Trem2 expression in microglia is required to maintain normal neuronal bioenergetics during development.

Triggering receptor expressed on myeloid cells 2 (Trem2) is a myeloid cell-specific gene expressed in brain microglia, with variants that are associated with neurodegenerative diseases, including Alzheimer's disease. Trem2 is essential for microglia-mediated synaptic refinement, but whether Trem2 contributes to shaping neuronal development remains unclear. Here, we demonstrate that Trem2 plays a key role in controlling the bioenergetic profile of pyramidal neurons during development. In the absence of Trem2, developing neurons in the hippocampal cornus ammonis (CA)1 but not in CA3 subfield displayed compromised energetic metabolism, accompanied by reduced mitochondrial mass and abnormal organelle ultrastructure. This was paralleled by the transcriptional rearrangement of hippocampal pyramidal neurons at birth, with a pervasive alteration of metabolic, oxidative phosphorylation, and mitochondrial gene signatures, accompanied by a delay in the maturation of CA1 neurons. Our results unveil a role of Trem2 in controlling neuronal development by regulating the metabolic fitness of neurons in a region-specific manner.

MSIpixel: a fully automated pipeline for compound annotation and quantitation in mass spectrometry imaging experiments.

Mass spectrometry imaging (MSI) is commonly used to map the spatial distribution of small molecules within complex biological matrices. One of the major challenges in imaging MS-based spatial metabolomics is molecular identification and metabolite annotation, to address this limitation, annotation is often complemented with parallel bulk LC-MS2-based metabolomics to confirm and validate identifications. Here we applied MSI method, utilizing data-dependent acquisition, to visualize and identify unknown molecules in a single instrument run. To reach this aim we developed MSIpixel, a fully automated pipeline for compound annotation and quantitation in MSI experiments. It overcomes challenges in molecular identification, and improving reliability and comprehensiveness in MSI-based spatial metabolomics.