse maturation phenotypes of CD4+ T cells. The traditional method of manual data analysis was compared to a multidimensional clustering tool, FLOw Clustering with K (FLOCK) in two cohorts of 47 untreated HIV-infected individuals and 21 age and sex matched healthy controls. In order to reduce the subjectivity of FLOCK, we developed an "artificial reference", using 2% of all CD4+ gated T cells from each of the HIV-infected individuals. Principle component analyses demonstrated that using an artificial reference lead to a better separation of the HIV-infected individuals from the healthy controls as compared to using a single HIV-infected subject as a reference or analyzing data manually. Multiple correlation analyses between laboratory parameters and pathological CD4+ clusters revealed that the CD4/CD8 ratio was the preeminent surrogate marker of CD4+ T cells dysfunction using all three methods. Increased frequencies of an early-differentiated CD4+ T cell cluster with high CD38, HLA-DR and PD-1 expression were best correlated (Rho = -0.80, P value = 1.96×10-11) with HIV disease progression as measured by the CD4/CD8 ratio. The novel approach described here can be used to identify cell clusters that distinguish healthy from HIV infected subjects and is biologically relevant for HIV disease progression. These results further emphasize that a simple measurement of the CD4/CD8 ratio is a useful biomarker for assessment of combined CD4+ T cell dysfunction in chronic HIV disease.

No MeSH data available.


Related in: MedlinePlus