Biology subjects

Classe, M.

Publications and source records attributed to Classe, M..

5 recordsLinked to original sources

bioRxiv2026

A conserved molecular marker for connections between two evolutionarily distinct visual centers

Sensory systems share common circuit organization motifs across mammalian species, however, anatomical subdivisions show varying degrees of complexity depending on ecological niche and species-specific sensory requirements. While coarse neuroanatomical connections seem preserved within the visual system, it remains unknown if molecularly defined cell-types share a similar degree of conservation, regarding not only their functional properties but also connectivity. Here we analyze the organization, molecular marker expression, and connections between two prominent visual centers, the superior colliculus (SC) and the dorsal lateral geniculate nucleus of the thalamus (dLGN), in the mouse and the tree shrew, a highly visual, diurnal species closely related to primates. Previous attempts to link molecular markers to subdivisions and connectivity of the dLGN have shown lack of conservation across species, thus preventing the systematic investigation of brain-wide interactions involved in vision. Leveraging recent single-cell and single-nucleus RNA sequencing studies, our results unravel a conserved molecular marker that shows spatial restriction in the dLGN and correlates with the location of connections from the SC in both the mouse and the tree shrew. We extend our findings by confirming the presence of this molecular marker in the human dLGN. These results provide a molecular definition and genetic access point for SC to dLGN connections in the mouse and tree shrew, enabling cell-type specific studies of the parallel processing of visual information.

neuroscience↗

bioRxiv2024

ENACT: End-to-End Analysis of Visium High Definition (HD) Data

MotivationSpatial transcriptomics (ST) enables the study of gene expression within its spatial context in histopathology samples. To date, a limiting factor has been the resolution of sequencing based ST products. The introduction of the Visium High Definition (HD) technology opens the door to cell resolution ST studies. However, challenges remain in the ability to accurately map transcripts to cells and in assigning cell types based on the transcript data. ResultsWe developed ENACT, the first tissue-agnostic pipeline that integrates advanced cell segmentation with Visium HD transcriptomics data to infer cell types across whole tissue sections. Our pipeline incorporates novel bin-to-cell assignment methods, enhancing the accuracy of single-cell transcript estimates. Validated on diverse synthetic and real datasets, our approach is both scalable and effective offering a robust solution for spatially resolved transcriptomics analysis. Availability and implementationENACT source code is available at https://github.com/Sanofi-Public/enact-pipeline. Experimental data is available at https://zenodo.org/records/13887921. Supplementary information: Supplementary data are available at BiorXiv online.

bioinformatics↗

bioRxiv2024

A new spatial multi-omics approach to deeply characterize human cancer tissue using a single tissue section

In the ever-changing world of digital pathology, being able to extract a maximum amount of information from a patient tissue sample is of paramount importance for better diagnosis, disease characterization, and therapeutic strategies. Recent technologies such as multiplex immunofluorescence imaging and spatial transcriptomic now enable a deep analysis of protein and gene expression while retaining the spatial context of the tissue. Here, we describe an innovative approach combining a 34-protein Phenocycler panel and transcriptome analysis using Visium on a single head and neck squamous cell carcinoma section. While protein analysis reveals the complexity of the immune phenotypes involved in the disease, transcriptome analysis reveals the intricate cellular states of cancer cells that coexist within the patients tumor. Finally, integrating both omics modalities, we uncover unique comparison of gene and protein expression of spatially resolved cellular subspaces.

cancer biology↗

bioRxiv2024

Deep learning uncovers histological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patients.

Over the last decade, Hippo signaling has emerged as a major tumor-suppressing pathway. Its dysregulation is associated with abnormal expression of YAP1 and TEAD-family genes. Recent works have highlighted the role of YAP1/TEAD activity in several cancers and its potential therapeutic implications. Therefore, identifying patients with a dysregulated Hippo pathway is key to enhancing treatment impact. Although recent studies have derived RNAseq-based signatures, there remains a need for a reproducible and cost-effective method to measure the pathway activation. In recent years, deep learning applied to histology slides have emerged as an effective way to predict molecular information from a data modality available in clinical routine. Here, we trained models to predict YAP1/TEAD activity from H&E-stained histology slides in multiple cancers. The robustness of our approach was assessed in seven independent validation cohorts. Finally, we showed that histological markers of disease aggressiveness were associated with dysfunctional Hippo signaling.

bioinformatics↗

bioRxiv2022

TGFβ receptor inhibition unleashes interferon-β production by tumor-associated macrophages and enhances radiotherapy efficacy

BackgroundTransforming growth factor-beta (TGF{beta}) can limit the efficacy of cancer treatments, including radiotherapy (RT), by inducing an immunosuppressive tumor environment. The association of TGF{beta} with impaired T cell infiltration and antitumor immunity is known, but the mechanisms by which TGF{beta} participates in immune cell exclusion and limits the efficacy of antitumor therapies warrant further investigations. MethodsWe used the clinically relevant TGF{beta} receptor 2 (TGF{beta}R2)-neutralizing antibody MT1 and the small molecule TGF{beta}R1 inhibitor LY3200882 and evaluated their efficacy in combination with RT against murine orthotopic models of head and neck and lung cancer. ResultsWe demonstrated that TGF{beta} pathway inhibition strongly increased the efficacy of RT. TGF{beta}R2 antibody upregulated interferon beta (IFN{beta}) expression in tumor-associated macrophages (TAMs) within the irradiated tumors and favored T cell infiltration at the periphery and within the core of the tumor lesions. We highlighted that both the antitumor efficacy and inhibition of immune exclusion observed with the combination of MT1 and RT were dependent on type I interferon signaling. ConclusionsThese data shed new light on the role of TGF{beta} in limiting the efficacy of RT, identifying a novel mechanism involving the inhibition of macrophage-derived type I interferon production, and fostering the use of TGF{beta}R inhibition in combination with RT in therapeutic strategies for the management of head and neck and lung cancer.

cancer biology↗