Balancing spatial resolution and proteome depth in LC-MS based spatial proteomics

Le résumé fourni par la source

Abstract Spatial proteomics aims to resolve protein composition within intact tissues, yet extraction-based liquid chromatography–mass spectrometry (LC–MS) workflows face an inherent trade-off: smaller sampling units increase spatial specificity, whereas larger sampling units provide greater proteome depth and robustness. As analytical sensitivity improves, sampling-unit size therefore becomes a key experimental design parameter. Current extraction-based LC-MS workflows typically rely on laser capture microdissection (LCM), where sample recovery and scalability can become limiting at low input. Spatially resolved laser- activated cell sorting (SLACS) offers an alternative tissue-isolation strategy based on single-pulse near-infrared laser activation. Here, we use SLACS to systematically examine the resolution– sensitivity trade-off across sampling units ranging from single-cell-equivalent to larger low-input tissue regions. Few-cell sampling retained substantial proteomic information relative to larger regions while increasing spatial specificity. Applied to the mouse somatosensory cortex, SLACS generated deep, layer-resolved proteomic profiles from regions corresponding to approximately 60 cells and preserved major layer-specific molecular patterns at inputs as low as approximately 6 cells. These results highlight sampling-unit size as an important experimental design parameter in extraction-based spatial proteomics and support few-cell sampling as a practical compromise between spatial specificity, proteome depth and robustness.

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