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Transcriptomic analysis and high throughput functional characterization of human induced pluripotent stem cell derived sensory neurons

Truong, V.; Brougher, J.; Strassmaier, T.; Lu, I.; George, D.; Price, T. J.; Randolph, A.; Obergrussberger, A. R.; Haedo, R.; Fertig, N.; Walsh, P.

2024-08-24 cell biology
10.1101/2024.08.23.607310 bioRxiv
Show abstract

Peripheral sensory neurons are a primary effector in pain neurotransmission, and have become a useful cellular model for the study of pain. While rodent tissue has historically served as a source of these neurons, it has become increasingly clear that pain mechanisms in rodents and humans are substantially divergent. Sensory neurons harvested from cadaveric human tissue serve as a superior translational model for studying pain mechanisms, however their relative paucity limits their widespread utility. Theoretically, sensory neurons manufactured from human pluripotent stem cells (hPSCs) could help bridge this translational gap given their relative abundance and potential similarity to primary human tissue. However, hPSC-derived sensory neurons manufactured with the most common methodologies correlate poorly to human tissue both transcriptionally and functionally. In the present work, we compare a novel population of hPSC-derived sensory neurons to previously published datasets and find this novel population to more closely resemble human primary dorsal root ganglia transcriptionally. Furthermore, we evaluate the heterogeneity of this novel population via single nucleus RNA sequencing and find it resembles specific nociceptor and mechanoreceptor subsets found in vivo. Finally, we assay the functionality of this population with high throughput automated patch clamp electrophysiology with respect to voltage-gated sodium (Nav) and potassium channels (Kv), and ligand-gated ionotropic GABA and P2X receptors. Overall, we find this population of hPSC-derived sensory neurons to be of relatively high fidelity, and suitable for interrogating numerous potential pain targets on a fully humanized platform.

Published in Neurobiology of Pain (predicted rank #5) · training set

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