Traditional histological tissue-based diagnostics have been slow and often require large numbers of sections to interrogate for several different proteins with immunohistochemical stains. Often, due to small biopsy size, there is not sufficient material available to carry out all the desired tests on a patient sample. Mass Spectrometry Imaging (MSI) enables the simultaneous detection of hundreds to thousands of analytes from a single tissue section. In recent years, great strides have been made to standardize MSI workflows and data analysis to make it an accessible tool for clinicians to use in patient diagnoses. This commentary highlights the advances by Janßen et al. toward this goal that are discussed in this issue (Proteomics Clin Appl., https://doi.org/10.1002/prca.202100068).
Keywords: machine learning; mass spectrometry imaging; tissue diagnostics.
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