Strataquest

Automated quantification of axons within the nerves samples was performed using StrataQuest version 5.1.249 and TissueQuest version 4.0.1.0128 (TissueGnostics, Vienna, Austria) as described previously and validated by Gesslbauer et al. (2017) (link). Per sample three cross-sections were selected for quantification analysis. The results were calculated using a custom-made script made specifically for this staining protocol (“Fibers_v3_16bit”). Axons were identified and quantified according to the following criteria. NF signals were used as the focus channel as this identifies all axons. The ChAT-positive axons were counted when overlapping with NF signals. All single positive as well as double positive axons were counted and visualized in the nerve cross section. Manual post-analysis correction of the falsely identified axons was applied to every single sample in alle three cross-sections. The variance between different cross-sections from the same sample remained under 3%.
For axonal quantification of the cross-sections stained using anti-NF and anti-MBP, QuPath version 0.3.0 was used (Bankhead et al., 2017 (link)). NF-positive axons were detected using the cell detection module. Subsequently, object classification via a single measurement classifier was used to classify MBP-positive axons by thresholding for mean MBP intensity in a 1 μm encircling each single axon.

StrataQuest (TissueGnostics, Vienna, Austria) was used to measure L. donovani amastigote co-localization in and distribution of Man^R+ and CD11c⁺ cells in the skin. Amastigotes were detected by segmentation on the tdTomato signal and host nuclei were detected by segmentation on a mask of YOYO-1 signal minus tdTomato signal. Host nuclei were used as a seed and a mask expanded based on Brilliant Violet 421 signal of Man^R and Alexa Fluor 647 signal of CD11c to determine cell type. The epidermis mask was demarcated by consolidating the signal of Brilliant Violet 421 signal of Man^R, Dylight 650 CD11c and YOYO-1 into a grayscale image and segmented using StrataQuest algorithms to distinguish from the dermis. Tissue morphology was used to define masks for the dermis and hypodermis. The tissue and cell masks were used to determine the location of the amastigotes. Host cells were classed as infected when at least one amastigote was found within them. However, for technical reasons, we did not differentiate between infected host cells by number of infecting amastigotes.

Sections were deparaffinized, rehydrated, and subjected to antigen retrieval by autoclaving (10 min, 120°C, 30 psi) for 10 min in the citrate target retrieval solution. Primary rabbit anti-β-tubulin-IV antibody (Abcam) was added overnight at 4°C in 10% BSA-PBS (1:100). Sections were washed with PBS and then were incubated in dark with FITC-labeled goat anti-rabbit IgG (KPL, Gaithersburg, MD, United States) for 2 h at room temperature. Sections were washed three times in dark with PBS and sealed with ProLong Gold Antifade Reagent with DAPI (Cell Signaling Technology, Danvers, MA, United States). The section was scanned by Tissue FAXS and analyzed with StrataQuest (Tissue Gnostics GmbH, Vienna, Austria) and Tissue quest software.

We used the image analysis software StrataQuest (TissueGnostics) for image analysis with an HP Z 440 computer containing an Intel Xeon E5-1620 v3 quad-core processor and 32 gigabytes of RAM. To optimize image analysis, nuclear and cytoplasm segmentation parameters were interactively fine-tuned in multiple tissue samples with repeated standardized experiments. The results were examined for plausibility using the function ‘backward connection’. This function allows for visualization of the currently selected events and control of the appropriateness of the segmentation parameters used. The segmentation values with the most plausible results were then selected for the whole experiment (Supplementary Tables 1 and 2).
Regions with artifacts, debris and air bubbles were visually identified and manually excluded. The epithelial layer and lamina propria were manually outlined as subregions using the software function ‘create region of interest’ to survey both tissues separately.

To determine CD146 protein expression in ccRCC, IHC staining of CD146 was conducted on cancer and paracancer tissues for 140 ccRCC cases from our cohort. For IHC staining, a tissue microarray (TMA) was obtained from the tissue bank at the Department of Urology of the Chinese PLA General Hospital. IHC staining of TMA tissues was performed with antibodies against CD146 (Abcam, ab75769). The standard protocols were followed as previously described (38 (link)). Slides were scanned using an Axio Image Z2 Microscope (Zeiss) and the TissueFAXS imaging system (TissueGnostics GmbH, Austria). All images were analyzed by TissueQuest and StrataQuest software (TissueGnostics GmbH, Austria). Staining intensity was scored 0 (negative), 1 (weak), 2 (moderate), and 3 (strong). Staining range was scored on a 4-point scale (0 = 0%, 1 = 1%∼24%, 2 = 25%∼49%, and 3 = 50%∼100%). The final IHC score was obtained by multiplying the intensity scores with the staining range. ccRCC patients with a final IHC score ≥4 were included in the high CD146 group, whereas those with a final IHC score<4 were included in the low CD146 group (39 (link)).