Tissuequest

Paraffin-embedded sections were stained with hematoxylin and eosin, Sirius red (ScyTek Laboratories Inc.), or anti-phospho-Histone H2A.X (Cell Signaling, 1:400) with biotinylated IgG and horseradish peroxidase-conjugated ABC reagent (Vector Laboratories, Burlingame, CA). The positive area of Sirius red staining and the number of brown color stained-H2A gamma positive cells were analyzed by Tissue Quest (TissueGnostics). Frozen sections were fixed with cold acetone, and immunostained with anti-AQP3 (Millipore, 1:100), anti-F4/80 (eBioScience, 1:400), or anti-desmin (R&D Systems, 10 μg/ml) antibodies.

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.

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.

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)).

The immunostained and riboprobes reacted sections were digitalized at 20× magnification utilizing a TissueFaxs Plus System coupled onto a Zeiss^® Axio Imager Z2 Microscope (Jena, Germany). Regions of interest were then acquired using the TissueFaxs (TissueGnostics^®, Vienna, Austria). TrkB immunostaining was scored [50 (link)] (0: no staining, low (1): under 30% of cells positive, middle (2): 30–66% of cells positive, high (3): more than 66% of cells positive in cancer cell nests) and Mann–Whitney test was used to detect differences between HNSCC and UPPP. Dot Plots, frequency diagrams, and heat maps were created using TissueQuest (TissueGnostics^®) [51 (link),52 (link)].

HUVECs/HUVSMCs were treated with various concentrations of GA or equivalent volume of the DMSO vehicle control at ~30% confluency and allowed to grow for an additional 24 h. Light microscopy images from the same areas of the well were obtained immediately after pre-treatment as well as 24 h and the total number of cells in each field of view was quantified with the help of ImageJ software (NIH, Bethesda, MD, USA). The cell number was converted into doubling time using the following formula:
TissueGnostics/TissueQuest microscopy and software were utilized to simultaneously automatically assess the number of Ki67-positive cells. In brief, HUVECs were fixed with methanol for 15 min at 4°C, air-dried and blocked with Casein/BSA block buffer for 30 min, incubated with the anti-Ki67 antibody overnight, washed with PBS and nuclei were visualized by DAPI staining, automatically detected and defined as region of interest (ROI). ROI-associated ki67 fluorescence was automatically detected and expressed as percentage fluorescence-positive nuclei with fluorescence signal above background using TissueFAXS microscopy (TissueGnostics AG, Austria) and TissueQuest (TissueGnostics AG, Austria) software.

Tissue microarray for FGL1 IHC staining was obtained from the tissue bank at Urology Department of the Chinese PLA General Hospital, Beijing, China. The standard protocols were followed as previously described (23 (link)). Slides were scanned using Axio Image Z2 Microscope (Zeiss) and TissueFAXS imaging system (TissueGnostics GmbH, Austria). All images were analyzed by TissueQuest and StrataQuest software (TissueGnostics GmbH, Austria). As previously described (24 (link)), staining intensity was scored as follows: 0 (negative), 1 (weak), 2 (moderate), and 3 (strong), whereas the staining range was scored as: 0 (0%), 1 (1%–24%), 2 (25%–49%), and 3 (50%–100%). The IHC staining score was obtained by multiplying the intensity scores with staining range. The IHC staining score ranged from 0 to 9. Scores less than two were considered as negative staining, 2–3 indicated weak staining, 4–6 was moderate staining, and >6 was strong staining. Patients with an IHC staining score ≥4 were included in the high expression group, whereas those with an IHC staining score <4 were included in the low expression group.