Biology subjects

THOMAS, M.

Publications and source records attributed to THOMAS, M..

3 recordsLinked to original sources

bioRxiv2026

Harnessing TfR1 for Cross-Species Systemic Delivery of siRNAs to Deep Brain Regions Using Single-Domain Antibodies

Despite their therapeutic potential across a wide range of central nervous system (CNS) disorders, nucleic acid-based therapeutics are limited by inefficient delivery to deep brain regions at clinically viable doses. Transferrin receptor 1 (TfR1) has emerged as an attractive target for receptor-mediated transcytosis across the blood-brain barrier (BBB), enabling systemic delivery of biologics such as lysosomal enzymes and monoclonal antibodies. In this study, we demonstrated the translational potential of recently described TfR1-targeting camelid-derived single-domain antibodies (VHHs) for CNS delivery of siRNAs. When conjugated 1:1 to different tool siRNAs, these VHHs promote rapid and robust intracellular uptake, resulting in potent RNAi activity at low nanomolar concentrations in neural cells. Systemic administration of VHH-siRNA conjugates in wild-type mice, hTfR1 transgenic-mice and non-human primates revealed a favourable pharmacokinetic profile characterized by rapid TfR-dependent distributional clearance and efficient functional uptake in deep brain structures. This translated into durable target knockdown of 50-80% at both mRNA and protein levels and with ED50 below 1 mg/kg siRNA. Collectively, these findings establish our TfR1 targeting VHHs as a fit-for-purpose platform for the systemic delivery of therapeutic oligonucleotides to deep brain structures at clinically relevant doses, opening new avenues for the treatment of diverse CNS disorders. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/726486v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@560424org.highwire.dtl.DTLVardef@12a1220org.highwire.dtl.DTLVardef@10c2addorg.highwire.dtl.DTLVardef@1d246ff_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2026

Preconceptional immunomodulation partially corrects pregnancy associated abnormalities induced by endometriosis in a mice model, with a normalization of transcriptional alteration observed in the developing fetal maternal interface at the single cell level.

Endometriosis is a chronic disease of gynaecological origin that affects approximately 10% of women worldwide and has a significant impact on patients lives. Women with endometriosis attempting to conceive may experience infertility and have a higher risk of obstetric complications. They are also more likely to miscarry and have placental defects during pregnancy. The impact of endometriosis on pregnancy remains unclear. Defects in implantation and placentation may be present, but these processes are difficult to study in human subjects. To address this issue, we used a surgically induced mouse model of endometriosis and examined pregnancy in the CBAxDBA crossbreed, which is commonly employed to study pregnancy immunology. We examined early (E9.5) and late (E18.5) gestational stages. In this model, we confirmed that the presence of endometriosis-like lesions resulted in fewer implantations and a higher proportion of fetal resorption, which is analogous to miscarriage. When an immune tolerance was induced previous to the endometriosis induction, we observed that endometriotic lesions were smaller, and gestational complications were mostly corrected. At early gestation (E9.5), fetal-maternal interfaces were retrieved to assess the potential impact of endometriosis and immunomodulation on placental development using single-cell RNA-sequencing. Our data show that most changes induced by endometriosis occur in decidual stromal cells, which originate from the endometrium. In these cells, we observed a consistent upregulation of Gata4, a transcription factor previously found to be elevated in the endometrium of women with endometriosis. We also observed downregulation of Prap1, which is an indicator of uterine receptivity and successful implantation in mice. In the mice where immune tolerance was induced, the endometriosis associated transcriptomic changes were partially reversed. In immune cells, endometriosis leads to an inflammation-associated signature. Additionally, the interferon gamma response is reduced in NK cells, which has been shown to be crucial for spiral artery remodelling and decidual integrity, which may be involved in the observed increased resorption rate. Overall, our findings provide new insights into endometriosis-associated infertility and pregnancy complications in a mouse model that can be partially corrected by immune modulation. These results suggest that targeting the immune system should be considered in future studies to improve pregnancy outcomes in patients with endometriosis.

molecular biology↗

bioRxiv2022

An end-to-end pipeline based on open source deep learning tools for reliable analysis of complex 3D images of Medaka ovaries

Computational analysis of bio-images by deep learning (DL) algorithms has made exceptional progress in recent years and has become much more accessible to non-specialists with the development of ready-to-use tools. The study of oogenesis mechanisms and female reproductive success in fish has also recently benefited from the development of efficient three-dimensional (3D) imaging protocols on entire ovaries. Such large datasets have a great potential for the generation of new quantitative data on oogenesis but are, however, complex to analyze due to imperfect fluorescent signals and the lack of efficient image analysis workflows. Here, we applied two open-source DL tools, Noise2Void and Cellpose, to analyze the oocyte content of medaka ovaries at larvae and adult stages. These tools were integrated into end-to-end analysis pipelines that include image pre-processing, cell segmentation, and image post-processing to filter and combine labels. Our pipelines thus provide effective solutions to accurately segment complex 3D images of entire ovaries with either irregular fluorescent staining or low autofluorescence signal. In the future, these pipelines will be applicable to extensive cellular phenotyping in fish for developmental or toxicology studies. Summary statementAn accessible image analysis method for biologists, which includes easy-to-use deep learning algorithms, designed for accurate quantitative measurement of ovarian content from complex 3D fluorescent images.

cell biology↗