Electrophysiological Analyses of Human Dorsal Root Ganglia and Human Induced Pluripotent Stem Cell-derived Sensory Neurons From Male and Female Donors.

Human induced pluripotent stem cell-derived sensory neurons (hiPSC-SNs) and human dorsal root ganglia neurons (hDRG-N) are popular tools in the field of pain research; however, few groups make use of both approaches. For screening and analgesic validation purposes, important characterizations can be determined of the similarities and differences between hDRG-N and hiPSC-SNs. This study focuses specifically on the electrophysiology properties of hDRG-N in comparison to hiPSC-SNs. We also compared hDRG-N and hiPSC-SNs from both male and female donors to evaluate potential sex differences. We recorded neuronal size, rheobase, resting membrane potential, input resistance, and action potential waveform properties from 83 hiPSCs-SNs (2 donors) and 108 hDRG-N neurons (8 donors). We observed several statistically significant electrophysiological differences between hDRG-N and hiPSC-SNs, such as size, rheobase, input resistance, and several action potential waveform properties. Correlation analysis also revealed many properties that were positively or negatively correlated, some of which were differentially correlated between hDRG-N and hiPSC-SNs. This study shows several differences between hDRG-N and hiPSC-SNs and allows a better understanding of the advantages and disadvantages of both for use in pain research. We hope this study will be a valuable resource for pain researchers considering the use of these human in vitro systems for mechanistic studies and/or drug development projects. PERSPECTIVE: hiPSC-SNs and hDRG-N are popular tools in the field of pain research. This study allows for a better functional understanding of the pros and cons of both tools.

Machine learning elucidates electrophysiological properties predictive of multi- and single-firing human and mouse dorsal root ganglia neurons.

Human and mouse dorsal root ganglia (hDRG and mDRG) neurons are important tools in understanding the molecular and electrophysiological mechanisms that underlie nociception and drive pain behaviors. One of the simplest differences in firing phenotypes is that neurons are single-firing (exhibit only one action potential) or multi-firing (exhibit 2 or more action potentials). To determine if single- and multi-firing hDRG exhibit differences in intrinsic properties, firing phenotypes, and AP waveform properties, and if these properties could be used to predict multi-firing, we measured 22 electrophysiological properties by whole-cell patch-clamp electrophysiology of 94 hDRG neurons from 6 male and 4 female donors. We then analyzed the data using several machine learning models to determine if these properties could be used to predict multi-firing. We used 1000 iterations of Monte Carlo Cross Validation to split the data into different train and test sets and tested the Logistic Regression, k-Nearest Neighbors, Random Forest, Supported Vector Classification, and XGBoost machine learning models. All models tested had a greater than 80% accuracy on average, with Supported Vector Classification and XGBoost performing the best. We found that several properties correlated with multi-firing hDRG neurons and together could be used to predict multi-firing neurons in hDRG including a long decay time, a low rheobase, and long first spike latency. We also found that the hDRG models were able to predict multi-firing with 90% accuracy in mDRG. Targeting the neuronal properties that lead to multi-firing could elucidate better targets for treatment of chronic pain.

Depletion of JunB increases adipocyte thermogenic capacity and ameliorates diet-induced insulin resistance.

The coexistence of brown adipocytes with low and high thermogenic activity is a fundamental feature of brown adipose tissue heterogeneity and plasticity. However, the mechanisms that govern thermogenic adipocyte heterogeneity and its significance in obesity and metabolic disease remain poorly understood. Here we show that in male mice, a population of transcription factor jun-B (JunB)-enriched (JunB+) adipocytes within the brown adipose tissue exhibits lower thermogenic capacity compared to high-thermogenic adipocytes. The JunB+ adipocyte population expands in obesity. Depletion of JunB in adipocytes increases the fraction of adipocytes exhibiting high thermogenic capacity, leading to enhanced basal and cold-induced energy expenditure and protection against diet-induced obesity and insulin resistance. Mechanistically, JunB antagonizes the stimulatory effects of PPARγ coactivator-1α on high-thermogenic adipocyte formation by directly binding to the promoter of oestrogen-related receptor alpha, a PPARγ coactivator-1α downstream effector. Taken together, our study uncovers that JunB shapes thermogenic adipocyte heterogeneity, serving a critical role in maintaining systemic metabolic health.