Back

Sickle cell disease patient plasma sensitizes iPSC-derived sensory neurons from sickle cell disease patients

Allison, R. L.; Burand, A.; Nieves Torres, D.; Brandow, A. M.; Stucky, C. L.; Ebert, A.

2023-01-10 neuroscience
10.1101/2023.01.10.523446 bioRxiv
Show abstract

Individuals living with sickle cell disease (SCD) experience severe recurrent acute and chronic pain. In order to develop novel therapies, it is necessary to better understand the neurobiological mechanisms underlying SCD pain. There are many barriers to gaining mechanistic insight into pathogenic SCD pain processes, such as differential gene expression and function of sensory neurons between humans and mice with SCD, as well as the limited availability of patient samples. These can be overcome by utilizing SCD patient-derived induced pluripotent stem cells (iPSCs) differentiated into sensory neurons (SCD iSNs). Here, we characterize the key gene expression and function of SCD iSNs to establish a model for higher-throughput investigation of intrinsic and extrinsic factors that may contribute to increased SCD patient pain. Importantly, identified roles for C-C Motif Chemokine Ligand 2 (CCL2) and endothelin 1 (ET1) in SCD pain can be recapitulated in SCD iSNs. Further, we find that plasma taken from SCD patients during acute pain increases SCD iSN calcium response to the nociceptive stimulus capsaicin compared to those treated with paired SCD patient plasma at baseline or healthy control plasma samples. Together, these data provide the framework necessary to utilize iSNs as a powerful tool to investigate the neurobiology of SCD and identify potential intrinsic mechanisms of SCD pain which may extend beyond a blood-based pathology. Key PointsO_LISickle cell disease (SCD) stem cell derived sensory neurons (iSNs) recapitulate important SCD phenotypes in vitro. C_LIO_LISCD patient plasma sensitizes SCD iSNs to TRPV1 stimulation by capsaicin. C_LI

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.