2025_HellaBolck_ccRCC_screens
COLLECTION:
2025_HellaBolck_ccRCC_screens
Article:
Deep visual multi-omics profiling reveals mechanisms that underly cancer cell differentiation and aggressiveness in clear cell renal cell carcinoma
Hella A. Bolck, Ede Migh, Andras Kriston, Natalia H. Zajac, Susanne Kreutzer, Tiberiu Totu, Peter Leary, Ferenc Kovacs, Dorothea Rutishauser, Sybille Pfammatter, Jonas Grossmann, Cassandra Litchfield, Marija Buljan, Niels J. Rupp, Peter Horvath, Holger Moch
doi: https://doi.org/10.1101/2025.01.31.635927
DESCRIPTION OF THE FILES IN THE COLLECTION:
Files and images.
MAIN CONTACT:
Dr. Ede Migh, mighede@gmail.com
MAIN AFFILIATION:
Institute of Biochemistry, BRC, Hungary, Szeged
KEYWORDS:
Artificial intelligence (AI)
Digital pathology
Image analysis
Tumor biology
DESCRIPTION:
The dataset contains diagnostic hematoxylin and eosin (H&E)-stained sections from clear cell renal cell carcinoma (ccRCC). For each ccRCC case, we selected one to four representative formalin-fixed, paraffin-embedded tissue blocks and cut 10 µm sections using PEN MembraneSlides 1.0 . These were stained with H&E according to standard procedures. Slides were digitized scanning z-stacks with seven z-slices in 0.5 µm increments using a Hamamatsu C9600 scanner at ×40 optical magnification. The scanner resolution was 0.228 µm per pixel.
COPYRIGHT:
* Copyright (c) 2025,
* BRC, Hungary, Szeged
* All rights reserved.
* This material is free; you can redistribute it and/or modify it under the terms of the CC BY 4.0.
* This material is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE
