Mass cytometry of the developing mouse nervous system

For more than a century, various technological and biological limitations have hindered attempts to comprehensively taxonomize cells present in the mammalian nervous system. Although single-cell transcriptomics (scRNA-seq) are rapidly expanding our appreciation of the cellular diversity of neural tissues, the numerous mechanisms involved in translating RNA into functional and properly trafficked proteins means these results provide only a recipe for individual cell identities and states. To better understand the molecular profiles driving neural cell identity and functions, protein-based measurements are necessary. Herein, we generated the first single-cell protein-based atlases of the developing mouse brain (Chapter II) and dorsal root ganglia (DRG, Chapter III) by using mass cytometry, a cutting-edge technique similar to flow cytometry that employs metal-conjugated antibodies and time-of-flight detection to quantify expression of up to 50 biomarkers. Our results quantify simultaneous expression of neurofilaments, transcription factors, surface receptors, adhesion molecules, enzymes, glycoproteins, and other relevant molecules in millions of cells. By examining tissues collected at daily timepoints from embryonic day 11.5 until postnatal day 4, we surveyed how the molecular profiles and abundances of cells change in the brain and DRG during this critical period of development. The results are corroborated by immunohistochemistry (IHC) and confocal fluorescence microscopy of brain and DRG tissue slices. We also evaluated the relationship between mRNA and protein expression by comparing our single-cell protein measurements with scRNA-seq data published in La Manno, et al. [Nature (596):92–96, 2021] and Sharma, et al. [Nature (577):392–398, 2022] for mouse brain and DRG, respectively. The results, which were confirmed by IHC and RNAscope for select makers, indicate discordances between mRNA and protein during mouse nervous system development. Pseudotime-based trajectory analyses employing the URD algorithm replicate canonical molecular transitions for cortical excitatory neurons, somatosensory neurons, glial precursors, oligodendrocytes, and Schwann cells; moreover, they predict two distinct pathways for producing oligodendrocyte progenitor cells in the forebrain. Our findings provide the highest resolution profile of single-cell protein expression in mouse brain and DRG available to date. Moreover, our methods and analytical strategies lay the foundation for future protein-based and multiomic approaches to precisely identify neural cells – an important step for building a complete atlas of the nervous system.