Raphael Gottardo

Fred Hutchinson Cancer Research Center, Seattle, Washington, United States

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Publications (80)411.32 Total impact

  • Cytometry Part A 07/2015; 87(7):591-3. DOI:10.1002/cyto.a.22707 · 3.07 Impact Factor
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    ABSTRACT: Advances in flow cytometry and other single-cell technologies have enabled high-dimensional, high-throughput measurements of individual cells as well as the interrogation of cell population heterogeneity. However, in many instances, computational tools to analyze the wealth of data generated by these technologies are lacking. Here, we present a computational framework for unbiased combinatorial polyfunctionality analysis of antigen-specific T-cell subsets (COMPASS). COMPASS uses a Bayesian hierarchical framework to model all observed cell subsets and select those most likely to have antigen-specific responses. Cell-subset responses are quantified by posterior probabilities, and human subject-level responses are quantified by two summary statistics that describe the quality of an individual's polyfunctional response and can be correlated directly with clinical outcome. Using three clinical data sets of cytokine production, we demonstrate how COMPASS improves characterization of antigen-specific T cells and reveals cellular 'correlates of protection/immunity' in the RV144 HIV vaccine efficacy trial that are missed by other methods. COMPASS is available as open-source software.
    Nature Biotechnology 05/2015; 33(6). DOI:10.1038/nbt.3187 · 39.08 Impact Factor
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    ABSTRACT: An important aspect of immune monitoring for vaccine development, clinical trials, and research is the detection, measurement, and comparison of antigen-specific T-cells from subject samples under different conditions. Antigen-specific T-cells compose a very small fraction of total T-cells. Developments in cytometry technology over the past five years have enabled the measurement of single-cells in a multivariate and high-throughput manner. This growth in both dimensionality and quantity of data continues to pose a challenge for effective identification and visualization of rare cell subsets, such as antigen-specific T-cells. Dimension reduction and feature extraction play pivotal role in both identifying and visualizing cell populations of interest in large, multi-dimensional cytometry datasets. However, the automated identification and visualization of rare, high-dimensional cell subsets remains challenging. Here we demonstrate how a systematic and integrated approach combining targeted feature extraction with dimension reduction can be used to identify and visualize biological differences in rare, antigen-specific cell populations. By using OpenCyto to perform semi-automated gating and features extraction of flow cytometry data, followed by dimensionality reduction with t-SNE we are able to identify polyfunctional subpopulations of antigen-specific T-cells and visualize treatment-specific differences between them. © 2015 International Society for Advancement of Cytometry. © 2015 International Society for Advancement of Cytometry.
    Cytometry Part A 04/2015; DOI:10.1002/cyto.a.22623 · 3.07 Impact Factor
  • eLife Sciences 03/2015; 4. DOI:10.7554/eLife.07586 · 8.52 Impact Factor
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    ABSTRACT: Tenofovir gel is being evaluated for vaginal and rectal pre-exposure prophylaxis against HIV transmission. Because this is a new prevention strategy, we broadly assessed its effects on the mucosa. In MTN-007, a phase-1, randomized, double-blinded rectal microbicide trial, we used systems genomics/proteomics to determine the effect of tenofovir 1% gel, nonoxynol-9 2% gel, placebo gel or no treatment on rectal biopsies (15 subjects/arm). We also treated primary vaginal epithelial cells from four healthy women with tenofovir in vitro. After seven days of administration, tenofovir 1% gel had broad-ranging effects on the rectal mucosa, which were more pronounced than, but different from, those of the detergent nonoxynol-9. Tenofovir suppressed anti-inflammatory mediators, increased T cell densities, caused mitochondrial dysfunction, altered regulatory pathways of cell differentiation and survival, and stimulated epithelial cell proliferation. The breadth of mucosal changes induced by tenofovir indicates that its safety over longer-term topical use should be carefully monitored.
    eLife Sciences 02/2015; 4. DOI:10.7554/eLife.04525 · 8.52 Impact Factor
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    ABSTRACT: Bioconductor is an open-source, open-development software project for the analysis and comprehension of high-throughput data in genomics and molecular biology. The project aims to enable interdisciplinary research, collaboration and rapid development of scientific software. Based on the statistical programming language R, Bioconductor comprises 934 interoperable packages contributed by a large, diverse community of scientists. Packages cover a range of bioinformatic and statistical applications. They undergo formal initial review and continuous automated testing. We present an overview for prospective users and contributors.
    Nature Methods 01/2015; 12(2):115-21. DOI:10.1038/nmeth.3252 · 25.95 Impact Factor
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    ABSTRACT: Finding one or more cell populations of interest, such as those correlating to a specific disease, is critical when analyzing flow cytometry data. However, labelling of cell populations is not well defined, making it difficult to integrate the output of algorithms to external knowledge sources. We developed flowCL, a software package that performs semantic labelling of cell populations based on their surface markers and applied it to labelling of the Federation of Clinical Immunology Societies Human Immunology Project Consortium lyoplate populations as a use case. By providing automated labelling of cell populations based on their immunophenotype, flowCL allows for unambiguous and reproducible identification of standardized cell types. Code, R script and documentation are available under the Artistic 2.0 license through Bioconductor(1). rbrinkman@bccrc.ca. © The Author (2014). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
    Bioinformatics 12/2014; 31(8). DOI:10.1093/bioinformatics/btu807 · 4.62 Impact Factor
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    ABSTRACT: To evaluate the role of V3-specific IgG antibodies (Abs) in the RV144 clinical HIV vaccine trial, which reduced HIV-1 infection by 31.2%, the anti-V3 Ab response was assessed. Vaccinees' V3 Abs were highly cross-reactive with cyclic V3 peptides (cV3s) from diverse virus subtypes. Sieve analysis of CRF01_AE breakthrough viruses from 43 vaccine- and 66 placebo-recipients demonstrated an estimated vaccine efficacy of 85% against viruses with amino acids mismatching the vaccine at V3 site 317 (p=0.004) and 52% against viruses matching the vaccine at V3 site 307 (p=0.004). This analysis was supported by data showing vaccinees' plasma Abs were less reactive with I(307) replaced with residues found more often in vaccinees' breakthrough viruses. Simultaneously, viruses with mutations at F(317) were less infectious, possibly due to the contribution of F(317) to optimal formation of the V3 hydrophobic core. These data suggest that RV144-induced V3-specific Abs imposed immune pressure on infecting viruses and inform efforts to design an HIV vaccine.
    11/2014; 1(1):37-45. DOI:10.1016/j.ebiom.2014.10.022
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    ABSTRACT: flowDensity facilitates reproducible, high-throughput analysis of flow cytometry data by automating a predefined manual gating approach. The algorithm is based on a sequential bivariate gating approach that generates a set of predefined cell populations. It chooses the best cut-off for individual markers using characteristics of the density distribution. The Supplementary Material is linked to the online version of the manuscript. Availability and implementation: R source code freely available through BioConductor (http://master.bioconductor.org/packages/devel/bioc/html/flowDensity.html.). Data available from FlowRepository.org (dataset FR-FCM-ZZBW).
    Bioinformatics 10/2014; 31(4). DOI:10.1093/bioinformatics/btu677 · 4.62 Impact Factor
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    ABSTRACT: The RV144 clinical trial showed the partial efficacy of a vaccine regimen with an estimated vaccine efficacy (VE) of 31% for protecting low-risk Thai volunteers against acquisition of HIV-1. The impact of vaccine-induced immune responses can be investigated through sieve analysis of HIV-1 breakthrough infections (infected vaccine and placebo recipients). A V1/V2-targeted comparison of the genomes of HIV-1 breakthrough viruses identified two V2 amino acid sites that differed between the vaccine and placebo groups. Here we extended the V1/V2 analysis to the entire HIV-1 genome using an array of methods based on individual sites, k-mers and genes/proteins. We identified 56 amino acid sites or "signatures" and 119 k-mers that differed between the vaccine and placebo groups. Of those, 19 sites and 38 k-mers were located in the regions comprising the RV144 vaccine (Env-gp120, Gag, and Pro). The nine signature sites in Env-gp120 were significantly enriched for known antibody-associated sites (p = 0.0021). In particular, site 317 in the third variable loop (V3) overlapped with a hotspot of antibody recognition, and sites 369 and 424 were linked to CD4 binding site neutralization. The identified signature sites significantly covaried with other sites across the genome (mean = 32.1) more than did non-signature sites (mean = 0.9) (p < 0.0001), suggesting functional and/or structural relevance of the signature sites. Since signature sites were not preferentially restricted to the vaccine immunogens and because most of the associations were insignificant following correction for multiple testing, we predict that few of the genetic differences are strongly linked to the RV144 vaccine-induced immune pressure. In addition to presenting results of the first complete-genome analysis of the breakthrough infections in the RV144 trial, this work describes a set of statistical methods and tools applicable to analysis of breakthrough infection genomes in general vaccine efficacy trials for diverse pathogens.
  • AIDS Research and Human Retroviruses 10/2014; 30 Suppl 1:A15-6. DOI:10.1089/aid.2014.5017a.abstract · 2.46 Impact Factor
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    ABSTRACT: Male circumcision provides partial protection against multiple sexually transmitted infections (STIs), including HIV, but the mechanisms are not fully understood. To examine potential vulnerabilities in foreskin epithelial structure, we used Wilcoxon paired tests adjusted using the false discovery rate method to compare inner and outer foreskin samples from 20 healthy, sexually active Peruvian males who have sex with males or transgender females, ages 21-29, at elevated risk of HIV infection. No evidence of epithelial microtrauma was identified, as assessed by keratinocyte activation, fibronectin deposition, or parakeratosis. However, multiple suprabasal tight junction differences were identified: 1) inner foreskin stratum corneum was thinner than outer (p = 0.035); 2) claudin 1 had extended membrane-bound localization throughout inner epidermis stratum spinosum (p = 0.035); 3) membrane-bound claudin 4 was absent from inner foreskin stratum granulosum (p = 0.035); and 4) occludin had increased membrane deposition in inner foreskin stratum granulosum (p = 0.042) versus outer. Together, this suggests subclinical inflammation and paracellular transport modifications to the inner foreskin. A setting of inflammation was further supported by inner foreskin epithelial explant cultures secreting higher levels of GM-CSF (p = 0.029), IP-10 (p = 0.035) and RANTES (p = 0.022) than outer foreskin, and also containing an increased density of CCR5+ and CD4+ CCR5+ cells (p = 0.022). Inner foreskin dermis also secreted more RANTES than outer (p = 0.036), and had increased density of CCR5+ cells (p = 0.022). In conclusion, subclinical changes to the inner foreskin of sexually active males may support an inflammatory state, with availability of target cells for HIV infection and modifications to epidermal barriers, potentially explaining the benefits of circumcision for STI prevention.
    PLoS ONE 09/2014; 9(9):e108954. DOI:10.1371/journal.pone.0108954 · 3.53 Impact Factor
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    ABSTRACT: The phase III RV144 HIV-1 vaccine trial estimated vaccine efficacy (VE) to be 31.2%. This trial demonstrated that the presence of HIV-1-specific IgG-binding Abs to envelope (Env) V1V2 inversely correlated with infection risk, while the presence of Env-specific plasma IgA Abs directly correlated with risk of HIV-1 infection. Moreover, Ab-dependent cellular cytotoxicity responses inversely correlated with risk of infection in vaccine recipients with low IgA; therefore, we hypothesized that vaccine-induced Fc receptor-mediated (FcR-mediated) Ab function is indicative of vaccine protection. We sequenced exons and surrounding areas of FcR-encoding genes and found one FCGR2C tag SNP (rs114945036) that associated with VE against HIV-1 subtype CRF01_AE, with lysine at position 169 (169K) in the V2 loop (CRF01_AE 169K). Individuals carrying CC in this SNP had an estimated VE of 15%, while individuals carrying CT or TT exhibited a VE of 91%. Furthermore, the rs114945036 SNP was highly associated with 3 other FCGR2C SNPs (rs138747765, rs78603008, and rs373013207). Env-specific IgG and IgG3 Abs, IgG avidity, and neutralizing Abs inversely correlated with CRF01_AE 169K HIV-1 infection risk in the CT- or TT-carrying vaccine recipients only. These data suggest a potent role of Fc-gamma receptors and Fc-mediated Ab function in conferring protection from transmission risk in the RV144 VE trial.
    The Journal of clinical investigation 08/2014; 124(9). DOI:10.1172/JCI75539 · 13.77 Impact Factor
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    ABSTRACT: Flow cytometry is used increasingly in clinical research for cancer, immunology and vaccines. Technological advances in cytometry instrumentation are increasing the size and dimensionality of data sets, posing a challenge for traditional data management and analysis. Automated analysis methods, despite a general consensus of their importance to the future of the field, have been slow to gain widespread adoption. Here we present OpenCyto, a new BioConductor infrastructure and data analysis framework designed to lower the barrier of entry to automated flow data analysis algorithms by addressing key areas that we believe have held back wider adoption of automated approaches. OpenCyto supports end-to-end data analysis that is robust and reproducible while generating results that are easy to interpret. We have improved the existing, widely used core BioConductor flow cytometry infrastructure by allowing analysis to scale in a memory efficient manner to the large flow data sets that arise in clinical trials, and integrating domain-specific knowledge as part of the pipeline through the hierarchical relationships among cell populations. Pipelines are defined through a text-based csv file, limiting the need to write data-specific code, and are data agnostic to simplify repetitive analysis for core facilities. We demonstrate how to analyze two large cytometry data sets: an intracellular cytokine staining (ICS) data set from a published HIV vaccine trial focused on detecting rare, antigen-specific T-cell populations, where we identify a new subset of CD8 T-cells with a vaccine-regimen specific response that could not be identified through manual analysis, and a CyTOF T-cell phenotyping data set where a large staining panel and many cell populations are a challenge for traditional analysis. The substantial improvements to the core BioConductor flow cytometry packages give OpenCyto the potential for wide adoption. It can rapidly leverage new developments in computational cytometry and facilitate reproducible analysis in a unified environment.
    PLoS Computational Biology 08/2014; 10(8):e1003806. DOI:10.1371/journal.pcbi.1003806 · 4.83 Impact Factor
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    ABSTRACT: Advances in high-throughput, single cell gene expression are allowing interrogation of cell heterogeneity. However, there is concern that the cell cycle phase of a cell might bias characterizations of gene expression at the single-cell level. We assess the effect of cell cycle phase on gene expression in single cells by measuring 333 genes in 930 cells across three phases and three cell lines. We determine each cell's phase non-invasively without chemical arrest and use it as a covariate in tests of differential expression. We observe bi-modal gene expression, a previously-described phenomenon, wherein the expression of otherwise abundant genes is either strongly positive, or undetectable within individual cells. This bi-modality is likely both biologically and technically driven. Irrespective of its source, we show that it should be modeled to draw accurate inferences from single cell expression experiments. To this end, we propose a semi-continuous modeling framework based on the generalized linear model, and use it to characterize genes with consistent cell cycle effects across three cell lines. Our new computational framework improves the detection of previously characterized cell-cycle genes compared to approaches that do not account for the bi-modality of single-cell data. We use our semi-continuous modelling framework to estimate single cell gene co-expression networks. These networks suggest that in addition to having phase-dependent shifts in expression (when averaged over many cells), some, but not all, canonical cell cycle genes tend to be co-expressed in groups in single cells. We estimate the amount of single cell expression variability attributable to the cell cycle. We find that the cell cycle explains only 5%-17% of expression variability, suggesting that the cell cycle will not tend to be a large nuisance factor in analysis of the single cell transcriptome.
    PLoS Computational Biology 07/2014; 10(7):e1003696. DOI:10.1371/journal.pcbi.1003696 · 4.83 Impact Factor
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    ABSTRACT: The RV144 HIV-1 vaccine trial demonstrated partial efficacy of 31% against HIV-1 infection. Studies into possible correlates of protection found that antibodies specific to the V1/V2 region of envelope correlated inversely with infection risk and that viruses isolated from trial participants contained genetic signatures of vaccine-induced pressure in the V1/V2 region. We explored the hypothesis that the genetic signatures in V1/V2 could be partly attributed to selection by vaccine primed T cells. We performed a T-cell based sieve analysis of breakthrough viruses in the RV144 trial and found evidence of predicted HLA binding escape that was greater in vaccine versus placebo recipients. The predicted escape depended on class I HLA A*02 and A*11 restricted epitopes in the MN-strain rgp120 vaccine immunogen. Though we hypothesized that this was indicative of post-acquisition selection pressure, we also found that vaccine efficacy (VE) was greater in A*02(+) compared to A*02(-) participants (VE=54% vs. 3%, p=0.05). Vaccine efficacy against viruses with a lysine residue at site 169, important to antibody binding and implicated in vaccine-induced immune pressure, was also greater in A*02(+) participants (VE=74% vs. 15%, p=0.02). Additionally, a reanalysis of vaccine-induced immune responses focused on those that were shown to correlate with infection risk, suggested that the humoral response may have differed in A*02(+) participants. These exploratory and hypothesis-generating analyses indicate there may be an association between a class I HLA allele and vaccine efficacy, highlighting the importance of considering HLA alleles and host immune genetics in HIV vaccine trials. The RV144 trial was the first to show efficacy against HIV-1 infection. Subsequently, much effort has been directed towards understanding the mechanisms of protection, including this T-cell based sieve analysis which compared the genetic sequences of viruses isolated from infected vaccine and placebo recipients. Though we hypothesized that the observed sieve effect indicated post-acquisition T-cell selection, we also found that vaccine efficacy was greater for participants who expressed HLA A*02, an allele implicated in the sieve analysis. Though HLA alleles have been associated with disease progression and viral load in HIV-1 infection, these data are the first to suggest the association of a class I HLA allele and vaccine efficacy. While these statistical analyses do not provide mechanistic evidence of protection in RV144, they generate testable hypotheses for the HIV vaccine community and they highlight the importance of assessing the impact of host immune genetics in vaccine-induced immunity and protection.
    Journal of Virology 05/2014; DOI:10.1128/JVI.01164-14 · 4.65 Impact Factor
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    ABSTRACT: Semen contains relatively ill-defined regulatory com-ponents that likely aid fertilization, but which could also interfere with defense against infection. Each ejaculate contains trillions of exosomes, membrane-enclosed subcellular microvesicles, which have im-munosuppressive effects on cells important in the genital mucosa. Exosomes in general are believed to mediate inter-cellular communication, possibly by transferring small RNA molecules. We found that seminal exosome (SE) preparations contain a sub-stantial amount of RNA from 20 to 100 nucleotides (nts) in length. We sequenced 20–40 and 40–100 nt fractions of SE RNA separately from six semen donors. We found various classes of small non-coding RNA, including microRNA (21.7% of the RNA in the 20–40 nt fraction) as well as abundant Y RNAs and tRNAs present in both fractions. Specific RNAs were consistently present in all donors. For example, 10 (of ∼2600 known) microRNAs constituted over 40% of mature microRNA in SE. Additionally, tRNA fragments were strongly enriched for 5'-ends of 18– 19 or 30–34 nts in length; such tRNA fragments re-press translation. Thus, SE could potentially deliver regulatory signals to the recipient mucosa via trans-fer of small RNA molecules.
    Nucleic Acids Research 05/2014; 42(11). DOI:10.1093/nar/gku347 · 9.11 Impact Factor
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    ABSTRACT: Flow cytometry datasets from clinical trials generate very large datasets and are usually highly standardized, focusing on endpoints that are well defined apriori. Staining variability of individual makers is not uncommon and complicates manual gating, requiring the analyst to adapt gates for each sample, which is unwieldy for large datasets. It can lead to unreliable measurements, especially if a template-gating approach is used without further correction to the gates. In this article, a computational framework is presented for normalizing the fluorescence intensity of multiple markers in specific cell populations across samples that is suitable for high-throughput processing of large clinical trial datasets. Previous approaches to normalization have been global and applied to all cells or data with debris removed. They provided no mechanism to handle specific cell subsets. This approach integrates tightly with the gating process so that normalization is performed during gating and is local to the specific cell subsets exhibiting variability. This improves peak alignment and the performance of the algorithm. The performance of this algorithm is demonstrated on two clinical trial datasets from the HIV Vaccine Trials Network (HVTN) and the Immune Tolerance Network (ITN). In the ITN data set we show that local normalization combined with template gating can account for sample-to-sample variability as effectively as manual gating. In the HVTN dataset, it is shown that local normalization mitigates false-positive vaccine response calls in an intracellular cytokine staining assay. In both datasets, local normalization performs better than global normalization. The normalization framework allows the use of template gates even in the presence of sample-to-sample staining variability, mitigates the subjectivity and bias of manual gating, and decreases the time necessary to analyze large datasets. © 2013 International Society for Advancement of Cytometry.
    Cytometry Part A 03/2014; 85(3). DOI:10.1002/cyto.a.22433 · 3.07 Impact Factor
  • Nature Biotechnology 01/2014; 32(2):146-148. DOI:10.1038/nbt.2777 · 39.08 Impact Factor
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    ABSTRACT: A major challenge for the development of a highly effective AIDS vaccine is the identification of mechanisms of protective immunity. To address this question, we used a nonhuman primate challenge model with simian immunodeficiency virus (SIV). We show that antibodies to the SIV envelope are necessary and sufficient to prevent infection. Moreover, sequencing of viruses from breakthrough infections revealed selective pressure against neutralization-sensitive viruses; we identified a two-amino-acid signature that alters antigenicity and confers neutralization resistance. A similar signature confers resistance of human immunodeficiency virus (HIV)-1 to neutralization by monoclonal antibodies against variable regions 1 and 2 (V1V2), suggesting that SIV and HIV share a fundamental mechanism of immune escape from vaccine-elicited or naturally elicited antibodies. These analyses provide insight into the limited efficacy seen in HIV vaccine trials.
    Nature 12/2013; DOI:10.1038/nature12893 · 42.35 Impact Factor

Publication Stats

1k Citations
411.32 Total Impact Points

Institutions

  • 2010–2015
    • Fred Hutchinson Cancer Research Center
      • Division of Vaccine and Infectious Disease
      Seattle, Washington, United States
    • Université Paris-Sud 11
      Orsay, Île-de-France, France
  • 2003–2015
    • University of Washington Seattle
      • • Department of Biostatistics
      • • Department of Medicine
      • • Department of Statistics
      Seattle, Washington, United States
  • 2012
    • Institut de recherches cliniques de Montréal
      Montréal, Quebec, Canada
  • 2006–2011
    • University of British Columbia - Vancouver
      • Department of Statistics
      Vancouver, British Columbia, Canada
  • 2009
    • Université de Montréal
      Montréal, Quebec, Canada