An Organ-Specific Metabolite Annotation Approach for Ambient Mass Spectrometry Imaging Reveals Spatial Metabolic Alterations of a Whole Mouse Body

Rapid and accurate metabolite annotation
in mass spectrometry imaging
(MSI) can improve the efficiency of spatially resolved metabolomics
studies and accelerate the discovery of reliable in situ disease biomarkers. To date, metabolite annotation tools in MSI
generally utilize isotopic patterns, but high-throughput fragmentation-based
identification and biological and technical factors that influence
structure elucidation are active challenges. Here, we proposed an
organ-specific, metabolite-database-driven approach to facilitate
efficient and accurate MSI metabolite annotation. Using data-dependent
acquisition (DDA) in liquid chromatography coupled with tandem mass
spectrometry (LC–MS/MS) to generate high-coverage product ions,
we identified 1620 unique metabolites from eight mouse organs (brain,
liver, kidney, heart, spleen, lung, muscle, and pancreas) and serum.
Following the evaluation of the adduct form difference of metabolite
ions between LC–MS and airflow-assisted desorption electrospray
ionization (AFADESI)-MSI and deciphering organ-specific metabolites,
we constructed a metabolite database for MSI consisting of 27,407
adduct ions. An automated annotation tool, MSIannotator, was then
created to conduct metabolite annotation in the MSI dataset with high
efficiency and confidence. We applied this approach to profile the
spatially resolved landscape of the whole mouse body and discovered
that metabolites were distributed across the body in an organ-specific
manner, which even spanned different mouse strains. Furthermore, the
spatial metabolic alteration in diabetic mice was delineated across
different organs, exhibiting that differentially expressed metabolites
were mainly located in the liver, brain, and kidney, and the alanine,
aspartate, and glutamate metabolism pathway was simultaneously altered
in these three organs. This approach not only enables robust metabolite
annotation and visualization on a body-wide level but also provides
a valuable database resource for underlying organ-specific metabolic
mechanisms.

质谱成像（mass spectrometry imaging, MSI）中快速且精准的代谢物注释，可提升空间分辨代谢组学研究的效率，加速可靠原位疾病生物标志物的发现。截至目前，质谱成像领域的代谢物注释工具通常依托同位素模式展开，但基于高通量碎裂的鉴定流程，以及影响结构解析的生物学与技术因素，仍是当前亟待攻克的挑战。本研究提出一种器官特异性、基于代谢物数据库的研究方法，以助力高效且精准的质谱成像代谢物注释。通过液相色谱-串联质谱（liquid chromatography coupled with tandem mass spectrometry, LC–MS/MS）中的数据依赖性采集（data-dependent acquisition, DDA）生成高覆盖度产物离子，我们从8种小鼠器官（脑、肝、肾、心、脾、肺、肌肉与胰腺）及血清中鉴定出1620种独特代谢物。在评估液相色谱-质谱与气流辅助解吸电喷雾电离（airflow-assisted desorption electrospray ionization, AFADESI）-MSI间代谢物离子的加合物形式差异，并解析器官特异性代谢物后，我们构建了包含27407个加合物离子的质谱成像代谢物数据库。随后开发了自动化注释工具MSIannotator，可高效且高置信度地完成质谱成像数据集的代谢物注释工作。我们将该方法应用于绘制小鼠全身的空间分辨代谢图谱，发现代谢物以器官特异性方式分布于全身，该特征甚至在不同小鼠品系中均存在。此外，本研究还描绘了糖尿病小鼠不同器官间的空间代谢组改变，结果显示差异表达代谢物主要富集于肝、脑与肾，且丙氨酸、天冬氨酸与谷氨酸代谢通路在这三个器官中均发生了改变。该方法不仅可实现全身体水平下可靠的代谢物注释与可视化，还为后续器官特异性代谢机制研究提供了极具价值的数据库资源。