Virtual Calibration Quantitative Mass Spectrometry Imaging for Accurately Mapping Analytes across Heterogenous Biotissue

It
is highly challenging to quantitatively map multiple analytes
in biotissues without specific chemical labeling. Quantitative mass
spectrometry imaging (QMSI) has this potential but still poses technical
issues for its variant ionization efficiency across a complicated,
heterogeneous biomatrices. Herein, a self-developed air-flow-assisted
desorption electrospray ionization (AFADESI) is introduced to present
a proof of concept method, virtual calibration (VC) QMSI. This method
screens and utilizes analyte response-related endogenous metabolite
ions from each mass spectrum as native internal standards (IS). Through
machine-learning-based regression and clustering, tissue-specific
ionization variation can be automatically recognized, predicted, and
normalized region by region or pixel by pixel. Therefore, the quantity
of analytes can be accurately mapped across highly structural biosamples including
whole body, kidney, brain, tumor, etc. VC-QMSI has
the advantages of simple sample preparation without laborious isotopic
IS synthesis, extrapolation for those unknown tissues or regions without
previous investigation, and automatic spatial recognition without
histological guidance. This strategy is suitable for mass spectrometry
imaging using a variety of in situ ionization techniques.
It is believed that VC-QMSI has wide applicability for drug candidate's
discovery, molecular mechanism elucidation, biomarker validation,
and clinical diagnosis.