Ongoing pain has been linked to ongoing activity (OA) in human C-fiber nociceptors, but rodent models of pain-related OA have concentrated on allodynia rather than ongoing pain, and on OA generated in non-nociceptive Aβ fibers rather than C-fiber nociceptors. Little is known about how ongoing pain or nociceptor OA is generated. To define neurophysiological alterations underlying nociceptor OA, we have used isolated dorsal root ganglion neurons that continue to generate OA after removal from animals displaying ongoing pain. We subclassify OA as either spontaneous activity generated solely by alterations intrinsic to the active neuron or as extrinsically driven OA. Both types of OA were implicated previously in nociceptors in vivo and after isolation following spinal cord injury, which produces chronic ongoing pain. Using novel automated algorithms to analyze irregular changes in membrane potential, we have found, in a distinctive, nonaccommodating type of probable nociceptor, induction by spinal cord injury of 3 alterations that promote OA: (1) prolonged depolarization of resting membrane potential, (2) a hyperpolarizing shift in the voltage threshold for action potential generation, and (3) an increase in the incidence of large depolarizing spontaneous fluctuations (DSFs). Can DSFs also be enhanced acutely to promote OA in neurons from uninjured animals? A low dose of serotonin failed to change resting membrane potential but lowered action potential threshold. When combined with artificial depolarization to model inflammation, serotonin also strongly potentiated DSFs and OA. These findings reveal nociceptor specializations for generating OA that may promote ongoing pain in chronic and acute conditions.

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300/300 words) 35Extracting biological signals from non-linear, dynamic and stochastic experimental data can be 36 challenging, especially when the signal is non-stationary. Many currently available methods make 37 assumptions about the data structure (e.g., signal is periodic, sufficient recording time) and modify 38 the raw data in pre-processing using filters and/or transformations. With an agnostic approach to 39 biological data analysis as a goal, we implemented a signal detection algorithm in Python that 40 quantifies the dimensional properties of waveform deviations from baseline via a running fit 41 function. We call the resulting free program frequency-independent biological signal identification 42 (FIBSI). We demonstrate the utility of FIBSI on two disparate types of experimental data: in vitro 43 whole-cell current-clamp electrophysiological recordings of rodent sensory neurons (i.e., 44 nociceptors) and in vivo fluorescence image time-lapse movies capturing gastrointestinal motility 45 in larval zebrafish. In rodent nociceptors, depolarizing fluctuations in membrane potential are 46 irregular in shape and difficult to distinguish from noise. Using FIBSI, we determined that 47 Author Summary (172/200 words) 60Biologists increasingly work with large, complex experimental datasets. Those datasets often 61 encode biologically meaningful signals along with background noise that is recorded along with 62 the biological data during experiments. Background noise masks the real signal but originates 63 from other sources, for example from the equipment used to perform the measurements or 64 environmental disturbances. When it comes to analyzing the data, distinguishing between the real 65 biological signals and the background noise can be very challenging. Many existing programs 66 designed to help scientists with this problem are either difficult to use, not freely available, or only 67 appropriate to use on very specific types of datasets. The research presented here embodies our 68 goal of helping others to analyze their data by employing a powerful but novice-friendly program 69 that describes multiple features of biological activity in its raw form without abstract 70 transformations. We show the program's applicability using two different kinds of biological activity 71 measured in our labs. It is our hope that this will help others to analyze complex datasets more 72 easily, thoroughly, and rigorously. 73 157