Session: Parallel session 7 - Club jeune FPS
A new spatial multiomics and multimodal workflow for deeper biological sample analysis in human pancreatic cancer.
For cancer research, fresh human samples are precious, often with limited access. Therefore, it’s crucial to document the maximum information from a restricted quantity of material. We propose an original spatial multiomics and multimodal workflow combining Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) and ImmunoHistoChemistry (IHC) to obtain morphological, lipidomic and proteomic data with reduced material requirement.
To develop our workflow, we used tumor (T) and non-tumor (NT) pancreatic samples from patients resected for pancreatic adenocarcinoma. First, a single frozen section was analyzed for lipid detection, followed by secondary detection of non-targeted protein after appropriate washes. A third analysis level consisted of targeted protein detection using the Tag-Mass approach (MALDI-IHC). The final step in our workflow is conventional IHC.
We use an atmospheric pressure MALDI-Orbitrap, allowing acquisitions with minimum impact on sample morphology. Raw data from MS were visualized and annotated with METASPACE, using public and homemade databases for lipid and protein identification, respectively. To develop these databases, peptides and proteins identified with conventional LC/MS analysis of a large number of T and NT human pancreas samples were used. The first database was based on experimental peptides, and the second one on in silico digestion of asociated proteins.
Combining these different approaches, we obtain a huge amount of data. This strategy is also suitable to visualize heterogeneity, which is not possible in a bulk sample. Indeed, different modalities are acquired over the same area allowing the overlay of results and selection of areas of interest.
Our new spatial multiomics and multimodal workflow will enable a better global analysis of tissues for research or diagnostic purposes, and opens the possibility of targeted analysis by focusing on well-defined regions such as specific cells or tissue structures.
To develop our workflow, we used tumor (T) and non-tumor (NT) pancreatic samples from patients resected for pancreatic adenocarcinoma. First, a single frozen section was analyzed for lipid detection, followed by secondary detection of non-targeted protein after appropriate washes. A third analysis level consisted of targeted protein detection using the Tag-Mass approach (MALDI-IHC). The final step in our workflow is conventional IHC.
We use an atmospheric pressure MALDI-Orbitrap, allowing acquisitions with minimum impact on sample morphology. Raw data from MS were visualized and annotated with METASPACE, using public and homemade databases for lipid and protein identification, respectively. To develop these databases, peptides and proteins identified with conventional LC/MS analysis of a large number of T and NT human pancreas samples were used. The first database was based on experimental peptides, and the second one on in silico digestion of asociated proteins.
Combining these different approaches, we obtain a huge amount of data. This strategy is also suitable to visualize heterogeneity, which is not possible in a bulk sample. Indeed, different modalities are acquired over the same area allowing the overlay of results and selection of areas of interest.
Our new spatial multiomics and multimodal workflow will enable a better global analysis of tissues for research or diagnostic purposes, and opens the possibility of targeted analysis by focusing on well-defined regions such as specific cells or tissue structures.