Biology subjects

Jagla, B.

Publications and source records attributed to Jagla, B..

5 recordsLinked to original sources

bioRxiv2026

CyFj11: FlowJo v11 Workspace Import and Legacy Format Export for R-Based Flow Cytometry Analysis

AO_SCPLOWBSTRACTC_SCPLOWHigh-dimensional flow cytometry measures immune cells at single-cell resolution, enabling systematic characterization of cell populations at scale. But harnessing this potential requires seamless interoperability between the interactive gating tools used by biologists and the statistical environments used for detailed downstream analysis. FlowJo, one of the most widely used commercial cytometry analysis software packages, now stores workspaces in a format that existing R tools cannot read, leaving researchers unable to import their gating strategies into R, or to return R-based results to FlowJo for visual review or collaborative sharing, without manual reconstruction. We present CyFj11, an R package that closes this gap, enabling import of FlowJo v11 gating hierarchies into R and export of R-defined gates back to FlowJo (throughout this paper, "import" refers to bringing a FlowJo v11 workspace into R, and "export" to writing an R-derived GatingSet back out to FlowJo). Using a combination of synthetic test scenarios and a real-world immunophenotyping dataset, we show that population counts in FlowJo 10 and 11 matched R-derived values with Pearson correlation coefficients exceeding 0.99. CyFj11 is platform-independent, requires no additional software infrastructure, and is freely available at https://github.com/C3BI-pasteur-fr/CyFj11.

bioinformatics↗

bioRxiv2025

NK cells undergo transcriptional and functional reprogramming following Streptococcus pneumoniae infection

Natural Killer (NK) cells are cytotoxic lymphocytes and key mediators of innate immunity, essential for combating viral infections and cancer. Notably, they exhibit immunological memory, generating a stronger response upon re-exposure to the same stimulus. While NK cell memory holds promise for infection control, its role in bacterial infections remains poorly understood. Previously, we demonstrated that Streptococcus pneumoniae induces long-term, specific, and protective NK cell memory. In this study, we performed single-cell RNA-seq to uncover how NK cells respond to S. pneumoniae infection. Our findings reveal that challenged Memory (cMemory) NK cells undergo transcriptional reprogramming following S. pneumoniae infection and have a differential transcriptional response upon reinfection. In addition, we identified distinct cMemory NK cell subpopulations, with responding cMemory NK cells displaying a general enhanced activation, proliferation, and cytotoxic activity. These findings support a novel role for NK cells in the context of bacterial infections, thereby opening avenues for harnessing the potential of innate immune memory for therapeutic applications.

immunology↗

bioRxiv2023

High-dimensional spectral cytometry panels for whole blood immune phenotyping

The need to understand the mechanisms and pathways of immune responses in pathogenic conditions such as cancer and autoimmunity requires awareness of natural immune variability in healthy subjects. To this end, various systems immunology studies have been established. Among them, the Milieu Interieur (MI) study was established to define the boundaries of a healthy immune response and identify determinants of immune response variation. MI used immunophenotyping of a 1000 healthy donor cohort by flow cytometry as a principal outcome for immune variance at steady state. For the 10-year longitudinal MI study, we have developed two high-dimensional spectral flow cytometry panels that allow deep characterization of innate and adaptive whole blood immune cells (35 and 34 fluorescent markers, respectively) and standardized the protocol for sample handling, staining, acquisition, and data analysis. This permits the reproducible quantification of over 182 immune cell phenotypes through robust immunophenotyping at a single site. This highly standardized protocol was applied to samples from patients with autoimmune/inflammatory diseases. It is currently used for characterization of the impact of age and environmental factors on peripheral blood immune phenotypes of >400 donors from the initial MI cohort.

immunology↗

bioRxiv2022

A single cell transcriptomic and clonal analysis depicts valvulogenesis

Congenital heart malformations often include mitral valve defects which remain largely unexplained. During embryogenesis, a restricted population of endocardial cells within the atrioventricular canal (AVC) undergoes endothelial to mesenchymal transition (EndMT) to give rise to mitral valvular cells. However, the identity, fate decisions of these progenitors as well as the distribution of their derivatives in valve leaflets remain unknown. Here, we use scRNA-seq of genetically labeled mouse AVC endocardial cells and of micro-dissected embryonic and postnatal mitral valves to characterize the developmental road. We uncovered the genetic, cell signaling and metabolic processes underlying specification of the progenitors and how they contribute to subtypes of endothelial and interstitial embryonic and postnatal valvular cells. Using clonal genetic tracing with multicolor reporter, we describe specific modes of growth of endocardial cell-derived clones which build up in a proper manner functional valve leaflets. Our data reveal how both genetic and metabolic specification mechanisms specifically drive the fate of a subset of endocardial cells toward valve progenitors and their distinct clonal contribution to the formation of the valve.

developmental biology↗

bioRxiv2020

SCHNAPPs - Single Cell sHiNy APPlication(s)

MotivationSingle-cell RNA-sequencing (scRNAseq) experiments are becoming a standard tool for bench-scientists to explore the cellular diversity present in all tissues. On one hand, the data produced by scRNASeq is technically complex, with analytical workflows that are still very much an active field of bioinformatics research, and on the other hand, a wealth of biological background knowledge is often needed to guide the investigation. Therefore, there is an increasing need to develop applications geared towards bench-scientists to help them abstract the technical challenges of the analysis, so that they can focus on the Science at play. It is also expected that such applications should support closer collaboration between bioinformaticians and bench-scientists by providing reproducible science tools. ResultsWe present SCHNAPPs, a computer program designed to enable bench-scientists to autonomously explore and interpret single cell RNA-seq expression data and associated annotations. The Shiny-based application allows selecting genes and cells of interest, performing quality control, normalization, clustering, and differential expression analyses, applying standard workflows from Seurat (Stuart et al., 2019) or Scran (Lun et al., 2016) packages, and most of the common visualizations. An R-markdown report can be generated that tracks the modifications, and selected visualizations facilitating communication and reproducibility between bench-scientist and bioinformatician. The modular design of the tool allows to easily integrate new visualizations and analyses by bioinformaticians. We still recommend that a data analysis specialist oversees the analysis and interpretation. AvailabilityThe SCHNAPPs application, docker file, and documentation are available on GitHub: https://c3bi-pasteur-fr.github.io/UTechSCB-SCHNAPPs; Example contribution are available at the following GitHub site: https://github.com/baj12/SCHNAPPsContributions.

bioinformatics↗