Biology subjects

Chalopin, D.

Publications and source records attributed to Chalopin, D..

6 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↗

bioRxiv2026

Uncertainty-aware benchmarking reveals ambiguous transcripts in mRNA-lncRNA classification

BackgroundLong non-coding RNAs (lncRNAs) have gained significant attention in recent years, yet distinguishing them from protein-coding transcripts remains challenging. Indeed, many lncRNAs share mRNA-like processing and existing sequence-derived signals do not fully capture the coding/non-coding boundary. Recent GENCODE annotation efforts revealed tens of thousands of novel lncRNA sequences as well as the reclassification of some lncRNAs into the protein-coding class, highlighting the need to better characterize transcript features associated with classification uncertainty and errors. ResultsWe performed uncertainty-aware benchmarking by retraining and evaluating eight transcript classifiers under a controlled protocol on a label-stable GENCODE v46-v47 subset. Beyond conventional model evaluation metrics, we quantified inter-tool agreement and entropy-based uncertainty to stratify transcripts into consensus, discordant, and consensus-error groups. To expand standard sequence and ORF-derived signals, we incorporated repeat-derived features from mature transcripts and non-B DNA motif features across gene bodies. Although aggregate performance was high, [~]45% of transcripts showed inter-tool discordance, particularly among lncRNAs. Feature analyses linked low-uncertainty predictions to strong coding-like signals, whereas high-uncertainty profiles exhibited mixed signatures. Alongside classical predictors in global importance analyses, repeat-derived features appear as main contributors. ConclusionsBy combining controlled benchmarking with transcript-level agreement and uncertainty stratification, together with extended feature profiling, we identified patterns associated with classifier disagreement and misclassification. This novel framework provides practical guidance for interpreting predictions, motivating the development of more robust coding/non-coding classifiers, while also shedding light on the sequence properties that distinguish lncRNA sequences.

bioinformatics↗

bioRxiv2025

Immunosuppressive myeloid cells induce mesenchymal-like breast cancer stem cells by a mechanism involving membrane-bound TGF-β1.

Suppressive myeloid cells play a central role in cancer escape from anti-tumor immunity. Beyond their immunosuppressive function, these cells are capable of exerting multiple other pro-tumoral activities, including the promotion of cancer cell survival, invasion and metastasis. The ability of some myeloid subsets to induce cancer stemness has recently emerged. Here we demonstrated that human immunosuppressive myeloid cells, generated in vitro or isolated from breast cancer patients, promoted the acquisition of mesenchymal-like breast cancer stemness properties. This cancer-stemness-inducing function was restricted to a myeloid subset expressing the glycoprotein CD52. Single cell transcriptomic- and surface proteome-based interactome analysis pointed towards membrane-bound TGF-{beta}1 as a potential factor involved in cancer stemness induction. Functional inhibition of the TGF-{beta}1 pathway blocked the emergence of cancer stem cells induced by suppressive myeloid cells. These results therefore identified the underlying mechanisms of a new tumor-promoting function of immunosuppressive myeloid cells, which may potentially be targeted. HighlightsO_LIImmunosuppressive CD33highCD52+ myeloid cells induce mesenchymal-like cancer stem cells C_LIO_LICancer stemness induction requires membrane bound TGF-{beta}1 C_LIO_LIBlockade of the TGF-{beta}1 pathway prevents cancer stemness induction C_LI

cancer biology↗

bioRxiv2024

SeuratIntegrate: an R package to facilitate the use of integration methods with Seurat

MotivationIntegrating multiple datasets has become an increasingly common task in scRNA-seq analysis. The advent of single-cell atlases adds further complexity to this task, as they often involve combining data with complex, nested batch effects - such as those arising from multiple studies, organs or disease states. Accurate data integration is essential to distinguish cell types with sufficient granularity, thereby reflecting true biological patterns, and to create reliable reference datasets for the community. In this context, the latest version of Seurat (v5) introduced a multi-layered object structure to facilitate the integration of scRNA-seq datasets in a unified manner. However, the panel of available batch-correction methods remains limited to five algorithms within Seurat, restricting users from accessing a broader diversity of available tools, particularly Python-based methods. Furthermore, no existing R tool assists the user in making an informed decision in selecting the most appropriate integration approach. ResultsTo overcome these challenges, we developed SeuratIntegrate, an open source R package that extends Seurats functionality. SeuratIntegrate supports eight integration methods, incorporating both R- and Python-based tools, and enables performance evaluation of integration through several scoring methods. This functionality allows for a more versatile and informed integration process. AvailabilitySeuratIntegrate is available at https://github.com/cbib/Seurat-Integrate/. The package is released under the MIT License.

bioinformatics↗

bioRxiv2024

Convergent transcriptomic and genomic adaptation in xeric rodents

Repeated adaptations rely in part on convergent genetic changes. The extent of convergent changes at the genomic scale is debated and may depend on the interplay between different factors. Rodents have repeatedly adapted to life in arid conditions, notably with altered renal morphology and physiology. This occurred at different time periods, allowing us to test the importance of time in convergent genomic evolution. We analyzed kidney transcriptomes from 34 species to quantify and characterize convergent evolution at the level of gene expression, tissue composition, and coding sequences. We found that several genes showed convergent expression changes, some of which also carried convergent changes in their coding sequence. We then subdivided these data to test the influence of evolutionary history. First, within the subfamily Murinae, we found more convergent gene expression, reflecting convergent changes in cell proportions. Second, we compared data for recent (within genera) and ancient (between genera) adaptations, and observed more convergent changes in the latter. Our study shows that adaptation to xeric environments in rodents involves repeated changes in tissue composition, gene expression and coding sequences, and that the degree of convergent evolution increases with both the age of the adaptations and species relatedness.

evolutionary biology↗

bioRxiv2022

TREM1+ regulatory myeloid cells expand in steatohepatitis-HCC and associate with poor prognosis and therapeutic resistance to anti-PD-1 blockade

Hepatocellular carcinoma (HCC) is an inflammation-associated cancer arising from viral and non-viral etiologies. Immune checkpoint blockade primarily benefits patients with viral HCC. Expansion of suppressive myeloid cells is a hallmark of chronic inflammation and cancer, but their heterogeneity in HCC is not fully resolved and might underlie immunotherapy resistance in the steatohepatitis setting. Here, we present a high resolution atlas of hepatic innate immune cells from patients with HCC that unravels a steatohepatitis contexture characterized by the emergence of high entropy myeloid cell states and myeloid-biased NK cell differentiation. We identify a discrete population of tumor-infiltrating myeloid cells, predominant in the steatohepatitis setting, that expresses a variety of myeloid lineage-affiliated genes, including granulocyte, macrophage and dendritic cell features, and can be identified in HCC tumors based on selective dual expression of TREM1 and CD163. Functional characterization reveals that TREM1+ CD163+ myeloid cells highly express TGF{beta} and IL-13RA, localize to HCC fibrotic lesions, and potently suppress T cell effector functions ex vivo, a function further potentiated by TREM1 engagement. We refer to this population as TREM1+ CD163+ regulatory myeloid cells (TREM1+CD163+ Mreg). Deconvolution analyses in large cohorts of patients with HCC and other solid tumors reveals that the density of TREM1+ CD163+ Mreg increases in advanced stages, associates with poor prognosis, and therapeutic resistance to PD-1 blockade. Our data support myeloid subset-targeted immunotherapies to treat HCC and identify TREM1 as a therapeutic target. HIGHLIGHTSO_LIAtlas of hepatic innate immune cells (100,000 transcriptomes) from patients with HCC C_LIO_LICore signatures to identify, discriminate and localize innate lymphoid and myeloid cells C_LIO_LIA population of TREM1+CD163+ myeloid cells, referred to as TREM1+CD163+ Mreg, expands in steatohepatitis HCC C_LIO_LITREM1+CD163+ Mreg express granulocyte- and macrophage/dendritic cell-lineage genes C_LIO_LITREM1+CD163+ Mreg potently suppress T cell effector functions, which is potentiated by TREM1 engagement by cognate ligands C_LIO_LITREM1+CD163+ Mreg produce high levels of TGF{beta} and populate fibrotic lesions C_LIO_LIThe density of TREM1+CD163+ Mreg increases in advanced HCC and associate with poor patient survival C_LIO_LIThe density of TREM1+CD163+ Mreg associates with resistance to immune checkpoint blockade in other solid tumors C_LI

immunology↗