Scalable high-plex pipeline for whole-brain proteomics profiling

Abstract Knowing the molecular signature of distinct brain cell types in the human brain is relevant for basic neuroscience research and understanding neurological disease. While past and ongoing large-scale scRNAseq and spatial transcriptomics efforts have provided a comprehensive mRNA expression atlas, there lacks a similar proteomics atlas due to the limited profiling throughput of current methods. Additionally, studies have shown expression discrepancy between the transcription and translation levels, especially during development. Therefore, mapping proteomes, not just transcriptomes, is crucial to providing a comprehensive understanding of the cell-type molecular signatures. Current methods to profile the proteome have disadvantages such as low multiplexity, slow acquisition speed, and high data storage costs. We propose improving upon these current methods by increasing multiplexity with a 100- plex antibody panel, increasing acquisition speed with the creation of a novel ultra-speed light-sheet microscope and data management system, and lowering storage costs by implementing high-fidelity lossy compression. Finally, we will implement this scalable strategy to generate 100-plex proteomic atlases of the whole mouse brain across 6 postnatal stages.