Mass spectrometry imaging (MSI) provides the opportunity to
investigate tumor biology from an entirely novel biochemical
perspective and could lead to the identification of a new pool of
cancer biomarkers. Effective clinical translation of histology-driven
MSI in systems oncology requires precise colocalization of morphological
and biochemical features as well as advanced methods
for data treatment and interrogation. Currently proposed MSI
workflows are subject to several limitations, including nonoptimized
raw data preprocessing, imprecise image coregistration,
and limited pattern recognition capabilities. Here we outline a
comprehensive strategy for histology-driven MSI, using desorption
electrospray ionization that covers (i) optimized data preprocessing
for improved information recovery; (ii) precise image
coregistration; and (iii) efficient extraction of tissue-specific molecular
ion signatures for enhanced biochemical distinction of different
tissue types. The proposed workflow has been used to investigate
region-specific lipid signatures in colorectal cancer tissue. Unique
lipid patterns were observed using this approach according to
tissue type, and a tissue recognition system using multivariate
molecular ion patterns allowed highly accurate (>98%) identification
of pixels according to morphology (cancer, healthy mucosa,
smooth muscle, and microvasculature). This strategy offers unique
insights into tumor microenvironmental biochemistry and should
facilitate compilation of a large-scale tissue morphology-specific
MSI spectral database with which to pursue next-generation, fully
automated histological approaches.