Advancing peristalsis deciphering in mouse small intestine by multi-parameter tracking

Assessing gastrointestinal motility lacks simultaneous evaluation of intraluminal pressure (ILP), circular muscle (CM) and longitudinal muscle (LM) contraction, and lumen emptying. In this study, a sophisticated machine was developed that synchronized real-time recordings to quantify the intricate interplay between CM and LM contractions, and their timings for volume changes using high-resolution cameras with machine learning capability, the ILP using pressure transducers and droplet discharge (DD) using droplet counters. Results revealed four distinct phases, B_Phase, N_Phase, D_Phase, and A_Phase, distinguished by pressure wave amplitudes. Fluid filling impacted LM strength and contraction frequency initially, followed by CM contraction affecting ILP, volume, and the extent of anterograde, retrograde, and segmental contractions during these phases that result in short or long duration DD. This comprehensive analysis sheds light on peristalsis mechanisms, understand their sequence and how one parameter influenced the other, offering insights for managing peristalsis by regulating smooth muscle contractions.