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Peptide-MHC cellular microarray with innovative data analysis system for simultaneously detecting multiple CD4 T-cell responses.

Ge X, Gebe JA, Bollyky PL, James EA, Yang J, Stern LJ, Kwok WW - PLoS ONE (2010)

Bottom Line: The practice of studying immune responses to complicated pathogens with this tool demands extensive knowledge of T cell epitopes and the availability of peptide:MHC complexes for array fabrication as well as a specialized data analysis approach for result interpretation.The data analysis system is reliable for T cell specificity and functional testing.Peptide:MHC cellular microarrays can be used to obtain multi-parametric results using limited blood samples in a variety of translational settings.

View Article: PubMed Central - PubMed

Affiliation: Benaroya Research Institute at Virginia Mason, Seattle, Washington, USA.

ABSTRACT

Background: Peptide:MHC cellular microarrays have been proposed to simultaneously characterize multiple Ag-specific populations of T cells. The practice of studying immune responses to complicated pathogens with this tool demands extensive knowledge of T cell epitopes and the availability of peptide:MHC complexes for array fabrication as well as a specialized data analysis approach for result interpretation.

Methodology/principal findings: We co-immobilized peptide:DR0401 complexes, anti-CD28, anti-CD11a and cytokine capture antibodies on the surface of chamber slides to generate a functional array that was able to detect rare Ag-specific T cell populations from previously primed in vitro T cell cultures. A novel statistical methodology was also developed to facilitate batch processing of raw array-like data into standardized endpoint scores, which linearly correlated with total Ag-specific T cell inputs. Applying these methods to analyze Influenza A viral antigen-specific T cell responses, we not only revealed the most prominent viral epitopes, but also demonstrated the heterogeneity of anti-viral cellular responses in healthy individuals. Applying these methods to examine the insulin producing beta-cell autoantigen specific T cell responses, we observed little difference between autoimmune diabetic patients and healthy individuals, suggesting a more subtle association between diabetes status and peripheral autoreactive T cells.

Conclusions/significance: The data analysis system is reliable for T cell specificity and functional testing. Peptide:MHC cellular microarrays can be used to obtain multi-parametric results using limited blood samples in a variety of translational settings.

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Related in: MedlinePlus

Illustration of data analysis.(A) Flow-chart; (B) a datasheet layout for a 8×12 cellular microarray assay. The contents in black or red boxes/ovals are relevant to generating final result highlighted by red arrow in the bar graph. The numeric indicators in the brackets of (A) and (B) are consistent.
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pone-0011355-g002: Illustration of data analysis.(A) Flow-chart; (B) a datasheet layout for a 8×12 cellular microarray assay. The contents in black or red boxes/ovals are relevant to generating final result highlighted by red arrow in the bar graph. The numeric indicators in the brackets of (A) and (B) are consistent.

Mentions: To assess clinical samples using the cellular microarray, particularly for comparative purposes, a standardized data analysis system is required to transform original fluorescence measurements into normalized results suitable for inter-/intra-assay comparison. We designed a 6-step protocol of mathematic/statistical calculations (Figure 2A): 1) the absolute fluorescence intensity (AFI) of each spotted area was measured as raw data; 2) a logarithmic transformation of the raw data was taken; 3) replicates were grouped and compared with the negative control (replicate spots of empty Class II MHC); 4) the variances of experimental replicates and control replicates were compared using an F-test; 5) based on F-test results, one of two types of 2-tailed non-paired t-tests was chosen to compare the means of the experimental group and the control group – a regular t-test for equal variances or a t-test with Welch's correction for unequal variances; 6) another logarithmic transformation was performed to convert the p-value of the t-test into a more readable score. This analysis system ranks Ag-specific T cell responses by evaluating the statistical difference between the experimental group and a ubiquitous negative reference. In this specialized detection system where no standard curve was available to normalize the activation level of a T cell response and the variation of fluorescence signal was high, the advantage of this data analysis approach was that both the AFI values and the variances (from either a sample or the negative control) contributed to the underlying p-values (or −log[p] scores). Since all −log[p] scores were calculated against a common internal negative control, these scores were independent of the particular assay and could be directly compared. To further validate the biological implication of this scoring method, we compared the cellular microarray results of serially diluted CD4 T cell lines with IFNgamma production in a parallel control experiment, in which the same numbers of T cells were seeded into a 96-well plate coated with peptide:MHC and anti-CD28/CD11a for the same period of stimulation (Figure 3). We found that those AFIs had less indicative value to reveal the number of Ag-specific T cells for the assays from different chamber. However, the −log[p] scores were not only correlated with the input of Ag-specific T cells (Figure 3F), but also correlated with IFNgamma production (from the control experiment) measured by conventional ELISA (Figure 3G–I). To facilitate batch data processing, a coding program based on this 6-step procedure was created with MS-Excel so that the −log[p] score for various peptide:MHC features could be calculated and reported equivalently (Figure 2B).


Peptide-MHC cellular microarray with innovative data analysis system for simultaneously detecting multiple CD4 T-cell responses.

Ge X, Gebe JA, Bollyky PL, James EA, Yang J, Stern LJ, Kwok WW - PLoS ONE (2010)

Illustration of data analysis.(A) Flow-chart; (B) a datasheet layout for a 8×12 cellular microarray assay. The contents in black or red boxes/ovals are relevant to generating final result highlighted by red arrow in the bar graph. The numeric indicators in the brackets of (A) and (B) are consistent.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2902358&req=5

pone-0011355-g002: Illustration of data analysis.(A) Flow-chart; (B) a datasheet layout for a 8×12 cellular microarray assay. The contents in black or red boxes/ovals are relevant to generating final result highlighted by red arrow in the bar graph. The numeric indicators in the brackets of (A) and (B) are consistent.
Mentions: To assess clinical samples using the cellular microarray, particularly for comparative purposes, a standardized data analysis system is required to transform original fluorescence measurements into normalized results suitable for inter-/intra-assay comparison. We designed a 6-step protocol of mathematic/statistical calculations (Figure 2A): 1) the absolute fluorescence intensity (AFI) of each spotted area was measured as raw data; 2) a logarithmic transformation of the raw data was taken; 3) replicates were grouped and compared with the negative control (replicate spots of empty Class II MHC); 4) the variances of experimental replicates and control replicates were compared using an F-test; 5) based on F-test results, one of two types of 2-tailed non-paired t-tests was chosen to compare the means of the experimental group and the control group – a regular t-test for equal variances or a t-test with Welch's correction for unequal variances; 6) another logarithmic transformation was performed to convert the p-value of the t-test into a more readable score. This analysis system ranks Ag-specific T cell responses by evaluating the statistical difference between the experimental group and a ubiquitous negative reference. In this specialized detection system where no standard curve was available to normalize the activation level of a T cell response and the variation of fluorescence signal was high, the advantage of this data analysis approach was that both the AFI values and the variances (from either a sample or the negative control) contributed to the underlying p-values (or −log[p] scores). Since all −log[p] scores were calculated against a common internal negative control, these scores were independent of the particular assay and could be directly compared. To further validate the biological implication of this scoring method, we compared the cellular microarray results of serially diluted CD4 T cell lines with IFNgamma production in a parallel control experiment, in which the same numbers of T cells were seeded into a 96-well plate coated with peptide:MHC and anti-CD28/CD11a for the same period of stimulation (Figure 3). We found that those AFIs had less indicative value to reveal the number of Ag-specific T cells for the assays from different chamber. However, the −log[p] scores were not only correlated with the input of Ag-specific T cells (Figure 3F), but also correlated with IFNgamma production (from the control experiment) measured by conventional ELISA (Figure 3G–I). To facilitate batch data processing, a coding program based on this 6-step procedure was created with MS-Excel so that the −log[p] score for various peptide:MHC features could be calculated and reported equivalently (Figure 2B).

Bottom Line: The practice of studying immune responses to complicated pathogens with this tool demands extensive knowledge of T cell epitopes and the availability of peptide:MHC complexes for array fabrication as well as a specialized data analysis approach for result interpretation.The data analysis system is reliable for T cell specificity and functional testing.Peptide:MHC cellular microarrays can be used to obtain multi-parametric results using limited blood samples in a variety of translational settings.

View Article: PubMed Central - PubMed

Affiliation: Benaroya Research Institute at Virginia Mason, Seattle, Washington, USA.

ABSTRACT

Background: Peptide:MHC cellular microarrays have been proposed to simultaneously characterize multiple Ag-specific populations of T cells. The practice of studying immune responses to complicated pathogens with this tool demands extensive knowledge of T cell epitopes and the availability of peptide:MHC complexes for array fabrication as well as a specialized data analysis approach for result interpretation.

Methodology/principal findings: We co-immobilized peptide:DR0401 complexes, anti-CD28, anti-CD11a and cytokine capture antibodies on the surface of chamber slides to generate a functional array that was able to detect rare Ag-specific T cell populations from previously primed in vitro T cell cultures. A novel statistical methodology was also developed to facilitate batch processing of raw array-like data into standardized endpoint scores, which linearly correlated with total Ag-specific T cell inputs. Applying these methods to analyze Influenza A viral antigen-specific T cell responses, we not only revealed the most prominent viral epitopes, but also demonstrated the heterogeneity of anti-viral cellular responses in healthy individuals. Applying these methods to examine the insulin producing beta-cell autoantigen specific T cell responses, we observed little difference between autoimmune diabetic patients and healthy individuals, suggesting a more subtle association between diabetes status and peripheral autoreactive T cells.

Conclusions/significance: The data analysis system is reliable for T cell specificity and functional testing. Peptide:MHC cellular microarrays can be used to obtain multi-parametric results using limited blood samples in a variety of translational settings.

Show MeSH
Related in: MedlinePlus