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Leica scn400 histology scanner

Manufactured by Leica Microsystems

The Leica SCN400 is a high-resolution digital slide scanner designed for the digitization of histological samples. It captures high-quality, whole-slide images at a resolution of up to 0.25 micrometers per pixel. The scanner utilizes advanced optics and imaging technology to provide reliable and efficient scanning of microscope slides.

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2 protocols using leica scn400 histology scanner

1

Immunohistochemical Analysis of Lung Markers

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TTF-1 and CK7 IHC was carried out on formalin-fixed, paraffin-embedded tissue 4 μm sections using anti-TTF-1 (mouse, 8GTG3/1, 1:200, Dako, Carpinteria, CA, USA) and anti-CK7 (mouse, OV-TL 12/30, 1:1000, Dako). Antibody incubation and detection were carried out at 37°C on a Menarini intelliPATH FLX (A. Menarini Diagnostics, UK) using Menarini's reagent buffer and detection kits unless otherwise noted. Antigen retrieval was carried out in a pressure cooker in citrate buffer pH6 for 2 min at 123°C. Pan-CK, CD44 and vimentin IHC was carried out on formalin-fixed, paraffin-embedded tissue 4 μm sections using anti pan-cytokeratin antibody (mouse, M3515, 1:60, ER1 20 min Dako), anti CD44 (mouse, DF1485, 1:100, ER2 10 min, Dako) and anti-Vimentin (mouse, V9, CC1 32 min, Roche). Pan-CK and CD44 staining was carried out on the LEICA Bond Max Platform and Vimentin staining on the Ventana Discovery Ultra platform (Roche). Appropriate positive, negative and isotype controls were included with the study sections (not shown). Digital images of whole-tissue sections acquired using a Leica SCN400 histology scanner (Leica Microsystems).
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2

Immunohistochemical Analysis of Tumor Samples

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Immunohistochemistry was performed on formalin-fixed, paraffin-embedded tumour tissue using antibodies against human Ki67(Cell Signaling #9027, 1:400 dilution), anti-human mitochondria (Abcam ab92824, 1:1000 dilution) and against endogenous NUT protein (Cell Signaling #3625, 1:50 dilution) used to detect NUT fusion proteins [7 (link), 10 (link)]. Digital images of whole tissue sections were acquired using a Leica SCN400 histology scanner (Leica Microsystems). Ki67-positive index was evaluated using Halo Software V. 3.2 (Indica lab). Regions of interest (ROIs) within the tissue sections were first identified using Halo Software via machine learning technology across pathological samples and tissue control, within these ROIs, nuclei were detected and classified as positive or negative based on IHC staining thresholds.
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