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-  2019 

Rapid MALDI mass spectrometry imaging for surgical pathology

DOI: 10.1038/s41698-019-0089-y

Keywords: Metabolomics, Surgical oncology, Molecular imaging, Metabolomics, Molecular medicine

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Abstract:

a Comparison of workflow for conventional versus rapid MALDI MSI. The left panel illustrates additional steps and time needed for conventional MALDI MSI, including matrix application/drying, high-resolution slide scanning, image registration, and data acquisition using a 1?kHz laser. In the rapid MALDI MSI method (right panel), by using a matrix pre-coated slide and cryomounting the sample directly into a pre-templated region, and acquiring data with a 10?kHz laser, significant improvements in turnaround time are achieved. A representative ion image (for m/z 885.6) is illustrated below the workflow. Additional ion images comparing conventional and rapid method can be found in Supplemental Information. b Proposed workflow for rapid MALDI MSI in the frozen section room. Surgical specimens are serially cryosectioned onto a glass slide and a matrix pre-coated ITO slide. The section on the glass slide is stained using H&E followed by high-speed digital imaging, while the rapid MALDI MSI method is applied to the templated region on the ITO slide in a similar time frame. Following data acquisition, either pathologist-guided or artificial intelligence (AI)-guided diagnostic approaches may be utilized. In the pathologist-guided mode, the digital histological image is fused to the ion images using a rapid non-linear transformation (Supplemental Fig. 1), allowing the pathologist to select a region or pixel of interest for further molecular characterization. This approach could be used to quickly evaluate, for example, the presence or absence of cancer or of pathogens as well as particular molecular features of the cancer cells, a feat which is currently unachievable in a similar time frame. In the AI-guided mode (dashed arrow), the MSI data could be analyzed directly without visual review using previously machine trained models to classify regions based on their mass spectral signatur

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