Inferring super-resolved spatial metabolomics from microscopy

Abstract

Current spatial metabolomics techniques have transformed our understanding of cellular metabolism, yet accessible methods are limited in spatial resolution due to sensitivity constraints. MetaLens, a deep generative approach, disrupts this trade-off by quantitatively propagating cellular-resolution in situ imaging mass spectrometry readouts to subcellular scales through integration with high-resolution light microscopy. MetaLens identifies subcellular metabolic domains with distinct molecular composition, enabling accessible label-free subcellular metabolomic analysis from microscopy.