Gain control of primary afferent neurotransmission at their intraspinal terminals occurs by several mechanisms including primary afferent depolarization (PAD). PAD produces presynaptic inhibition via a reduction in transmitter release. While it is known that descending monoaminergic pathways complexly regulate sensory processing, the extent these actions include modulation of afferent-evoked PAD remains uncertain. We investigated the effects of serotonin (5HT), dopamine (DA) and noradrenaline (NA) on afferent transmission and PAD. Responses were evoked by stimulation of myelinated hindlimb cutaneous and muscle afferents in the isolated neonatal mouse spinal cord. Monosynaptic responses were examined in the deep dorsal horn either as population excitatory synaptic responses (recorded as extracellular field potentials; EFPs) or intracellular excitatory postsynaptic currents (EPSCs). The magnitude of PAD generated intraspinally was estimated from electrotonically back-propagating dorsal root potentials (DRPs) recorded on lumbar dorsal roots. 5HT depressed the DRP by 76%. Monosynaptic actions were similarly depressed by 5HT (EFPs 54%; EPSCs 75%) but with a slower time course. This suggests that depression of monosynaptic EFPs and DRPs occurs by independent mechanisms. DA and NA had similar depressant actions on DRPs but weaker effects on EFPs. IC50 values for DRP depression were 0.6, 0.8 and 1.0 mM for 5HT, DA and NA, respectively. Depression of DRPs by monoamines was nearly-identical in both muscle and cutaneous afferentevoked responses, supporting a global modulation of the multimodal afferents stimulated. 5HT, DA and NA produced no change in the compound antidromic potentials evoked by intraspinal microstimulation indicating that depression of the DRP is unrelated to direct changes in the excitability of intraspinal afferent fibers, but due to metabotropic receptor activation. In summary, both myelinated afferent-evoked DRPs and monosynaptic transmission in the dorsal horn are broadly reduced by descending monoamine transmitters. These actions likely integrate with modulatory actions elsewhere to reconfigure spinal circuits during motor behaviors.
Single-cell analysis has the potential to provide us with a host of new knowledge about biological systems, but it comes with the challenge of correctly interpreting the biological information. While emerging techniques have made it possible to measure inter-cellular variability at the transcriptome level, no consensus yet exists on the most appropriate method of data analysis of such single cell data. Methods for analysis of transcriptional data at the population level are well established but are not well suited to single cell analysis due to their dependence on population averages. In order to address this question, we have systematically tested combinations of methods for primary data analysis on single cell transcription data generated from two types of primary immune cells, neutrophils and T lymphocytes. Cells were obtained from healthy individuals, and single cell transcript expression data was obtained by a combination of single cell sorting and nanoscale quantitative real time PCR (qRT-PCR) for markers of cell type, intracellular signaling, and immune functionality. Gene expression analysis was focused on hierarchical clustering to determine the existence of cellular subgroups within the populations. Nine combinations of criteria for data exclusion and normalization were tested and evaluated. Bimodality in gene expression indicated the presence of cellular subgroups which were also revealed by data clustering. We observed evidence for two clearly defined cellular subtypes in the neutrophil populations and at least two in the T lymphocyte populations. When normalizing the data by different methods, we observed varying outcomes with corresponding interpretations of the biological characteristics of the cell populations. Normalization of the data by linear standardization taking into account technical effects such as plate effects, resulted in interpretations that most closely matched biological expectations. Single cell transcription profiling provides evidence of cellular subclasses in neutrophils and leukocytes that may be independent of traditional classifications based on cell surface markers. The choice of primary data analysis method had a substantial effect on the interpretation of the data. Adjustment for technical effects is critical to prevent misinterpretation of single cell transcript data.
Natural and complementary therapies in conjunction with mainstream cancer care are steadily gaining popularity. Ginger extract (GE) confers significant health-promoting benefits owing to complex additive and/or synergistic interactions between its bioactive constituents. Recently, we showed that preservation of natural "milieu" confers superior anticancer activity on GE over its constituent phytochemicals, 6-gingerol (6G), 8-gingerol (8G), 10-gingerol (10G) and 6-shogaol (6S), through enterohepatic recirculation. Here we further evaluate and compare the effects of GE and its major bioactive constituents on cytochrome P450 (CYP) enzyme activity in human liver microsomes by monitoring metabolites of CYP-specific substrates using LC/MS/MS detection methods. Our data demonstrate that individual gingerols are potent inhibitors of CYP isozymes, whereas GE exhibits a much higher half-maximal inhibition value, indicating no possible herb-drug interactions. However, GE's inhibition of CYP1A2 and CYP2C8 reflects additive interactions among the constituents. In addition, studies performed to evaluate transporter-mediated intestinal efflux using Caco-2 cells revealed that GE and its phenolics are not substrates of P-glycoprotein (Pgp). Intriguingly, however, 10G and 6S were not detected in the receiver compartment, indicating possible biotransformation across the Caco-2 monolayer. These data strengthen the notion that an interplay of complex interactions among ginger phytochemicals when fed as whole extract dictates its bioactivity highlighting the importance of consuming whole foods over single agents. Our study substantiates the need for an in-depth analysis of hepatic biotransformation events and distribution profiles of GE and its active phenolics for the design of safe regimens.