Biology subjects

Thiebaut, R.

Publications and source records attributed to Thiebaut, R..

10 recordsLinked to original sources

bioRxiv2026

When Less Is Not More: DICEPro Mitigates the Impact of Incomplete Reference Matrices on Cellular Frequency Deconvolution.

Cellular deconvolution aims to estimate the frequencies of different cell populations from gene expression measurements in a biological sample. Supervised approaches, such as CIBERSORTx and DISSECT, critically depend on the reference signature matrix, which encodes the gene expression profiles of cell-types based on prior knowledge. Despite numerous deconvolution methods, the impact of missing cell populations in the reference matrix remains understudied. Here, we evaluate the robustness of state-of-the-art deconvolution approaches using simulations based on real dataset examples combined with statistical modeling, validated against published data, and multiple real benchmark datasets. Results show that deconvolution performance remains stable when the reference matrix includes most cell-types, but declines sharply as the matrix becomes incomplete, especially for abundant cell populations. To address the limitations of incomplete reference matrices, we introduce DICEPro, an optimization-based framework designed to enhance existing deconvolution methods. By systematically adjusting the reference signatures, DICEPro better accounts for missing or underrepresented cell populations, leading to improved precision and robustness. We show that DICEPro consistently boosts deconvolution performance across both simulated datasets, derived from real data examples, and multiple real biological datasets, offering a practical solution when standard methods are hindered by incomplete references.

bioinformatics↗

bioRxiv2026

Joint mechanistic modeling of viral and antibody responses to vaccines in non-human primates to quantify SARS-CoV-2 mechanistic correlates of protection

In a global SARS-CoV-2 landscape of hybrid immunity resulting from a vaccinated worldwide population and the emergence of new variants able to escape immunity, the identification and quantification of correlates of protection (CoPs) is crucial for the adaptation of next-generation SARS-CoV-2 vaccines. Antibodies and their neutralizing capacity have been identified as reliable mechanistic CoPs. Here, we proposed an original mechanistic model jointly describing viral and antibody dynamics, and their mutual interactions, observed after SARS-CoV-2 infection in non-human primates (NHPs) with distinct immunological backgrounds. From the model, the concentration of neutralizing antibodies being protective against viral infection spreading was derived using the reproduction number. Counterfactual simulations were also performed to better understand and validate immune mechanisms driving immune control. The model was estimated on viral, binding (bAb), and neutralizing (nAb) antibody dynamics, with data collected in 34 naive and convalescent NHPs. Animals were involved in a preclinical study evaluating two next-generation protein-based vaccines targeting the RBD of the Spike protein to CD40-expressing cells, and the original BNT162b2 mRNA vaccine against Delta SARS-CoV-2 infection. Our model validated the functionality of nAb to neutralize viruses as the primary mechanism of protection. An inhibitory antibody concentration against Delta variant of 20 AU/mL was deemed protective against Delta infection in naive animals. Moreover, we showed the strong benefit of hybrid immunity to induce faster and more protective antibody responses pointing out the crucial role of the memory B-cell immune response in viral control. Finally, an additional effect of natural immunity beyond antibodies and enhancing the elimination of infected cells was identified, suggesting the potential role of the T-cell response in viral control. Our model showed the benefit of combining information from both viral and antibody responses to better qualitatively and quantitatively inform on their protective capacity against Delta SARS-CoV-2 infection.

immunology↗

bioRxiv2022

Neglecting normalization impact in semi-synthetic RNA-seq data simulation generates artificial false positives

By reproducing differential expression analysis simulation results presented by Li et al, we identified a caveat in the data generation process. Data not truly generated under the null hypothesis led to incorrect comparisons of benchmark methods. We provide corrected simulation results that demonstrate the good performance of dearseq and argue against the superiority of the Wilcoxon rank-sum test as suggested by Li et al. Please see related Research article with DOI 10.1186/s13059-022-02648-4.

bioinformatics↗

bioRxiv2021

Design, immunogenicity, and efficacy of a pan-sarbecovirus dendritic-cell targeting vaccine

The emergence of SARS-CoV-2 variants of concern (VOCs) that escape pre-existing antibody neutralizing responses increases the need for vaccines that target conserved epitopes and induce cross-reactive B- and T-cell responses. We used a computational approach and sequence alignment analysis to design a new-generation subunit vaccine targeting conserved sarbecovirus B- and T-cell epitopes from Spike (S) and Nucleocapsid (N) to antigen-presenting cells expressing CD40 (CD40.CoV2). We demonstrate the potency of CD40.CoV2 to elicit high levels of cross-neutralizing antibodies against SARS-CoV-2, VOCs, and SARS-CoV-1 in K18-hACE2 transgenic mice, associated with improved viral control and survival after challenge. In addition, we demonstrate the potency of CD40.CoV2 in vitro to recall human multi-epitope, functional, and cytotoxic SARS-CoV-2 S- and N-specific T-cell responses that are unaffected by VOC mutations and cross-reactive with SARS-CoV-1 and, to a lesser extent, MERS epitopes. Overall, these findings provide a framework for a pan-sarbecovirus vaccine.

immunology↗

bioRxiv2021

High temporal resolution transcriptomic profiling delineates distinct patterns of interferon response following Covid-19 mRNA vaccination and SARS-CoV2 infection

Knowledge of the mechanisms underpinning the development of protective immunity conferred by mRNA vaccines is fragmentary. Here we investigated responses to COVID-19 mRNA vaccination via ultra-low-volume sampling and high-temporal-resolution transcriptome profiling (23 subjects across 22 timepoints, and with 117 COVID-19 patients used as comparators). There were marked differences in the timing and amplitude of the responses to the priming and booster doses. Notably, we identified two distinct interferon signatures. The first signature (A28/S1) was robustly induced both post-prime and post-boost and in both cases correlated with the subsequent development of antibody responses. In contrast, the second interferon signature (A28/S2) was robustly induced only post-boost, where it coincided with a transient inflammation peak. In COVID19 patients, a distinct phenotype dominated by A28/S2 was associated with longer duration of intensive care. In summary, high-temporal-resolution transcriptomic permitted the identification of post- vaccination phenotypes that are determinants of the course of COVID-19 disease.

immunology↗

bioRxiv2021

SARS-CoV-2 mechanistic correlates of protection: insight from modelling response to vaccines

The definition of correlates of protection is critical for the development of next generation SARS-CoV-2 vaccine platforms. Here, we propose a new framework for identifying mechanistic correlates of protection based on mathematical modelling of viral dynamics and data mining of immunological markers. The application to three different studies in non-human primates evaluating SARS-CoV-2 vaccines based on CD40-targeting, two-component spike nanoparticle and mRNA 1273 identifies and quantifies two main mechanisms that are a decrease of rate of cell infection and an increase in clearance of infected cells. Inhibition of RBD binding to ACE2 appears to be a robust mechanistic correlate of protection across the three vaccine platforms although not capturing the whole biological vaccine effect. The model shows that RBD/ACE2 binding inhibition represents a strong mechanism of protection which required significant reduction in blocking potency to effectively compromise the control of viral replication. One Sentence SummaryA framework for modelling the immune control of viral dynamics is applied to quantify the effect of several SARS-CoV-2 vaccine platforms and to define mechanistic correlates of protection.

immunology↗

bioRxiv2021

Gene Set Analysis for time-to-event outcome with the Generalized Berk-Jones statistic

Gene set analysis evaluates the collective impact of groups of genes on an outcome of interest, such as disease occurrence. By incorporating biological knowledge through predefined gene sets, this approach enhances the interpretability of results and improves statistical power compared to gene-wise analyses. In the context of time-to-event data, existing methods are limited and fail to account for potentially strong correlations within gene sets. Given the strong performance of the Generalized Berk-Jones (GBJ) statistic, which effectively incorporates correlation within the test statistic, we adapted this method to the time-to-event framework using a Cox model. We then compared its performance with established methods, including the Wald test, global test, and global boost test. Our proposed method, sGBJ, shows an over-control of Type I error, leading to reduced statistical power compared to other methods in numerical studies. We further benchmarked these methods in two different real-world contexts: gliomas and breast cancer. The Wald test emerged as the most effective, identifying the largest number of significant pathways while maintaining appropriate control of Type I error in simulation settings. sGBJ closely followed demonstrating good performances, without a significant loss of statistical power in analyzing these two real-world biomedical datasets.

bioinformatics↗

bioRxiv2021

Distribution-free complex hypothesis testing for single-cell RNA-seq differential expression analysis

SO_SCPLOWUMMARYC_SCPLOWState-of-the-art methods for single-cell RNA sequencing (scRNA-seq) Differential Expression Analysis (DEA) often rely on strong distributional assumptions that are difficult to verify in practice. Furthermore, while the increasing complexity of clinical and biological single-cell studies calls for greater tool versatility, the majority of existing methods only tackle the comparison between two conditions. We propose a novel, distribution-free, and flexible approach to DEA for single-cell RNA-seq data. This new method, called ccdf, tests the association of each gene expression with one or many variables of interest (that can be either continuous or discrete), while potentially adjusting for additional covariates. To test such complex hypotheses, ccdf uses a conditional independence test relying on the conditional cumulative distribution function, estimated through multiple regressions. We provide the asymptotic distribution of the ccdf test statistic as well as a permutation test (when the number of observed cells is not sufficiently large). ccdf substantially expands the possibilities for scRNA-seq DEA studies: it obtains good statistical performance in various simulation scenarios considering complex experimental designs (i.e. beyond the two condition comparison), while retaining competitive performance with state-of-the-art methods in a two-condition benchmark. We apply ccdf to a large publicly available scRNA-seq dataset of 84,140 SARS-CoV-2 reactive CD8+ T cells, in order to identify the diffentially expressed genes across 3 groups of COVID-19 severity (mild, hospitalized, and ICU) while accounting for seven different cellular subpopulations.

bioinformatics↗

bioRxiv2019

dearseq: a variance component score test for RNA-Seq differential analysis that effectively controls the false discovery rate

RNA-seq studies are growing in size and popularity. We provide evidence that the most commonly used methods for differential expression analysis (DEA) may yield too many false positive results in some situations. We present dearseq, a new method for DEA which controls the FDR without making any assumption about the true distribution of RNA-seq data. We show that dearseq controls the FDR while maintaining strong statistical power compared to the most popular methods. We demonstrate this behavior with mathematical proofs, simulations, and a real data set from a study of Tuberculosis, where our method produces fewer apparent false positives.

bioinformatics↗

bioRxiv2018

cytometree: a binary tree algorithm for automatic gating in cytometry analysis

MotivationFlow cytometry is a powerful technology that allows the high-throughput quantification of dozens of surface and intracellular proteins at the single-cell level. It has become the most widely used technology for immunophenotyping of cells over the past three decades. Due to the increasing complexity of cytometry experiments (more cells and more markers), traditional manual flow cytometry data analysis has become untenable due to its subjectivity and time-consuming nature.\n\nResultsWe present a new unsupervised algorithm called \"cytometree\" to perform automated population discovery (aka gating) in flow cytometry. cytometree is based on the construction of a binary tree, the nodes of which are subpopulations of cells. At each node, the marker distributions are modeled by mixtures of normal distribution. Node splitting is done according to a normalized difference of Akaike information criteria (AIC) between the two models. Post-processing of the tree structure and derived populations allows us to complete the annotation of the derived populations. The algorithm is shown to perform better than the state-of-the-art unsupervised algorithms previously proposed on panels introduced by the Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP I) project. The algorithm is also applied to a T-cell panel proposed by the Human Immunology Project Consortium (HIPC) program; it also outperforms the best unsupervised open-source available algorithm while requiring the shortest computation time.\n\nAvailabilityAn R package named \"cytometree\" is available on the CRAN repository.\n\nContactdaniel.commenges@u-bordeaux.fr; rodolphe.thiebaut@u-bordeaux.fr\n\nSupplementary informationSupplementary data are available.

bioinformatics↗