Super-resolved highly multiplexed immunofluorescence imaging for precise protein localization and podocyte ultrastructure.

Deep insights into the complex cellular and molecular changes occurring during (patho-)physiological conditions are essential for understanding the interactions and regulation of proteins. This understanding is crucial for research and diagnostics. However, the effectiveness of conventional immunofluorescence and light microscope, tools for visualizing the spatial distribution of cells or proteins, are limited both in resolution and multiplexity in complex tissues. This is mainly due to challenges such as the spectral overlap of fluorophore wavelengths, a limited range of antibody types, the inherent variability of samples and the optical resolution limit. The herein demonstrated combination of multiplex immunofluorescence imaging and super resolution microscopy offers a solution to these limitations by enabling the identification of different cell types and precise subcellular localization of proteins in tissue sections. In this study, we demonstrate the cyclic staining and de-staining of paraffin kidney sections, making it suitable for routine use and compatible with super-resolution microscopy for podocyte ultrastructural studies. We have further developed a computerized workflow for data processing which is accessible through available reagents and open-access code. As a proof of principle, we identified CDH2 as a marker for cellular lesions of sclerotic glomeruli in the nephrotoxic serum nephritis mouse model and cross-validated this finding with a human Nephroseq dataset indicating its translatability. In summary, our work represents an advance in multiplex imaging, which is crucial for understanding the localization of numerous proteins in a single FFPE kidney section and the compatibility with super-resolution microscopy to study ultrastructural changes of podocytes.

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 obtain spatial single-cell single-mRNA expression data from 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 were assigned. Furthermore, we show correlative and spatial single-cell expression quantification with super-resolved podocyte foot process morphometry. 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. Using this method, we demonstrate that ACE2 can be locally upregulated in podocytes upon injury. In a patient suffering from COVID-19-associated collapsing FSGS, ACE2 expression levels were correlated with intracellular SARS-CoV-2 abundance. As this method performs well with standard formalin-fixed paraffin-embedded samples and we provide pretrained deep learning networks embedded in a comprehensive image analysis workflow, this method can be applied immediately in a variety of settings.