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

Abstract

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.