Pancreatic cancer is a highly aggressive and rapidly
progressing
disease, often diagnosed in advanced stages due to the absence of
early noticeable symptoms. The KRAS mutation is a hallmark of pancreatic
cancer, yet the underlying mechanisms driving pancreatic carcinogenesis
remain elusive. Cancer cells display significant metabolic heterogeneity,
which is relevant to the pathogenesis of cancer. Population measurements
may obscure information about the metabolic heterogeneity among cancer
cells. Therefore, it is crucial to analyze metabolites at the single-cell
level to gain a more comprehensive understanding of metabolic heterogeneity.
In this study, we employed a 3D-printed ionization source for metabolite
analysis in both mice and human pancreatic cancer cells at the single-cell
level. Using advanced machine learning algorithms and mass spectral
feature selection, we successfully identified 23 distinct metabolites
that are statistically significantly different in KRAS mutant human
pancreatic cancer cells and mouse acinar cells bearing the oncogenic
KRAS mutation. These metabolites encompass a variety of chemical classes,
including organic nitrogen compounds, organic acids and derivatives,
organoheterocyclic compounds, benzenoids, and lipids. These findings
shed light on the metabolic remodeling associated with KRAS-driven
pancreatic cancer initiation and indicate that the identified metabolites
hold promise as potential diagnostic markers for early detection in
pancreatic cancer patients.