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Informatics-Based Discovery of Disease-Associated Immune Profiles

View Article: PubMed Central - PubMed

ABSTRACT

Advances in flow and mass cytometry are enabling ultra-high resolution immune profiling in mice and humans on an unprecedented scale. However, the resulting high-content datasets challenge traditional views of cytometry data, which are both limited in scope and biased by pre-existing hypotheses. Computational solutions are now emerging (e.g., Citrus, AutoGate, SPADE) that automate cell gating or enable visualization of relative subset abundance within healthy versus diseased mice or humans. Yet these tools require significant computational fluency and fail to show quantitative relationships between discrete immune phenotypes and continuous disease variables. Here we describe a simple informatics platform that uses hierarchical clustering and nearest neighbor algorithms to associate manually gated immune phenotypes with clinical or pre-clinical disease endpoints of interest in a rapid and unbiased manner. Using this approach, we identify discrete immune profiles that correspond with either weight loss or histologic colitis in a T cell transfer model of inflammatory bowel disease (IBD), and show distinct nodes of immune dysregulation in the IBDs, Crohn’s disease and ulcerative colitis. This streamlined informatics approach for cytometry data analysis leverages publicly available software, can be applied to manually or computationally gated cytometry data, is suitable for any clinical or pre-clinical setting, and embraces ultra-high content flow and mass cytometry as a discovery engine.

No MeSH data available.


Related in: MedlinePlus

Informatics-based identification of immune dysregulation in clinical inflammatory bowel diseases.(A) Bottom left, 6-parameter FACS panel used for analyzing expression of surface antigens on peripheral blood mononuclear cells (PBMC) from healthy adult donors and adult IBD patients. Gating strategy for FACS analysis of human PBMC; immune subsets used in downstream analysis are indicated by gates and text. (B) Percentages of major T cell subsets in a healthy control PBMC stock, determined by repeated FACS analysis as in (A), over 10 independent staining experiments. Each subset is quantified based on the percentages within relevant parent gates (as in (A)); coefficients of variation (CVs) are indicated for each subset by color-matched text. (C) Heat map showing hierarchical clustering of 7 disease endpoints and 24 immune phenotypes in healthy adults (n = 26) and IBD patients ((ulcerative colitis (UC), n = 50; Crohn’s disease (CD), n = 53). (D) Rank-ordered (Pearson r) correlation values of all disease endpoints and immune phenotypes relative to diagnosis group (i.e., healthy donors, group 1; UC patients, group 2; CD patients, group 3). Relevant disease endpoints and immune phenotypes are indicated by black and red text, respectively; the correlation of the reference variable with itself (r = 1.0) is shown at top left in grey. (E) Immune cell subsets (CD4+CD25hi–left; CD8+RO- Teff–middle; CD8+ naïve–right) identified by hierarchical clustering and ranked Pearson coefficients (as in (C, D)) perturbed in CD patient PBMC. (F) Immune cell subsets (CD4+ naive–left; CD4+ Teff–right) identified by hierarchical clustering and ranked Pearson coefficients (as in C and S3 File) perturbed in UC PBMC. Red lines indicate median values for each group. * P < .05, ** P < .01, *** P < .001, One-way ANOVA. Teff, effector/memory T cells. Only significant differences between groups are shown.
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pone.0163305.g003: Informatics-based identification of immune dysregulation in clinical inflammatory bowel diseases.(A) Bottom left, 6-parameter FACS panel used for analyzing expression of surface antigens on peripheral blood mononuclear cells (PBMC) from healthy adult donors and adult IBD patients. Gating strategy for FACS analysis of human PBMC; immune subsets used in downstream analysis are indicated by gates and text. (B) Percentages of major T cell subsets in a healthy control PBMC stock, determined by repeated FACS analysis as in (A), over 10 independent staining experiments. Each subset is quantified based on the percentages within relevant parent gates (as in (A)); coefficients of variation (CVs) are indicated for each subset by color-matched text. (C) Heat map showing hierarchical clustering of 7 disease endpoints and 24 immune phenotypes in healthy adults (n = 26) and IBD patients ((ulcerative colitis (UC), n = 50; Crohn’s disease (CD), n = 53). (D) Rank-ordered (Pearson r) correlation values of all disease endpoints and immune phenotypes relative to diagnosis group (i.e., healthy donors, group 1; UC patients, group 2; CD patients, group 3). Relevant disease endpoints and immune phenotypes are indicated by black and red text, respectively; the correlation of the reference variable with itself (r = 1.0) is shown at top left in grey. (E) Immune cell subsets (CD4+CD25hi–left; CD8+RO- Teff–middle; CD8+ naïve–right) identified by hierarchical clustering and ranked Pearson coefficients (as in (C, D)) perturbed in CD patient PBMC. (F) Immune cell subsets (CD4+ naive–left; CD4+ Teff–right) identified by hierarchical clustering and ranked Pearson coefficients (as in C and S3 File) perturbed in UC PBMC. Red lines indicate median values for each group. * P < .05, ** P < .01, *** P < .001, One-way ANOVA. Teff, effector/memory T cells. Only significant differences between groups are shown.

Mentions: To validate this approach in a clinical setting, we performed FACS analysis on frozen PBMCs from healthy adult donors (n = 26), and adult IBD patients (ulcerative colitis (UC), n = 50; Crohn’s disease (CD), n = 53). For proof-of-principle, we assessed a relatively small number (n = 24) of manually gated immune parameters reflecting frequencies of major CD3+ and CD3- lymphocyte subsets, including CD4+ and CD4- (CD8+) CD3+ T cells; CD4+CD25lo Tconv and CD4+CD25hi cells (within CD3+CD4+ T cells); and CCR7hiCD45RO- Tnaive, CCR7loCD45RO+ Teff, and CCR7loCD45RO- Teff cells (within both CD4+ and CD8+ Tconv cells) (Fig 3A). To ensure assay reliability, we performed repeated analyses on a control stock of healthy donor PBMC, run in parallel during each independent experiment on sets of healthy donor and IBD patient samples. Coefficients of variation (CVs) for each major T cell subset ranged between 6–15% (Fig 3B), indicating reliable detection.


Informatics-Based Discovery of Disease-Associated Immune Profiles
Informatics-based identification of immune dysregulation in clinical inflammatory bowel diseases.(A) Bottom left, 6-parameter FACS panel used for analyzing expression of surface antigens on peripheral blood mononuclear cells (PBMC) from healthy adult donors and adult IBD patients. Gating strategy for FACS analysis of human PBMC; immune subsets used in downstream analysis are indicated by gates and text. (B) Percentages of major T cell subsets in a healthy control PBMC stock, determined by repeated FACS analysis as in (A), over 10 independent staining experiments. Each subset is quantified based on the percentages within relevant parent gates (as in (A)); coefficients of variation (CVs) are indicated for each subset by color-matched text. (C) Heat map showing hierarchical clustering of 7 disease endpoints and 24 immune phenotypes in healthy adults (n = 26) and IBD patients ((ulcerative colitis (UC), n = 50; Crohn’s disease (CD), n = 53). (D) Rank-ordered (Pearson r) correlation values of all disease endpoints and immune phenotypes relative to diagnosis group (i.e., healthy donors, group 1; UC patients, group 2; CD patients, group 3). Relevant disease endpoints and immune phenotypes are indicated by black and red text, respectively; the correlation of the reference variable with itself (r = 1.0) is shown at top left in grey. (E) Immune cell subsets (CD4+CD25hi–left; CD8+RO- Teff–middle; CD8+ naïve–right) identified by hierarchical clustering and ranked Pearson coefficients (as in (C, D)) perturbed in CD patient PBMC. (F) Immune cell subsets (CD4+ naive–left; CD4+ Teff–right) identified by hierarchical clustering and ranked Pearson coefficients (as in C and S3 File) perturbed in UC PBMC. Red lines indicate median values for each group. * P < .05, ** P < .01, *** P < .001, One-way ANOVA. Teff, effector/memory T cells. Only significant differences between groups are shown.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC5036861&req=5

pone.0163305.g003: Informatics-based identification of immune dysregulation in clinical inflammatory bowel diseases.(A) Bottom left, 6-parameter FACS panel used for analyzing expression of surface antigens on peripheral blood mononuclear cells (PBMC) from healthy adult donors and adult IBD patients. Gating strategy for FACS analysis of human PBMC; immune subsets used in downstream analysis are indicated by gates and text. (B) Percentages of major T cell subsets in a healthy control PBMC stock, determined by repeated FACS analysis as in (A), over 10 independent staining experiments. Each subset is quantified based on the percentages within relevant parent gates (as in (A)); coefficients of variation (CVs) are indicated for each subset by color-matched text. (C) Heat map showing hierarchical clustering of 7 disease endpoints and 24 immune phenotypes in healthy adults (n = 26) and IBD patients ((ulcerative colitis (UC), n = 50; Crohn’s disease (CD), n = 53). (D) Rank-ordered (Pearson r) correlation values of all disease endpoints and immune phenotypes relative to diagnosis group (i.e., healthy donors, group 1; UC patients, group 2; CD patients, group 3). Relevant disease endpoints and immune phenotypes are indicated by black and red text, respectively; the correlation of the reference variable with itself (r = 1.0) is shown at top left in grey. (E) Immune cell subsets (CD4+CD25hi–left; CD8+RO- Teff–middle; CD8+ naïve–right) identified by hierarchical clustering and ranked Pearson coefficients (as in (C, D)) perturbed in CD patient PBMC. (F) Immune cell subsets (CD4+ naive–left; CD4+ Teff–right) identified by hierarchical clustering and ranked Pearson coefficients (as in C and S3 File) perturbed in UC PBMC. Red lines indicate median values for each group. * P < .05, ** P < .01, *** P < .001, One-way ANOVA. Teff, effector/memory T cells. Only significant differences between groups are shown.
Mentions: To validate this approach in a clinical setting, we performed FACS analysis on frozen PBMCs from healthy adult donors (n = 26), and adult IBD patients (ulcerative colitis (UC), n = 50; Crohn’s disease (CD), n = 53). For proof-of-principle, we assessed a relatively small number (n = 24) of manually gated immune parameters reflecting frequencies of major CD3+ and CD3- lymphocyte subsets, including CD4+ and CD4- (CD8+) CD3+ T cells; CD4+CD25lo Tconv and CD4+CD25hi cells (within CD3+CD4+ T cells); and CCR7hiCD45RO- Tnaive, CCR7loCD45RO+ Teff, and CCR7loCD45RO- Teff cells (within both CD4+ and CD8+ Tconv cells) (Fig 3A). To ensure assay reliability, we performed repeated analyses on a control stock of healthy donor PBMC, run in parallel during each independent experiment on sets of healthy donor and IBD patient samples. Coefficients of variation (CVs) for each major T cell subset ranged between 6–15% (Fig 3B), indicating reliable detection.

View Article: PubMed Central - PubMed

ABSTRACT

Advances in flow and mass cytometry are enabling ultra-high resolution immune profiling in mice and humans on an unprecedented scale. However, the resulting high-content datasets challenge traditional views of cytometry data, which are both limited in scope and biased by pre-existing hypotheses. Computational solutions are now emerging (e.g., Citrus, AutoGate, SPADE) that automate cell gating or enable visualization of relative subset abundance within healthy versus diseased mice or humans. Yet these tools require significant computational fluency and fail to show quantitative relationships between discrete immune phenotypes and continuous disease variables. Here we describe a simple informatics platform that uses hierarchical clustering and nearest neighbor algorithms to associate manually gated immune phenotypes with clinical or pre-clinical disease endpoints of interest in a rapid and unbiased manner. Using this approach, we identify discrete immune profiles that correspond with either weight loss or histologic colitis in a T cell transfer model of inflammatory bowel disease (IBD), and show distinct nodes of immune dysregulation in the IBDs, Crohn&rsquo;s disease and ulcerative colitis. This streamlined informatics approach for cytometry data analysis leverages publicly available software, can be applied to manually or computationally gated cytometry data, is suitable for any clinical or pre-clinical setting, and embraces ultra-high content flow and mass cytometry as a discovery engine.

No MeSH data available.


Related in: MedlinePlus