Biophysical model of muscle spindle encoding

Mechanosensory encoding in ex vivo muscle-nerve preparations

Our objective was to evaluate an ex vivo muscle-nerve preparation used to study mechanosensory signalling by low threshold mechanosensory receptors (LTMRs). Specifically, we aimed to assess how well the ex vivo preparation represents in vivo firing behaviours of the three major LTMR subtypes of muscle primary sensory afferents, namely type Ia and II muscle spindle (MS) afferents and type Ib tendon organ afferents. Using published procedures for ex vivo study of LTMRs in mouse hindlimb muscles, we replicated earlier reports on afferent firing in response to conventional stretch paradigms applied to non-contracting, that is passive, muscle. Relative to in vivo studies, stretch-evoked firing for confirmed MS afferents in the ex vivo preparation was markedly reduced in firing rate and deficient in encoding dynamic features of muscle stretch. These deficiencies precluded conventional means of discriminating type Ia and II afferents. Muscle afferents, including confirmed Ib afferents were often indistinguishable based on their similar firing responses to the same physiologically relevant stretch paradigms. These observations raise uncertainty about conclusions drawn from earlier ex vivo studies that either attribute findings to specific afferent types or suggest an absence of treatment effects on dynamic firing. However, we found that replacing the recording solution with bicarbonate buffer resulted in afferent firing rates and profiles more like those seen in vivo. Improving representation of the distinctive sensory encoding properties in ex vivo muscle-nerve preparations will promote accuracy in assigning molecular markers and mechanisms to heterogeneous types of muscle mechanosensory neurons.

Intrafusal cross-bridge dynamics shape history-dependent muscle spindle responses to stretch

Computational models can be critical to linking complex properties of muscle spindle organs to the sensory information that they encode during behaviours such as postural sway and locomotion where few muscle spindle recordings exist. Here, we augment a biophysical muscle spindle model to predict the muscle spindle sensory signal. Muscle spindles comprise several intrafusal muscle fibres with varied myosin expression and are innervated by sensory neurons that fire during muscle stretch. We demonstrate how cross-bridge dynamics from thick and thin filament interactions affect the sensory receptor potential at the spike initiating region. Equivalent to the Ia afferent's instantaneous firing rate, the receptor potential is modelled as a linear sum of the force and rate change of force (yank) of a dynamic bag1 fibre and the force of a static bag2/chain fibre. We show the importance of inter-filament interactions in (i) generating large changes in force at stretch onset that drive initial bursts and (ii) faster recovery of bag fibre force and receptor potential following a shortening. We show how myosin attachment and detachment rates qualitatively alter the receptor potential. Finally, we show the effect of faster recovery of receptor potential on cyclic stretch-shorten cycles. Specifically, the model predicts history-dependence in muscle spindle receptor potentials as a function of inter-stretch interval (ISI), pre-stretch amplitude and the amplitude of sinusoidal stretches. This model provides a computational platform for predicting muscle spindle response in behaviourally relevant stretches and can link myosin expression seen in healthy and diseased intrafusal muscle fibres to muscle spindle function.