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.

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.

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 (hDRG) neurons 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 and hiPSC-SNs. This study focuses specifically on electrophysiology properties of hDRG in comparison to hiPSC-SNs. We also compared hDRG 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 neurons (9 donors). We observed several statistically significant electrophysiological differences between hDRG and hiPSC-SNs, such as size, rheobase, input resistance, and several actional potential (AP) waveform properties. Correlation analysis also revealed many properties that were positively or negatively correlated, some of which were differentially correlated between hDRG and hiPSC-SNs. This study shows several differences between hDRG and hiPSC-SNs and allows 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.

Multiplexed Lipid Bilayers on Silica Microspheres for Analytical Screening Applications.

Most druggable targets are membrane components, including membrane proteins and soluble proteins that interact with ligands or receptors embedded in membranes. Current target-based screening and intermolecular interaction assays generally do not include the lipid membrane environment in presenting these targets, possibly altering their native structure and leading to misleading or incorrect results. To address this issue, an ideal assay involving membrane components would (1) mimic the natural membrane environment, (2) be amenable to high-throughput implementation, and (3) be easily multiplexed. In a step toward developing such an ideal target-based analytical assay for membrane components, we present fluorescently indexed multiplexed biomimetic membrane assays amenable to high-throughput flow cytometric detection. We build fluorescently multiplexed biomimetic membrane assays by using varying amounts of a fluorescently labeled lipid, NBD-DOPE [1,2-dioleoyl-sn-glycero-3-phosphoethanolamine-N-(7-nitro-2-1,3-benzoxadiazol-4-yl)], incorporated into a phospholipid membrane bilayer supported on 3 µm silica microspheres. Using flow cytometry, we demonstrate this multiplexed approach by measuring specific affinity of two well-characterized systems, the fluorescently labeled soluble proteins cholera toxin B subunit-Alexa 647 and streptavidin-PE/Cy5, to membranes containing different amounts of ligand targets of these proteins, GM1 and biotin-DOPE, respectively. This work will enable future efforts in developing highly efficient biomimetic assays for interaction analysis and drug screening involving membrane components.

Formation and dynamics of supported phospholipid membranes on a periodic nanotextured substrate.

We have studied and modeled the morphology and dynamics of fluid planar lipid bilayer membranes supported on a textured silicon substrate. The substrate is fabricated to have channels on its surface that are a few hundred nanometers across, with a channel depth of a few hundred nanometers perpendicular to the plane of observation. Using atomic force microscopy and quantitative fluorescence microscopy, we have shown that the bilayer assemblies conform to the underlying nanostructured substrate. As far as dynamics is concerned, when observed over length scales exceeding the dimensions of the nanostructured features, the macroscopic diffusion is anisotropic. However, the macroscopic anisotropy is well simulated using models of diffusion on the nanostructured surface that consider the lipids to diffuse homogeneously and isotropically on the supporting substrate. Consistent with previous observations on less well characterized or less periodic nanostructures, we find that the nanostructured substrate produces an effective anisotropy in macroscopic diffusion of the conformal membrane. More importantly, we demonstrate how quantitative analysis of dynamics probed by larger-scale fluorescence imaging can yield information on nanoscale thin-film morphology.