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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.


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Discrete immune phenotypes correspond with T cell transfer-induced weight loss or colitis in Rag1-/- mice.(A) Rank-ordered (Pearson r) correlation values of all disease endpoints and immune phenotypes relative to weight loss (disease severity index (DSI)), in FVB.Rag1-/- mice injected with wild type naïve CD4+ T cells as in Fig 1A. Relevant disease endpoints and immune phenotypes are indicated by black and red text, respectively. Correlation between weight loss and colitis scores is further shown in insert, where blue text indicates the Pearson r correlation value. (B) Rank-ordered (Pearson r) correlation values of all disease endpoints and immune phenotypes relative to colitis scores, determined by histology, in the same T cell-transferred FVB.Rag1-/- mice. Relevant immune phenotypes are indicated by red text; correlation with weight loss (DSI) is indicated by black text. For (A, B), the correlation of the reference variable with itself (r = 1.0) is shown at top left in grey. (C) Exemplar immune phenotypes that correlate with T cell transfer-induced weight loss (disease severity index (DSI)), (left), but not histologic colitis (right) in T cell-transferred FVB.Rag1-/- mice. (D) Exemplar immune phenotypes that correlate with T cell transfer-induced colitis (right), but not weight loss (disease severity index (DSI)) (left). Pearson r correlation values are show in red (for correlations achieving statistical significance) and blue (for correlations not statistically significant). * P < .05, ** P < .01, *** P < .001, Pearson correlation test.
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pone.0163305.g002: Discrete immune phenotypes correspond with T cell transfer-induced weight loss or colitis in Rag1-/- mice.(A) Rank-ordered (Pearson r) correlation values of all disease endpoints and immune phenotypes relative to weight loss (disease severity index (DSI)), in FVB.Rag1-/- mice injected with wild type naïve CD4+ T cells as in Fig 1A. Relevant disease endpoints and immune phenotypes are indicated by black and red text, respectively. Correlation between weight loss and colitis scores is further shown in insert, where blue text indicates the Pearson r correlation value. (B) Rank-ordered (Pearson r) correlation values of all disease endpoints and immune phenotypes relative to colitis scores, determined by histology, in the same T cell-transferred FVB.Rag1-/- mice. Relevant immune phenotypes are indicated by red text; correlation with weight loss (DSI) is indicated by black text. For (A, B), the correlation of the reference variable with itself (r = 1.0) is shown at top left in grey. (C) Exemplar immune phenotypes that correlate with T cell transfer-induced weight loss (disease severity index (DSI)), (left), but not histologic colitis (right) in T cell-transferred FVB.Rag1-/- mice. (D) Exemplar immune phenotypes that correlate with T cell transfer-induced colitis (right), but not weight loss (disease severity index (DSI)) (left). Pearson r correlation values are show in red (for correlations achieving statistical significance) and blue (for correlations not statistically significant). * P < .05, ** P < .01, *** P < .001, Pearson correlation test.

Mentions: After clustering, we highlighted either the DSI (Fig 2A) or colitis scores (Fig 2B) and used the nearest neighbor search feature in GenePattern (HierarchicalClusteringViewer module) to generate Pearson (r) coefficients for all immune phenotypes relative to each disease endpoint, ranked from high (positive Pearson coefficient; directly correlated) to low (negative Pearson coefficient; inversely correlated). As expected, the absolute percentage of weight loss was the strongest direct correlate of the DSI (r = 0.865; P = 0.0026), the time post-T cell transfer was among the strongest inverse correlates of the DSI (r = -0.646; P = 0.0503), and histologic colitis did not correlate with the DSI (r = -0.045) (Fig 2A).


Informatics-Based Discovery of Disease-Associated Immune Profiles
Discrete immune phenotypes correspond with T cell transfer-induced weight loss or colitis in Rag1-/- mice.(A) Rank-ordered (Pearson r) correlation values of all disease endpoints and immune phenotypes relative to weight loss (disease severity index (DSI)), in FVB.Rag1-/- mice injected with wild type naïve CD4+ T cells as in Fig 1A. Relevant disease endpoints and immune phenotypes are indicated by black and red text, respectively. Correlation between weight loss and colitis scores is further shown in insert, where blue text indicates the Pearson r correlation value. (B) Rank-ordered (Pearson r) correlation values of all disease endpoints and immune phenotypes relative to colitis scores, determined by histology, in the same T cell-transferred FVB.Rag1-/- mice. Relevant immune phenotypes are indicated by red text; correlation with weight loss (DSI) is indicated by black text. For (A, B), the correlation of the reference variable with itself (r = 1.0) is shown at top left in grey. (C) Exemplar immune phenotypes that correlate with T cell transfer-induced weight loss (disease severity index (DSI)), (left), but not histologic colitis (right) in T cell-transferred FVB.Rag1-/- mice. (D) Exemplar immune phenotypes that correlate with T cell transfer-induced colitis (right), but not weight loss (disease severity index (DSI)) (left). Pearson r correlation values are show in red (for correlations achieving statistical significance) and blue (for correlations not statistically significant). * P < .05, ** P < .01, *** P < .001, Pearson correlation test.
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pone.0163305.g002: Discrete immune phenotypes correspond with T cell transfer-induced weight loss or colitis in Rag1-/- mice.(A) Rank-ordered (Pearson r) correlation values of all disease endpoints and immune phenotypes relative to weight loss (disease severity index (DSI)), in FVB.Rag1-/- mice injected with wild type naïve CD4+ T cells as in Fig 1A. Relevant disease endpoints and immune phenotypes are indicated by black and red text, respectively. Correlation between weight loss and colitis scores is further shown in insert, where blue text indicates the Pearson r correlation value. (B) Rank-ordered (Pearson r) correlation values of all disease endpoints and immune phenotypes relative to colitis scores, determined by histology, in the same T cell-transferred FVB.Rag1-/- mice. Relevant immune phenotypes are indicated by red text; correlation with weight loss (DSI) is indicated by black text. For (A, B), the correlation of the reference variable with itself (r = 1.0) is shown at top left in grey. (C) Exemplar immune phenotypes that correlate with T cell transfer-induced weight loss (disease severity index (DSI)), (left), but not histologic colitis (right) in T cell-transferred FVB.Rag1-/- mice. (D) Exemplar immune phenotypes that correlate with T cell transfer-induced colitis (right), but not weight loss (disease severity index (DSI)) (left). Pearson r correlation values are show in red (for correlations achieving statistical significance) and blue (for correlations not statistically significant). * P < .05, ** P < .01, *** P < .001, Pearson correlation test.
Mentions: After clustering, we highlighted either the DSI (Fig 2A) or colitis scores (Fig 2B) and used the nearest neighbor search feature in GenePattern (HierarchicalClusteringViewer module) to generate Pearson (r) coefficients for all immune phenotypes relative to each disease endpoint, ranked from high (positive Pearson coefficient; directly correlated) to low (negative Pearson coefficient; inversely correlated). As expected, the absolute percentage of weight loss was the strongest direct correlate of the DSI (r = 0.865; P = 0.0026), the time post-T cell transfer was among the strongest inverse correlates of the DSI (r = -0.646; P = 0.0503), and histologic colitis did not correlate with the DSI (r = -0.045) (Fig 2A).

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