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A bioinformatic framework for immune repertoire diversity profiling enables detection of immunological status.

Greiff V, Bhat P, Cook SC, Menzel U, Kang W, Reddy ST - Genome Med (2015)

Bottom Line: The framework relies on Hill-based diversity profiles composed of a continuum of single diversity indices, which enable the quantification of the extent of immunological information contained in immune repertoires.Our framework is highly scalable as it easily allowed for the analysis of 1000 simulated immune repertoires; this exceeds the size of published immune repertoire datasets by one to two orders of magnitude.Our framework offers the possibility to advance immune-repertoire-based fingerprinting, which may in the future enable a systems immunogenomics approach for vaccine profiling and the accurate and early detection of disease and infection.

View Article: PubMed Central - PubMed

Affiliation: ETH Zürich, Department of Biosystems Science and Engineering, Basel, 4058 Switzerland.

ABSTRACT

Background: Lymphocyte receptor repertoires are continually shaped throughout the lifetime of an individual in response to environmental and pathogenic exposure. Thus, they may serve as a fingerprint of an individual's ongoing immunological status (e.g., healthy, infected, vaccinated), with far-reaching implications for immunodiagnostics applications. The advent of high-throughput immune repertoire sequencing now enables the interrogation of immune repertoire diversity in an unprecedented and quantitative manner. However, steadily increasing sequencing depth has revealed that immune repertoires vary greatly among individuals in their composition; correspondingly, it has been reported that there are few shared sequences indicative of immunological status ('public clones'). Disconcertingly, this means that the wealth of information gained from repertoire sequencing remains largely unused for determining the current status of immune responses, thereby hampering the implementation of immune-repertoire-based diagnostics.

Methods: Here, we introduce a bioinformatics repertoire-profiling framework that possesses the advantage of capturing the diversity and distribution of entire immune repertoires, as opposed to singular public clones. The framework relies on Hill-based diversity profiles composed of a continuum of single diversity indices, which enable the quantification of the extent of immunological information contained in immune repertoires.

Results: We coupled diversity profiles with unsupervised (hierarchical clustering) and supervised (support vector machine and feature selection) machine learning approaches in order to correlate patients' immunological statuses with their B- and T-cell repertoire data. We could predict with high accuracy (greater than or equal to 80 %) a wide range of immunological statuses such as healthy, transplantation recipient, and lymphoid cancer, suggesting as a proof of principle that diversity profiling can recover a large amount of immunodiagnostic fingerprints from immune repertoire data. Our framework is highly scalable as it easily allowed for the analysis of 1000 simulated immune repertoires; this exceeds the size of published immune repertoire datasets by one to two orders of magnitude.

Conclusions: Our framework offers the possibility to advance immune-repertoire-based fingerprinting, which may in the future enable a systems immunogenomics approach for vaccine profiling and the accurate and early detection of disease and infection.

No MeSH data available.


Related in: MedlinePlus

Diversity and Evenness profiles resolve stages of hematopoietic stem cell transplantation. a–d Hierarchical clustering was performed based on Euclidean distance for Diversity profiles and correlation-based distance for Evenness profiles of dataset 1 and visualized using heatmaps. The heatmaps depict the pairwise distances/Pearson correlation coefficients of all profiles determined (see Methods for further details). Both for CD4 and CD8 T-cell repertoires, Diversity (a, c) and Evenness (b, d) profiles from 'Month 2' (blue) after transplantation cluster together as do profiles of 'Baseline' measurements (green) and 'Month 12' (red) after transplantation (red color bar). Of note, for CD8 datasets, Diversity profiles cluster almost perfectly by each of the three statuses (Baseline, Month 2, Month 12). Diversity and Evenness profiles were calculated in a range of alpha = 0 to alpha = 10 with a step size of 0.2. Sample numbers: 24 per immunological status and T-cell population
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Fig4: Diversity and Evenness profiles resolve stages of hematopoietic stem cell transplantation. a–d Hierarchical clustering was performed based on Euclidean distance for Diversity profiles and correlation-based distance for Evenness profiles of dataset 1 and visualized using heatmaps. The heatmaps depict the pairwise distances/Pearson correlation coefficients of all profiles determined (see Methods for further details). Both for CD4 and CD8 T-cell repertoires, Diversity (a, c) and Evenness (b, d) profiles from 'Month 2' (blue) after transplantation cluster together as do profiles of 'Baseline' measurements (green) and 'Month 12' (red) after transplantation (red color bar). Of note, for CD8 datasets, Diversity profiles cluster almost perfectly by each of the three statuses (Baseline, Month 2, Month 12). Diversity and Evenness profiles were calculated in a range of alpha = 0 to alpha = 10 with a step size of 0.2. Sample numbers: 24 per immunological status and T-cell population

Mentions: In order to visualize possible immunological phenotypic differences of Diversity and Evenness profiles, we used hierarchical clustering. Diversity profiles were clustered by Euclidian distance to take into account the SR differences between repertoires, whereas Evenness profiles were clustered by correlation distance in order to exclusively focus on their shape (relative degree of clonal expansion). For dataset 1 (human, TCR-Vβ, baseline versus transplantation), we found that both Diversity and Evenness profiles cluster by 2 months, and baseline with 12 months, which is in line with the intuition that 12 months after hematopoietic stem cell transplantation the immune system has recovered the pre-surgery baseline state whereas 2 months after transplantation the T-cell repertoire has assumed a perturbed state (Fig. 4a–d). For dataset 2 (human, BCR, healthy versus CLL), we found that Diversity and Evenness profiles cluster samples of B-cell repertoires of healthy and CLL-afflicted patients well (Fig. 5a, b).Fig. 4


A bioinformatic framework for immune repertoire diversity profiling enables detection of immunological status.

Greiff V, Bhat P, Cook SC, Menzel U, Kang W, Reddy ST - Genome Med (2015)

Diversity and Evenness profiles resolve stages of hematopoietic stem cell transplantation. a–d Hierarchical clustering was performed based on Euclidean distance for Diversity profiles and correlation-based distance for Evenness profiles of dataset 1 and visualized using heatmaps. The heatmaps depict the pairwise distances/Pearson correlation coefficients of all profiles determined (see Methods for further details). Both for CD4 and CD8 T-cell repertoires, Diversity (a, c) and Evenness (b, d) profiles from 'Month 2' (blue) after transplantation cluster together as do profiles of 'Baseline' measurements (green) and 'Month 12' (red) after transplantation (red color bar). Of note, for CD8 datasets, Diversity profiles cluster almost perfectly by each of the three statuses (Baseline, Month 2, Month 12). Diversity and Evenness profiles were calculated in a range of alpha = 0 to alpha = 10 with a step size of 0.2. Sample numbers: 24 per immunological status and T-cell population
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4489130&req=5

Fig4: Diversity and Evenness profiles resolve stages of hematopoietic stem cell transplantation. a–d Hierarchical clustering was performed based on Euclidean distance for Diversity profiles and correlation-based distance for Evenness profiles of dataset 1 and visualized using heatmaps. The heatmaps depict the pairwise distances/Pearson correlation coefficients of all profiles determined (see Methods for further details). Both for CD4 and CD8 T-cell repertoires, Diversity (a, c) and Evenness (b, d) profiles from 'Month 2' (blue) after transplantation cluster together as do profiles of 'Baseline' measurements (green) and 'Month 12' (red) after transplantation (red color bar). Of note, for CD8 datasets, Diversity profiles cluster almost perfectly by each of the three statuses (Baseline, Month 2, Month 12). Diversity and Evenness profiles were calculated in a range of alpha = 0 to alpha = 10 with a step size of 0.2. Sample numbers: 24 per immunological status and T-cell population
Mentions: In order to visualize possible immunological phenotypic differences of Diversity and Evenness profiles, we used hierarchical clustering. Diversity profiles were clustered by Euclidian distance to take into account the SR differences between repertoires, whereas Evenness profiles were clustered by correlation distance in order to exclusively focus on their shape (relative degree of clonal expansion). For dataset 1 (human, TCR-Vβ, baseline versus transplantation), we found that both Diversity and Evenness profiles cluster by 2 months, and baseline with 12 months, which is in line with the intuition that 12 months after hematopoietic stem cell transplantation the immune system has recovered the pre-surgery baseline state whereas 2 months after transplantation the T-cell repertoire has assumed a perturbed state (Fig. 4a–d). For dataset 2 (human, BCR, healthy versus CLL), we found that Diversity and Evenness profiles cluster samples of B-cell repertoires of healthy and CLL-afflicted patients well (Fig. 5a, b).Fig. 4

Bottom Line: The framework relies on Hill-based diversity profiles composed of a continuum of single diversity indices, which enable the quantification of the extent of immunological information contained in immune repertoires.Our framework is highly scalable as it easily allowed for the analysis of 1000 simulated immune repertoires; this exceeds the size of published immune repertoire datasets by one to two orders of magnitude.Our framework offers the possibility to advance immune-repertoire-based fingerprinting, which may in the future enable a systems immunogenomics approach for vaccine profiling and the accurate and early detection of disease and infection.

View Article: PubMed Central - PubMed

Affiliation: ETH Zürich, Department of Biosystems Science and Engineering, Basel, 4058 Switzerland.

ABSTRACT

Background: Lymphocyte receptor repertoires are continually shaped throughout the lifetime of an individual in response to environmental and pathogenic exposure. Thus, they may serve as a fingerprint of an individual's ongoing immunological status (e.g., healthy, infected, vaccinated), with far-reaching implications for immunodiagnostics applications. The advent of high-throughput immune repertoire sequencing now enables the interrogation of immune repertoire diversity in an unprecedented and quantitative manner. However, steadily increasing sequencing depth has revealed that immune repertoires vary greatly among individuals in their composition; correspondingly, it has been reported that there are few shared sequences indicative of immunological status ('public clones'). Disconcertingly, this means that the wealth of information gained from repertoire sequencing remains largely unused for determining the current status of immune responses, thereby hampering the implementation of immune-repertoire-based diagnostics.

Methods: Here, we introduce a bioinformatics repertoire-profiling framework that possesses the advantage of capturing the diversity and distribution of entire immune repertoires, as opposed to singular public clones. The framework relies on Hill-based diversity profiles composed of a continuum of single diversity indices, which enable the quantification of the extent of immunological information contained in immune repertoires.

Results: We coupled diversity profiles with unsupervised (hierarchical clustering) and supervised (support vector machine and feature selection) machine learning approaches in order to correlate patients' immunological statuses with their B- and T-cell repertoire data. We could predict with high accuracy (greater than or equal to 80 %) a wide range of immunological statuses such as healthy, transplantation recipient, and lymphoid cancer, suggesting as a proof of principle that diversity profiling can recover a large amount of immunodiagnostic fingerprints from immune repertoire data. Our framework is highly scalable as it easily allowed for the analysis of 1000 simulated immune repertoires; this exceeds the size of published immune repertoire datasets by one to two orders of magnitude.

Conclusions: Our framework offers the possibility to advance immune-repertoire-based fingerprinting, which may in the future enable a systems immunogenomics approach for vaccine profiling and the accurate and early detection of disease and infection.

No MeSH data available.


Related in: MedlinePlus