Biology subjects

Tharaux, P.-L.

Publications and source records attributed to Tharaux, P.-L..

2 recordsLinked to original sources

bioRxiv2022

A Protective Inter-Organ Communication Response Against Life-Threatening Malarial Anemia

Anemia is a clinical hallmark and independent risk factor of malaria mortality, the disease caused by Plasmodium spp. infection. While malarial anemia arises from parasite-induced hemolysis, whether and how host metabolic adaptation to malaria regulates anemia severity is less understood. Here we demonstrate that reprogramming of organismal iron (Fe) metabolism by the kidneys is a central component of the host metabolic response regulating the pathogenesis of life-threatening malarial anemia. Renal proximal tubule epithelial cells (RPTEC) are the main cell compartment responsible for Fe storage and recycling during Plasmodium infection in mice. Transcriptional reprogramming of RPTEC couples immune resistance to Plasmodium infection to renal Fe export via the induction of the cellular Fe exporter SLC40A1/ferroportin 1. This integrated defense strategy is essential to deliver Fe to erythroblasts and support compensatory erythropoiesis to prevent the development of life-threatening anemia. Failure to mobilize Fe from RPTEC causes acute kidney injury (AKI) and is associated with life-threatening anemia in P. falciparum-infected individuals. These findings reveal an unexpected role of the kidneys in the control of organismal Fe metabolism and anemia severity during malaria.

pathology↗

bioRxiv2021

scoMorphoFISH: A Deep-Learning enabled toolbox for single-cell single-mRNA quantification and correlative (ultra-)morphometry

Increasing the information depth of single kidney biopsies can improve diagnostic precision, personalized medicine and accelerate basic kidney research. Until now, information on mRNA abundance and morphologic analysis has been obtained from different samples, missing out on the spatial context and single-cell correlation of findings. Herein, we present scoMorphoFISH, a modular toolbox to get spatial single-cell single-mRNA expression data optimized for routinely generated kidney biopsies. Deep-Learning was used to virtually dissect tissue sections in tissue compartments and cell types to which single-cell expression data was assigned. Furthermore, we show correlative and spatial single-cell expression quantification with super-resolved podocyte foot process morphometry on the same histological section. In contrast to bulk analysis methods, this approach will help to identify local transcription changes even in less frequent kidney cell types on a spatial single-cell level with single-mRNA resolution. As this method performs well with standard formalin-fixed paraffin-embedded samples and we provide pretrained DL-networks embedded in a comprehensive image analysis workflow, this method can be applied immediately in a variety of settings.

pathology↗