Biology subjects

Burel, J. G.

Publications and source records attributed to Burel, J. G..

9 recordsLinked to original sources

bioRxiv2026

Single-cell profiling of physically interacting immune cells using image-guided sorting

Single-cell transcriptomics of cell-cell complexes offers key insights into in vivo immune interactions, yet distinguishing true physical contacts from artifacts remains challenging. Here, we integrated spectral imaging cell sorting with physical cell deconvolution to separate circulating T cell-monocyte doublets from tuberculosis patients into physically interacting (in-contact) and non-interacting (coincidental) populations. Single-cell sequencing of individual cell partners revealed that physical contact is associated with robust signatures distinct from singlets, including unidirectional monocyte-to-T cell RNA transfer, heightened metabolic activity and T cell cytotoxicity, and a trend toward antigen-specific enrichment. These signatures spanned all T cell and monocyte subpopulations, reflecting a global program of T cell-monocyte engagement. Coincidental doublets displayed intermediate transcriptional profiles between singlet and in-contact states, suggesting the presence of recently disrupted or forming physical pairs. This framework provides high-fidelity deconvolution of cellular interactions, uncovering signatures governing T cell-monocyte communication in human disease.

immunology↗

bioRxiv2026

A high-resolution blood immune cell doublet atlas via imaging spectral cytometry

Circulating cell-cell complexes (doublets) provide critical insights into in vivo immune interactions. Here, we leverage high-dimensional imaging spectral cytometry to systematically map the landscape of immune doublets in human peripheral blood. By integrating morphometric imaging features with a 22-color spectral panel, we established a gating workflow that cleanly segregates genuine, physically interacting complexes from coincidental transits and platelet contaminants, and resolved 12 highly pure homotypic and heterotypic immune doublet configurations in peripheral blood mononuclear cells (PBMC). At steady state, each doublet population displayed unique abundance, affinity and surface phenotypic profiles relative to circulating singlets. Principal component analysis (PCA) of quantitative imaging features further revealed lineage-specific topologies distinguishing singlet cells, homotypic doublets, and heterotypic pairs. Finally, comparative cohort analysis demonstrated that while doublet frequencies, affinities and surface phenotypes remain comparable between healthy donors and patients with tuberculosis (TB) disease, imaging-derived features uncovered disease-specific alterations in doublets morphology. Specifically, high-dimensional clustering using imaging parameters showed that TB disease significantly enriches for a tightly synapsed T cell-monocyte cluster characterized by high CD3-CD14 pixel correlation. Collectively, this study establishes a robust framework for mapping circulating cell-cell networks and demonstrates the superior sensitivity of imaging features over conventional fluorescence for capturing functional immune interactions in health and disease.

immunology↗

bioRxiv2025

Enrichment of a CD4-CD8- NK-like cytotoxic Vδ1/3 T cell subset in tuberculosis disease

Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a leading cause of global morbidity and mortality. Although gamma-delta ({gamma}{delta}) T cells have increasingly been suggested to contribute to the TB immune response, quantitative and qualitative differences in this immune cell compartment between healthy and TB diseased individuals are not well-characterized. In this study, we used single-cell RNA sequencing to provide a high-resolution characterization of CD4-CD8- {gamma}{delta} T cells in peripheral blood across healthy Mtb-non-sensitized, healthy Mtb-sensitized, and TB disease pre-/post-treatment cohorts. We found upregulation of an activated and cytotoxic gene signature in {gamma}{delta} T cells of TB disease compared to both healthy cohorts. Strikingly, these differences persisted through one year following diagnosis of TB disease (corresponding to six months after completion of anti-TB therapy). We found that these transcriptomic differences were largely mediated by an NK-like cytotoxic V{delta}1 and V{delta}3 subset that was enriched in TB disease, with a unique V{delta}3 TCR gene usage. Our findings suggest long-lasting changes in the CD4-CD8- {gamma}{delta} T cell compartment and highlight V{delta}3 cells, a previously underappreciated {gamma}{delta} T cell subset, as potentially important in TB.

immunology↗

bioRxiv2025

Minimal immune cell subset differences in a cohort of close contacts of tuberculosis index cases

Understanding the perturbations in immune response across the spectrum of TB infection is still unclear. In this study, we followed a cohort of close contacts of pulmonary TB patients with serial QFT testing at 0, 3, 6, and 12 months, and stratified them into six subgroups: QFT-increasing (low/high), QFT converters (QFT- to QFT+), QFT+ stable, and QFT- individuals. Despite these distinct QFT trajectories, we observed minimal differences in immune cell frequencies, activation profiles, and T helper subset distributions among the QFT subgroups, suggesting limited immunological stratification based on QFT dynamics alone. Ex vivo immune phenotyping, including analysis of CD4, CD8, and NKT cell frequencies, memory T cell subsets, and activated T cells (HLA-DRCD38), failed to distinguish between QFT subgroups. Antigen-specific CD4 T cell responses assessed by the activation-induced marker (AIM) assay were elevated in QFT+ compared to QFT- individuals. These findings suggest that blood-based immune profiling may not capture subtle immunological transitions among QFT converters or individuals with increasing QFT responses. In contrast, individuals with active TB (ATB) showed clear immune perturbations. ATB patients at diagnosis exhibited significantly elevated frequencies of antigen-specific CD4 T cells, increased activated T cells, and higher frequencies of intermediate monocytes and NK cells compared to QFT+/QFT- contacts. Many of these immune features declined with treatment, indicating therapy-associated immune resolution. Additionally, shifts in T helper subsets and effector memory populations were observed over the course of treatment. These results suggest that while ex vivo immune profiling can robustly distinguish active TB from non-diseased states, it lacks the resolution to differentiate QFT subgroups based on QFT dynamics alone. This could reflect either immunological similarity among close contacts regardless of QFT status or limitations of blood-based phenotyping in detecting early or subclinical immune shifts. Our study highlights the challenge of immunologically distinguishing QFT-defined subgroups within close contacts using conventional ex vivo profiling approaches.

immunology↗

bioRxiv2024

A novel method for characterizing cell-cell interactions at single-cell resolution reveals unique signatures in blood T cell-monocyte complexes during infection

Communication between immune cells through direct contact is a critical feature of immune responses. Here, we developed a novel high-throughput method to study the transcriptome and adaptive immune receptor repertoire of single cells forming complexes without needing bioinformatic deconvolution. We found that T cells and monocytes forming complexes in blood during active tuberculosis (TB) and dengue hold unique transcriptomic signatures indicative of TCR/MCH-II immune synapses. Additionally, T cells in complexes showed enrichment for effector phenotypes, imaging and transcriptomic features of active TCR signaling, and increased immune activity at diagnosis compared to after anti-TB therapy. We also found evidence for bidirectional RNA exchange between T cells and monocytes, since complexes were markedly enriched for "dual-expressing" cells (i.e., co-expressing T cell and monocyte genes). Thus, studying immune cell complexes at a single-cell resolution offers novel perspectives on immune synaptic interactions occurring in blood during infection. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=59 SRC="FIGDIR/small/612103v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@f90023org.highwire.dtl.DTLVardef@178007corg.highwire.dtl.DTLVardef@167e53corg.highwire.dtl.DTLVardef@1faff48_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2024

Putting computational models of immunity to the test - an invited challenge to predict B. pertussis vaccination outcomes

Systems vaccinology studies have been used to build computational models that predict individual vaccine responses and identify the factors contributing to differences in outcome. Comparing such models is challenging due to variability in study designs. To address this, we established a community resource to compare models predicting B. pertussis booster responses and generate experimental data for the explicit purpose of model evaluation. We here describe our second computational prediction challenge using this resource, where we benchmarked 49 algorithms from 53 scientists. We found that the most successful models stood out in their handling of nonlinearities, reducing large feature sets to representative subsets, and advanced data preprocessing. In contrast, we found that models adopted from literature that were developed to predict vaccine antibody responses in other settings performed poorly, reinforcing the need for purpose-built models. Overall, this demonstrates the value of purpose-generated datasets for rigorous and open model evaluations to identify features that improve the reliability and applicability of computational models in vaccine response prediction.

immunology↗

bioRxiv2020

The challenge of distinguishing cell-cell complexes from singlet cells in non-imaging flow cytometry and single-cell sorting

Our recent work has highlighted that care needs to be taken when interpreting single cell data originating from flow cytometry acquisition or cell sorting: We found that doublets of T cells bound to other immune cells are often present in the live singlet gate of human peripheral blood samples acquired by flow cytometry. This hidden contamination generates atypical gene signatures of mixed cell lineage in what is assumed to be single cells, which can lead to data misinterpretation, such as the description of novel immune cell types. Here, based on the example of T cell-monocyte complexes, we identify experimental and data analysis strategies to help distinguishing between singlets and cell-cell complexes in non-imaging flow cytometry and single-cell sorting. We found robust molecular signatures in both T cell-monocyte and T cell-B cell complexes that can distinguish them from singlets at both protein and mRNA levels. Imaging flow cytometry with appropriate gating strategy (matching the one used in cell sorting) and direct microscopy imaging after cell sorting were the two methods of choice to detect the presence of cell-cell complexes in suspicious dual-expressing cells. We finally applied this knowledge to highlight the likely presence of T cell-B cell complexes in a recently published dataset describing a novel cell population with mixed T cell and B cell lineage properties.

immunology↗

bioRxiv2018

No cell is an island: circulating T cell:monocyte complexes are markers of immune perturbations

Our results highlight for the first time that a significant proportion of cell doublets in flow cytometry, previously believed to be the result of technical artefacts and thus ignored in data acquisition and analysis, are the result of true biological interaction between immune cells. In particular, we show that cell:cell doublets pairing a T cell and a monocyte can be directly isolated from human blood, and high resolution microscopy shows polarized distribution of LFA1/ICAM1 in many doublets, suggesting in vivo formation. Intriguingly, T cell:monocyte complex frequency and phenotype fluctuate with the onset of immune perturbations such as infection or immunization, reflecting expected polarization of immune responses. Overall these data suggest that cell doublets reflecting T cell-monocyte in vivo immune interactions can be detected in human blood and that the common approach in flow cytometry to avoid studying cell:cell complexes should be revisited.

immunology↗

bioRxiv2017

DAFi: A Directed Recursive Filtering and Clustering Approach to Data-Driven Identification of Cell Populations from Polychromatic Flow Cytometry Data

Computational methods for identification of cell populations from high-dimensional flow cytometry data are changing the paradigm of cytometry bioinformatics. Data clustering is the most common computational approach to unsupervised identification of cell populations from multidimensional cytometry data. We found that combining recursive filtering and clustering with constraints converted from the user manual gating strategy can effectively identify overlapping and rare cell populations from smeared data that would have been difficult to resolve by either a single run of data clustering or manual segregation. We named this new method DAFi: Directed Automated Filtering and Identification of cell populations. Design of DAFi preserves the data-driven characteristics of unsupervised clustering for identifying novel cell-based biomarkers, but also makes the results interpretable to experimental scientists as in supervised classification through mapping and merging the high-dimensional data clusters into the user-defined 2D gating hierarchy. By recursive data filtering before clustering, DAFi can uncover small local clusters which are otherwise difficult to identify due to the statistical interference of the irrelevant major clusters. Quantitative assessment of cell type specific characteristics demonstrates that the population proportions calculated by DAFi, while being highly consistent with those by expert centralized manual gating, have smaller technical variance than those from individual manual gating analysis. Visual examination of the dot plots showed that the boundaries of the DAFi-identified cell populations followed the natural shapes of the data distributions. To further exemplify the utility of DAFi, we show that DAFi can incorporate the FLOCK clustering method to identify novel cell-based biomarkers. Implementation of DAFi supports options including clustering, bisecting, slope-based gating, and reversed filtering to meet various auto-gating needs from different scientific use cases.

bioinformatics↗