Biology subjects

Colinge, J.

Publications and source records attributed to Colinge, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Single-cell atlas reveals age-related cellular shifts underlying fibrosis in murine synovium

Aging is a major risk factor for joint disease, yet the impact of physiological aging on the synovium remains poorly defined. Here we generate a single-cell atlas of murine ankle synovium across age and identify the sublining stromal-myeloid niche as a major site of age-associated remodeling. Aging shifted fibroblast states toward oxidative stress and matrix-remodeling programs, accompanied by sublining collagen accumulation, reduced cellularity, and loss of THY1+ sublining fibroblasts. In parallel, resident synovial macrophages exhibited altered inflammatory and phagocytic responses together with a preferential decline in TIM4+VSIG4- sublining macrophages, without overt local myeloid expansion despite systemic inflammaging. Macrophage depletion experiments further supported a link between sublining macrophages and extracellular matrix homeostasis. Together, these findings provide a reference framework for synovial aging and uncover niche-specific stromal and macrophage alterations associated with aging.

physiology↗

bioRxiv2025

Circulating DNA reveals nucleosome occupancy patterns that are associated with nucleosome-DNA affinity and are affected in cancer

The study of cell-free circulating DNA (cirDNA) fragments (fragmentomics) from liquid biopsies has received increasing attention. By constructing an atlas of these well-positioned nucleosomes, which we called WPNA, we found that their occupancy was associated with histone-DNA affinity, as evidenced by codon usage bias and differences in cirDNA fragment sizes. Moreover, WPNA nucleosome occupancy was different in healthy and cancer samples, thus allowing developing a high-performance machine learning approach for cancer detection (specificity and sensitivity >0.95 for seven cancer types). Cancer influenced WPNA nucleosome occupancy in a global manner, although distinct cancer types retained specific features. WPNA nucleosome occupancy at transcription factor binding sites revealed shared, pan-cancer regulation of transcriptional programs involved in hematopoietic cell differentiation and neutrophil biology, the main cirDNA sources. This work provides new fundamental insights into cirDNA and DNA sequence using cirDNA as a physical readout. It also bares translational significance by disclosing a new high-performance strategy for cancer detection from liquid biopsies.

genomics↗

bioRxiv2025

Addressing multiple facets of ligand-receptor network inference including single-cell proteomics

Distinct ligand-receptor interaction (LRI) inference tools often produce markedly different results, and their performance can vary considerably across datasets. Indeed, performance is influenced by differences in experimental designs and dataset-specific features, making it difficult to establish a universal LRI tool. To address this challenge, we expanded our SingleCellSignalR Bioconductor package to provide an integrated framework that incorporates alternative scoring strategies and adjustable analytical depth. We motivate this choice through the analysis of two single-cell transcriptomics datasets that exemplify contrasting experimental designs. Leveraging the new framework flexibility, we present a detailed analysis of paired single-cell proteomics and transcriptomics data, providing, to our knowledge, the first direct comparison of LRI inference across these complementary modalities at single-cell resolution. Finally, we demonstrate how the same framework seamlessly accommodates additional underexplored data types from the LRI perspective, including patient-derived mouse xenografts and bulk RNA sequencing of upstream-sorted cell populations.

bioinformatics↗

bioRxiv2025

Single- and double-strand circulating DNA fragmentomics for enhanced cancer detection performance

In early detection of cancer, the use of circulating cell-free DNA (cirDNA) obtained from blood samples is notable for its minimally invasive nature. We have developed an algorithm designed to discriminate cancer patients and healthy individuals based on cirDNA fragment end motif analysis assisted by machine learning, using data obtained from shallow whole genome sequencing (a method we call EMA). We applied EMA to cirDNA from the plasma of patients with stage II-III breast cancer, stage I-III non-small cell lung cancer, and metastatic colorectal cancer (mCRC). CirDNA from 158 individuals was prepared following the conventional double-stranded DNA library preparation (DSP). Using 3 bp end motifs, each tumor type was detected with a sensitivity of 0.87-1.00, a specificity of 0.95, and an AUC above 0.96. The three selected cancer types could be differentiated with an accuracy (ACC) above 0.94. Multi-cancer detection by pooling samples from the three cancer types showed ACC, AUC and sensitivity of 0.98, 0.99 and 0.98, respectively. Comparisons with 4 and 2 bp end motifs were conducted, and our main observations were confirmed using an external public dataset (N=366). We also performed a single-stranded DNA library preparation (SSP) using mCRC patients and healthy control cirDNA, which allowed us to make the first ever end motif analysis in the literature which compares the use of DSP and SSP. As compared to EMAD (use of DSP), EMAS (use of SSP) produced a very significant difference in end motif frequency and an improved cancer detection performance (ACC, AUC and sensitivity of 0.97, 1.00 and 0.99, respectively). Furthermore, optimal performance was produced when the full-size range was used for EMAS, whereas when the dataset was restricted to fragments of 115 - 220 bp for EMAD. EMAS strong performance, coupled with its compatibility with cost-effective shallow cirDNA sequencing, positions this methodology as a potentially transformative tool in early cancer screening.

genomics↗

bioRxiv2024

Testing and overcoming the limitations of Modular Response Analysis

Modular Response Analysis (MRA) is an effective method to infer biological networks from perturbation data. However, it has several limitations, such as strong sensitivity to noise, need of performing independent perturbations that hit a single node at a time, and linear approximation of dependencies within the network. Previously, we addressed MRA sensitivity to noise by revisiting MRA as a multilinear regression problem. Here, we provide new contributions to complement this theory. First, we overcame the need of perturbations to be independent, thereby augmenting MRA applicability. Second, using analysis of variance (ANOVA) and lack of fit tests, we assessed MRA compatibility with the data and identified the primary source of errors. If nonlinearity prevails, we propose a polynomial extension to the model. Third, we demonstrated how to effectively use the prior knowledge of the network studied. Finally, we added these innovations to our R software package MRARegress to provide a complete, extended theory around MRA and to facilitate its access by the community.

bioinformatics↗

bioRxiv2024

The transcription factor RIP140 regulates interferon γ signaling in breast cancer

RIP140 (receptor interacting protein of 140 kDa) is an important player in breast cancer (BC) by regulating key cellular pathways such as nuclear hormone receptors signaling. In order to identify additional genes specifically regulated by RIP140 in BC, we performed an RNA sequencing after silencing its expression in MCF-7 cells. We identified the interferon {gamma} (IFN{gamma}) signaling as being substantially repressed by RIP140 knock-down. Using the GBP1 (guanylate binding protein 1) gene as a reporter of IFN{gamma} signaling, we demonstrated its robust induction by RIP140 through an ISRE motif, leading to a significant reduction of its induction upon IFN{gamma} treatment. Furthermore, we showed that low levels of RIP140 amplified the IFN{gamma}-dependent inhibition of BC cell proliferation. In line with these data, reanalysis of transcriptomic data obtained in human BC samples, revealed that IFN{gamma} levels were associated with good prognosis only for BC patients exhibiting tumors expressing low levels of RIP140, thus confirming its effect on the anti-tumor activity of IFN{gamma} provided by our experimental data. Altogether, this study identifies RIP140 as a new regulator of IFN{gamma} signaling in breast tumorigenesis.

cancer biology↗

bioRxiv2023

Modeling the simultaneous dynamics of proteins in blood plasma and the cerebrospinal fluid in human in vivo

The analysis of protein dynamics or turnover in patients has the potential to reveal altered protein recycling such as in Alzheimer disease, and to provide informative data regarding drug efficacy, or certain biological processes. The observed protein dynamics in a solid tissue or a fluid is the net result of protein synthesis and degradation, but also transport across biological compartments. We report an accurate 3-biological compartment model able simultaneously account for the protein dynamics observed in blood plasma and the cerebrospinal fluid (CSF) including a hidden central nervous system (CNS) compartment. We successfully applied this model to 69 proteins of a single individual displaying similar or very different dynamics in plasma and CSF. This study put a strong emphasis on the methods and tools needed develop this type of model. We believe it will be useful to any researcher dealing with protein dynamics data modeling.

systems biology↗

bioRxiv2023

Enabling population protein dynamics through Bayesian modeling

The knowledge of protein dynamics or turnover in patients provides invaluable information related to certain diseases, drug efficacy, or biological processes. A great corpus of experimental and computational methods has been developed, including by us, in the case of human patients followed in vivo. Moving one step further, we propose here a new modeling approach to capture the highly relevant notion of population protein dynamics. Using two data sets, we show that models inspired by population pharmacokinetics can accurately capture protein turnover within a cohort of individuals, even in presence of substantial inter-individual variability. Such models pave the way for comparative studies searching for altered dynamics or biomarkers in diseases.

systems biology↗

bioRxiv2023

Cancer-associated Fibroblast Spatial Heterogeneity and EMILIN1 Expression in Cancer Stroma Modulate TGF-beta Activity and CD8+ T-Cell Infiltration in Breast Cancer

The tumor microenvironment (TME) and its multifaceted interactions with cancer cells are major targets for cancer treatment. Single-cell technologies have brought major insights into the TME, but the resulting complexity frequently precludes conclusions on function. Therefore, we combined single-cell RNA sequencing and spatial transcriptomic data to explore the relationship between different cancer-associated fibroblast (CAF) populations and immune cell exclusion in breast tumors. Our data show for the first time the degree of spatial organization of different CAF populations in breast cancer. We found that IL-iCAFs, Detox-iCAFs, and IFN{gamma}-iCAFs tended to cluster together, while Wound-myCAFs, TGF{beta}-myCAFs, and ECM-myCAFs formed another group that overlapped with elevated TGF-{beta} signaling. Differential gene expression analysis of areas with CD8+ T-cell infiltration/exclusion within the TGF-{beta} signaling-rich zones identified elastin microfibrillar interface protein 1 (EMILIN1) as a top modulated gene. EMILIN1, a TGF-{beta} inhibitor, was upregulated in IFN{gamma}-iCAFs directly modulating TGF{beta} immunosuppressive function. Histological analysis of 74 breast cancer samples confirmed that high EMILIN-1 expression in the tumor margins was related to high CD8+ T-cell infiltration, consistent with our spatial gene expression analysis. High EMILIN-1 expression was also associated with better prognosis of patients with breast cancer, underscoring its functional significance for the recruitment of cytotoxic T cells into the tumor area. In conclusion, our data show that correlating TGF-{beta} signaling to a CAF subpopulation is not enough because proteins with TGF-{beta}-modulating activity originating from other CAF subpopulations can alter its activity. Therefore, therapeutic targeting should remain focused on biological processes rather than on specific CAF subtypes.

cancer biology↗

bioRxiv2022

Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR

The study of cellular networks mediated by ligand-receptor interactions has attracted much attention recently owing to single-cell omics. However, rich collections of bulk data accompanied with clinical information exists and continue to be generated with no equivalent in single-cell so far. In parallel, spatial transcriptomic (ST) analyses represent a revolutionary tool in biology. A large number of ST projects rely on multicellular resolution, for instance the Visium platform, where several cells are analyzed at each location, thus producing localized bulk data. Here, we describe BulkSignalR, a R package to infer ligand-receptor networks from bulk data. BulkSignalR integrates ligand-receptor interactions with downstream pathways to estimate statistical significance. A range of visualization methods complement the statistics, including functions dedicated to spatial data. We demonstrate BulkSignalR relevance using different bulk datasets, including new Visium liver metastasis ST data, with experimental validation of selected interactions. A comparison with other ST packages shows the significantly higher quality of BulkSignalR inferences. BulkSignalR can be applied to any species thanks to its built-in generic ortholog mapping functionality.

bioinformatics↗

bioRxiv2022

Modular response analysis reformulated as a multilinear regression problem

MotivationModular response analysis (MRA) is a well-established method to infer biological networks from perturbation data. Classically, MRA requires the solution of a linear system and results are sensitive to noise in the data and perturbation intensities. Applications to networks of 10 nodes or more are difficult due to noise propagation. ResultsWe propose a new formulation of MRA as a multilinear regression problem. This enables to integrate all the replicates and potential, additional perturbations in a larger, over determined and more stable system of equations. More relevant confidence intervals on network parameters can be obtained and we show competitive performance for networks of size up to 100. Prior knowledge integration in the form of known null edges further improves these results. Availability and implementationThe R code used to obtain the presented results is available from GitHub: https://github.com/J-P-Borg/BioInformatics ContactPatrice.ravel@umontpellier.fr

systems biology↗

bioRxiv2022

Farnesyltransferase inhibition overcomes the adaptive resistance to osimertinib in EGFR-mutant NSCLC

Drug-tolerant "dormant" cells (DTC) have emerged as one of the major non-genetic mechanisms driving resistance to targeted therapy in lung cancer, although the sequence of events leading to entry and exit from dormancy remain poorly described. Here, we performed real-time monitoring of the cell cycle dynamics during the adaptive response to Epidermal Growth Factor Receptor tyrosine kinase inhibitors (EGFR-TKi) in a panel of EGFR-mutated lung cancer cell lines. We identified a rare population of S/G2 cycling cells (referred to as early escapers) that emerged in the first hours of treatment amongst stably arrested and progressively dying G1 cells. We determined that early escapers evolved from a non-proliferative differentiated alveolar type 1 (AT1) phenotype which was invariably associated with cytoskeletal remodeling through Rho/ROCK pathway activation. Using a panel of Rho-pathway inhibitors, we found that the farnesyltransferase inhibitor tipifarnib induced a complete clearance of EGFR-TKi-induced DTC thus fully preventing relapse in vitro. Using a xenograft model and a PDX model of EGFRL858R/T790M lung cancer, co-treatment with tipifarnib prevented relapse to osimertinib for up to 6 months with no evidence of toxicity. Among the farnesylated proteins regulated during osimertinib treatment, concomitant inhibition of RHOE and LaminB1 was sufficient to recapitulated FTIs effect. Osimertinib and tipifarnib co-treatment completely suppressed the emergence of AT1 phenotype, prevented mitosis of S/G2-treated cells and increased the apoptotic response through activation of ATF4-CHOP-dependent Integrated Stress Response (ISR) pathway. Our data strongly support the use of tipifarnib in combination with osimertinib in patients to effectively and durably prevent relapse.

cancer biology↗

bioRxiv2021

Application of Modular Response Analysis to Medium- to Large-Size Biological Systems

The development of high-throughput genomic technologies associated with recent genetic perturbation techniques such as short hairpin RNA (shRNA), gene trapping, or gene editing (CRISPR/Cas9) has made it possible to obtain large perturbation data sets. These data sets are invaluable sources of information regarding the function of genes, and they offer unique opportunities to reverse engineer gene regulatory networks in specific cell types. Modular response analysis (MRA) is a well-accepted mathematical modeling method that is precisely aimed at such network inference tasks, but its use has been limited to rather small biological systems so far. In this study, we show that MRA can be employed on large systems with almost 1,000 network components. In particular, we show that MRA performance surpasses general-purpose mutual information-based algorithms. Part of these competitive results was obtained by the application of a novel heuristic that pruned MRA-inferred interactions a posteriori. We also exploited a block structure in MRA linear algebra to parallelize large system resolutions. Author SummaryThe knowledge of gene and protein regulatory networks in specific cell types, including pathologic cells, is an important endeavor in the post-genomic era. A particular type of data obtained through the systematic perturbation of the actors of such networks enables the reconstruction of the latter and is becoming available at a large scale (networks comprised of almost 1,000 genes). In this work, we benchmark the performance of a classical methodology for such data called modular response analysis, which has been so far applied to networks of modest sizes. We also propose improvements to increase performance and to accelerate computations on large problems.

systems biology↗

bioRxiv2020

The immune landscape of primary central nervous system diffuse large B cell lymphoma

Primary central nervous system diffuse large B-cell lymphoma (PCNSL) is a rare and aggressive entity that resides in an immune-privileged site. The tumor microenvironment (TME) and the disruption of the immune surveillance influence lymphoma pathogenesis and immunotherapy resistance. Despite growing knowledge on heterogeneous therapeutic responses, no comprehensive description of the PCNSL TME is available. We investigated the immune subtypes of PCNSL and their association with molecular signaling and survival. Bulk mRNA-sequencing (n=20) and microarray (n=34) data were exploited to identify three immune subtypes of PCNSL: immune-rich, poor, and intermediate. The immune-rich subtype was associated to better survival and characterized by hyper-activation of STAT3 signaling and inflammatory signaling, e.g., IFN{gamma} and TNF-, resembling the hot subtype described in primary testicular lymphoma and solid cancer. WNT/{beta}-catenin, HIPPO, and NOTCH signaling were hyper-activated in the immune-poor subtype. HLA down-modulation was clearly associated with a low or intermediate immune infiltration and the absence of T-cell activation. Moreover, HLA class I down-regulation was also correlated with worse survival with implications on immune-intermediate PCNSL that frequently feature reduced HLA expression. A ligand-receptor intercellular network revealed high expression of two immune checkpoints, i.e., CTLA-4/CD86 and TIM-3/LAGLS9. Immunohistopathology and digital imaging showed that TIM-3 and galectin-9 proteins were clearly upregulated in PCNSL. Altogether, our study reveals that patient stratification according to immune subtypes, HLA status, and immune checkpoint molecule quantification should be considered prior to immune checkpoint inhibitor therapy. Moreover, TIM-3 protein should be considered an axis for future therapeutic development.

cancer biology↗

bioRxiv2020

MAGI1 inhibits the AMOTL2/p38 stress pathway and prevents luminal breast tumorigenesis

Alterations to cell polarization or to intercellular junctions are often associated with epithelial cancer progression, including breast cancers (BCa). We show here that the loss of the junctional scaffold protein MAGI1 is associated with bad prognosis in luminal BCa, and promotes tumorigenesis. E-cadherin and the actin binding scaffold AMOTL2 accumulate in MAGI1 deficient cells which are subjected to increased stiffness. These alterations are associated with low YAP activity, the terminal Hippo-pathway effector, but with an elevated ROCK and p38 Stress Activated Protein Kinase activities. Blocking ROCK prevented p38 activation, suggesting that MAGI1 limits p38 activity in part through releasing actin strength. Importantly, the increased tumorigenicity of MAGI1 deficient cells is rescued in the absence of AMOTL2 or after inhibition of p38, demonstrating that MAGI1 acts as a tumor-suppressor in luminal BCa by inhibiting an AMOTL2/p38 stress pathway.

cancer biology↗

bioRxiv2020

Loss of epithelial polarity redirects Notch signaling and triggers a Xrp1 response during neoplastic growth in Drosophila

Aggressive neoplastic growth can be initiated by a limited number of genetic alterations, such as the well-established cooperation between loss of cell architecture and hyperactive signaling pathways. However, our understanding of how these different alterations interact and influence each other remains very incomplete. Using Drosophila paradigms of imaginal wing disc epithelial growth, we have monitored the changes in Notch pathway activity according to the polarity status of cells (scrib mutant). We show that the scrib mutation impacts the direct transcriptional output of the Notch pathway, without altering the global distribution of Su(H), the Notch dedicated transcription factor. The Notch-dependent neoplasms require however, the action of a group of transcription factors, similar to those previously identified for Ras/scrib neoplasm (namely AP-1, Stat92E, Ftz-F1, and bZIP factors), further suggesting the importance of this transcription factor network during neoplastic growth. Finally our work highlights some Notch/scrib specificities, in particular the role of the PAR domain containing bZIP transcription factor and Notch direct target Pdp1 for neoplastic growth.

developmental biology↗

bioRxiv2020

aiMeRA: A generic modular response analysis R package and its application to estrogen and retinoic acid receptors crosstalk

Modular response analysis (MRA) is a widely used modeling technique to uncover coupling strengths in molecular networks under a steady-state condition by means of perturbation experiments. We propose an extension of this methodology to search genomic data for new associations with a network modeled by MRA and to improve the predictive accuracy of MRA models. These extensions are illustrated by exploring the cross talk between estrogen and retinoic acid receptors, two nuclear receptors implicated in several hormone-driven cancers such as breast. We also present a novel, rigorous and elegant mathematical derivation of MRA equations, which is the foundation of this work and of an R package that is freely available at https://github.com/bioinfo-ircm/aiMeRA/. This mathematical analysis should facilitate MRA understanding by newcomers. Author summaryEstrogen and retinoic acid receptors play an important role in several hormone-driven cancers and share co-regulators and co-repressors that modulate their transcription factor activity. The literature shows evidence for crosstalk between these two receptors and suggests that spatial competition on the promoters could be a mechanism. We used MRA to explore the possibility that key co-repressors, i.e., NRIP1 (RIP140) and LCoR could also mediate crosstalk by exploiting new quantitative (qPCR) and RNA sequencing data. The transcription factor role of the receptors and the availability of genome-wide data enabled us to explore extensions of the MRA methodology to explore genome-wide data sets a posteriori, searching for genes associated with a molecular network that was sampled by perturbation experiments. Despite nearly two decades of use, we felt that MRA lacked a systematic mathematical derivation. We present here an elegant and rather simple analysis that should greatly facilitate newcomers understanding of MRA details. Moreover, an easy-to-use R package is released that should make MRA accessible to biology labs without mathematical expertise. Quantitative data are embedded in the R package and RNA sequencing data are available from GEO.

systems biology↗

bioRxiv2019

SingleCellSignalR: Inference of intercellular networks from single-cell transcriptomics

Single-cell transcriptomics offers unprecedented opportunities to infer the ligand-receptor interactions underlying cellular networks. We introduce a new, curated ligand-receptor database and a novel regularized score to perform such inferences. For the first time, we try to assess the confidence in predicted ligand-receptor interactions and show that our regularized score outperforms other scoring schemes while controlling false positives. SingleCellSignalR is implemented as an open-access R package accessible to entry-level users and available from https://github.com/SCA-IRCM. Analysis results come in a variety of tabular and graphical formats. For instance, we provide a unique network view integrating all the intercellular interactions, and a function relating receptors to expressed intracellular pathways. A detailed comparison with related tools is conducted. Among various examples, we demonstrate SingleCellSignalR on mouse epidermis data and discover an oriented communication structure from external to basal layers.

bioinformatics↗