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A novel multiomics and multimodal workflow for deeper global and spatial analysis using a limited amount of biological samples: Proof-of-concept in human pancreatic cancer.

Abstract #37 Posted on24 February 20259 May 2025 9 May 2025 14h17

Abstract

Human samples are precious, extracting maximum information from it is crucial. We propose multiomics and multimodal workflow combining Bottom-up LC-MS/MS, MALDI Mass Spectrometry Imaging (MALDI-MSI) and immunohistochemistry (IHC) to obtain morphological, lipidomic and proteomic data with reduced material requirements.

To develop our workflow, we used human tumoral (T) and non-tumoral (NT) pancreatic samples from resected pancreatic adenocarcinoma.
For LC-MS/MS approach, 48 samples of T and NT regions were microdissected from FFPE tissue. They were prepared and proteolysed using the PreOmics iST-FFPE kit, optimized for our samples. Peptides were analyzed using Fusion Lumos mass spectrometer. The adapted workflow enabled the identification of hundreds of proteins, demonstrating the feasibility of deep proteomic profiling from limited material.
For spatial approach, we used frozen sections of T or NT sample. IHC, lipid and peptide detections were performed on the same slide, via an AP-MALDI-Orbitrap. Data are treated using public databases for lipids and home-made database (with proteins identified by LC-MS/MS and in silico digestion) for peptides. Targeted analyses can be run focusing on well-defined areas like specific cells or structures.

Combining LC-MS/MS, MSI and IHC, we obtain a huge amount of data from a low quantity of material. This novel approach enhances the understanding of pancreatic cancer and facilitates the discovery of prognostic signatures and biomarkers.

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To develop our workflow, we used human tumoral (T) and non-tumoral (NT) pancreatic samples from resected pancreatic adenocarcinoma.
For LC-MS/MS approach, 48 samples of T and NT regions were microdissected from FFPE tissue. They were prepared and proteolysed using the PreOmics iST-FFPE kit, optimized for our samples. Peptides were analyzed using Fusion Lumos mass spectrometer. The adapted workflow enabled the identification of hundreds of proteins, demonstrating the feasibility of deep proteomic profiling from limited material. 
For spatial approach, we used frozen sections of T or NT sample. IHC, lipid and peptide detections were performed on the same slide, via an AP-MALDI-Orbitrap. Data are treated using public databases for lipids and home-made database (with proteins identified by LC-MS/MS and in silico digestion) for peptides. Targeted analyses can be run focusing on well-defined areas like specific cells or structures.

Combining LC-MS/MS, MSI and IHC, we obtain a huge amount of data from a low quantity of material. This novel approach enhances the understanding of pancreatic cancer and facilitates the discovery of prognostic signatures and biomarkers.

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