Probes for PPIB

Background and Aims Although high-throughput single-cell transcriptomic analysis, super-resolution light microscopy and deep-learning methods are broadly used, the gold-standard to evaluate kidney biopsies is still the histologic assessment of formalin-fixed and paraffin embedded (FFPE) samples with parallel ultrastructural evaluation. Recently, we and others have shown that super-resolution fluorescence microscopy can be used to study glomerular ultrastructure in human biopsy samples. Additionally, in the last years mRNA in situ hybridization techniques have been improved to increase specificity and sensitivity to enable transcriptomic analysis with single-mRNA resolution (smFISH). Method For smFISH, we used the fluorescent multiplex RNAscope kit with probes targeting ACE2, WT1, PPIB, UBC and POLR2A. To find an on-slide reference gene, the normfinder algorithm was used. The smFISH protocol was combined with a single-step anti-podocin immunofluorescence enabled by VHH nanobodies. Podocytes were labeled by tyramide-signal amplified immunofluorescence using recombinant anti-WT1 antibodies. Slides were imaged using confocal laser scanning, as well as 3D structured illumination microscopy. Deep-learning networks to segment glomeruli and cell nuclei (UNet and StarDist) were trained using the ZeroCostDL4Mic approach. Scripts to automate analysis were developed in the ImageJ1 macro language. Results First, we show robust functionality of threeplex smFISH in archived routine FFPE kidney biopsy samples with single-mRNA resolution. As variations in sample preparation can negatively influence mRNA-abundance, we established PPIB as an ideal on-slide reference gene to account for different RNA-integrities present in biopsy samples. PPIB was chosen for its most stable expression in microarray dataset of various glomerular diseases determined by the Normfinder algorithm as well as its smFISH performance. To segment glomeruli and to label glomerular and tubulointerstitial cell subsets, we established a combination of smFISH and immunofluorescence. As smFISH requires intense tissue digestion to liberate cross-linked RNAs, immunofluorescence protocols had to be adapted: For podocin, a small-sized single-step label approach enabled by small nanobodies and for WT1, tyramide signal amplification was used. For enhanced segmentation performance, we used deep learning: First, a network was customized to recognize DAPI+ cell nuclei and WT1/DAPI+ podocyte nuclei. Second, a UNet was trained to segment glomeruli in podocin-stained tissue sections. Using these segmentation masks, we could annotate PPIB-normalized single mRNA transcripts to individual cells. We established an ImageJ script to automatize transcript quantification. As a proof-of-principle, we demonstrate inverse expression of WT1 and ACE2 in glomerular vs. tubulointerstitial single cells. Furthermore, in the podocyte subset, WT1 highly clustered whereas no significant ACE2 expression was found under baseline conditions. Additionally, when imaged with super-resolution microscopy, podocyte filtration slit morphology could be visualized The optical resolution was around 125 nm and therefore small enough to resolve individual foot processes. The filtration slit density as a podocyte-integrity marker did not differ significantly from undigested tissue sections proving the suitability for correlative podocyte foot process morphometry with single-podocyte transcript analysis. Conclusion Here we present a modular toolbox which combines algorithms for multiplexed, normalized single-cell gene expression with single mRNA resolution in cellular subsets (glomerular, tubulointerstitial and podocytes). Additionally, this approach enables correlation with podocyte filtration slit ultrastructure and gross glomerular morphometry.

Chronic lung allograft dysfunction (CLAD) is the leading cause of death among lung transplant recipients. Eosinophils, effector cells of type 2 immunity, are implicated in the pathobiology of many lung diseases and prior studies suggest their presence associates with acute rejection or CLAD after lung transplantation.Does histological allograft injury or respiratory microbiology correlate with the presence of eosinophils in bronchoalveolar lavage fluid (BALF)? Does early posttransplant BALF eosinophilia associate with future CLAD development, including after adjustment for other known risk factors?We analyzed BALF cell count, microbiology, and biopsy data from a multicenter cohort of 531 lung recipients with 2592 bronchoscopies over the first posttransplant year. Generalized estimating equation models were utilized to examine the correlation of allograft histology or BALF microbiology with the presence of BALF eosinophils. Multivariable Cox regression was used to determine the association between ≥1% BALF eosinophils in the first posttransplant year and definite CLAD. Expression of eosinophil-relevant genes was quantified in CLAD and transplant control tissues.The odds of BALF eosinophils being present was significantly higher at the time of acute rejection and non-rejection lung injury histologies and during pulmonary fungal detection. Early posttransplant ≥1% BALF eosinophils significantly and independently increased the risk for definite CLAD development (adjusted hazard ratio 2.04, p=0.009). Tissue expression of eotaxins, IL13 related genes, and the epithelial-derived cytokines IL33 and thymic stromal lymphoprotein were significantly increased in CLAD.BALF eosinophilia was an independent predictor of future CLAD risk across a multicenter lung recipient cohort. Additionally, type 2 inflammatory signals were induced in established CLAD. These data underscore the need for mechanistic and clinical studies to clarify the role of type 2 pathway-specific interventions in CLAD prevention or treatment.