2026-10-10T12:27:28Z
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oai:figshare.com:article/33870438
2026-09-17T04:04:12Z
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InSitu Untargeted Metabolite Identification
and Spatial Mapping by Clustering Resampling Mass Spectrometry Imaging
Yu Wang (12152)
Chaoting Shi (15454188)
Yilin Liu (1991839)
Yu Ye (533827)
Zhuolin Jin (23005455)
Wenjian Gu (25001127)
Fan Wu (43375)
Fei Ran (11075277)
Wenlin Wu (461424)
Bing Xia (76986)
Yan Zhou (6523)
Biophysics
Biochemistry
Space Science
Biotechnology
Chemical Sciences not elsewhere classified
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
Developmental Biology
Cancer
untargeted metabolite identification
tandem mass spectrometry
spatially resolved analysis
single tissue section
representative points testing
countless pixel scans
overall msi throughput
msi collected 1
175 ms2 species
structural fragmentation data
transforming ms2 acquisition
insitu </ italic
msi data acquisition
structural elucidation
msi workflow
msi allows
msi ),
situ </
ms2 acquisition
vastly enhance
spatial resolution
spatial mapping
spatial localization
rat brain
molecular distributions
integrated device
innovatively converts
inherent trade
increase compared
driven process
dependent procedure
conventional techniques
biological tissues
Mass spectrometry imaging enables the spatially resolved
analysis
of molecular distributions in biological tissues. However, <i>in situ</i> untargeted metabolite identification remains challenging
due to the inherent trade-off between spatial resolution and <i>in situ</i> tandem mass spectrometry (MS2) acquisition. Here,
we developed clustering resampling mass spectrometry imaging (CR-MSI),
a method that innovatively converts <i>in situ</i> MS2 acquisition
from countless pixel scans to a few representative points testing.
CR-MSI allows the acquisition of both spatial localization and structural
fragmentation data for metabolites from a single tissue section. Additionally,
we have engineered an integrated device that automates the CR-MSI
workflow, transforming MS2 acquisition from an empirical, operator-dependent
procedure to an objective, data-driven process. Moreover, the synchronization
between MSI data acquisition and processing has improved the efficiency
and overall MSI throughput. When applied to rat brain, CR-MSI collected
1,175 MS2 species, representing a 410% increase compared with conventional
techniques. This approach will vastly enhance the structural elucidation
of untargeted mass spectrometry imaging.
2026-09-17T00:00:00Z
Dataset
Media
10.1021/acs.analchem.6c02449.s001
https://figshare.com/articles/media/InSitu_Untargeted_Metabolite_Identification_and_Spatial_Mapping_by_Clustering_Resampling_Mass_Spectrometry_Imaging/33870438
CC BY-NC 4.0