The dataset contains non-exhaustive spatial annotation (i.e., set of representative ROIs) of 4 distinct tissue types from the diagnostic specimen of 14 operable pancreatic ductal adenocarcinoma (PDAC) patients. Brief description of the 4 tissue types: - Neoplastic parenchyma (NP): Cancerous region, composed of dedifferentiated, proliferating cells. - Neoplastic stroma (NS): Connective tissue surrounding the NP regions. Infiltrating stroma is a well-known phenomenon of PDAC, also known as the desmoplastic reaction. - Adjacent parenchyma (AP): histologically benign pancreatic parenchyma, composed of acinar structures. - Adjacent stroma (AS): connective tissue surrounding the AP regions. NB. The non-exhaustive property of the annotation means that not all of the regions complying to the description above are annotated. Also there are certainly regions in the specimen not falling into any of the above categories (immune cell clusters, veins, islets of Langerhans, etc.). The dataset contains slides from 2 batches with slightly different characteristics: Batch 1: - Number of patients: 5 - Thickness of the section: 10 μm - Coloring of the annotation: * NP - red * NS - purple * AP - dark green * AS - light green - Associated files: PaPr[01-05].czi Batch 2: - Number of patients: 9 - Thickness of the section: 20 μm - Coloring of the annotation: * NP - red * NS - yellow * AP - green * AS - cyan - Associated files: PaPr[06-14].czi NB. Patients 9,10 and 11 have 2 associated slides PaPr[9,10,11]-[1,2] and slide 1 contains only neoplastic, whilst slide 2 contains only adjacent regions. The files are in .czi format, and we recommend using the freely available ZEN Blue software for opening them. https://www.zeiss.com/microscopy/int/products/microscope-software/zen-lite.html The regions annotated in this dataset were subject to laser capture microdissection and subsequently analyzed by LC-MS/MS proteomics. The regions corresponding to the same tissue type within patients have been pooled to obtain sufficient material for quantitative proteomics (one sample/patient/tissue type). The dataset is described in details in the following publication: https://doi.org/10.1158/0008-5472.CAN-22-0396

2022