Discussion
AR with cinnabar-like red color secretory cavities have been
traditionally used in Chinese medicine for thousand years. However,
little is known about the chemical basis of the red secretory cavities,
not only between A. lancea natural accessions but also within
different tissue types. Although previous studies have shown that AR is
rich of certain types of sesquiterpenes and polyacetylenes (Zhang et
al., 2021), the spatial distribution of those secondary/specialized
metabolites is less understood. According to the geographical variation
in the components of AR, here we systematically analysed three
representative A. lancea natural accessions (Guo et al., 2008;
Takeda et al., 1996), each of which has distinct secretory cavities
(red, non-red & mixed SCs). This allowed rational comparative
metabolomics with multivariate statistical analysis to identify the
potential metabolites that underly the cinnabar-like red color of SC in
AR.
Metabolites in plants are often distributed in complex matrices that
stock arrays of different classes of chemicals. Some highly valuable
compounds are accumulated only in specialized structures, such as
the
glandular secretory trichomes on Artemisia annua leaves have the
capacity to secrete and store artemisinin (Shi et al., 2018),
monoterpenoids in pericarp secretory
cavities of citrus plants (Voo et al., 2012), and morphine in the
laticifers of the aerial organs of opium poppy (Onoyovwe et al., 2013).
Accessing the patchy mixtures of specialized metabolites thus are
challenging and requires reliable spatial dissection and visualization
of metabolomics techniques. We first applied untargeted metabolite
analysis using LCM-GC-MS, enabling quantitative comparisons in a
diversity of samples. Although the spatial resolution of LCM-GC-MS is
limited, it allowed robust comparisons not only between SC and non-SC
regions but also SCs that differ among natural accessions. We also
applied various of DESI approaches and found that the DESI/PI-MSI (which
has a relatively soft ionization step) was able to detect the biomarkers
(polyacetylenes) identified by previous comparative metabolomics (Figure
S6).