Resolving Spectral Complexity in 4D Lipidomics Using a Two‐Dimensional Deconvolution Framework

ABSTRACT

The application of data‐independent acquisition (DIA) in 4D lipidomics has been constrained by spectral interference due to fragment ion overlap, a bottleneck that existing one‐dimensional deconvolution methods fail to fully resolve. Here, we overcome this limitation by introducing a two‐dimensional liquid chromatography‐ion mobility (LC‐IM) deconvolution framework that unlocks the full potential of 4D lipidomics. By mathematically modeling the orthogonal LC‐IM separation dimensions, our method reconstructs high‐quality MS/MS spectra from highly complex DIA data, effectively disentangling co‐eluting lipid interferences. We demonstrate the power of this approach by annotating 491 lipids from 1 µL human plasma at a 1% false discovery rate, a two‐fold increase in coverage compared to traditional methods. Beyond bulk analysis, we showcase its unique capability for spatial lipidomics, enabling deep profiling of laser‐microdissected tissue regions equivalent to only hundreds of cells, revealing metabolic reprogramming in human hepatocellular carcinoma. We further integrate this workflow with six‐plex isobaric labeling to achieve high‐throughput, high‐accuracy quantification in spatial tissue mapping. This transition from one‐ to two‐dimensional deconvolution establishes a robust, sensitive platform for deep lipidome characterization, bridging the gap between proteomics‐grade throughput and lipidomic structural complexity.