Data for LC-IM 2-D deconvolution algorithm

While data-independent acquisition (DIA) is a highly efficient strategy in proteomics, its application in lipidomics is hindered by spectral interference due to fragment overlapping. Existing solutions for DIA lipidomics have largely relied on one-dimensional deconvolution which lack the capability to fully resolve these interferences. Here, we present a two-dimensional LC-ion mobility deconvolution method capable of interpreting lipidomic DIA data acquired on a 4D lipidomics platform. Our method leverages the orthogonal LC-IM separation to resolve co-eluted lipids, enabling mathematical deconvolution across a second dimension to reconstruct high-quality MS/MS spectra from the high-dimensional data. We annotated 491 lipids from 1 µL of plasma at a 1% false discovery rate, achieving a 2× increase inlipidome coverage. Furthermore, we demonstrate that this approachsupports deep lipidome profiling in spatial tissue analysis coupled with laser microdissection (from tissue regions equivalent to hundreds of cells) and 6-channel multiplexed DIA with isobaric labeling for high-throughput spatial lipidome analysis. In summary, transitioning from traditional one-dimensional to LC-ion mobility dual deconvolution establishes this work as an indispensable tool for 4D lipidomics, enabling sensitive and high-coverage lipidome analysis for fundamental and biomedical research.