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Bardou, M.

Publications and source records attributed to Bardou, M..

3 recordsLinked to original sources

Met-Vision stratifies single cell metabolism revealing coexisting energetic states in tissue macrophages redistributed by inflammation

The function of tissue-associated macrophages is tightly linked to their energy metabolism. Yet, the diversity of macrophage metabolic profiles coexisting in tissues at homeostasis or during immune challenges is incompletely understood. Here, we introduce Met-Vision, an imaging-based pipeline for single-cell functional profiling and classification of energy metabolism. Across multiple tissue contexts, we identified that macrophages do not adopt a uniform metabolic profile but typically co-exist in four discrete metabolic states with distinct dependence on OXPHOS and metabolic plasticity. Inflammation reconfigured the distribution of macrophage metabolic profiles that remained heterogeneous. Notably, inflammation-derived nitric oxide finely tuned the distribution of macrophage energetic states. These findings challenge the view of homogenous metabolic activation and reveal a layer of metabolic diversity in tissue at steady state and during inflammation. The ability to stratify macrophage energy metabolic profiles with Met-Vision should help guide the development of metabolism-targeted therapies for inflammatory diseases, cancer, and metabolic disorders. Significance statementThis study introduces Met-Vision and reveal that tissue macrophages occupy four distinct energetic states, challenging the notion of uniform metabolic activation and opening avenues for guiding metabolism-targeted therapies.

immunology↗

pyHiM, a new open-source, multi-platform software package for spatial genomics based on multiplexed DNA-FISH imaging

The three-dimensional (3D) nuclear organization of chromatin in eukaryotes plays a crucial role in gene regulation, DNA replication, and DNA damage repair. While genome-wide ensemble methods have enhanced our understanding of chromatin organization, they lack the ability to capture single-cell heterogeneity and preserve spatial information. To overcome these limitations, a new family of imaging-based methods has emerged, giving rise to the field of spatial genomics. In this study, we present pyHiM, an open-source and modular software toolbox specifically designed for the robust, automatic analysis of sequential spatial genomics data. pyHiM enables the reconstruction of chromatin traces in individual cells from raw, multicolor images, offering novel, robust and validated algorithms, extensive documentation, and tutorials. Its user-friendly graphical interface and command-line interface allow for easy installation and execution on various hardware platforms. The software employs a modular architecture, allowing independent execution of analysis steps and customization according to sample specificity and computing resources. pyHiM supports preprocessing, spot detection, mask detection, and trace generation, generating human-readable reports and intermediate results for data validation and further analysis. Moreover, it offers additional features for data formatting, result display, and post-processing. pyHiMs scalability and parallelization capabilities enable the analysis of large, complex datasets in a reasonable time frame. Overall, pyHiM aims to facilitate the democratization and standardization of spatial genomics analysis, foster collaborative developments, and promote the growth of a user community to drive discoveries in the field of chromatin organization.

genomics↗

Nanometric axial localization of single fluorescent molecules with modulated excitation

Strategies have been developed in LIDAR to perform distance measurements for non-coherent emission in sparse samples based on excitation modulation. Super-resolution fluorescence microscopy is also striving to perform axial localization but through entirely different approaches. Here we revisit the amplitude modulated LIDAR approach to reach nanometric localization precision and we successfully adapt it to bring distinct advantages to super-resolution microscopy. The excitation pattern is performed by interference enabling the decoupling between spatial and time modulation. The localization of a single emitter is performed by measuring the relative phase of its linear fluorescent response to the known shifting excitation field. Taking advantage of a tilted interfering configuration, we obtain a typical axial localization precision of 7.5 nm over the entire field of view and the axial capture range, without compromising on the acquisition time, the emitter density or the lateral localization precision. The interfering pattern being robust to optical aberrations, this modulated localization (ModLoc) strategy is particularly well suited for observations deep in the samples. Images performed on various biological samples show that the localization precision remains nearly constant up to several micrometers.

biophysics↗