ORCID Profile
0000-0003-3307-0446
Current Organisation
Sorbonne University
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: The American Association of Immunologists
Date: 06-2016
Abstract: Broadening our understanding of the abundance and phenotype of B cell subsets that are induced or perturbed by exogenous Ags will improve the vaccine evaluation process. Mass cytometry (CyTOF) is being used to increase the number of markers that can be investigated in single cells, and therefore characterize cell phenotype at an unprecedented level. We designed a panel of CyTOF Abs to compare the B cell response in cynomolgus macaques at baseline, and 8 and 28 d after the second homologous immunization with modified vaccinia virus Ankara. The spanning-tree progression analysis of density-normalized events (SPADE) algorithm was used to identify clusters of CD20+ B cells. Our data revealed the phenotypic complexity and ersity of circulating B cells at steady-state and significant vaccine-induced changes in the proportions of some B cell clusters. All SPADE clusters, including those altered quantitatively by vaccination, were characterized phenotypically and compared using double hierarchical clustering. Vaccine-altered clusters composed of previously described subsets including CD27hiCD21lo activated memory and CD27+CD21+ resting memory B cells, and subphenotypes with novel patterns of marker coexpression. The expansion, followed by the contraction, of a single memory B cell SPADE cluster was positively correlated with serum anti-vaccine Ab titers. Similar results were generated by a different algorithm, automatic classification of cellular expression by nonlinear stochastic embedding. In conclusion, we present an in-depth characterization of B cell subphenotypes and proportions, before and after vaccination, using a two-step clustering analysis of CyTOF data, which is suitable for longitudinal studies and B cell subsets and biomarkers discovery.
Publisher: Wiley
Date: 26-04-2017
DOI: 10.1002/CYTO.A.23107
Abstract: Comparative immune-profiling of innate responses in humans and non-human primates is important to understand the pathogenesis of infectious and chronic inflammatory diseases as well as for the preclinical development of vaccines and immune therapies. However, direct comparisons of the two species are rare and were never performed using mass cytometry. Here, whole-blood-derived leukocytes from healthy humans and cynomolgus macaques were analyzed with mass cytometry. Two similar panels of around 30 monoclonal antibodies targeting human markers associated with innate myeloid cells to stain fixed human and macaque leukocytes were constructed. To compare the circulating innate cells from the two primate species, an analysis pipeline combining a clustering analysis by the Spanning-tree Progression Analysis of Density-normalized Events (SPADE) algorithm with a two-step hierarchical clustering of cells nodes and markers was used. Identical SPADE settings were applied to both datasets, except for the 20 clustering markers which slightly differed. A correlation analysis designed to compare the phenotypes of human and macaque cell nodes and based on 16 markers, including 15 shared clustering markers and CD19 for humans or CD20 for macaques, revealed similarities and differences between staining patterns. This study unique by the number of in iduals (26 humans and 5 macaques) and the use of mass cytometry certainly contributes to better assess the advantages and limits of the use of non-human primates in preclinical research. © 2017 International Society for Advancement of Cytometry.
Publisher: Oxford University Press (OUP)
Date: 22-02-2019
Abstract: A better understanding of innate responses induced by vaccination is critical for designing optimal vaccines. Here, we studied the ersity and dynamics of the NK cell compartment after prime-boost immunization with the modified vaccinia virus Ankara using cynomolgus macaques as a model. Mass cytometry was used to deeply characterize blood NK cells. The NK cell subphenotype composition was modified by the prime. Certain phenotypic changes induced by the prime were maintained over time and, as a result, the NK cell composition prior to boost differed from that before prime. The key phenotypic signature that distinguished NK cells responding to the boost from those responding to the prime included stronger expression of several cytotoxic, homing, and adhesion molecules, suggesting that NK cells at recall were functionally distinct. Our data reveal potential priming or imprinting of NK cells after the first vaccine injection. This study provides novel insights into prime-boost vaccination protocols that could be used to optimize future vaccines.
Publisher: Oxford University Press (OUP)
Date: 12-2017
DOI: 10.1093/BIOINFORMATICS/BTW708
Abstract: Flow, hyperspectral and mass cytometry are experimental techniques measuring cell marker expressions at the single cell level. The recent increase of the number of markers simultaneously measurable has led to the development of new automatic gating algorithms. Especially, the SPADE algorithm has been proposed as a novel way to identify clusters of cells having similar phenotypes in high-dimensional cytometry data. While SPADE or other cell clustering algorithms are powerful approaches, complementary analysis features are needed to better characterize the identified cell clusters. We have developed SPADEVizR, an R package designed for the visualization, analysis and integration of cell clustering results. The available statistical methods allow highlighting cell clusters with relevant biological behaviors or integrating them with additional biological variables. Moreover, several visualization methods are available to better characterize the cell clusters, such as volcano plots, streamgraphs, parallel coordinates, heatmaps, or distograms. SPADEVizR can also generate linear, Cox or random forest models to predict biological outcomes, based on the cell cluster abundances. Additionally, SPADEVizR has several features allowing to quantify and to visualize the quality of the cell clustering results. These analysis features are essential to better interpret the behaviors and phenotypes of the identified cell clusters. Importantly, SPADEVizR can handle clustering results from other algorithms than SPADE. SPADEVizR is distributed under the GPL-3 license and is available at chitchek-lab/SPADEVizR. Supplementary data are available at Bioinformatics online.
Publisher: Springer Science and Business Media LLC
Date: 15-02-2018
DOI: 10.1038/S41598-018-21222-2
Abstract: Understanding the innate immune response to vaccination is critical in vaccine design. Here, we studied blood innate myeloid cells after first and second immunization of cynomolgus macaques with the modified vaccinia virus Ankara. The inflammation at the injection site was moderate and resolved faster after the boost. The blood concentration of inflammation markers increased after both injections but was lower after the boost. The numbers of neutrophils, monocytes, and dendritic cells were transiently affected by vaccination, but without any major difference between prime and boost. However, phenotyping deeper those cells with mass cytometry unveiled their high phenotypic ersity with subsets responding differently after each injection, some enriched only after the primary injection and others only after the boost. Actually, the composition in subphenotype already differed just before the boost as compared to just before the prime. Multivariate analysis identified the key features that contributed to these differences. Cell subpopulations best characterizing the post-boost response were more activated, with a stronger expression of markers involved in phagocytosis, antigen presentation, costimulation, chemotaxis, and inflammation. This study revisits innate immunity by demonstrating that, like adaptive immunity, innate myeloid responses differ after one or two immunizations.
Publisher: Public Library of Science (PLoS)
Date: 09-09-2010
Publisher: Elsevier BV
Date: 2018
DOI: 10.1016/J.YMETH.2017.09.005
Abstract: Cytometry is an experimental technique used to measure molecules expressed by cells at a single cell resolution. Recently, several technological improvements have made possible to increase greatly the number of cell markers that can be simultaneously measured. Many computational methods have been proposed to identify clusters of cells having similar phenotypes. Nevertheless, only a limited number of computational methods permits to compare the phenotypes of the cell clusters identified by different clustering approaches. These phenotypic comparisons are necessary to choose the appropriate clustering methods and settings. Because of this lack of tools, comparisons of cell cluster phenotypes are often performed manually, a highly biased and time-consuming process. We designed CytoCompare, an R package that performs comparisons between the phenotypes of cell clusters with the purpose of identifying similar and different ones, based on the distribution of marker expressions. For each phenotype comparison of two cell clusters, CytoCompare provides a distance measure as well as a p-value asserting the statistical significance of the difference. CytoCompare can import clustering results from various algorithms including SPADE, viSNE/ACCENSE, and Citrus, the most current widely used algorithms. Additionally, CytoCompare can generate parallel coordinates, parallel heatmaps, multidimensional scaling or circular graph representations to visualize easily cell cluster phenotypes and the comparison results. CytoCompare is a flexible analysis pipeline for comparing the phenotypes of cell clusters identified by automatic gating algorithms in high-dimensional cytometry data. This R package is ideal for benchmarking different clustering algorithms and associated parameters. CytoCompare is freely distributed under the GPL-3 license and is available on chitchek-lab/CytoCompare.
No related grants have been discovered for Nicolas Tchitchek.