From the synaptome of the mouse hippocampus, we identified 12 synapse-associated clusters potentially corresponding to various synaptic states and subcellular structures such as neuron-astrocyte junctions, neuron-oligodendrocyte junctions, and axon initial segments [2]. By comparing the synaptome to the matched single-nucleus transcriptome, we readily identified the neuron compartment-dependent RNA expression preferences. The effective intron detection warranted by the total-RNA approach allows the characterization of synaptic splicing landscapes. We observed that transcripts responsible for synaptogenesis tend to be transported in unspliced forms. Next, we characterized the synaptic pathology (synaptopathy) in an Alzheimer’s Disease mouse model 5xFAD. We characterized gene expression changes associated with synaptopathy. As a result, we pinned down the most vulnerable pathways to the synapses, such as the complement-mediated synaptic pruning [3]. We also demonstrated that synaptome profiling could be applied to postmortem human brain tissues. Characterization of synaptopathy of various neurological and psychiatric diseases is greatly desired, which we believe will shed new light on the etiology of the diseases and unveil new therapeutic targets for treatment.

Beyond profiling gene expression in synapses, synaptome profiling could provide an alternative approach to map neural connectivity (“connectome”) using high-throughput next-generation sequencing platforms. This sequencing-based connectome mapping method requires the introduction of a neuron-unique barcode using viruses with no trans-synaptic crossing, similar to the MAPseq/BARseq [4]. The neuron-neuron connections can be identified through the simultaneous detection of two neuron-specific barcodes in the synaptosomes in a high-throughput manner (Fig. 1B). Meanwhile, the neuron identity (both barcode and neuron type) could be retrieved from the single-nucleus transcriptome of the same sample (Fig. 1B). Considering the scale of 108 neurons and at least 1012 synapses in a single mouse brain, we reason that the major technical challenge is a reasonable sampling of this ultra-large number of neurons and connectivity. Integration of split-and-pool strategy [5] could potentially boost the throughput of MATQ-Drop to the order of billions, thereby enabling the connectome construction at the whole-brain level.

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