Biology subjects

Thibault, G.

Publications and source records attributed to Thibault, G..

At least 19 recordsLinked to original sources

bioRxiv2026

CROCHET: a versatile pipeline for automated analysis and visual atlas creation from single-cell spatialomic data

Spatial biology technologies offer a unique opportunity to link tissue composition with function. However, analytical methods for quantifying and interpreting highly complex spatial data remain limited. We present CROCHET (ChaRacterization Of Cellular HEterogeneity in Tissues), an end-to-end analysis pipeline for construction of spatially resolved cell atlases from raw data covering millions of cells across large sample cohorts. Its modular architecture supports the integration of diverse data modalities and novel analytical methods for image processing and segmentation, spatialomics quantification and downstream analyses. With comprehensive, open-source, user-friendly, interactive, and visual analysis modules, CROCHET aims to democratize spatial omics for a broad community of users.

bioinformatics↗

bioRxiv2025

Enterococcus faecalis redox metabolism activates the unfolded protein response to impair wound healing

Enterococcus faecalis is an opportunistic pathogen that thrives in biofilm-associated infections and delays wound healing, yet how it impairs host tissue responses is unclear. Here, we identified extracellular electron transport (EET) as a previously unrecognized source of ROS in E. faecalis and show that this activity directly triggers the unfolded protein response (UPR) in epithelial cells and delays epithelial cell migration. ROS detoxification with catalase suppressed E. faecalis-induced UPR and rescued epithelial cell migration, while exogenous H2O2 was sufficient to restore UPR activation in EET-deficient strains. Importantly, UPR disruption by pharmacological inhibition also impaired cell migration, highlighting a critical role for UPR homeostasis in wound repair. Our findings establish EET as a novel virulence mechanism that links bacterial redox metabolism to host cell stress and impaired repair, offering new avenues for therapeutic intervention in chronic infections.

microbiology↗

bioRxiv2025

Impact of Choline intake during pregnancy on maternal cognition and hippocampal gene signature in old age

Women are twice as likely to have Alzheimers disease (AD) than men and multiparity has been suggested to be a risk factor for dementia. The present study evaluated whether the lack of certain nutrients during pregnancy influences cognition while pregnant and in old age in mouse model. Non-targeted NMR analysis revealed significantly lower levels of numerous plasma nutrients and metabolites including choline and its derivatives on gestation day 7 compared to day 1. Novel object recognition and Morris Water Maze tests revealed impaired cognition in pregnant mice compared to nonpregnant controls. Choline deprivation worsened the cognitive impairment during pregnancy and choline supplementation alleviated it. Furthermore, choline availability during pregnancy affected cognition and general health in old age, with mice given a choline-deficient diet during pregnancy performed more poorly. RNA-Seq analysis indicates lasting effect of choline intake during pregnancy on hippocampal gene signatures in old age. Choline deprivation was associated with more upregulation of proinflammatory genes, whereas choline supplementation showed upregulation of neuroprotective genes such as Prl, Gh, and hemoglobin (Hba and Hbb subunits). Together, the study shows that choline supplementation benefits cognitive health in women during pregnancy and in old age.

animal behavior and cognition↗

bioRxiv2025

A spatially resolved single cell proteomic atlas of Small Bowel Adenocarcinoma

Small bowel adenocarcinoma (SBA) is a rare malignancy marked with a poor prognosis. The cellular and proteomic heterogeneity within the tumor immune microenvironment (TIME) of SBA is a likely driver of prognosis, disease progression and response to therapy. We have addressed a major gap in knowledge of the TIME in SBA using highly multiplexed, protein imaging of the SBA tumor-immune ecosystem generating a comprehensive, single-cell level, spatial, proteomic atlas of TIME in > 600,000 cells from 136 tumor and matched normal samples from clinically and genomically annotated SBA patients (N=37). The SBA TIME Atlas informs on spatial distribution and interactions of tumor-intrinsic processes, diverse immune cell types, immune checkpoints, and vascularization. Enrichment of proliferating epithelial tissue, stem cells, and likely pro-tumor immune signatures in the tumor niche is contrasted by the representation of naive and early-effector T-cells in the epithelial compartments of adjacent normal tissues. Epithelial-stem-immune cell spatial architectures within tumor and matched-normal niches were strongly associated with patient survival, suggesting malignancy is driven by spatial architecture beyond the tumor microenvironment. The blueprints of therapeutically actionable immune checkpoints at the interface between epithelial and microenvironmental T-cells as well as macrophages have established a guideline for precision immunotherapies tailored to the TIME composition in SBA. We expect that this SBA atlas will contribute to a deeper understanding of the immune contexture in this rare disease as well as other gastrointestinal cancers and help guide future precision immune-oncology strategies.

cancer biology↗

bioRxiv2025

Enterococcus faecalis-derived lactic acid facilitates persistent and polymicrobial wound infections by suppressing macrophage activation

Macrophage activation is essential for innate immunity, and its suppression enables pathogen persistence. We show that Enterococcus faecalis suppresses macrophage activation through lactic acid-mediated extracellular acidification. Mutants lacking lactate dehydrogenase (ldh), and thus unable to acidify the environment, fail to inhibit NF-{kappa}B. E. faecalis-derived lactic acid acts via MCT-1 and GPR81 through two distinct but complementary mechanisms that culminate in the reduction of NF-{kappa}B activity. Lactic acid acts through MCT-1 to inhibit ERK and STAT3 phosphorylation, leading to reduced STAT3 binding to the Myd88 promoter, and reduced MyD88 protein levels. Lactic acid signaling to GPR81 mediates the phosphorylation of the transcriptional factor YAP, ultimately attenuating NF-{kappa}B signaling. In a murine wound infection model, this lactic acid-driven immunosuppressive niche enables prolonged E. faecalis persistence and enhances the fitness of co-infecting bacteria such as Escherichia coli. These findings reveal how bacterial lactic acid subverts innate immunity to support chronic and polymicrobial infections. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/635924v2_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@2e1565org.highwire.dtl.DTLVardef@17eaf85org.highwire.dtl.DTLVardef@356139org.highwire.dtl.DTLVardef@821581_HPS_FORMAT_FIGEXP M_FIG C_FIG Highlights- E. faecalis lactate dehydrogenase mutants fail to acidify the environment and cannot suppress NF-{kappa}B signaling. - Lactic acid is necessary and sufficient to drive NF-{kappa}B suppression in macrophages. - Lactic acid NF-{kappa}B suppression is relayed through two complementary routes: MCT- 1 transport and the lactate sensor GPR81. - MCT-1-dependent signaling blunts ERK/STAT3 phosphorylation, lowering MyD88 levels; whereas GPR81 drives YAP phosphorylation, reducing NF-{kappa}B activation. - E. faecalis-derived lactic acid drives immunosuppression, potentiates persistence, and promotes multi-species wound infection in vivo.

microbiology↗

bioRxiv2024

Yeast Knowledge Graphs Database for Exploring Saccharomyces cerevisiae and Schizosaccharomyces pombe

Biomedical literature contains an extensive wealth of information on gene and protein function across various biological processes and diseases. However, navigating this vast and often restricted-access data can be challenging, making it difficult to extract specific insights efficiently. In this study, we introduce a high-throughput pipeline that leverages OpenAIs Generative Pre-Trained Transformer Model (GPT) to automate the extraction and analysis of gene function information. We applied this approach to 84,427 publications on Saccharomyces cerevisiae and 6,452 publications on Schizosaccharomyces pombe, identifying 3,432,749 relationships for budding yeast and 421,198 relationships for S. pombe. This resulted in a comprehensive, searchable online Knowledge Graph database, available at yeast.connectome.tools and spombe.connectome.tools, which offers users extensive access to various interactions and pathways. Our analysis underscores the power of integrating artificial intelligence with bioinformatics, as demonstrated through key insights into important nodes like Hsp104 and Atg8 proteins. This work not only facilitates efficient data extraction in yeast research but also presents a scalable model for similar studies in other biological systems. HIGHLIGHTSO_LIGenerated Yeast Knowledge Graphs from full-text research articles. C_LIO_LIAnalyzed over 90,000 publications for Saccharomyces and Schizosaccharomyces species. C_LIO_LIExtracted millions of relationships using GPT-based natural language processing. C_LIO_LIYeast Knowledge Graphs accessible through interactive web platforms and APIs. C_LIO_LIAdvanced tool enabling insights into gene networks and functional interactions. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC="FIGDIR/small/626523v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@15afd5corg.highwire.dtl.DTLVardef@afa4bcorg.highwire.dtl.DTLVardef@16858f2org.highwire.dtl.DTLVardef@1a7a19f_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

AxonFinder: Automated segmentation of tumor innervating neuronal fibers

Neurosignaling is increasingly recognized as a critical factor in cancer progression, where neuronal innervation of primary tumors contributes to the diseases advancement. This study focuses on segmenting individual axons within the prostate tumor microenvironment, which have been challenging to detect and analyze due to their irregular morphologies. We present a novel deep learning-based approach for the automated segmentation of axons, AxonFinder, leveraging a U-Net model with a ResNet-101 encoder, based on a multiplexed imaging approach. Utilizing a dataset of whole-slide images from low-, intermediate-, and high-risk prostate cancer patients, we manually annotated axons to train our model, achieving significant accuracy in detecting axonal structures that were previously hard to segment. Our analysis includes a comprehensive assessment of axon density and morphological features across different CAPRA-S prostate cancer risk categories, providing insights into the correlation between tumor innervation and cancer progression. Our paper suggests the potential utility of neuronal markers in the prognostic assessment of prostate cancer in aiding the pathologists assessment of tumor sections and advancing our understanding of neurosignaling in the tumor microenvironment.

systems biology↗

bioRxiv2023

Towards Generalizability and Robustness in Biological Object Detection in Electron Microscopy Images

Machine learning approaches have the potential for meaningful impact in the biomedical field. However, there are often challenges unique to biomedical data that prohibits the adoption of these innovations. For example, limited data, data volatility, and data shifts all compromise model robustness and generalizability. Without proper tuning and data management, deploying machine learning models in the presence of unaccounted for corruptions leads to reduced or misleading performance. This study explores techniques to enhance model generalizability through iterative adjustments. Specifically, we investigate a detection tasks using electron microscopy images and compare models trained with different normalization and augmentation techniques. We found that models trained with Group Normalization or texture data augmentation outperform other normalization techniques and classical data augmentation, enabling them to learn more generalized features. These improvements persist even when models are trained and tested on disjoint datasets acquired through diverse data acquisition protocols. Results hold true for transformerand convolution-based detection architectures. The experiments show an impressive 29% boost in average precision, indicating significant enhancements in the models generalizibality. This underscores the models capacity to effectively adapt to diverse datasets and demonstrates their increased resilience in real-world applications.

bioengineering↗

bioRxiv2023

Efficient semi-supervised semantic segmentation of electron microscopy cancer images with sparse annotations

Electron microscopy (EM) enables imaging at nanometer resolution and can shed light on how cancer evolves to develop resistance to therapy. Acquiring these images has become a routine task; however, analyzing them is now the bottleneck, as manual structure identification is very time-consuming and can take up to several months for a single sample. Deep learning approaches offer a suitable solution to speed up the analysis. In this work, we present a study of several state-of-the-art deep learning models for the task of segmenting nuclei and nucleoli in volumes from tumor biopsies. We compared previous results obtained with the ResUNet architecture to the more recent UNet++, FracTALResNet, SenFormer, and CEECNet models. In addition, we explored the utilization of unlabeled images through semi-supervised learning with Cross Pseudo Supervision. We have trained and evaluated all of the models on sparse manual labels from three fully annotated in-house datasets that we have made available on demand, demonstrating improvements in terms of 3D Dice score. From the analysis of these results, we drew conclusions on the relative gains of using more complex models, semi-supervised learning as well as next steps for the mitigation of the manual segmentation bottleneck.

bioengineering↗

bioRxiv2023

Multiplex imaging of localized prostate tumors reveals changes in mast cell type composition and spatial organization of AR-positive cells in the tumor microenvironment

Mapping spatial interactions of cancer, immune and stromal cells present novel opportunities for patient stratification and for advancing immunotherapy. While single-cell studies revealed significant molecular heterogeneity in prostate tumors, there is currently no understanding of how immune cell heterogeneity impacts spatial coordination between tumor and stromal cells in localized tumors. Here, we used cyclic immunofluorescent imaging on whole-tissue sections to uncover novel spatial associations between cancer and stromal cells in low- and high-grade prostate tumors and tumor-adjacent normal tissues. Our results provide a spatial map of 699,461 single-cells that show epigenetic and molecular differences in distinct clinical grades. We report unique populations of mast cells that differentially express CD44, CD90 and Granzyme B (GZMB) and demonstrate GZMB+ mast cells are spatially associated with M2 macrophages in prostate tumors. Finally, we uncover recurrent neighborhoods that are primarily driven by androgen receptor positive (AR+) stromal cells and identify transcriptional networks active in AR+ prostate stroma.

cancer biology↗

bioRxiv2023

SEG: Segmentation Evaluation in absence of Ground truth labels

Identifying individual cells or nuclei is often the first step in the analysis of multiplex tissue imaging (MTI) data. Recent efforts to produce plug-and-play, end-to-end MTI analysis tools such as MCMICRO1- though groundbreaking in their usability and extensibility - are often unable to provide users guidance regarding the most appropriate models for their segmentation task among an endless proliferation of novel segmentation methods. Unfortunately, evaluating segmentation results on a users dataset without ground truth labels is either purely subjective or eventually amounts to the task of performing the original, time-intensive annotation. As a consequence, researchers rely on models pre-trained on other large datasets for their unique tasks. Here, we propose a methodological approach for evaluating MTI nuclei segmentation methods in absence of ground truth labels by scoring relatively to a larger ensemble of segmentations. To avoid potential sensitivity to collective bias from the ensemble approach, we refine the ensemble via weighted average across segmentation methods, which we derive from a systematic model ablation study. First, we demonstrate a proof-of-concept and the feasibility of the proposed approach to evaluate segmentation performance in a small dataset with ground truth annotation. To validate the ensemble and demonstrate the importance of our method-specific weighting, we compare the ensembles detection and pixel-level predictions - derived without supervision - with the datas ground truth labels. Second, we apply the methodology to an unlabeled larger tissue microarray (TMA) dataset, which includes a diverse set of breast cancer phenotypes, and provides decision guidelines for the general user to more easily choose the most suitable segmentation methods for their own dataset by systematically evaluating the performance of individual segmentation approaches in the entire dataset.

bioinformatics↗

bioRxiv2021

Robust Segmentation of Cellular Ultrastructure on Sparsely Labeled 3D Electron Microscopy Images using Deep Learning

A deeper understanding of the cellular and subcellular organization of tumor cells and their interactions with the tumor microenvironment will shed light on how cancer evolves and guide effective therapy choices. Electron microscopy (EM) images can provide detailed view of the cellular ultrastructure and are being generated at an ever-increasing rate. However, the bottleneck in their analysis is the delineation of the cellular structures to enable interpretable rendering. We have mitigated this limitation by using deep learning, specifically, the ResUNet architecture, to segment cells and subcellular ultrastructure. Our initial prototype focuses on segmenting nuclei and nucleoli in 3D FIB-SEM images of tumor biopsies obtained from patients with metastatic breast and pancreatic cancers. Trained with sparse manual labels, our method results in accurate segmentation of nuclei and nucleoli with best Dice score of 0.99 and 0.98 respectively. This method can be extended to other cellular structures, enabling deeper analysis of inter- and intracellular state and interactions.

cancer biology↗

bioRxiv2021

Noise Reconstruction & Removal Network: A New Architecture to Denoise FIB-SEM Images

Recent advances in Focused Ion Beam-Scanning Electron Microscopy (FIB-SEM) allows the imaging and analysis of cellular ultrastructure at nanoscale resolution, but the collection of labels and/or noise-free data sets has several challenges, often immutable. Reasons range from time consuming manual annotations, requiring highly trained specialists, to introducing imaging artifacts from the prolonged scanning during acquisition. We propose a fully unsupervised Noise Reconstruction and Removal Network for denoising scanning electron microscopy images. The architecture, inspired by gated recurrent units, reconstructs and removes the noise by synthesizing the sequential data. At the same time the fully unsupervised training guides the network in distinguishing true signal from noise and gives comparable results to supervised architectures. We demonstrate that this new network specialized on 3D electron microscopy data sets, achieves comparable and even better results than supervised networks.

bioengineering↗

bioRxiv2021

Transfer Learning for Inference of Metastatic Origin from Whole Slide Histology

Accurate diagnosis of metastatic cancer is essential for prescribing optimal control strategies to halt further spread of metastasizing disease. While pathological inspection aided by immunohistochemistry staining provides a valuable gold standard for clinical diagnostics, deep learning methods have emerged as powerful tools for identifying clinically relevant features of whole slide histology relevant to a tumors metastatic origin. Although deep learning models require significant training data to learn effectively, transfer learning paradigms provide mechanisms to circumvent limited training data by first training a model on related data prior to fine-tuning on smaller data sets of interest. In this work we propose a transfer learning approach that trains a convolutional neural network to infer the metastatic origin of tumor tissue from whole slide images of hematoxylin and eosin (H&E) stained tissue sections and illustrate the advantages of pre-training network on whole slide images of primary tumor morphology. We further characterize statistical dissimilarity between primary and metastatic tumors of various indications on patch-level images to highlight limitations of our indication-specific transfer learning approach. Using a primary-to-metastatic transfer learning approach, we achieved mean class-specific areas under receiver operator characteristics curve (AUROC) of 0.779, which outperformed comparable models trained on only images of primary tumor (mean AUROC of 0.691) or trained on only images of metastatic tumor (mean AUROC of 0.675), supporting the use of large scale primary tumor imaging data in developing computer vision models to characterize metastatic origin of tumor lesions.

pathology↗

bioRxiv2020

Toward reproducible, scalable, and robust data analysis across multiplex tissue imaging platforms

The emergence of megascale single-cell multiplex tissue imaging (MTI) datasets necessitates reproducible, scalable, and robust tools for cell phenotyping and spatial analysis. We developed open-source, graphics processing unit (GPU)-accelerated tools for intensity normalization, phenotyping, and microenvironment characterization. We deploy the toolkit on a human breast cancer (BC) tissue microarray stained by cyclic immunofluorescence and benchmark our cell phenotypes against a published MTI dataset. Finally, we demonstrate an integrative analysis revealing BC subtype-specific features.

systems biology↗

bioRxiv2020

An Integrated Clinical, Omic, and Image Atlas of an Evolving Metastatic Breast Cancer

Mechanisms of therapeutic resistance manifest in metastatic cancers as tumor cell intrinsic alterations and extrinsic microenvironmental influences that can change during treatment. To support the development of methods for the identification of these mechanisms in individual patients, we present here an Omic and Multidimensional Spatial (OMS) Atlas generated from four serial biopsies of a metastatic breast cancer patient during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata including treatment times and doses, anatomic imaging, and blood-based response measurements to exploratory analytics including comprehensive DNA, RNA, and protein profiles, images of multiplexed immunostaining, and 2- and 3-dimensional scanning electron micrographs. These data reveal aspects of therapy-associated heterogeneity and evolution of the cancers genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples showing how integrative analyses of these data provide insights into potential mechanisms of response and resistance, and suggest novel therapeutic vulnerabilities.

cancer biology↗

bioRxiv2020

Yeast FIT2 homolog is necessary to maintain cellular proteostasis by regulating lipid homeostasis

Lipid droplets (LDs) have long been regarded as inert cytoplasmic organelles with the primary function of housing excess intracellular lipids. More recently, LDs have been strongly implicated in conditions of lipid and protein dysregulation. The fat storage inducing transmembrane (FIT) family of proteins comprises of evolutionarily conserved endoplasmic reticulum (ER)-resident proteins that have been reported to induce LD formation. Here, we establish a model system to study the role of S. cerevisiae FIT homologues (ScFIT), SCS3 and YFT2, in proteostasis and stress response pathways. While LD biogenesis and basal ER stress-induced unfolded protein response (UPR) remain unaltered in ScFIT mutants, SCS3 was found to be essential for proper stress-induced UPR activation and for viability in the absence of the sole yeast UPR transducer IRE1. Devoid of a functional UPR, scs3{Delta} mutants exhibited accumulation of triacylglycerol within the ER along with aberrant LD morphology, suggesting a UPR-dependent compensatory mechanism for LD maturation. Additionally, SCS3 was necessary to maintain phospholipid homeostasis. Strikingly, the absence of the ScFIT proteins results in the downregulation of the closely-related Heat Shock Response (HSR) pathway. In line with this observation, global protein ubiquitination and the turnover of both ER and cytoplasmic misfolded proteins is impaired in ScFIT{Delta} cells, while a screen for interacting partners of Scs3 identifies components of the proteostatic machinery as putative targets. Taken together, these suggest that ScFIT proteins may modulate proteostasis and stress response pathways with lipid metabolism at the interface between the two cellular processes.

cell biology↗

bioRxiv2020

VISTA: Virtual ImmunoSTAining for pancreatic disease quantification in murine cohorts

Mechanistic studies of pancreatic disease progression using animal models require objective and quantifiable assessment of tissue changes among animal cohorts. Disease state quantification, however, relies heavily on tissue immunostaining, which can be expensive, labor- and time-intensive, and all too often produces uneven staining that is prone to variable interpretation between experts and inaccurate quantification. Here we develop a fully automated semantic segmentation tool using deep learning for the rapid and objective quantification of histologic features using hematoxylin and eosin (H&E) stained pancreatic tissue sections acquired from murine pancreatic cancer models. The tool was successfully trained to segment and quantify multiple histopathologic features of pancreatic pre-cancer evolution, including normal acinar structures, the ductal phenotype of acinar-to ductal metaplasia (ADM), dysplasia, and the expanding stromal compartment. Disease quantifications produced by our computational tool were highly correlated to the results obtained by immunostaining markers of normal and diseased tissue (DAPI, amylase, and cytokeratins; correlation score= 0.9, 0.95, and 0.91, respectively) and were able to accurately reproduce immunostain patterns. Moreover, our tool was able to distinguish ADM from dysplasia, which are not reliably distinguished by immunostaining, and avoid the pitfalls of uneven or poor-quality staining. Using this tool, we quantified the changes in histologic feature abundance for murine cohorts with oncogenic Kras-driven disease at 2 months and 5 months of age (n=12, n=13). The calculated changes in histologic feature abundance were consistent with biological expectations, showing an expansion of the stromal compartment, a reduction of normal acinar tissue, and an increase in both ADM and dysplasia as disease progresses (p= 2e-6, 6e-7, 4e-4, and 3e-5, respectively). These results demonstrate the tools efficacy for accurate and rapid quantification of multiple histologic features using an objective and automated platform. Our tool promises to rapidly accelerate and improve the quantification of altered pancreatic disease progression in animal studies.

bioinformatics↗