Biology subjects

Dartigues, B.

Publications and source records attributed to Dartigues, B..

5 recordsLinked to original sources

bioRxiv2026

TileScope quantifies spatial gene expression relationships beyond colocalization

Gene expression patterns within the same tissue region can exhibit similar correlation or enrichment, but still differ in their organization within regions and at their boundaries. We present TileScope, a framework for analysing genes and transcriptional programs within predefined tissue regions, their components and their contacts with neighbouring regions. TileScope combines regional enrichment, within-region correlation, cross-correlation over distance and expression differences near specific boundaries. Repeated spatial partitions and consensus aggregation show how these measurements depend on the grouping of spots or cells. Across 30 controlled constructions derived from glioblastoma and hepatocellular carcinoma data representing ten spatial expression scenarios, combined measurements separated arrangements that individual statistics left ambiguous, including coincident patches, gradients and expression localised at region boundaries. In 30 human glioblastoma sections, TileScope distinguished shared regional enrichment from internal covariation and identified hypoxia-associated expression localised at region boundary despite negligible within-region correlation. In newly acquired Visium profiles of control and myeloid-targeted MCT1-knockout (KO) orthotopic GL261 mouse gliomas, proliferation and myeloid programs were enriched in tumour peripheries but showed opposite within-periphery-region correlations. Proliferation was higher near the tumour-facing boundary in both samples, whereas myeloid expression was higher near the tumour-facing boundary in the control and farther within periphery in the KO condition. Xenium applications on glioblastoma and breast cancer samples demonstrated feasibility with irregularly spaced individual cells and different regional annotations. TileScope provides spatially explicit descriptions of gene and program relationships, linking their organization within tissue compartments to their sensitivity to aggregation.

bioinformatics↗

bioRxiv2024

Exploiting a metabolic vulnerability in brain tumour stem cells using a brain-penetrant drug with safe profile

Glioblastoma (GB) remains one of the most treatment refractory and fatal tumour in humans. GB contains a population of self-renewing stem cells, the brain tumour stem cells (BTSC) that are highly resistant to therapy and are at the origin of tumour relapse. Here, we report, for the first time, that mubritinib potently impairs stemness and growth of patient-derived BTSCs harboring different oncogenic mutations. Mechanistically, by employing bioenergetic assays and rescue experiments, we provide compelling evidence that mubritinib acts on complex I of the electron transport chain to impair BTSC stemness pathways, self-renewal and proliferation. Global gene expression profiling revealed that mubritinib alters the proliferative, neural-progenitor-like, and the cell-cycling state signatures. We employed in vivo pharmacokinetic assays to establish that mubritinib crosses the blood-brain barrier. Using preclinical models of patient-derived and syngeneic murine orthotopic xenografts, we demonstrated that mubritinib delays GB tumourigenesis, and expands lifespan of animals. Interestingly, its combination with radiotherapy offers survival advantage to animals. Strikingly, thorough toxicological and behavioral studies in mice revealed that mubritinib does not induce any damage to normal cells and has a well-tolerated and safe profile. Our work warrants further exploration of this drug in in-human clinical trials for better management of GB tumours.

cancer biology↗

bioRxiv2023

Benchmarking the identification of a single degraded protein to explore optimal search strategies for ancient proteins

Palaeoproteomics is a rapidly evolving discipline, and practitioners are constantly developing novel strategies for the analyses and interpretations of complex, degraded protein mixtures. The community has also established standards of good practice to interrogate our data. However, there is a lack of a systematic exploration of how these affect the identification of peptides, post-translational modifications (PTMs), proteins and their significance (through the False Discovery Rate) and correctness. We systematically investigated the performance of a wide range of sequencing tools and search engines in a controlled system: the experimental degradation of the single purified bovine {beta}-lactoglobulin (BLG), heated at 95 {degrees}C and pH 7 for 0, 4 and 128 days. We target BLG since it is one of the most robust and ubiquitous proteins in the archaeological record. We tested different reference database choices, a targeted dairy protein one, and the whole bovine proteome and the three digestion options (tryptic-, semi-tryptic- and non-specific searches), in order to evaluate the effects of search space and the identification of peptides. We also explored alternative strategies, including open search that allows for the global identification of PTMs based upon wide precursor mass tolerance and de novo sequencing to boost sequence coverage. We analysed the samples using Mascot, MaxQuant, Metamorpheus, pFind, Fragpipe and DeNovoGUI (pepNovo+, DirecTag, Novor), benchmarked these tools and discuss the optimal strategy for the characterisation of ancient proteins. We also studied physicochemical properties of the BLG that correlate with bias in the identification coverage.

bioinformatics↗

bioRxiv2021

DeepSpot: a deep neural network for RNA spot enhancement in smFISH microscopy images

Detection of RNA spots in single molecule FISH microscopy images remains a difficult task especially when applied to large volumes of data. The small size of RNA spots combined with high noise level of images often requires a manual adaptation of the spot detection thresholds for each image. In this work we introduce DeepSpot, a Deep Learning based tool specifically designed to enhance RNA spots which enables spot detection without need to resort to image per image parameter tuning. We show how our method can enable the downstream accurate detection of spots. The architecture of DeepSpot is inspired by small object detection approaches. It incorporates dilated convolutions into a module specifically designed for the Context Aggregation for Small Object (CASO) and uses Residual Convolutions to propagate this information along the network. This enables DeepSpot to enhance all RNA spots to the same intensity and thus circumvents the need for parameter tuning. We evaluated how easily spots can be detected in images enhanced by our method, by training DeepSpot on 20 simulated and 1 experimental datasets, and have shown that more than 97% accuracy is achieved. Moreover, comparison with alternative deep learning approaches for mRNA spot detection (deepBlink) indicated that DeepSpot allows more precise mRNA detection. In addition, we generated smFISH images from mouse fibroblasts in a wound healing assay to evaluate whether DeepSpot enhancement can enable seamless mRNA spot detection and thus streamline studies of localized mRNA expression in cells.

bioinformatics↗

bioRxiv2019

DypFISH: Dynamic Patterned FISH to Interrogate RNA and Protein Spatial and Temporal Subcellular Distribution

Advances in single cell RNA sequencing have allowed for the identification and characterization of cellular subtypes based on quantification of the number of transcripts in each cell. However, cells may differ not only in the number of mRNA transcripts that they exhibit, but also in their spatial and temporal distribution, intrinsic to the definition of their cellular state. Here we describe DypFISH, an approach to quantitatively investigate the spatial and temporal subcellular localization of RNA and protein, by combining micropatterning of cells with fluorescence microscopy at high resolution. We introduce a range of analytical techniques for quantitatively interrogating single molecule RNA FISH data in combination with protein immunolabeling over time. Strikingly, our results show that constraining cellular architecture reduces variation in subcellular mRNA and protein distributions, allowing the characterization of their localization and dynamics with high reproducibility. Many tissues contain cells that exist in similar constrained architectures. Thus DypFISH reveals reproducible patterns of clustering, strong correlative influences of mRNA-protein localization on MTOC orientation when they are present and interdependent dynamics globally and at specific subcellular locations which can be extended to physiological systems.

systems biology↗