Limits...
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

An informatics approach to correlating immune phenotpyes with weight loss or colitis in a T cell transfer mouse model of IBD.(A) Weight loss in FVB.Rag1-/- mice (n = 9) injected with wild type naïve CD4+ T cells. Weights are shown relative to day 0 (pre-transfer baseline). Bold red trace shows mean weight loss for the group; green and blue traces show individual mice displaying mild or aggressive weight loss, respectively. Examples of disease severity index (DSI) calculations are shown in color-coded text. (B) Quantitative colitis scores (n = 9) from the same group of T cell-transferred FVB.Rag1-/- mice shown in (A). H&E-stained colon tissues were scored blindly as in [17]; representative micrographs (at right) show mild (score of 1) and severe (score of 3) inflammation (20x magnification). Red horizontal bar indicates mean colitis scores for the group. (C) Left, 10-parameter FACS panel used for analyzing ex vivo expression of surface antigens on leukocytes isolated from spleen, mesenteric lymph nodes (MLN), and colon lamina propria (colon) of FVB.Rag1-/- mice injected as in (A). Right, Gating strategy for surface FACS analysis; immune subsets used in downstream analysis are indicated by gates, text, and where appropriate, percentages. (D) Left, 11-parameter FACS panel used for analyzing ex vivo expression of intracellular transcription factors and cytokines in leukocytes isolated from T cell-transferred FVB.Rag1-/- mice as above. Right, Gating strategy for intracellular FACS analysis; immune subsets used in downstream analysis are indicated by gates, text, and where appropriate, percentages. (E) Heat map showing hierarchical clustering of 7 disease endpoints and 57 immune phenotypes in T cell-transferred FVB.Rag1-/- mice as above. Dendrograms (far left) show the clustering relationship between the mice based on all disease endpoints and immunophenotypes.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC5036861&req=5

pone.0163305.g001: An informatics approach to correlating immune phenotpyes with weight loss or colitis in a T cell transfer mouse model of IBD.(A) Weight loss in FVB.Rag1-/- mice (n = 9) injected with wild type naïve CD4+ T cells. Weights are shown relative to day 0 (pre-transfer baseline). Bold red trace shows mean weight loss for the group; green and blue traces show individual mice displaying mild or aggressive weight loss, respectively. Examples of disease severity index (DSI) calculations are shown in color-coded text. (B) Quantitative colitis scores (n = 9) from the same group of T cell-transferred FVB.Rag1-/- mice shown in (A). H&E-stained colon tissues were scored blindly as in [17]; representative micrographs (at right) show mild (score of 1) and severe (score of 3) inflammation (20x magnification). Red horizontal bar indicates mean colitis scores for the group. (C) Left, 10-parameter FACS panel used for analyzing ex vivo expression of surface antigens on leukocytes isolated from spleen, mesenteric lymph nodes (MLN), and colon lamina propria (colon) of FVB.Rag1-/- mice injected as in (A). Right, Gating strategy for surface FACS analysis; immune subsets used in downstream analysis are indicated by gates, text, and where appropriate, percentages. (D) Left, 11-parameter FACS panel used for analyzing ex vivo expression of intracellular transcription factors and cytokines in leukocytes isolated from T cell-transferred FVB.Rag1-/- mice as above. Right, Gating strategy for intracellular FACS analysis; immune subsets used in downstream analysis are indicated by gates, text, and where appropriate, percentages. (E) Heat map showing hierarchical clustering of 7 disease endpoints and 57 immune phenotypes in T cell-transferred FVB.Rag1-/- mice as above. Dendrograms (far left) show the clustering relationship between the mice based on all disease endpoints and immunophenotypes.

Mentions: Immunophenotypic data from FlowJo as above were collated in Microsoft Excel together with clinical, pre-clinical, and human demographic data (converted to single numeric values; as in Fig 1A and 1B and Table 1). To enable analyses in GenePattern, Microsoft Excel spreadsheets containing the data were converted to gct files as per instructions found in the GenePattern File Formats Guide (http://software.broadinstitute.org/cancer/software/genepattern/file-formats-guide#GCT). gct files were then analyzed using the HierarchicalClustering module in GenePattern (http://genepattern.broadinstitute.org) using both row and column clustering (Pearson correlation) and log-transformation. Two-dimensional hierarchical clustering data output files (atr, cdt, gtr) where analyzed in the HierarchicalClusteringViewer module to generate heatmaps. Within the HierarchicalClusteringViewer software (run through a Java applet), “nearest neighbor searches” were performed to quantify similarity between select clinical or pre-clinical disease endpoints of interest and all other data. Pearson correlation was used for nearest neighbor searches unless noted otherwise. Euclidian or Manhattan distances were also tested in independent nearest neighbor searches to compare results with those generated using Pearson coefficients (S2 File). Follow-up analysis and graphing was performed using GraphPad Prism software.


Informatics-Based Discovery of Disease-Associated Immune Profiles
An informatics approach to correlating immune phenotpyes with weight loss or colitis in a T cell transfer mouse model of IBD.(A) Weight loss in FVB.Rag1-/- mice (n = 9) injected with wild type naïve CD4+ T cells. Weights are shown relative to day 0 (pre-transfer baseline). Bold red trace shows mean weight loss for the group; green and blue traces show individual mice displaying mild or aggressive weight loss, respectively. Examples of disease severity index (DSI) calculations are shown in color-coded text. (B) Quantitative colitis scores (n = 9) from the same group of T cell-transferred FVB.Rag1-/- mice shown in (A). H&E-stained colon tissues were scored blindly as in [17]; representative micrographs (at right) show mild (score of 1) and severe (score of 3) inflammation (20x magnification). Red horizontal bar indicates mean colitis scores for the group. (C) Left, 10-parameter FACS panel used for analyzing ex vivo expression of surface antigens on leukocytes isolated from spleen, mesenteric lymph nodes (MLN), and colon lamina propria (colon) of FVB.Rag1-/- mice injected as in (A). Right, Gating strategy for surface FACS analysis; immune subsets used in downstream analysis are indicated by gates, text, and where appropriate, percentages. (D) Left, 11-parameter FACS panel used for analyzing ex vivo expression of intracellular transcription factors and cytokines in leukocytes isolated from T cell-transferred FVB.Rag1-/- mice as above. Right, Gating strategy for intracellular FACS analysis; immune subsets used in downstream analysis are indicated by gates, text, and where appropriate, percentages. (E) Heat map showing hierarchical clustering of 7 disease endpoints and 57 immune phenotypes in T cell-transferred FVB.Rag1-/- mice as above. Dendrograms (far left) show the clustering relationship between the mice based on all disease endpoints and immunophenotypes.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5036861&req=5

pone.0163305.g001: An informatics approach to correlating immune phenotpyes with weight loss or colitis in a T cell transfer mouse model of IBD.(A) Weight loss in FVB.Rag1-/- mice (n = 9) injected with wild type naïve CD4+ T cells. Weights are shown relative to day 0 (pre-transfer baseline). Bold red trace shows mean weight loss for the group; green and blue traces show individual mice displaying mild or aggressive weight loss, respectively. Examples of disease severity index (DSI) calculations are shown in color-coded text. (B) Quantitative colitis scores (n = 9) from the same group of T cell-transferred FVB.Rag1-/- mice shown in (A). H&E-stained colon tissues were scored blindly as in [17]; representative micrographs (at right) show mild (score of 1) and severe (score of 3) inflammation (20x magnification). Red horizontal bar indicates mean colitis scores for the group. (C) Left, 10-parameter FACS panel used for analyzing ex vivo expression of surface antigens on leukocytes isolated from spleen, mesenteric lymph nodes (MLN), and colon lamina propria (colon) of FVB.Rag1-/- mice injected as in (A). Right, Gating strategy for surface FACS analysis; immune subsets used in downstream analysis are indicated by gates, text, and where appropriate, percentages. (D) Left, 11-parameter FACS panel used for analyzing ex vivo expression of intracellular transcription factors and cytokines in leukocytes isolated from T cell-transferred FVB.Rag1-/- mice as above. Right, Gating strategy for intracellular FACS analysis; immune subsets used in downstream analysis are indicated by gates, text, and where appropriate, percentages. (E) Heat map showing hierarchical clustering of 7 disease endpoints and 57 immune phenotypes in T cell-transferred FVB.Rag1-/- mice as above. Dendrograms (far left) show the clustering relationship between the mice based on all disease endpoints and immunophenotypes.
Mentions: Immunophenotypic data from FlowJo as above were collated in Microsoft Excel together with clinical, pre-clinical, and human demographic data (converted to single numeric values; as in Fig 1A and 1B and Table 1). To enable analyses in GenePattern, Microsoft Excel spreadsheets containing the data were converted to gct files as per instructions found in the GenePattern File Formats Guide (http://software.broadinstitute.org/cancer/software/genepattern/file-formats-guide#GCT). gct files were then analyzed using the HierarchicalClustering module in GenePattern (http://genepattern.broadinstitute.org) using both row and column clustering (Pearson correlation) and log-transformation. Two-dimensional hierarchical clustering data output files (atr, cdt, gtr) where analyzed in the HierarchicalClusteringViewer module to generate heatmaps. Within the HierarchicalClusteringViewer software (run through a Java applet), “nearest neighbor searches” were performed to quantify similarity between select clinical or pre-clinical disease endpoints of interest and all other data. Pearson correlation was used for nearest neighbor searches unless noted otherwise. Euclidian or Manhattan distances were also tested in independent nearest neighbor searches to compare results with those generated using Pearson coefficients (S2 File). Follow-up analysis and graphing was performed using GraphPad Prism software.

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